Archive for the ‘ Anti-Aging ’ Category

Writer Elisabeth Rosenthal has worked as a physician and says it’s far more lucrative in the U.S. health system to provide a lifetime of treatments than a cure. Her new book is An American Sickness.

Health care is a trillion-dollar industry in America, but are we getting what we pay for? Dr. Elisabeth Rosenthal, a medical journalist who formerly worked as a medical doctor, warns that the existing system too often focuses on financial incentives over health or science.

“We’ve trusted a lot of our health care to for-profit businesses and it’s their job, frankly, to make profit,” Rosenthal says. “You can’t expect them to act like Mother Teresas.”

Rosenthal’s new book, An American Sickness, examines the deeply rooted problems of the existing health-care system and also offers suggestions for a way forward. She notes that under the current system, it’s far more lucrative to provide a lifetime of treatments than a cure.

“One expert in the book joked to me … that if we relied on the current medical market to deal with polio, we would never have a polio vaccine,” Rosenthal says. “Instead we would have iron lungs in seven colors with iPhone apps.”

Interview Highlights

On what consolidation of hospitals is doing to the price of care

In the beginning, this was a good idea: Hospitals came together to share efficiencies. You didn’t need every hospital ordering bed sheets. You didn’t need every hospital doing every procedure. You could share records of patients so the patient could go to the medical center that was most appropriate.

Now that consolidation trend has kind of snowballed and skyrocketed to a point … that in many parts of the country, major cities only have one, maybe two, hospital systems. And what you see with that level of consolidation is it’s kind of a mini-monopoly.

What happens, of course, when you have a mini-monopoly is you have an enormous sway over price. And so, what we see in research over and over again is that the cities that have the most hospital consolidation tend to have the highest prices for health care without any benefit for patient results. So consolidation, which started as a good idea in many places, has evolved to a point where it’s not benefiting patients anymore, it is benefiting profits.

An American Sickness

How Healthcare Became Big Business and How You Can Take It Back

by Elisabeth Rosenthal

Hardcover, 406 pages


On the ways the health-care industry stands to profit more from lifetime treatment than it does from curing disease

If you’re a pharmaceutical manufacturer and you have a problem like diabetes, for example, if I invented a pill tomorrow that would cure diabetes — that would kill a multi-billion dollar business market. It’s far better to have treatments, sometimes really great treatments … [that] go on for life. That’s much better than something that will make the disease go away overnight.

On how prices will rise to whatever the market will bear

Another concept that I think is unique to medicine is what economists call “sticky pricing,” which is a wonderful term. It basically means … once one drugmaker, one hospital, one doctor says “Hey we could charge $10,000 for that procedure or that medicine.” Maybe it was $5,000 two months ago, but once everyone sees that someone’s getting away with charging $10,000, the prices all go up to that sticky ceiling. …

What you see often now is when generic drugs come out … the price doesn’t go down to 20 percent of the branded price, it maybe goes down to 90 percent of the branded price. So we’re not getting what we should get from a really competitive market where we, the consumers, are making those choices.

On initiating conversations early on with doctors about fees and medical bills

You should start every conversation with a doctor’s office by asking “Is there a concierge fee? Are they affiliated with a hospital? Which hospital are they affiliated with? Is the office considered part of a hospital?” In which case you’re going to be facing hospital fees in addition to your doctor’s office fees. You ask your doctor always … “If I need a lab test, if I need an X-ray, will you send me to an in-network provider so I don’t get hit by out-of-network fees?” …

Often that will be a little hard for your doctor, because they may have to fill out a different requisition, but it’s worth asking. And any doctor who won’t help you in that way, I think, isn’t attuned to the financial cost that we’re bearing today.

On getting charged for “drive-by doctors” brought in by the hospital or primary doctor

You do have to say “Who are you? Who called you?” and “Am I going to be billed for this?” And it’s tragic that in recovery people have to think in this kind of keep-on-your-guard, somewhat adversarial way, but I think if we don’t push back against the system in the way it bills, we’re complicit in allowing it to continue.

On how to decipher coded medical bills

Don’t be alarmed by the “prompt payment discount.” Go back to the hospital and say, “I want a fully itemized bill. I want to know what I’m paying for.” Some of it will be in codes, some of it will be in medical abbreviations. I’ve discovered you can Google those codes and find out what you’re being charged for, often, and most importantly, you might find you’re being charged for stuff that obviously you know you didn’t have.

Elizabeth Rosenthal is editor-in-chief of Kaiser Health News, an editorially independent news program of the Henry J. Kaiser Family Foundation and a partner of NPR’s. Neither KFF nor KHN is affiliated with Kaiser Permanente. Radio producer Sam Briger and web producers Bridget Bentz and Molly Seavy-Nesper contributed to this story.

Source: Elizabeth Rosenthal Explains How U.S. Health Care Became Big Business : Shots – Health News : NPR

Older adults who favored this eating style lost less brain volume, study finds

Source: More Signs Mediterranean Diet May Boost Your Brain – News –

The ads started popping up about a decade ago on social media. Instead of selling alcohol with sex and romance, these ads had an edgier theme: Harried mothers chugging wine to cope with everyday stress. Women embracing quart-sized bottles of whiskey, and bellying up to bars to knock back vodka shots with men.

In this new strain of advertising, women’s liberation equaled heavy drinking, and alcohol researchers say it both heralded and promoted a profound cultural shift: Women in America are drinking far more, and far more frequently, than their mothers or grandmothers did, and alcohol consumption is ending their marriages, alienating them from their children and killing them in record numbers.

White women are particularly likely to drink dangerously, with more than a quarter (25%) drinking multiple times a week and the share of binge drinking up 40 percent since 1999, according to a Washington Post analysis of federal health data. In 2013, more than a million women of all races wound up in emergency rooms as a result of heavy drinking, with women in middle age most likely to suffer severe intoxication.

This behavior has contributed to a startling increase in early mortality. The rate of alcohol-related deaths for white women ages 35 to 54 has more than doubled since 1999, according to The Post analysis, accounting for 8 percent of deaths in this age group in 2015.

“It is a looming health crisis,” said Katherine M. Keyes, an alcohol researcher at Columbia University.

Although independent researchers are increasingly convinced that any amount of alcohol poses serious health risks, American women are still receiving mixed messages. Parts of the federal government continue to advance the idea that moderate drinking may be good for you. Meanwhile, many ads for alcohol — particularly on social media — appear to promote excessive drinking, which is universally recognized as potentially deadly. These ads also appear to violate the industry’s code of ethics, according to a Post analysis of alcohol marketing.

For example, when girl-power heroine Amy Schumer guzzled Bandit boxed wine in the movie “Trainwreck,” Bandit’s producer, Trinchero Family Estates, promoted the scene on social media. Young women responded with photos of themselves chugging Bandit. Within months, Trinchero said, sales of boxed wines — sometimes called “binge in a box” — jumped 22 percent.

“We saw it first with tobacco, marketing it to women as their right to smoke. Then we saw lung cancer deaths surpass deaths from breast cancer,” said Rear Adm. Susan Blumenthal, a former assistant surgeon general and an expert on women’s health issues. “Now it’s happening with alcohol, and it’s become an equal rights tragedy.”

Alcohol marketing is regulated primarily by industry trade groups, but dozens of studies have found lapses in their record of enforcing the rules. As a result, an international group of public health experts convened by the World Health Organization’s regional office in Washington, D.C., plans to call in January for governments worldwide to consider legislation similar to laws adopted a decade ago to sharply curtail tobacco advertising.

Officials with the Distilled Spirits Council of the United States, one of the largest U.S. trade groups, defend their record of oversight, saying it has received high marks from federal regulators.

DISCUS tells members that ads should not “in any way suggest that intoxication is socially acceptable conduct.” The Beer Institute tells members that their “marketing materials should not depict situations where beer is being consumed rapidly, excessively.” And the Wine Institute prohibits ads that make “any suggestion that excessive drinking or loss of control is amusing or a proper subject for amusement” or that directly associate use of wine with “social, physical or personal problem solving.”

But these rules appear regularly to be flouted, particularly on alcohol companies’ websites and social-media feeds, which are soaking up a growing share of the more than $2 billion the industry is expected to spend on advertising this year. And the trade groups acknowledge that they do not investigate or act on possible violations unless they receive a formal complaint.

Some of the edgiest ads appear on social media — Facebook, Twitter, Instagram — where they can be narrowly targeted toward the inboxes and desperate little lives of the most eager consumers.

Jokes about becoming inebriated are common.

Women also are frequently shown drinking to cope with daily stress. In one image that appeared on a company website, two white women wearing prim, narrow-brimmed hats, button earrings and wash-and-set hair confer side by side. “How much do you spend on a bottle of wine?” one asks. The other answers, “I would guess about half an hour …” At the bottom is the name of the wine:

Mommy’s Time Out.

Another ad on a company website features a white woman wearing pearls and an apron. “The most expensive part of having kids is all the wine you have to drink,” it says above the name of the wine:

Mad Housewife.

This spring, Mad Housewife offered a Mother’s Day promotion: a six-pack of wine called

Mommy’s Little Helper.

“The rise in hazardous drinking among women is not all due to the ads. But the ads have played a role in creating a cultural climate that says it’s funny when women drink heavily,” said Jean Kilbourne, who has produced several films and books about alcohol marketing to women. “Most importantly, they’ve played a role in normalizing it.

Source: Booze causing ‘crisis’ for women | TribLIVE

World’s Oldest PersonBorn on 29 November 1899, Emma Morano is 116 years old and is currently the world’s oldest living person. She is believed to be the last person living born in the 19th century. So, what’s her secret to longevity? Well, she has been following the same diet for around 90 years. She has three eggs per day (two raw, one cooked), fresh Italian pasta and a dish of raw meat.

Source: 50 Unique Women Who Deserve The Spotlight – Page 24 of 50 – Viral Scoop

Ten years ago, Shinya Yamanaka revolutionized biological research with his discovery of how to turn ordinary skin cells into stem cells with just four key genes.

Source: Reflecting on the Discovery of the Decade: Induced Pluripotent Stem Cells | Gladstone Institutes

Mice were rejuvenated through a four-gene cocktail that allowed them to repair aging signs including loss of hair and organs malfunction. There were no signs of cancer and compared to untreated mice, the reprogrammed mice looked younger, with better organ function, improved cardiovascular performance and lived 30 percent longer.

Source: Study shows aging might be reversible thanks to cells group

No, your pubic hair cannot prevent STDs.

Source: Myths about pubic hair you must stop believing t1216 | Photo Galleries of Weight Loss, Diet Plan, Healthy Recipes, Sexual Health |

The mis information begins with a study that has no causation links to anything just much innuendo and misdirection from facts.

Source: Not to shave? In life (and TV), pubic hair is staying on

Dr. Mercola discusses the role of B vitamins and other valuable nutrients to support brain health.

Reprinted with the kind permission of Dr. Mercola.

By Dr. Mercola

A number of studies have investigated the impact of vitamin supplementation to prevent and/or treat cognitive dysfunction and decline.

It’s well-established that healthy fats such as animal-based omega-3 fats are really important for brain health, but other nutrients such as vitamins are also necessary for optimal brain function.

Most recently, a Korean studyconcluded that giving a multivitamin supplement to seniors suffering from mild cognitive impairment and depression helped improve both conditions.

B vitamins in particular, especially folate (B9, aka folic acid in its synthetic form) and vitamins B6 and B12, have made headlines for their powerful role in preventing cognitive decline and more serious dementia such as Alzheimer’s disease.

Mental fogginess and problems with memory are actually two of the top warning signs that you have vitamin B12 deficiency, indicating its importance for brain health.

B Vitamins and Omega-3 — An Important Combo for Brain Health

Although Dr. Michael Greger’s video is a good review on the research about B vitamins, being a vegetarian he does not include information about animal-based omega-3 fats, which are also beneficial in reducing dementia.
Low plasma concentrations of omega-3 and high levels of the amino acid homocysteine are associated with brain atrophy, dementia, and Alzheimer’s. Vitamins B6, B9, and B12 help convert homocysteine into methionine — a building block for proteins.
If you don’t get enough of these B vitamins, this conversion process is impaired and as a result your homocysteine levels increase. Conversely, when you increase intake of folic acid (folate), vitamin B6, and vitamin B12, your homocysteine levels decrease.
In one placebo-controlled trial2 published in 2015, 168 seniors diagnosed with mild cognitive impairment were randomly assigned to receive either placebo, or daily supplementation with 0.8 mg of folic acid, 20 mg of vitamin B6, and 0.5 mg of B12.
It’s worth noting that these are quite high doses — far above the U.S. RDA. All participants underwent cranial magnetic resonance imaging (MRI) scans at the outset of the study, and at the end, two years later.
The effect of the vitamin B supplementation was analyzed and compared to their omega-3 fatty acid concentrations at baseline. Interestingly, only those who had high omega-3 levels reaped beneficial effects from the B vitamins.
As noted by the authors:

“There was a significant interaction between B vitamin treatment and plasma combined omega-3 fatty acids (eicosapentaenoic acid and docosahexaenoic acid) on brain atrophy rates.

In subjects with high baseline omega-3 fatty acids (>590 ?mol/L), B vitamin treatment slowed the mean atrophy rate by 40 percent compared with placebo.
B vitamin treatment had no significant effect on the rate of atrophy among subjects with low baseline omega-3 fatty acids (<390 ?mol/L). High baseline omega-3 fatty acids were associated with a slower rate of brain atrophy in the B vitamin group but not in the placebo group…
It is also suggested that the beneficial effect of omega-3 fatty acids on brain atrophy may be confined to subjects with good B vitamin status.”

B Vitamins Significantly Slow Brain Shrinkage

As mentioned above, elevated homocysteine is linked to brain degeneration, and B vitamins are known to suppress homocysteine.
A 2010 study,3 in which participants again received higher than normal doses of B vitamins, also found that people receiving B vitamins experienced far less brain shrinkage than the placebo group.
Here the participants received either a placebo or 800 micrograms (mcg) folic acid, 500 mcg B12, and 20 mg B6. The study was based on the presumption that by controlling homocysteine levels you might be able to reduce brain shrinkage, thereby slowing the onset of Alzheimer’s.
Indeed, after two years those who received the vitamin B regimen suffered significantly less brain shrinkage compared to those who had received a placebo. Those who had the highest levels of homocysteine at the start of the trial experienced brain shrinkage at half the rate of those taking a placebo.
Research Shows B Vitamins Specifically Slow Alzheimer’s Disease
A 2013 study4 takes this research a step further, showing that not only do B vitamins slow brain shrinkage, but they specifically slow shrinkage in brain regions known to be most severely impacted by Alzheimer’s disease. Moreover, in those specific areas the shrinkage is decreased by as much as seven-fold!
The brain scans clearly show the difference between placebo and vitamin supplementation on brain atrophy. As in the studies above, participants taking high doses of folic acid and vitamins B6 and B12 lowered their blood levels of homocysteine, and brain shrinkage was decreased by as much as 90 percent.
As noted by the authors:

” … B vitamins lower homocysteine, which directly leads to a decrease in GM [gray matter] atrophy, thereby slowing cognitive decline.

Our results show that B vitamin supplementation can slow the atrophy of specific brain regions that are a key component of the AD [Alzheimer’s disease] process and that are associated with cognitive decline.”

B12-Rich Foods Reduce Risk of Alzheimer’s in Later Years
Other supporting research includes a small Finnish study5 published in 2010. It found that people who consume vitamin B12-rich foods may reduce their risk of Alzheimer’s in their later years.
For each unit increase in the marker of vitamin B12 (holotranscobalamin), the risk of developing Alzheimer’s was reduced by 2 percent. This makes a strong case for ensuring your diet includes plenty of B vitamin foods, such as meat, poultry, eggs, dairy products and wild-caught fish.
Leafy green vegetables, beans, and peas also provide some of the B vitamins, but if you eat an all vegetarian or vegan diet, you’re at a significantly increased risk of vitamin B12 deficiency, as B12 is naturally present in foods that come from animals, including meat, fish, eggs, milk and milk products.
In such a case, supplementation is really important. Another concern is whether your body can adequately absorb the B12. It’s the largest vitamin molecule we know of, and because of its hefty size, it’s not easily absorbed.
This is why many, if not most, oral B12 supplements fail to deliver any benefits. Vitamin B12 requires a gastric protein called intrinsic factor to bind to it, which allows it to be absorbed in the end of your small intestine (terminal ileum). The intrinsic factor is absorbed first, pulling the attached B12 molecule along with it.
As you grow older, your ability to produce intrinsic factor decreases, thereby increasing your risk for vitamin B12 deficiency. Use of metformin (Glucophage, Glucophage XR, Fortamet, Riomet, and Glumetza) may also inhibit your B12 absorption, especially at higher doses. Drinking four or more cups of coffee a day can reduce your B vitamin stores by as much as 15 percent, and use of antacids will also hinder your body’s ability to absorb B12.
Other Valuable Vitamins for Brain Health
Besides B vitamins, vitamins C and D are also important for optimal brain health.6 Vitamin C plays a role in the production of neurotransmitters, including serotonin, which has antidepressant activity. Vitamin C has also been shown to improve IQ, memory, and offer protection against age-related brain degeneration and strokes.
In one study,7 the combination of vitamin C and E (which work synergistically) helped reduce the risk of dementia by 60 percent. Vitamin C also has detoxifying effects, and due to its ability to cross your blood-brain barrier, it can help remove heavy metals from your brain.
Vitamin D, a steroid hormone produced in your skin in response to sun exposure, also has profound effects on your brain. Pregnant women need to be particularly cognizant of this, as vitamin D deficiency during pregnancy can prevent proper brain development in the fetus, plus a host of other problems. After birth, children need vitamin D for continued brain development, and in adulthood, optimal levels have been shown to help prevent cognitive decline.8,9
Where to Find These Valuable Brain Nutrients
There’s nothing “normal” about cognitive decline. More often than not, it’s due to poor lifestyle choices, starting with a nutrient-deficient diet that is too high in sugars, non-vegetable carbs, unhealthy fats like trans fats, and too many toxins (pesticides and artificial additives, etc).
As a general rule, I recommend getting most if not all of your nutrition from REAL FOOD, ideally organic to avoid toxic pesticides, and locally grown. Depending on your situation and condition however, you may need one or more supplements.
To start, review the following listing of foods that contain the brain nutrients discussed in this article: animal-based omega-3s, vitamins B6, B9, and B12, C, and D. If you find that you rarely or never eat foods rich in one or more of these nutrients, you may want to consider taking a high-quality, ideally food-based supplement. I’ve made some suggestions to keep in mind when selecting a good supplement.


Nutrient Dietary Sources Supplement Recommendations
Animal-based omega-3 Fatty fish that is low in mercury, such as wild-caught Alaskan salmon, sardines, and anchovies, as well as organic grass-fed beef.10

Sardines, in particular, are one of the most concentrated sources of omega-3 fats, with one serving containing more than 50 percent of your recommended daily value.

Antarctic krill oil is a sustainable choice. It also has the added benefit of containing natural astaxanthin, which helps prevent oxidation.

Another good option is wild-caught Alaskan salmon oil.

Vitamin B6 Turkey, beef, chicken, wild-caught salmon, sweet potatoes, potatoes, sunflower seeds, pistachios, avocado, spinach and banana.11,12 Nutritional yeast is an excellent source of B vitamins, especially B6.13One serving (2 tablespoons) contains nearly 10 mg of vitamin B6.

Not to be confused with Brewer’s yeast or other active yeasts, nutritional yeast is made from an organism grown on molasses, which is then harvested and dried to deactivate the yeast.

It has a pleasant cheesy flavor and can be added to a number of different dishes. For tips, see this vegan blog post.14

Folate (B9) Fresh, raw, and organic leafy green vegetables, especially broccoli, asparagus, spinach, and turnip greens, and a wide variety of beans, especially lentils, but also pinto beans, garbanzo beans, navy and black beans, and kidney beans.15 Folic acid is a synthetic type of B vitamin used in supplements; folate is the natural form found in foods.

Think: folate comes from foliage(edible leafy plants).

For folic acid to be of use, it must first be activated into its biologically active form — L-5-MTHF.

This is the form able to cross the blood-brain barrier to give you the brain benefits noted.

Nearly half of the population has difficulty converting folic acid into the bioactive form due to a genetic reduction in enzyme activity.

For this reason, if you take a B vitamin supplement, make sure it contains natural folate rather than synthetic folic acid.

Nutritional yeast is an excellent source.16

Vitamin B12 Vitamin B12 is found almost exclusively in animal tissues, including foods like beef and beef liver, lamb, snapper, venison, salmon, shrimp, scallops, poultry, eggs, and dairy products.

The few plant foods that are sources of B12 are actually B12 analogs that block the uptake of true B12.

Also consider limiting sugar and eating fermented foods.

The entire B group vitamin series is produced within your gut, assuming you have healthy gut flora.

Eating real food, ideally organic, along with fermented foods will provide your microbiome with important fiber and beneficial bacteria to help optimize your internal vitamin B production.

Nutritional yeast is also high in B12, and is highly recommended for vegetarians and vegans.

One serving (2 tbsp) provides nearly 8 micrograms (mcg) of natural vitamin B12.17

Sublingual (under-the-tongue) fine mist spray or vitamin B12 injections are also effective, as they allow the large B12 molecule to be absorbed directly into your bloodstream.

Vitamin C Sweet peppers, chili peppers, Brussel sprouts, broccoli, artichoke, sweet potato, tomato, cauliflower, kale, papaya, strawberries, oranges, kiwi, grapefruit, cantaloupe, and lemon.

To boost your intake of fruits and vegetables, consider juicing. As an alternative, you can also make fermented vegetables at home.

The vitamin C in sauerkraut (fermented cabbage) is about six times higher than in the same helping of unfermented cabbage, so it’s an excellent way to boost your vitamin C intake.

The most effective form of oral vitamin C is liposomal vitamin C.

It’s not associated with many of the complications of traditional vitamin C or ascorbic acid (such as gastrointestinal distress), which will allow you to achieve higher intracellular concentrations.

You can expect a significant rise in plasma vitamin C concentration at doses between 30 and 100 mg/day.

Taking vitamin C frequently throughout the day is more effective than taking one large dose once a day.

Vitamin D Vitamin D is created naturally when your skin is exposed to sunshine.

While you can get some vitamin D from grass-fed meats and other whole foods and fortified foods, sun exposure is an ideal primary source.

When taking supplemental vitamin D, also be sure to increase your intake of vitamin K2 and magnesium, either from food or a supplement.

Sources and References

1 Journal of Nursing Scholarship February 15, 2016 DOI: 10.1111/jnu.12201

2 American Journal of Clinical Nutrition July 2015: 102(1); 215-221

3 PLoS ONE 5(9): e12244.

4 PNAS 2013 Jun 4;110(23):9523-8

5 Neurology. 2010 Oct 19;75(16):1408-14.

6 Be Brain Fit, Brain Vitamins

7 Psychology Today November 20, 2015

8 Journal of Neurology, Neurosurgery, and Psychiatry 2009 Jul;80(7):722-9

9 Vitamin D Council, Cognitive Impairment

10, Omega-3 Oils

11 Worlds Healthiest Foods, Vitamin B6

12, Top 10 Foods High in Vitamin B6

13, 17 Self Nutrition Data, Nutritional Yeast

14 Fat Free Vegan Kitchen, Nutritional Yeast

15 Worlds Healthiest Foods, Folate

16 Chalkboard, Nutritional Yeast

Source: The Importance of B Vitamins for Brain Health and Combating Dementia

Symptoms of small intestinal bacterial overgrowth, according to the SIBO Center for Digestive Health, include:






Heartburn (reflux or GERD)


Food sensitivities


Joint pain


Skin rashes

Respiratory symptoms (such as asthma)

Mood symptoms (such as depression)

Brain symptoms (such as autism)


Steatorrhea (fatty stools)

Iron deficiency anemia


Abdominal pain

Vitamin B12 deficiency


Research from the University of Southern California published in the Journal of the American Medical Association indicated that the symptoms of SIBO were nearly universal to those symptoms associated with irritable bowel syndrome.

Source: 21 Symptoms of Small Intestinal Bacterial Overgrowth

For decades, neuroscientists and physicians have tried to get to the bottom of the age-old mystery of post-traumatic stress disorder, to explain why only some people are vulnerable and why they experience so many symptoms and so much disability.

All experts in the field now agree that PTSD indeed has its roots in very real, physical processes within the brain — and not in some sort of psychological “weakness.” But no clear consensus has emerged about what exactly has gone “wrong” in the brain.

In a Perspective article published this week in Neuron, a pair of University of Michigan Medical School professors — who have studied PTSD from many angles for many years — put forth a theory of PTSD that draws from and integrates decades of prior research. They hope to stimulate interest in the theory and invite others in the field to test it.

The bottom line, they say, is that people with PTSD appear to suffer from disrupted context processing. That’s a core brain function that allows people and animals to recognize that a particular stimulus may require different responses depending on the context in which it is encountered. It’s what allows us to call upon the “right” emotional or physical response to the current encounter.

A simple example, they write, is recognizing that a mountain lion seen in the zoo does not require a fear or “flight” response, while the same lion unexpectedly encountered in the backyard probably does.

For someone with PTSD, a stimulus associated with the trauma they previously experienced — such as a loud noise or a particular smell — triggers a fear response even when the context is very safe. That’s why they react even if the noise came from the front door being slammed, or the smell comes from dinner being accidentally burned on the stove.

Context processing involves a brain region called the hippocampus, and its connections to two other regions called the prefrontal cortex and the amygdala. Research has shown that activity in these brain areas is disrupted in PTSD patients. The U-M team thinks their theory can unify wide-ranging evidence by showing how a disruption in this circuit can interfere with context processing and can explain most of the symptoms and much of the biology of PTSD.

“We hope to put some order to all the information that’s been gathered about PTSD from studies of human patients, and of animal models of the condition,” says Israel Liberzon, M.D., a professor of psychiatry at U-M and a researcher at the VA Ann Arbor Healthcare System who also treats veterans with PTSD. “We hope to create a testable hypothesis, which isn’t as common in mental health research as it should be. If this hypothesis proves true, maybe we can unravel some of the underlying pathophysiological processes, and offer better treatments.”

Liberzon and his colleague, James Abelson, M.D., Ph.D., describe in their piece models of PTSD that have emerged in recent years, and lay out the evidence for each. The problem, they say, is that none of these models sufficiently explains the various symptoms seen in patients, nor all of the complex neurobiological changes seen in PTSD and in animal models of this disorder.

The first model, abnormal fear learning, is rooted in the amygdala — the brain’s ‘fight or flight’ center that focuses on response to threats or safe environments. This model emerged from work on fear conditioning, fear extinction and fear generalization.

The second, exaggerated threat detection, is rooted in the brain regions that figure out what signals from the environment are “salient,” or important to take note of and react to. This model focuses on vigilance and disproportionate responses to perceived threats.

The third, involving executive function and regulation of emotions, is mainly rooted in the prefrontal cortex — the brain’s center for keeping emotions in check and planning or switching between tasks.

By focusing only on the evidence bolstering one of these theories, researchers may be “searching under the streetlight,” says Liberzon. “But if we look at all of it in the light of context processing disruption, we can explain why different teams have seen different things. They’re not mutually exclusive.”

The main thing, says Liberzon, is that “context is not only information about your surroundings — it’s pulling out the correct emotion and memories for the context you are in.”

A deficit in context processing would lead PTSD patients to feel “unmoored” from the world around them, unable to shape their responses to fit their current contexts. Instead, their brains would impose an “internalized context” — one that always expects danger — on every situation.

This type of deficit, arising in the brain from a combination of genetics and life experiences, may create vulnerability to PTSD in the first place, they say. After trauma, this would generate symptoms of hypervigilance, sleeplessness, intrusive thoughts and dreams, and inappropriate emotional and physical outbursts.

Liberzon and Abelson think that testing the context processing theory will enhance understanding of PTSD, even if all of its details are not verified. They hope the PTSD community will help them pursue the needed research, in PTSD patients and in animal models. They put forth specific ideas in the Neuron paper to encourage that, and are embarking on such research themselves.

The U-M/VA team is currently recruiting people with PTSD — whether veterans or not — for studies involving brain imaging and other tests.

In the meantime, they note that there is a growing set of therapeutic tools that can help patients with PTSD, such as cognitive behavioral therapy mindfulness training and pharmacological approaches. These may work by helping to anchor PTSD patients in their current environment, and may prove more effective as researchers learn how to specifically strengthen context processing capacities in the brain.

Source: What’s really going on in PTSD brains? Experts suggest new theory — ScienceDaily

February 13, 2013

Shocking Alien Fears Force Pope From Office

By: Sorcha Faal, and as reported to her Western Subscribers


A stunning Ministry of Foreign Affairs (MFA) report prepared for President Putin, which is circulating in the Kremlin today, states that Pope Benedict XVI was forced to resign this past week over Catholic Church fears that this 85-year-old leader of over 1 billion Christians was “mentally and physically unprepared” to deal with the coming revelation about the truth of alien beings.

In our 22 January report, Russia Orders Obama: Tell World About Aliens, Or We Will, we detailed how the issue of extraterrestrial beings was brought to the forefront of the World Economic Forum (WEF) with the naming in their 2013 Executive Summary of the danger posed to our world over the discovery of alien life with their stating: “Proof of life elsewhere in the universe could have profound psychological implications for human belief systems.”

Also noted in our previous report were Prime Minister Medvedev’s 7 December 2012 off-air comments to reporters which were recorded and wherein he stated: “Along with the briefcase with nuclear codes, the president of the country is given a special ‘top secret’ folder. This folder in its entirety contains information about aliens who visited our planet… Along with this, you are given a report of the absolutely secret special service that exercises control over aliens on the territory of our country… More detailed information on this topic you can get from a well-known movie called Men In Black… I will not tell you how many of them are among us because it may cause panic.”

Spurring Pope Benedict XVI to become the first leader of the Catholic Church to resign in nearly 600 years, this MFA report says, was the appearance over Los Cristianos, Spain on 21 August 2011 of the long prophesized “bird of prey” interplanetary spacecraft, and which was followed nearly 3 weeks ago with a fleet of them appearing in the skies over Mexico City.

To fully understand the significance of these “bird of prey” UFO’s, this report continues, files relating to the 27 September 1989 Voronezh Incident must be studied in length, especially as it relates to the “messages” delivered to eyewitnesses from the “giants”.

In an 11 October 1989 New York Times article about the Voronezh Incident titled U.F.O. Landing Is Fact, Not Fantasy, the Russians Insist it says:

“It is not a joke, nor a hoax, nor a sign of mental instability, nor an attempt to drum up local tourism by drawing the curious, the Soviet press agency Tass insisted today in discussions of what it called an extraterrestrial visit to southern Russia.

Residents of the city of Voronezh insisted today that lanky, three-eyed extraterrestrial creatures had indeed landed in a local park and gone for a stroll and that a seemingly fantastic report about the event carried Monday by the official press agency Tass was absolutely true.

The three-eyed creature, about nine feet tall and fashionably dressed in silvery overalls and bronze boots and with a disk on its chest, disappeared, then landed and came out for a promenade with a companion and a robot.

The aliens seemed to communicate with each other, producing the mysterious appearance of a shining triangle, and activated the robot with a touch.”

Regarding these “messages” from the Voronezh “giants”, this MFA report says, was the warning to human beings that when these “bird of prey” UFO’s descend upon Earth the whole planet will be in peril.

The Voronezh “giants” further related, this report says, that the alien beings associated with these “bird of prey” UFO’s were the cause of the 14 April 1561 massive “sky battle” over Nuremberg, Germany which was depicted in a famous 16th century woodcut by Hans Glaser [photo 3rd left] and described by the residents as: “A very frightful spectacle.” “The sky appeared to fill with cylindrical objects from which red, black, orange and blue white disks and globes emerged. Crosses and tubes resembling cannon barrels also appeared whereupon the objects promptly began to fight one another.”

Important to note is that the Catholic Christian faith headed by Pope Benedict XVI, as well as nearly every other religion on Earth, all prophesize in their teachings a time when the “gods” will return to our planet and engage in a battle that could very well bring our entire planet to the brink of destruction.

Equally important to note about Pope Benedict XVI’s shock resignation is how it eerily compares with Saint Malachy, who as an Irish saint and Archbishop of Armagh, in the 12th Century, received a vision of 112 Popes later attributed to the apocalyptic list of Prophecy of the Popes. He was the first Irish saint to be canonized by Pope Clement III in 1199.

American authors Tom Horn and Cris Putnam in their 2012 book “Petrus Romanus: The Final Pope is Here” about Saint Malachy’s prophecies told interviewers last Spring that Pope Benedict XVI would resign by late 2012, or early 2013, and described the next Pope to follow as “Petrus Romanus,” or “Peter the Roman,” writing: “In the final persecution of the Holy Roman Church there will reign Peter the Roman, who will feed his flock among many tribulations; after which the seven-hilled city will be destroyed and the dreadful Judge will judge the people.”

Though the masses of people reading of the things this report contains will, undoubtedly, ridicule them, the same cannot be said of the elite moneyed classes who, even at this writing, are protecting themselves from “something” at such a fever-pitched pace it is destabilizing the entire global economy, and as exampled by the highly respected Zero Hedge news service in their article titled “What Do They Know That We Don’t?” and which, in part, says:

“Friday evening when no one was supposed to pay attention, Google announced that Executive Chairman Eric Schmidt would sell 3.2 million of his Google shares in 2013, 42% of the 7.6 million shares he owned at the end of last year—after having already sold 1.8 million shares in 2012. But why would he sell 5 million shares, about 53% of his holdings, with Google stock trading near its all-time high?

“Part of his long-term strategy for individual asset diversification and liquidity,” Google mollified us, according to the Wall Street Journal. Soothing words. Nothing but “a routine diversification of assets.”

Routine? He didn’t sell any in 2008 as the market was crashing. He didn’t sell at the bottom in early 2009. And he didn’t sell during the rest of 2009 as Google shares were soaring, nor in 2010, as they continued to soar. In 2011, he eased out of about 300,000 shares, a mere rounding error in his holdings. But in 2012, he opened the valves, and in 2013, he’d open the floodgates. So it’s not “routine.”

Mr. Schmidt isn’t alone. Corporate insiders were “aggressively selling their shares,” reported Mark Hulbert. And they were doing so “at an alarming pace.” The buy sell-to-buy ratio had risen to 9.2-to-1; insiders had sold over 9 times as many shares as they’d bought. They’d been aggressive sellers for weeks.

Instantly, soothing voices were heard: “don’t be alarmed,” they said. But Mr. Schmidt and his colleagues at the top of corporate America, multi-billionaires many of them, are immensely well connected, not only to each other but also to the Fed, whose twelve regional Federal Reserve Banks they own and control.”

To why Google Chairman Schmidt did not attend this years World Economic Forum, where the danger of aliens was being discussed, opting instead for a visit to North Korea (who announced yesterday that they had exploded another nuclear weapon) and when coupled with the information contained in this MFA report, is far from being “soothing”, and is, instead, something well all should be very alarmed about as the end is much nearer than the beginning as those with “eyes to see” and “ears to hear” already know.

February 13, 2012 © EU and US all rights reserved. Permission to use this report in its entirety is granted under the condition it is linked back to its original source at WhatDoesItMean.Com. Freebase content licensed under CC-BY and GFDL.

[Ed. Note: Western governments and their intelligence services actively campaign against the information found in these reports so as not to alarm their citizens about the many catastrophic Earth changes and events to come, a stance that the Sisters of Sorcha Faal strongly disagrees with in believing that it is every human beings right to know the truth. Due to our missions conflicts with that of those governments, the responses of their ‘agents’ against us has been a longstanding misinformation/misdirection campaign designed to discredit and which is addressed in the report “Who Is Sorcha Faal?”.]

You May Already Be To Late…But It Has Begun!

They Are Going To Come For You…Why Are You Helping Them?

Source: Shocking Alien Fears Force Pope From Office

How Can We Defy Aging?Many anti-aging studies are being conducted nowadays with the hope of discovering new methods of finding out which substances can halt the progress of aging in the body. Recently one study has shown that a vitamin, nicotinamide riboside, can stop the aging process in body organs.Nicotinamide riboside is a substance that has the potential of rejuvenating stem cells, permitting higher regeneration processes in aged mice.Nicotinamide riboside (NR) is pretty robust. It has already been proven in several experiences to be effective in boosting metabolism. And now, researchers at EPFL’s Laboratory of Integrated Physiology (LISP has reported about the positive effects of this vitamin on stem cells such as restoration.This vitamin has recently been studied in mice. As mice age, the regenerative ability of organs such as the liver and kidneys and some muscle groups diminishes. Their capability to repair cells following damage can also be affected. This results in some of the problems which are typical of getting older.Nicotinamide and AgingThe researchers wanted to appreciate how regeneration deteriorated with age. To do so, they determined the molecular chain that regulates how mitochondria, known as the “powerhouse” of the cell, can perform and how they are altered by age. The function that mitochondria play in metabolism has already been amply established, however, according to the researchers they were able to show for the first time that their ability to function properly was important for stem cells”.Under average conditions, these stem cells reacting to indicators sent by the body regenerate damaged organs by producing new distinctive cells. The lead author added, “We demonstrated that fatigue in stem cells was one of the main causes of poor regeneration or even degeneration in certain tissues or organs”.Because of this, the researchers desired to “revitalize” stem cells in the muscle tissues of aged mice. And so they did by means of concentrating on the molecules that support the mitochondria to function adequately. According to them, they “gave nicotinamide riboside to 2-year-old mice, which is an advanced age for them. This substance, which is close to vitamin B3, is a precursor of NAD+, a molecule that plays a key role in mitochondrial activity. And our results are extremely promising: muscular regeneration is much better in mice that received NR, and they lived longer than the mice that didn’t get it”.Other previous studies have showed a comparable effect on stem cells of the brain and epidermis. According to the lead author, “This work could have very important implications in the field of regenerative medicine. We are not talking about introducing foreign substances into the body but rather restoring the body’s ability to repair itself with a product that can be taken with food.”Up to now, no negative results have been found using NR, even at excessive doses. However caution should be observed because it seems to boost the functioning of all cells, which would comprise the abnormal ones. More studies are required.

Source: Nicotinamide Can Stop Aging of Organs, Study Shows – Doctor Tipster

Mechanisms and methods of methonine restriction

Life Extension Benefits of Methionine Restriction

by Ben Best




Methionine is the only essential amino acid containing sulfur. Methionine is the precursor of the other sulfur-containing amino acids: cysteine, taurine, homocysteine, and cystathione. Methionine is essential for the synthesis of proteins and many other biomoleules required for survival. Rats fed a diet without methionine develop fatty liver disease which can be corrected by methionine supplements [DIGESTIVE DISEASES AND SCIENCES; Oz,HS; 53(3):767-776 (2008)]. Dietary methionine is essential for DNA methylation. Reduced DNA methylation results in genetic instability, aberrant gene expression, and increased cancer.

The above paragraph is the first paragraph from the section on methionine in my article dealing with the Methionine Cycle. Material in that article is useful background for the information below. Note, however, that there is an inverse correlation between lifespan and the methionine content of protein in the heart muscle of eight mammalian species [MECHANISMS OF AGEING AND DEVELOPMENT; Ruiz,MC; 126(10):1106-1114 (2005)]. The sulfur-containing amino acids methionine and cysteine are the most readily oxidized of any of the amino acids — both as free amino acids or in proteins. Methionine is oxidized to methionine sulfoxide, but methionine sulfoxide reductases enzymatically regenerate methionine [BIOPHYSICA ET BIOCHEMICA ACTA; Lee,BC; 1790 (11): 1471-1477 (2009)].

(return to contents)


Substantial evidence indicates that as much as half of the life-extension benefits of CRAN (Calorie Restriction with Adequate Nutrition) are due to restriction of the single amino acid methionine. In a study of rats given 20% the dietary methionine of control rats, mean lifespan increased 42% and maximum lifespan increased 44% [THE FASEB JOURNAL;Richie,JP; 8(15):1302-1307 (1994)]. Blood glutathione levels were 81% higher in the methionine-restricted rats at maturity, and 164% higher in old age. In other studies, methionine-restricted rats showed greater insulin sensitivity and reduced fat deposition [AMERICAN JOURNAL OF PHYSIOLOGY; Hasek,BE; 299:R728-R739 (2010) and AGING CELL; Malloy,VL; 5(4):305-314 (2006)].

An experiment on mice given 35% the methionine of controls showed only a 7% increase in median life span [JOURNALS OF GERONTOLOGY; Sun,L; 64(7):711-722 (2009)]. Another mouse study showed lowered serum insulin, IGF−1, glucose, and thyroid hormone for methionine at one-third the normal intake. There was significant mouse mortality for methionine less than one-third normal intake, but with one-third intake of methionine maximum lifespan was significantly increased [AGING CELL; Miller,RA; 4(3):119-125 (2005)]. Rats generally show greater longevity benefits from CRAN than mice.

Mitochondrial free radical generation is believed by many biogerontologists to be a significant contributor to aging damage. Rats given 20% the dietary methionine of control rats show significantly decreased free radical generation from complex I and complex III of liver mitochondria as well as from complex I of heart mitochondria — associated with reduced oxidative damage to mitochondrial DNA and protein [THE FASEB JOURNAL;Sanz,A; 20(8):1064-1073 (2006)]. These results are comparable to the reduced mitochondrial free radical generation seen in CRAN rats [ENDOCRINOLOGY; Gredilla,R; 146(9):3713-3717 (2005)]. Rats given 60% rather than 20% of the methionine of control rats showed nearly the same amount of reduced mitochondrial free radical generation and damage [BIOCHEMICA ET BIOPHYSICA ACTA; Lopez-Torres,M; 1780(11):1337-1347 (2008)]. Body weight was not reduced with 60% dietary methionine, leading to the conclusion that such reduction would not result in reduced growth in children [REJUVENATION RESEARCH; Caro,P; 12(6):421-434 (2009)]. It was concluded that methionine restriction is the sole reason for reduced mitochondrial free radical generation and damage associated with CRAN [Ibid.] and protein restriction [BIOGERONTOLOGY; Caro,P; 9(3):183-196 (2008)].

Evidence for the suggestion that methionine oxidation plays a significant role in lifespan can be found in the considerable lifespan extension benefits seen in transgenic fruit flies that overexpress a gene for repairing oxidized methionine in protein [PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES (USA); Ruan,H; 99(5):2748 (2002)]. The sulfur-containing amino acids methionine and cyteine are more easily oxidized in proteins than other amino acids [JOURNAL OF PHYSIOLOGY)], which is apparently related to the reduced free radical generation in mitochondria seen in methionine restriction. Both the fruit fly experiment and the methionine restriction experiments indicate a significant impact on lifespan from methionine oxidation.

It has been suggested that glycine supplementation has the same effect as methionine restriction. An experiment with glycine supplementation in rats showed a 30% extension in maximum lifespan [FASEB JOURNAL; Brind,J; 25:528.2 (2011)]. Additionally, three grams of glycine daily has been shown to improve sleep quality in young (average age 31) female Japanese adults [SLEEP AND BIOLOGICAL RHYTHM; Inagawa,K; 4:75-77 (2006)]

(return to contents)

TABLE 1 Lysine, Threonine and Methionine in Food
(milligrams amino acid per gram protein)
Food Sulfur-containing amino acid Lysine Threonine
Nuts,Seeds 46 ± 17 45 ± 14 36 ± 3
Animal foods 38 85 ± 12 44
Cereals 37 ± 5 31 ± 10 32 ± 4
Fruits 27 ± 6 25 ± 12 29 ± 7
Legumes 25 ± 3 64 ± 10 38 ± 3


The adjoining table (my Table 1) from [AMERICAN JOURNAL OF CLINICAL NUTRITION; Young,VR; 59(suppl):1203s-1212s (1994)] indicates the essential amino acids most likely to be limited in plant protein foods. Cereal protein contains comparable sulfur-containing amino acids (including methionine) per gram as animal foods, whereas fruit and legume protein contain about 65% as much methionine. Nuts and seeds are particularly high in methionine, on average 20% higher in methionine than animal protein, although the absolute amount of protein in animal foods tends to be higher, which makes total methionine intake generally higher in animal foods. Vegetables are not shown in Table 1, but as described in Table 4 in the AMERICAN JOURNAL OF CLINICAL NUTRITION paper from which Table 1 is taken, vegetables are on average in the 1-2% range for percent protein and fruits are in the 0.5-1% protein range — so neither fruits nor vegetables should be considered serious sources of protein (green peas are an exceptional vegetable with 5.4% protein, and avacado is an exceptional fruit with 2% protein). Cereals are typically 7-13% protein and legumes are typically 20-30% protein (soybeans are exceptionally high in protein even for legumes, being in the range of 35-45% protein).

The dry weight of beef, broccoli, peanuts, and peas is about one-third protein, whereas cereals and fruits are less than 10% dry weight protein. Unlike many other plant proteins, legumes are not particularly low in lysine, and they are close to animal protein in threonine content. Vegetarians attempting to achieve complete protein often combine cereals (which are relatively high in methionine for plant protein) with legumes (which are relatively high in lysine for plant protein).


Lentils and other beans contain high amounts of phytic acid (phosphate-rich inositol), which can chelate positively-charged multivalent mineral ions (especially iron, zinc, magnesium, and calcium), preventing absorption. Soaking lentils and beans in warm water overnight not only makes them easier to cook, it allows some of the phytates to be soaked-out (and thrown-away with the water). Acidic solution (such as vinegar) better removes the phytates. Cooking also helps destroy phytates.

Although it would be very difficult to determine a diet providing optimum methionine for maximum human lifespan — even on the basis of rat experiments — evidence is convincing that reducing dietary methionine can help extend lifespan. The Table 2, listing milligrams of methionine per 100 grams of food (rather than per gram of protein, as in Table 1), could be helpful. Table values are based on [FOOD VALUES OF PORTIONS COMMONLY USED by Jean Pennington (1989)].


TABLE 2 Methionine in Foods
(milligrams/100 grams of food)
Food Methionine
Cheese, parmesan (dry) 971
Skim milk (dry) 907
Tuna (light) 862
Cheese, Swiss (processed) 792
Corned beef 711
Cheese, Cheddar 661
Salmon 631
Cheese, American (processed) 579
Extra lean beef 572
Walnuts, black 479
Egg white 394
Whole boiled egg 392
Pistashio nuts 386
Peanuts 289
Walnuts, Persian (English) 286
Cashew nuts 279
Cheerios 254
Oatmeal 250
Broad (Fava) beans 239
Soybeans 224
Barley 208
Tofu (firm) 202
Grape nuts (cereal) 200
Shredded wheat (cereal) 193
Wheaties (cereal) 168
Rice 167
Almonds 161
Yogurt 155
White beans 146
Black turtle beans 141
Navy beans 131
Kidney (red) beans 130
Chickpeas (garbanzos) 116
Blackeyed peas (cowpeas) 110
Lima beans 100
Macadamia nuts 93
Millet 85
Peas (raw) 82
Adzuki beans 79
Lentils 77
Corn 70
Spaghetti 51
Sweet potato (baked) 42
Mushrooms 40
Avacado 39
Mung beans 35
Broccoli 34
Potato 33
Pinto beans 33
Amaranth 30
Cauliflower 28
Oranges 22
Tomato paste 19
Kale 18
Banana 17
Blueberries 11
Onion 10
Tomato 8
Apple 2
Grapefruit 2
Strawberries 1


The absolute methionine content of a food is better evaluated knowing what the water, fat, carbohydrate, fiber, and protein content of that food is. A higher protein content and a lower methionine content is better than having a low methionine content because the food is low in protein and high in water, fat, or carbohydrate. Lima beans and rice are relatively high in both carbohydrate and methionine. Onions and strawberries are low in methionine, but are high in water and low in protein.

The data for Table 3 is taken from [NUTRITIVE VALUE OF FOODS; USDA Bulletin 72 (1981)], but is adjusted to give percent protein by dry weight. Percent water in the food is not related to the other columns. Fiber content is not given, and I suspect that fiber is equated with carbohydrate. I may have made a few errors, and I suspect that the data contains a few errors (garbage-in, garbage-out). But for the most part I think the data is good, my transcription is accurate, and my calculations are correct.


TABLE 3 Percent Macronutrients (dry weight)
and Percent Water (whole food)
Food Protein Carbohydrate Fat Water
Egg, white 100 0 0 88
Tuna solid,white, water 97 0 3 63
Salmon (baked) 81 0 19 67
Tuna chunk,light,oil 77 0 23 61
Corned beef 69 0 31 59
Ground beef,lean 57 0 43 56
Cheese, Parmesan (grated) 55 5 40 18
Ham 54 0 46 53
Ground beef,regular 53 0 47 54
Cheese, Swiss 47 7 47 42
Egg, whole 46 8 46 75
Yogurt, nonfat 43 57 0 80
Soybeans 41 20 39 71
Cheese, American processed 40 0 60 39
Milk, nonfat 39 59 2 91
Sesame seeds 29 14 57 5
Lentils, cooked 29 69 2 72
Sausage 29 0 71 45
Peas, split 27 71 2 70
Walnut, black 26 13 61 4
Frankfurter 26 5 68 54
Chickpeas (garbanzos) 23 75 6 60
Pinto beans, cooked 23 75 2 65
Pistachio nuts 22 26 52 4
Mushroom, cooked 25 67 8 91
Lima beans, cooked 24 74 2 64
Cashew nuts 16 35 49 2
Macaroni (enriched) 15 83 2 64
Tomato paste 16 80 3 74
Bread, whole wheat 16 76 7 38
Bread,1/3 wht (Pmnk) 16 78 6 37
Spaghetti (enriched), ckd 15 83 2 64
Egg noodles 15 80 4 70
Walnut, Persian (English) 15 19 65 4
Bread,2/3 wht (rye) 14 79 7 37
Onions 14 85 0 91
Corn 13 87 0 76
Potato (baked+skin) 9 91 0 71
Rice, brown 9 89 2 70
Avacado flesh (Florida) 8 46 46 80
Strawberries (raw) 8 83 8 92
Rice, white 7 93 0 73


Brown rice would be more nutritious than white rice, except that the fats in germ that is removed to make white rice can go rancid. Ingestion of Advanced Glycation End-Products (AGES) is detrimental to health.

Table 4gives the percent fat obtained for selected items in the above table, and breaks down the fat into percent saturated, monosaturated, and polyunsaturated fat. Numbers are rounded to the nearest whole number, which is why the total percentages don’t always add to 100. Monosaturated fats and polyunsaturated fats are preferred to unsaturated fats except where there is rancidity. Again, ingestion of Advanced Glycation End-Products (AGES) is detrimental to health. I had no data for non-fat cheese, the only kind of cheese that I eat.


TABLE 4 Percent Fat Types
Food Saturated Monosaturated Polyunsaturated % Fat
Cheese, American processed 67 30 4 52
Ground beef,lean 45 50 4 56
Egg, whole 44 52 4 48
Corned beef 44 52 4 29
Frankfurter 39 51 10 63
Ham 39 49 12 45
Sausage 37 50 11 69
Salmon (baked) 24 48 28 18
Tuna chunk,light,oil 22 30 48 18
Avacado flesh (Florida) 22 60 18 80
Cashew nuts 21 62 18 63
Soybeans 15 22 62 33
Pistachio nuts 13 71 16 52
Walnut, Persian (English) 9 24 66 64
Walnut, black 7 24 70 59


Table 5 gives relative proportions of all of the essential amino acids (plus tyrosine) for some representative high-protein animal foods as well as for some low-methionine plant foods.

Lysine is given after methionine because lysine is most often the limiting amino acid (the essential amino acid found in the smallest quantity relative to requirement) in cereals, nuts, and seeds — but lysine in abundant in legumes, for which methionine is typically the limiting amino acid [AMERICAN JOURNAL OF CLINICAL NUTRITION; Young,VR; 59(suppl):1203s-1212s (1994)]. Lysine is therefore listed second in the table. Leucine is listed third because of its paradoxical ability to reduce fat in high doses [DIABETES; Zhang,Y; 56(6):1647-1654 (2007)] and low doses [DIABETES; Cheng,Y; 59(1):17-25 (2010)]. Leucine and threonine are the limiting amino acid in vegetables and fruits, although vegetables and fruits are too low in protein to be considered significant proteins sources. Trytophan restriction has been shown to have a modest (compared to methionine restriction) ability to extend lifespan in rats [ MECHANISMS OF AGEING AND DEVELOPMENT; Ooka,H; 43(1):79-98 (1988)], reputedly by opposing an age-related increase in brain serotonin.

Tyramine was evaluated because of claims that high dietary tyramine could have adverse reactions with monoamine oxidase inhibitors (I take deprenyl). But none of the foods listed have seriously high levels of tyramine, so tyramine is not really a concern.

Again, this data is taken from  [FOOD VALUES OF PORTIONS COMMONLY USED by Jean Pennington (1989)]. I have adjusted the Pennington data to be standardized for 100 grams of food, rather than reproducing the variable quantities of food given, which makes comparison difficult. I may have made transcription errors, but probably not many (if any).


TABLE 5 Low Methionine Beans/Grains
Essential amino acids (+tyramine)
(milligrams/100 grams food)
Met = Methionine
Lys = Lysine
Leu = Leucine
Thr = Threonine
Try = Typtophan
Iso = Isoleucine
Phe = Phenylalanine
Val = Valine
His = Histidine
Tyr = Tyrosine
Food Met Lys Leu Thr Try Iso Phe Val His Tyr
Skim milk,dry 907 2867 3543 1633 510 2187 1746 2420 980 1747
American cheese 579 2225 1982 729 329 1036 1139 1343 914 1229
Walnuts, black 479 732 1729 739 325 993 1086 1304 489 761
Egg white 394 642 882 451 155 618 636 761 230 406
Walnuts, Persian (English) 286 293 1007 454 193 575 636 732 364 446
Cashew nuts 279 829 1304 600 239 743 804 1054 404 496
Soybeans, cooked 224 1108 1355 723 242 807 869 831 449 630
Whey, dry 200 967 1067 567 233 567 567 300 233 367
Rice, cooked 167 292 542 333 83 292 375 458 208 375
Yogurt, nonfat 169 514 578 235 32 312 312 474 142 289
Kidney (red) beans 130 595 693 365 103 383 469 454 241 244
Chickpeas (garbanzos) 116 593 631 329 85 380 475 372 244 220
Blackeyed peas (cowpeas) 110 523 592 294 95 314 451 368 240 250
Lima beans, cooked 100 523 673 337 92 411 470 469 238 276
Peas (raw) 82 317 323 203 37 195 200 235 106 113
Adzuki beans 79 567 632 255 72 300 398 387 198 224
Lentils, cooked 77 779 809 400 100 482 551 554 314 298
Corn, cooked 70 141 359 133 23 133 155 191 91 126
Broadbeans (Fava) 62 468 572 270 72 306 97 338 193 241
Spaghetti, cooked 51 109 220 133 41 170 177 _ 80 113
Mushrooms 40 211 129 94 46 82 80 97 57 46
Potato, baked 33 126 124 75 32 84 92 117 45 77
Pinto beans, cooked 33 564 656 346 97 363 444 430 229 231
Amaranth 30 109 167 85 27 102 114 118 44 68
Avacado flesh (Florida) 29 75 99 53 17 57 54 78 23 39
Mung beans, cooked 25 123 131 58 27 39 98 85 97 52
Tomato paste 19 108 105 86 26 73 80 77 60 51
Onion 10 56 41 28 18 42 30 28 19 29


Confusion can be caused by the variable amounts of proteins in the foods. Some foods have high water content (such as onion), or high carbohydrate content (such as rice), or high fat content (such as nuts). To compare relative amounts of methionine in the proteins in the foods, I have created Table 6 in which I have adjusted the values to reflect milligrams of amino acid per gram of protein, rather than the per 100 grams of food used in the previous table. To do this, I first calculate dry weight [(100 − % water) / 100] and then divide by % protein. (Note that Persian/English walnuts contain 60% the protein of black walnuts, mostly because of higher fat content. This creates a misimpression that Persian/English walnuts are much lower in methionine than black walnuts.)

I make no guarantee that I have made no transcription errors in manually copying data from either table to my calculator.



TABLE 6 Low Methionine Beans/Grains
Essential amino acids
(milligrams/gram protein)
Met = Methionine
Lys = Lysine
Leu = Leucine
Thr = Threonine
Try = Typtophan
Iso = Isoleucine
Phe = Phenylalanine
Val = Valine
His = Histidine
% P = % Protein (dry weight)
% W = % Water
Food Met Lys Leu Thr Try Iso Phe Val His % P % W
Rice (dry) 24 42 77 48 12 42 54 65 30 7 70
Milk,nonfat 24 75 92 43 13 57 45 63 26 40 91
Cheese, American 24 91 81 30 13 42 47 55 37 40 39
Corn, cooked 22 45 115 43 7.4 43 50 61 29 13 76
Sesame seeds 21 20 47 25 13 26 33 34 18 29 5
Walnuts, Persian (English) 20 20 70 32 13 40 44 51 25 15 4
Walnuts, black 19 29 69 30 13 40 44 52 20 26 4
Yogurt, skim 19 60 67 27 3.7 36 36 55 17 43 80
Soybeans, cooked 19 93 114 61 20 68 73 70 38 41 71
Cashew nuts 18 53 83 38 15 47 51 67 26 16 2
Avacado flesh (Florida) 18 47 62 33 11 36 34 49 14 8 80
Mushroom, cooked 18 94 57 42 20 36 36 45 25 25 91
Chickpeas (garbanzos) 13 64 69 36 9.2 41 52 40 27 23 60
Potato 13 48 48 29 12 32 35 45 17 9 71
Lentils, cooked 9.5 96 100 49 12 59 68 68 39 29 72
Spaghetti, cooked 9.4 20 41 25 7.6 31 33 _ 15 15 64
Onion 8 45 33 22 14 34 24 22 15 14 91
Tomato paste 4.5 26 25 21 6 18 19 19 14 16 74
Pinto beans, cooked 4 70 81 43 12 45 55 53 28 23 65


I am searching for foods that are high in protein, but low in methionine, as a source of protein. Preferably the foods should be high in the essential amino acids (other than methionine), and low in fat (especially saturated fat) and low in carbohydrate. As sources of protein, the data in the Table 6 are important in proportion to the percent protein in the food, especially when the water content is low. As long as protein is adequate in the diet overall, other foods that are low in protein and high in water are not much of a concern from a methionine restriction point of view. Legumes offer the best tradeoff of low methionine, and high protein (high essential amino acids), particularly lentils and pinto beans. Adzuki beans would be a contender except that the high fiber content makes them hard to process. I prefer to get my fiber from other sources.

Table 7 was created by dividing methionine amount into the amounts of the other essential amino acids shown in Table 5. Thus, the numbers in the lysine column reflect how many times the lysine content of the food exceed the methionine content.


TABLE 7 Ratio of Essential amino acids to methionine
(Essential amino acids)/methionine
Lys = Lysine
Leu = Leucine
Thr = Threonine
Try = Typtophan
Iso = Isoleucine
Phe = Phenylalanine
Val = Valine
His = Histidine
Food Lys Leu Thr Try Iso Phe Val His
Pinto beans, cooked 17 20 10 2.9 11 13 13 6.9
Lentils, cooked 10 11 5.2 1.3 6.3 7.2 7.2 4.1
Broadbeans (Fava) 7.5 9.2 4.3 1.2 6.4 1.6 5.5 3.1
Adzuki beans 7.2 8.0 3.2 0.91 3.8 5.0 4.9 2.5
Tomato paste 5.7 5.5 4.5 1.4 3.8 4.2 4.1 3.2
Onion 5.6 4.1 2.8 1.8 4.2 3.0 2.8 1.9
Mushrooms 5.3 3.2 2.6 1.2 2.0 2.0 2.4 1.4
Lima beans, cooked 5.2 6.7 3.4 0.92 4.1 4.7 4.7 2.4
Chickpeas (garbanzos) 5.1 5.4 2.8 0.73 3.3 4.1 3.2 2.1
Soybeans, cooked 4.9 6.0 3.2 1.1 3.6 3.9 3.7 2.0
Mung beans, cooked 4.9 5.2 2.3 1.1 1.6 3.9 3.4 3.9
Blackeyed peas (cowpeas) 4.8 5.4 2.7 0.86 2.9 4.1 3.3 2.2
Whey, dry 4.8 5.3 2.8 1.2 2.8 1.5 1.2 1.8
Kidney (red) beans 4.6 5.3 2.8 0.79 2.9 3.6 3.5 1.9
Peas (raw) 3.9 3.9 2.5 0.45 2.4 2.4 2.9 1.3
Potato, baked 3.8 3.8 2.3 0.97 2.5 2.8 3.5 1.4
Cheese, American 3.8 3.4 1.3 0.57 1.8 2.0 2.3 1.6
Amaranth 3.6 5.6 2.8 0.9 3.4 3.8 3.9 1.5
Skim milk,dry 3.2 3.9 1.8 0.56 2.4 1.9 2.7 1.1
Cashew nuts 3.0 4.7 2.2 0.86 2,7 2.9 3.8 1.4
Yogurt, nonfat 3.0 3.4 1.4 0.19 1.8 1.8 2.8 0.84
Avacado flesh (Florida) 2.6 3.4 1.8 0.59 2.0 1.9 2.7 0.79
Spaghetti, cooked 2.1 4.3 2.6 0.80 3.3 3.5 _ 1.6
Corn, cooked 2.0 5.1 1.9 0.32 1.9 2.2 2.7 1.3
Rice, cooked 1.7 3.2 2.0 0.50 1.7 2.2 2.7 1.2
Egg white 1.6 2.2 1.1 0.39 1.6 1.6 1.9 0.58
Walnuts, black 1.5 3.6 1.5 0.68 2.1 2.3 2.7 1.0
Walnuts, Persian (English) 1.0 3.5 1.6 0.67 2.0 2.2 2.6 1.3


(return to contents)


Pinto beans and lentils are the high-protein foods that show the best low-methionine, high-lysine profile, by a large margin. Lentils, however, are easier to soak before cooking to remove phytates, and produce a bit less odiferous flatulance than pinto beans. Both legumes, however, are high in phytic acid and raffinose oligosaccharides. Humans lack the enzyme to digest raffinose, which passes to the lower intestine where bacteria possessing the digestive enzyme create gases which can be quite odiferous.

Soaking pinto beans for 16 hours at room temperature only reduces raffinose oligosaccharides by 10%, and 90 minutes of cooling only cuts the raffinose oligosaccharide content in half [JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY; Song,D; 54(4):1296-1301 (2006)].

Just as the objective of calorie restriction is not to live without calories, methionine is an essential amino acid that can be reduced to 60% normal consumption to obtain most of the benefit [BIOGERONTOLOGY; Caro,P; 9(3):183-196 (2008)]. That dietary objective can be met without the need to consume legumes.

(return to contents)



She was proud to be a vegan and wanted her son to live like she did. But her family members said she took her food choices too far — her diet became a danger, in their eyes, something closer to an obsession than a healthy lifestyle.

“She was going to live on water and sunlight,” her sister-in-law told CBS Pittsburgh.

When the 33-year-old woman from western Pennsylvania, Elizabeth Hawk, began feeding her 11-month-old child sparse meals of only fruit and nuts, however, that was beyond the pale.

The boy developed what the sister-in-law, Brandy Hawk, described as a severe rash. He seemed to have lost control of his motor skills, she said, rendering his hands useless. Elizabeth Hawk said allergies were the reason for his apparent malaise, not the diet.

That argument did not convince Jerry Hawk, Elizabeth’s separated husband and the father of the child. He removed his son from his estranged wife’s care, taking the boy to a Children & Youth Services agency in nearby Fayette County. From there, reported, the agency took the child to a hospital in West Virginia.

An attending physician said the lack of nutritious food, according to Pennsylvania’s WKBN, caused a “failure to thrive.” Malnourishment had hindered the boy’s ability to develop, and ignoring the skin condition could have led to septic shock.

It is not inevitable that a vegan-only menu would doom young children to sickness or starvation, as The Washington Post wrote in July. But a commitment to veganism can make raising a healthy child more challenging, as parents must ensure that a child ingests sufficient calories and the correct balance of nutrients. In 2001, for instance, a pair of vegetarian nutritionists published recommendations for vegan infants in the Journal of the American Dietetic Association:

“For the first 4 to 6 months, breast milk should be the sole food with soy-based infant formula as an alternative. Commercial soymilk should not be the primary beverage until after age 1 year. Breastfed vegan infants may need supplements of vitamin B-12 if maternal diet is inadequate; older infants may need zinc supplements and reliable sources of iron and vitamins D and B-12. Timing of solid food introduction is similar to that recommended for non-vegetarians. Tofu, dried beans, and meat analogs are introduced as protein sources around 7-8 months. Vegan diets can be planned to be nutritionally adequate and support growth for infants.”

The young boy now lives with his father. Brandy Hawk, the sister-in-law, told CBS Pittsburgh the child is “doing great” and has “turned completely around.”

Elizabeth Hawk faces charges of child endangerment and was released on her own recognizance. A preliminary hearing has been set for Nov. 14, reported.

Source: Vegan mom fed her 11-month-old only fruit and nuts. Now she faces child endangerment charges. – The Washington Post

The idea of using the body’s immune system to fight cancer has been around for a century, but only in the past half a dozen years have dramatic breakthroughs begun rocking the medical world.

“That’s when the tsunami came,” says Drew Pardoll, director of the Bloomberg-Kimmel Institute for Cancer Immunology at Johns Hopkins University, and those advances are spawning hundreds of clinical trials nationwide, plus generating intense interest from patients, physicians and investors. Yet researchers remember the past anti-cancer efforts that fizzled after initially showing promise — which explains why most say daunting hurdles and years of perseverance are still ahead.

Here’s a primer about the new treatments and how they work:

What is cancer immunotherapy?

Immunotherapy is a significantly different approach from conventional treatments such as chemotherapy or radiation. The latter attack the malignancy itself, while immunotherapy aims to empower the immune system to kill it.

Because of the immune system’s unique power, says the nonprofit Cancer Research Institute, this therapy could prove a formidable weapon against many kinds of cancer and offer long-term protection with reduced side effects.

Which immunotherapies are sparking excitement? 

Two types of immunotherapy are drawing most of the interest: checkpoint inhibitors, which remove “brakes” from the immune system, allowing it to see and go after cancer; and CAR T-cell therapy, which involves a more customized attack.

“Checkpoint” inhibitors are designed to block the ability of certain proteins to blunt or weaken the response of the immune system so it can’t recognize and go after abnormal cells. In normal times, such checkpoint proteins keep the immune system from being too aggressive and damaging the body. But cancer sometimes hijacks them and uses them to suppress the immune system’s response to disease.

The Food and Drug Administration has cleared four checkpoint inhibitors for adults: Yervoy, also known as ipilimumab; Keytruda, or pembrolizumab; Opdivo, or nivolumab, and Tecentriq, or atezolizumab. The drugs are approved for malignancies including melanoma and Hodgkin lymphoma, as well as lung, kidney and bladder cancer. The treatments also are being tested in a wide range of other cancers.

Former president Jimmy Carter was treated with Keytruda, surgery and radiation for advanced melanoma last year. He announced in December that all signs of his cancer had disappeared.

In CAR T-cell therapy, T cells — a key part of the immune system — are removed from a patient, genetically modified in the lab to target a specific cancer and infused back into the person. This treatment, available only in clinical trials, is being tested mainly for leukemia and lymphoma. The Food and Drug Administration is likely to approve the first CAR T-cell treatment next year or in 2018.

Of these two immunotherapy approaches, most research and investor interest is focused on checkpoint inhibitors. That’s because they are off-the-shelf treatments that are much easier to administer than customized T-cell therapy, said Crystal Mackall, a former National Cancer Institute researcher who’s now leading immunotherapy trials for Stanford University School of Medicine.

What are some of the main challenges in immunotherapy?

Among the biggest challenges are increasing the response rate among patients and turning initial responses into long-lasting remissions. CAR T-cell therapy often produces a high remission rate in blood-disorder trials, but a significant percentage of patients relapse.

Checkpoint inhibitors induce responses — signaling a tumor has been shrunk or stabilized — in an average of just about 20 percent of patients, said oncologist Elizabeth Jaffee, the deputy director of the Sidney Kimmel Comprehensive Cancer Center at Hopkins. Researchers need to understand why only some cases and some cancers respond. Why, for example, the treatment benefits melanoma but not pancreatic cancer. They think the key to improving effectiveness will be coming up with combination treatments, as happened with AIDS. Jaffee points out that the tide was turned against that disease only after researchers figured out how to use a “cocktail” of medications to keep people with HIV from developing AIDS.

Nationwide, combination trials are testing the simultaneous use of two or more checkpoint inhibitors, a checkpoint inhibitor with a CAR T-cell therapy or an immunotherapy plus radiation and chemotherapy. But combining these can increase safety risks.

Jill O’Donnell-Tormey, chief executive of the Cancer Research Institute, said researchers also are trying to understand tumors’ “micro-environments,” which contain cells and other factors that appear to sometimes suppress the immune system’s response to cancer. The institute, along with the American Association for Cancer Research and two European groups, sponsored the three-day conference in New York.

What are immunotherapy’s downsides?

By revving up the immune system, immunotherapy can cause sometimes serious damage to healthy tissue and organs. Researchers are working on ways to limit or even reverse the potential toxicity, but much work needs to be done.

CAR T-cell therapy poses two types of safety risks. Almost all patients get sick with flu-like symptoms, including high fever and pain, a week or so after the treatment; some end up in intensive care. The treatment also can cause brain swelling that can be fatal.

Yet standard treatments have major side effects as well. Chemotherapy and radiation, when used for children with leukemia, can cause long-term problems such as secondary cancers, infertility and heart damage. In many ways, researchers say, immunotherapy is less toxic over the long term and might eventually be a good first-line alternative to chemo and radiation.

Immunotherapy can carry higher price tags. For example, Merck’s checkpoint inhibitor, Keytruda, costs about $150,000 a year. Once CAR T-cell therapies are approved by the Food and Drug Administration, they may cost hundreds of thousands of dollars a year, according to some analysts. If the treatments are used as directed by the agency, chances are good that insurance will pay for at least some of that.

Does immunotherapy work for children?

Immunotherapy in kids is a mixed picture.

Checkpoint inhibitors are only now being tested extensively in children, so it will take time to see how well they work. But very early-stage studies suggest that they may not be as effective as in adults. One theory holds that these drugs work better in cancers with many mutations — and pediatric cancers tend to have many fewer mutations.

CAR T-cell treatment, on the other hand, is being widely tested in children and has shown impressive effectiveness against acute lymphoblastic leukemia, the most common childhood leukemia.

How do I find immunotherapy treatments?

Talk first to your doctor, who should be able to help you find appropriate medication or clinical trials for unapproved treatment. Trials sponsored by the National Cancer Institute can be found at Studies also are listed on the website –though that doesn’t signify government endorsement or approval. Another resource is the Cancer Research Institute’s Clinical Trial Finder. 

Read more:

Family hopes immunotherapy will save young girl with tumor

Long-term survival rates lengthen for melanoma patients on immunotherapy

Brain cancer replaces leukemia as leading cause of cancer deaths in children

While confident that immunotherapy will play an increasing role in cancer treatment, researchers must overcome some obstacles.

Source: Cancer immunotherapy is moving fast. Here’s what you need to know. – The Washington Post

Driven by technological progress, human life expectancy has increased greatly since the nineteenth century. Demographic evidence has revealed an ongoing reduction in old-age mortality and a rise of the maximum age at death, which may gradually extend human longevity. Together with observations that lifespan in various animal species is flexible and can be increased by genetic or pharmaceutical intervention, these results have led to suggestions that longevity may not be subject to strict, species-specific genetic constraints. Here, by analysing global demographic data, we show that improvements in survival with age tend to decline after age 100, and that the age at death of the world’s oldest person has not increased since the 1990s. Our results strongly suggest that the maximum lifespan of humans is fixed and subject to natural constraints.

Source: Evidence for a limit to human lifespan : Nature : Nature Research

Source: Next Big Future: Research towards enabling minds as healthy and productive as twenty year olds in people who are 60-80

Source: Next Big Future: Toxic air pollution nanoparticles discovered in the human brain and could be a possible cause of Alzheimer’s disease

Source: Next Big Future: Ten percent have immune systems that ignore HIV and thus the immune system is saved and AIDS does not develop

Source: Next Big Future: Towards pills that can mimick many of the benefits of exercise

Source: Next Big Future: DARPA funds seven teams to modulate nerves to treat disease

Skeletal muscle secretes several bioactive proteins from within the cell into extracellular fluid. The secretion of several proteins, whose levels increase in response to exercise, can mediate exercise-induced benefits such as metabolic improvement, anti-inflammation, and muscle hypertrophy. We recently found a novel muscle-secreted protein SPARC which may be fundamental for the colon cancer prevention mechanism of regular exercise, demonstrated by various epidemiological studies. Many other proteins, along with c-miRNAs in exosome and metabolites, secreted from muscle have yet to be identified. In the future, the presence and beneficial function of more unknown bioactive factors are expected to be discovered, which strengthens the development of sports science.

Source: BioDiscovery Skeletal muscle: novel and intriguing characteristics as a secretory organ

An estimated 180,890 American men will be diagnosed with prostate cancer this year. The disease will also take the lives of 26,120 patients. But according to a new 10-year study conducted on more than 1,500 men in the United Kingdom, those who are diagnosed may want to hold off on starting aggressive treatment right away.Typically, men who are diagnosed with prostate cancer are given several options: Have surgery to remove all or part of the gland, undergo radiotherapy to reduce any tumors, or take a “watch and wait” active monitoring approach, which involves additional screenings and biopsies but no treatment, as the cancer can grow so slowly that it often doesn’t present a medical problem for those who have it. The study, published Wednesday in the New England Journal of Medicine, found that men who received treatment ― either surgery or radiotherapy ― were better able to limit their cancer from spreading. But this didn’t necessarily mean immediate treatment led to better overall outcomes. Among the men who took a “watch and wait” approach, nearly half didn’t need any additional treatment. As a result, they avoided the negative side effects that come with surgery and radiation, such as bowel and urinary incontinence, sexual dysfunction and life-threatening cardiovascular issues. Indeed, no matter what approach the men were randomized to, they weren’t likely to die of either the cancer itself, cancer treatment or other causes after ten years. That said, the patients in this study are still being followed because deaths from prostate cancer are usually measured after 15 to 20 years. “This paper really underscores that an active surveillance approach is good for many patients, but there are some who still need upfront treatment,” said Dr. Timothy J. Daskivich, a urologic oncologist and director of health services research for the Cedars-Sinai department of surgery in Los Angeles. “Time will tell if we can sort these patients out in the future.”While some men will need immediate treatment, further developments that help urologists identify low-risk and high-risk patients will be key to making sure that only men who need it the most will have to undergo surgery or radiation.How outcomes differed according to the treatmentThe Prostate Testing for Cancer and Treatment (ProtecT) trial recruited U.K. men ages 50 to 69 from 1999 to 2009. Of 2,664 men who received a diagnosis of prostate cancer, 1,643 agreed to be randomized to any one of three of the most common prostate cancer treatments: active monitoring (545 men), radical prostatectomy (553 men) or radiotherapy (545 men).The men were not further classed into low- or high-risk groups based on the features of their tumor or the levels of prostate-specific antigens in their blood. This is what the researchers found: There was no difference in death rates. The scientists followed up with the men after a median of 10 years and found that while there were less prostate cancer-specific deaths in the groups that got radiation or surgery, the difference was not significant, and all groups had at least a 98.8 percent survival rate when it came to prostate cancer-specific deaths. In all, the death rate from prostate cancer across all groups was about one percent after a median of ten years.There was a difference in the rate of cancer spread. Of the men who were randomized to the active monitoring group, 112 experienced disease progression, including cancer spread, which was higher than in the surgery and radiation groups (46 and 46, respectively). While there appears to be a slight advantage to getting treated immediately after a prostate cancer diagnosis to avoid cancer progression, longer-term follow up is needed to see if these results are significant, the researchers wrote. What this means for U.S. menIn an opinion piece that accompanied the study, Dr. Anthony V. D’Amico of the Dana-Farber Cancer Institute concluded that when compared with surgery or radiation, active monitoring leads to increased cancer spread, and that active monitoring should only be an option for men who already have another life-shortening disease that is expected to result in death after less than 10 years. Daskivich, who was not involved with the study, was more optimistic about active monitoring’s place in prostate cancer care, and included “low-risk” men among those who should consider active monitoring instead of surgery or radiation, even though the study didn’t stratify men according to high or low risk cancers. However, doctors still need more tools that help them confidently sort patients according to risk.“It’s all about treatment selection ― picking out patients who have higher risk disease who should get treated upfront, and those who have lower risk features who don’t need to be treated and managed with active surveillance,” said Daskivich. “That’s going to be the challenge in the coming years.”The results of the study won’t change much for most middle-aged men in the U.S., Daskivich

Source: Treating Prostate Cancer Is Often No Better Than Doing Nothing | Huffington Post

After three months, more than a third of study participants grew back more than half of their lost hair

Source: Arthritis drug Xeljanz may help with the hair loss condition alopecia – CBS News

Drug deaths over the past 15 years have been rising so rapidly that experts say they’ve rarely, if ever, seen anything like it.

This is America on drugs: A visual guide

Updated 11:28 AM ET, Fri September 23, 2016

In modern history, few things have caused such a sharp spike in US deaths as drug overdoses.

CNN reached out to every state for the latest statistics on drug deaths, with half providing data from 2015. It found that drugs deaths continue to rise rapidly in many states.


Epidemiologists in several states blame the increasing number of drug-related deaths on greater use of heroin and synthetic opioids, such as fentanyl.
“If you look at the cause of death, we just don’t normally see increases like this,” said Robert Anderson, the chief of the mortality statistics branch at the National Center for Health Care Statistics at the Centers for Disease Control and Prevention.


Drugs are the leading cause of accidental death in this country. Fatal overdoses surpassed shooting deaths and fatal traffic accidents years ago.
For perspective on how fast drug deaths have risen, Anderson said, consider the sharp rise in heart disease in the early half of the 20th century. It took about 50 years for the rate of heart disease to double. It took drug deaths a fraction of that time.
The only thing comparable might be the HIV epidemic when it first reached the United States in the late 1980s, when there were no drugs to treat it. But unlike with HIV, where demonstrators took to the streets to demand help, the drug epidemic often happened out of the spotlight.
That might be because drug deaths have disproportionately hit small towns and rural America, mainly in Appalachia and in the Southwest, far away from the eye of the national media. It became a particularly dangerous problem for middle-age white men and women.


Heroin-related deaths increased 439% from 1999 to 2014. As of 2014, heroin-related deaths had more than tripled in five years and quintupled in 10 years.
In 2014, opioids were involved in 28,647 deaths — 61% of all US drug overdose deaths — and 10,574 were related to heroin, in particular. Data from 2014 reflects “two distinct but interrelated trends,” the CDC notes, a longterm increase in overdose deaths due to prescription opioids and a surge in illicit opioid overdose deaths, mostly related to heroin.


In 2010, West Virginia moved into the top spot on the list of states with the highest number of drug deaths. From 2014 to 2015 alone, the number of deaths in that state increased by 12%. New Hampshire saw a 24% increase in deaths in that same time period.
How to get help

Struggling with addiction or know someone who is? Here are several organizations that help addicts beat back their habits and regain their lives.

The state that has struggled the longest is New Mexico. Its Rio Arriba County has the highest number of drug deaths for a single county in the United States, according to data analysis of more than 15 years of records from the CDC and state departments of health. Looking at drug death data from 1999 to 2014, New Mexico most often holds the No. 1 spot for the highest number of deaths.
The sharp uptick in deaths seems to coincide with Americans’ increasing use of drugs like illicit fentanyl.
Pop star Prince died of a fentanyl overdose in April. The pain reliever is often given to cancer patients and is more than 100 times as strong as morphine and 30 to 50 times more powerful than heroin.


Appalachia has struggled with a number of high-profile overdose cases recently.
West Virginia is home to six of the top 20 counties in the country with the largest concentrations of drug-related deaths. Kentucky has the most, with nine counties on that list. Ohio has also been hard-hit by the epidemic.

Source: This is America on drugs: A visual guide –

The opinions voiced in this material are for general information only and are not intended to provide specific advice or recommendations to any individual. For your individual planning and investing needs, please see your investment professional.

Jonathan DeYoe has been a financial advisor in San Francisco for the past two decades, giving him a first-row seat to the unprecedented explosion of wealth creation ushered in by tech industry. Here are his 10 best pieces of money advice.  

1. Put your money where your happiness is.

It is an incredible understatement to say the San Francisco Bay Area is an expensive place to live. Whether you come from money or just joined Facebook, you will have to make trade-offs to keep your head above water here — make the tradeoffs that are appropriate for you.

You don’t have to drive a Tesla, you aren’t required to live in a rad pad in the Mission, and you don’t need designer duds or the newest iGadget. Give up the trappings of success that hold no personal meaning for you and focus your financial resources on activities and affordable luxuries that build your particular brand of happiness, like a rock-climbing course and killer burritos.

2. Invest in yourself early and often.

If you are an engineer or scientist, you must stay on top of your technical game, but don’t hesitate to spend money on coaching or classes to develop your communication and leadership skills, as well.

If you are a professional, constantly hone your craft. Read broadly within your industry, enroll in continuing education, obtain advanced professional designations, and find opportunities to network with new people.

The dollars you dedicate to increasing your intellectual capacity and enhancing your ability to work well with others can boost your income substantially. Lifelong learning and professional development both lead to long-term success. The sooner you embark upon rigorous self-improvement, the longer you’ll enjoy the fruits of your labors, so invest in yourself now.

3. Don’t count your chickens before they’re hatched.

Equity compensation in the form of RSUs and stock-options can be a wonderful addition to your income and asset base. Over the years, I have seen many folks become wealthy through their company stock programs.

However, I have watched just as many stock compensation packages go up in smoke. Never forget that your stock has NO real value until you are fully vested and someone is willing to give you cash money for it on the open market. Just because a VC gives your company a sky-high valuation does not mean you’ll receive that valuation if (not when) the stock ever trades publicly.

Do not borrow against your stock. Do not pledge your stock as collateral to buy a massive house on Russian Hill. Do not count your stock among your REAL assets until it is actually part of your real assets. Better yet, don’t even count the eggs in your basket until you’ve hatched and sold them.

4. Get your foot in the front door.

Yes. The cost of housing in the Bay Area is ridiculous! When I read a 2015 San Francisco Chronicle article claiming that a Mountain View, California, resident was renting a tent in their backyard with bathroom access but no kitchen privileges for $900, I knew that we had all gone off the deep-end.

Today the median sales price for San Francisco homes is over $1.1 Million! No one is happy about real estate prices in the greater Bay Area, but if you are planning to stay here for five to seven years or more, consider buying a home. It doesn’t have to be beautiful or close-in. Alameda and Contra Costa counties are still relatively affordable. Just get your foot in the front door.

If you stay on the sidelines, don’t be surprised if the market continues to run away from you. Expect rare short-term dips, like we saw in 2008-2009, to effervesce quickly due to decades of housing policy that limited building.

And while many cities have strong rent-control laws, remaining a renter means your housing costs will continue to grow — perhaps pricing you out of the rental market and into that tent in someone’s backyard.

5. Turn a passion into a side hustle into a business.

First and foremost, do not neglect your day job. If your 9-to-5 office gig pays the bills and affords you ample pocket money, pursuing your passion for cooking by taking a second job as a sous-chef in a neighborhood restaurant won’t help you get ahead. You will burn out.

Nonetheless, there are hundreds of creative ways to capitalize on your hidden and not so hidden talents. My 11-year-old son bakes pies for neighbors, cat sits, and walks dogs. If you like baking or pets, why not?

You prefer to drive? Try Lyft or Uber. You love to write? Start a blog and learn how to drive traffic with social media. You’re a crack web designer? Register on freelance sites like Upwork or You have a spare bedroom? You get the idea!A driver displays Uber and Lyft ride sharing signs in his car windscreen in Santa Monica, California, U.S., May 23, 2016.  REUTERS/Lucy Nicholson/Files Finding a side hustle — like driving for Uber or Lyft — is a great idea, so long as you’re passionate about it and it won’t burn you out.Thomson Reuters

6. Create a financial road map.

Where do you want to go in life? As with any journey, if you have a specific destination in mind, you will need to take specific steps to get there. Planning your route is essential.

No one can afford to experience everything they want, but you can accomplish what is most important to you by creating a financial road map. Decide what tradeoffs you’re willing to make to achieve your goals. Take staycations until you’ve saved the down payment on a new house? Live with your old car six more months so that you can afford that new motorcycle next year? Drive Uber on week-ends to cover the cost of coding classes?

Where are you now? In debt? $20,000 away from that down payment? Underemployed? No need for shame. Accept your today and plan for a better tomorrow. What tradeoffs will you make? How much do you need to save? How are you going to get where you want to be? Planning makes things happen for you! NOT planning lets them happen to you.

7. Make your health a priority.

There are actually significant financial benefits to being healthy.

It probably comes as no surprise that healthier people have higher energy levels, improved resistance to illness, improved moods, higher self-esteem, better brain function, reduced fatigue, and less anxiety. But research indicates that healthier people may earn more and spend less, as well.

Good health while you’re young gives you the energy and focus to work harder and smarter, which can lead to better raises and more promotions, which translates into increased lifetime earnings. And good health later in life means fewer doctors visits, fewer medications, and hopefully decreased long-term care expenses as you age.

8. Save at least 10% of every dime you make.

Or, as the familiar saying goes, “Pay yourself first.”

Once you got your first “real” job and started earning more, you probably started spending more, too. If that trend continues every time you get a promotion or better job, you will never get ahead. At some point, you must make a conscious decision to save a specific portion of your income every single month. These savings will form the foundation upon which your entire financial life can be built.

Start by saving at least 10% of your gross salary every paycheck, and increase your savings 1% each year until you are saving 20% of your income. Use those initial savings to establish a cash emergency fund with six months to two years of living expenses. At the same time, take advantage of the tax breaks and “free” money you get from participating in your company’s 401(k) matching program. Next, pay-off your high interest debt. Then max out your 401(k), ROTH, and IRA combo, after consulting with your tax professional. The final step is to save even more in a taxable investment account and/or pay down your low interest debts.

9. Invest 90% of your liquid assets in an appropriately allocated, broadly diversified, and annually rebalanced basket of publicly traded securities.

I expect I will get some healthy Bay Area blow-back for this statement: Your investing prowess will not lead to “outperformance” in the long run.

Timing the markets, stock selection, and economic predictions may be an enduring part of the investment landscape, but none of those strategies offer a repeatable process for financial success. Luck often plays a much bigger role than skill when it comes to investment performance.

There is plenty of research on portfolio construction available to anyone willing to look. There is no evidence to support the idea that recent past performance will persist into the future or that folks dedicated to the timing and selection have been or will be successful doing so. Stock-picking requires repeated luck. Asset allocation, diversification, and rebalancing rely on something we can control, our consistent behavior, patience, and discipline.

10. Always be mindful of the big picture.

The course of human social and economic history expresses itself in a very long upward trend. That upward trend is often punctuated by short-term market upheavals, which are amplified by Wall Street and the financial press.

Stock markets and the financial media constantly over-correct in both directions in a seemingly endless cycle. Upside yields to downside. Excitement leads to despair. The good news? Today’s losses sow the seeds of future gain. You can’t consistently predict short-term outcomes because the economic and market details are ever-changing. Nonetheless, the big picture remains the same. Instead of reacting and over-reacting to the markets whims, be mindful of the big picture and stick to your thoughtfully constructed investment program and financial plan.

Jonathan K. DeYoe, AIF and CPWA, is the author of Mindful Money: Simple Practices for Reaching Your Financial Goals and Increasing Your Happiness Dividend. He is the founder and president of DeYoe Wealth Management in Berkeley, California, and blogs at the Happiness Dividend Blog. Financial planning and investment advisory services offered through DeYoe Wealth Management, Inc., a registered investment adviser.

Source: Best pieces of money advice from a San Francisco wealth advisor – Business Insider

There are also natural compounds that elevate sirtuins—one is resveratrol, which is already sold as a dietary supplement today. Another is called NAD. NAD—Nicotinamide adenine dinucleotide—is one of the most compelling bits of chemistry related to aging. Its presence in the body is directly correlated with the passage of time: An elderly man will have about half the levels of NAD is his body as a young person. There’s no amount of healthy eating or exercise that can stop the decline. But in a scientific

Source: One Of The World’s Top Aging Researchers Has A Pill To Keep You Feeling Young | Co.Exist | ideas + impact

Archival documents reveal how the sugar industry secretly funded heart disease research by Harvard professors

Archival documents reveal how the sugar industry secretly funded heart disease research by Harvard professors


The sugar industry has a long history of skewing nutrition science, a new report suggests. By combing through archival documents from the 1950s and 1960s, researchers from the University of California, San Francisco (UCSF), report that the sugar industry sponsored research that turned attention away from the sweetener’s link to heart disease and toward fat and cholesterol as the bigger culprits.

The documents the researchers reviewed in their report, published Monday in JAMA Internal Medicine, included correspondence between the Sugar Research Foundation (SRF) and nutrition professors at the Harvard School of Public Health. The letters discussed the SRF’s effort to respond to growing research linking sugar to coronary heart disease.

In 1954, SRF then-president Henry Hass gave a speech to the American Society of Sugar Beet Technologists that highlighted opportunities for the sugar industry to expand by encouraging people to adopt a low-fat diet. He said:

“Leading nutritionists are pointing out the chemical connection between [Americans’] high-fat diet and the formation of cholesterol which partly plugs our arteries and capillaries, restricts the flow of blood, and causes high blood pressure and heart trouble… if you put [the middle-aged man] on a low-fat diet, it takes just five days for the blood cholesterol to get down to where it should be… If the carbohydrate industries were to recapture this 20 percent of the calories in the US diet (the difference between the 40 percent which fat has and the 20 percent which it ought to have) and if sugar maintained its present share of the carbohydrate market, this change would mean an increase in the per capita consumption of sugar more than a third with a tremendous improvement in general health.”

What appears to have happened next were efforts by the SRF to increase skepticism over sugar’s link to heart troubles. In 1967, an SRF-funded report led by Harvard nutrition professors was published in the New England Journal of Medicine. The report reviewed the available evidence that linked various nutrients to heart disease and argued that epidemiological and animal studies that linked sugar with heart disease were limited, and suggested the available science wasn’t up to snuff. The review also highlighted studies that linked saturated fat to heart problems, without the same critiques. The review was published in the journal without disclosing the sugar industry’s funding or role in making the study happen in the first place. (Later, in 1984, the NEJM began requiring disclosure of conflicts of interest.)

The Sugar Association—which is the current name of the SRF—released a statement saying, in part: “We acknowledge that the Sugar Research Foundation should have exercised greater transparency in all of its research activities, however, when the studies in question were published funding disclosures and transparency standards were not the norm they are today. Beyond this, it is challenging for us to comment on events that allegedly occurred 60 years ago, and on documents we have never seen.”


It’s not the first time researchers have found links between sugar industry connections and nutrition science. The same team of UCSF researchers behind the new study previously used sugar industry documents to reveal how advocacy groups influenced federal cavity prevention recommendations.

“What struck me was that I thought the evidence the researchers summarized in the review was stronger and more consistent for a sugar effect [on coronary heart disease] than for a fat effect,” says study author Stanton Glantz of UCSF. “No matter how good the evidence was linking sugar to heart disease, there was something wrong with it. But for fat, the evidence was fine. They set up a false dichotomy.”

In an editorial published alongside new study, Marion Nestle, a professor in the Department of Nutrition and Food Studies at NYU, writes that the Harvard professors who conducted the review knew what the funders wanted and provided those findings. “Whether they did this deliberately, unconsciously, or because they genuinely believed saturated fat to be the greater threat is unknown,” Nestle writes. “But science is not supposed to work this way. The documents make this review seem more about public relations than science.”

Study author Cristin Kearns of UCSF says she was surprised by the complexity of the sugar industry strategy. “It was such a sophisticated way to protect the industry’s interests so early on,” she says. “It’s overwhelming to unravel the different ways the industry has influenced this debate. The scope is probably much greater than we imagined.”

For its part, the Sugar Association said, in statement: “The Sugar Association is always seeking to further understand the role of sugar and health, but we rely on quality science and facts to drive our assertions.”

Source: How the Sugar Lobby Skewed Health Research | TIME

An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease

  • Steve HorvathEmail authorView ORCID ID profile,
  • Michael Gurven,
  • Morgan E. Levine,
  • Benjamin C. Trumble,
  • Hillard Kaplan,
  • Hooman Allayee,
  • Beate R. Ritz,
  • Brian Chen,
  • Ake T. Lu,
  • Tammy M. Rickabaugh,
  • Beth D. Jamieson,
  • Dianjianyi Sun,
  • Shengxu Li,
  • Wei Chen,
  • Lluis Quintana-Murci,
  • Maud Fagny,
  • Michael S. Kobor,
  • Philip S. Tsao,
  • Alexander P. Reiner,
  • Kerstin L. Edlefsen,
  • Devin Absher and
  • Themistocles L. Assimes
Contributed equally
Genome Biology201617:171

DOI: 10.1186/s13059-016-1030-0

Received: 6 July 2016

Accepted: 18 July 2016

Published: 11 August 2016



Epigenetic biomarkers of aging (the “epigenetic clock”) have the potential to address puzzling findings surrounding mortality rates and incidence of cardio-metabolic disease such as: (1) women consistently exhibiting lower mortality than men despite having higher levels of morbidity; (2) racial/ethnic groups having different mortality rates even after adjusting for socioeconomic differences; (3) the black/white mortality cross-over effect in late adulthood; and (4) Hispanics in the United States having a longer life expectancy than Caucasians despite having a higher burden of traditional cardio-metabolic risk factors.


We analyzed blood, saliva, and brain samples from seven different racial/ethnic groups. We assessed the intrinsic epigenetic age acceleration of blood (independent of blood cell counts) and the extrinsic epigenetic aging rates of blood (dependent on blood cell counts and tracks the age of the immune system). In blood, Hispanics and Tsimane Amerindians have lower intrinsic but higher extrinsic epigenetic aging rates than Caucasians. African-Americans have lower extrinsic epigenetic aging rates than Caucasians and Hispanics but no differences were found for the intrinsic measure. Men have higher epigenetic aging rates than women in blood, saliva, and brain tissue.


Epigenetic aging rates are significantly associated with sex, race/ethnicity, and to a lesser extent with CHD risk factors, but not with incident CHD outcomes. These results may help elucidate lower than expected mortality rates observed in Hispanics, older African-Americans, and women.


DNA methylation Epigenetic clock Race Gender Aging Coronary heart disease Hispanic paradox Black/white mortality cross-over


Many demographic and epidemiological studies explore the effects of chronological age, race/ethnicity, and sex on mortality rates and susceptibility to chronic disease [1, 2, 3, 4, 5], but it remains an open research question whether race/ethnicity and sex affect molecular markers of aging directly. To what extent clinical biomarkers of inflammation, dyslipidemia, and immune senescence relate to cellular markers of aging also remains an open question. One major challenge is the lack of agreement on how to define and measure biological aging rates [6]. Many biomarkers of aging have been proposed ranging from clinical markers (such as whole-body functional evaluations and gait speed) to molecular markers such as telomere length [7, 8]. Available biomarkers capture only particular aspects of aging. For example, African Americans have been shown to have longer telomere lengths than Caucasians [9], despite significantly higher levels of inflammation, lower average life expectancies, and higher disease incidence. To date, no studies have employed epigenetic measures to estimate and compare molecular aging rates among gender or racial/ethnic groups.

Measures incorporating DNA methylation levels have recently given rise to a new class of biomarkers that appear informative of aging given that age has a profound effect on DNA methylation levels in most human tissues and cell types [10, 11, 12, 13, 14, 15, 16, 17, 18]. Several recent studies have measured the epigenetic age of tissue samples by combining the DNA methylation levels of multiple dinucleotide markers, known as Cytosine phosphate Guanines or CpGs [19, 20, 21]. We recently developed the epigenetic clock (based on 353 CpGs) to measure the age, known as “DNA methylation age” or “epigenetic age,” of assorted human cell types (CD4+ T cells or neurons), tissues, and organs—including blood, brain, breast, kidney, liver, lung [20], and even prenatal brain samples [22]. The epigenetic clock is an attractive biomarker of aging because it applies to most human tissues and its accurate measurement of chronological age is unprecedented.

The following evidence shows that the epigenetic clock captures aspects of biological age. First, the epigenetic age of blood has been found to be predictive of all-cause mortality even after adjusting for chronological age and a variety of known risk factors [23, 24, 25]. Second, the blood of the offspring of Italian semi-supercentenarians (i.e. participants who reached an age of at least 105 years) has a lower epigenetic age than that of age-matched controls [26]. Third, the epigenetic age of blood relates to frailty [27] and cognitive/physical fitness in the elderly [28]. The utility of the epigenetic clock method has been demonstrated in applications surrounding obesity [29], Down’s syndrome [30], HIV infection [31], Parkinson’s disease [32], Alzheimer’s disease-related neuropathologies [33], lung cancer [34], and lifetime stress [35]. Here, we apply the epigenetic clock to explore relationships between epigenetic age and race/ethnicity, sex, risk factors of coronary heart disease (CHD), and the CHD outcome itself.


Blood datasets and racial/ethnic groups

An overview of our DNA methylation datasets can be found in Table 1. We analyze multiple sources of DNA: mostly blood, saliva, and lymphoblastoid cell lines. In addition, brain datasets were used to compare men and women (Table 2). We considered the following racial/ethnic groups (Table 1): 1387 African Ancestry (African Americans and two groups from Central Africa), 2932 Caucasian (non-Hispanic whites), 657 Hispanic, 127 East Asians (mainly Han Chinese), and 59 Tsimane Amerindians.

Table 1

Overview of the DNA methylation datasets. The rows correspond to the datasets used in this article. Columns report the tissue source, DNA methylation platform, number of participants, access information, and citation and a reference to the use in this text

Tissue source


Participants (n)

Women (n)

African Ancestry, Caucasian, Hispanic, Tsimane, East Asian (n)

Mean age (years) (range)




1. Women’s Health Initiative (blood)




676, 353, 433, 0, 0

63 (50–80)


Current article


2. Bogalusa (blood)




288, 681, 0, 0, 0

43 (29–51)


Current article


3. PEG (blood)




0, 289, 46, 0, 0

70 (36–91)


Current article


4. Saliva from PEG




0, 166, 93, 0, 0

69 (36–88)


Current article


5. Older Tsimane and others




0, 235, 38, 37, 0

66 (35–92)


Current article


6. Younger Tsimane and Caucasians




0, 24, 0, 22, 0

15 (2–35)


Current article


7. East Asians vs. Caucasians (PSP samples removed)




0, 279, 0, 0, 33

68 (34–93)


Li, 2014 [73]


8. African populations




256, 0, 0, 0, 0

40 (16–90)


Fagny, 2015 [42]


9. Cord blood




92, 70, 0, 0, 0

0 (0–0)


Adkins, 2011 [44]

10. Male saliva




0, 59, 32, 0, 0

29 (21–55)


Liu, 2010 [74]

11. Female saliva




0, 27, 15, 0, 0

27 (21–55)


Liu, 2010 [74]

12. Lymphoblastoid cell lines




75, 68, 0, 0, 94

34 (5–73)


Heyn, 2013 [88]

Additional file 1

Table 2

Description of brain datasets for evaluating the effect of gender. Additional details can be found in “Methods


Participants (n)

Men (%)

Age mean ± SE [min, max]

Brain region

Brain tissue samples (n)

Study 1


41 %

84.0 ± 9.8 [40, 105]









Study 2


68 %

48.0 ± 23.2 [16, 96]









Study 3


63 %

44.3 ± 9.6 [19, 68]



Study 4


62 %

64.4 ± 17.4 [25, 96]





Study 5


66 %

52.3 ± 29.8 [1, 102]





Study 6


37 %

88.5 ± 6.6 [66, 108]



CRBLM cerebellum, DLPFC dorsolateral prefrontal cortex, EC entorhinal cortex, FCTX frontal cortex, PFCTX prefrontal cortex, PONS pons, STG superior temporal gyrus, TCTX temporal cortex

Accuracy of the epigenetic clock

DNAm age, also referred to as epigenetic age, was calculated in human samples profiled with the Illumina Infinium 450 K platform using a previously described method [20]. As expected, we found DNAm age to have a strong linear relationship with chronological age in blood and saliva (correlations in the range of 0.65–0.93, Figs. 1, 2, 3, 4, and 5) and in lymphoblastoid cell lines (r = 0.59; Additional file 1). Based on a spline regression line, we defined a “universal” measure of epigenetic age acceleration, denoted “Age Accel.” in our figures, as the difference between the observed DNAm age value and the value predicted by a spline regression model in Caucasians. The term “universal” refers to the fact that this measure can be defined in a vast majority of tissues and cell types with the notable exception of sperm [20]. A positive value of the universal age acceleration measure indicates that DNA methylation age is higher than that predicted from the regression model for Caucasian participants of the same age. Our intrinsic and extrinsic age acceleration measures (see “Methods”) only apply to blood data. A measure of intrinsic epigenetic age acceleration (IEAA) measures cell-intrinsic epigenetic aging effects that are not confounded by extra-cellular differences in blood cell counts. The measure of IEAA is an incomplete measure of the age-related functional decline of the immune system because it does not track age-related changes in blood cell composition, such as the decrease of naïve CD8+ T cells and the increase in memory or exhausted CD8+ T cells [36, 37, 38]. The measure of extrinsic epigenetic age acceleration (EEAA) only applies to whole blood and aims to measure epigenetic aging in immune-related components. It keeps track of both intrinsic epigenetic changes and age-related changes in blood cell composition (see “Methods”). The estimated blood cell counts, which are used in these measures, correlate strongly with corresponding flow cytometric measurements from the MACS study (Additional file 2): r = 0.63 for CD8 + T cells, r = 0.77 for CD4+ T, r = 0.67 B cell, r = 0.68 naïve CD8+ T cell, r = 0.86 for naïve CD4+ T, and r = 0.49 for exhausted CD8+ T cells.
Fig. 1

Intrinsic epigenetic age acceleration in Caucasians and Hispanics. ad DNA methylation age (y-axis) versus chronological age (x-axis) in (a) Women’s Health Initiative, (b) blood data from PEG, (c) dataset 5, (d) saliva data from PEG. Dots corresponds to participants and are colored by ethnic group (gray = Caucasian, blue = Hispanic). The gray line depicts a spline regression line through Caucasians. We define two measures of age acceleration based on DNAm age. eg The bar plots relate the universal measure of epigenetic age acceleration to race/ethnicity, which is defined as residual to the spline regression line through Caucasians, i.e. the vertical distance of a point from the line. By definition, the mean age acceleration in Caucasians is zero. h, m Results after combining the three blood datasets using Stouffer’s meta-analysis method. i Age acceleration residual versus ethnicity in the saliva data from PEG. jm The y-axis reports the mean value of IEAA, which is defined as residual from a multivariate regression model that regresses DNAm age on age and several measures of blood cell counts. Each bar plot reports 1 standard error and the p value from a group comparison test (ANOVA). n Age acceleration in blood versus age acceleration in saliva for the subset of PEG participants for whom both data were available
Fig. 2

Intrinsic epigenetic age acceleration in Tsimane, Hispanics, East Asians, and Caucasians. ac DNA methylation age (y-axis) versus chronological age (x-axis) in (a) dataset 5, (b) dataset 6, (c) dataset 7. Dots corresponds to participants and are colored by race/ethnicity (green = African American, gray = Caucasian, blue = Hispanic, red = Tsimane, orange = East Asians). The gray line depicts a spline regression line through Caucasians. We define two measures of age acceleration based on DNAm age. df The bar plots relate the universal measure of epigenetic age acceleration to race/ethnicity, which is defined as residual to the spline regression line through Caucasians, i.e. the vertical distance of a point from the line. gi The y-axis reports the mean value of IEAA, which is defined as residual from a multivariate regression model that regresses DNAm age on age and several measures of blood cell counts. Each bar plot reports 1 standard error and the p value from a group comparison test (ANOVA)
Fig. 3

Intrinsic epigenetic age acceleration versus African or European Ancestry. ac DNA methylation age (y-axis) versus chronological age (x-axis) in (a) Women’s Health Initiative, (b) Bogalusa study. Dots corresponds to participants and are colored by race/ethnicity (green = African Ancestry, gray = Caucasian). The gray line depicts a spline regression line through Caucasians. We define two measures of age acceleration based on DNAm age. c, d The bar plots relate the universal measure of epigenetic age acceleration to race/ethnicity, which is defined as residual to the spline regression line through Caucasians. e, h Results after combining the two blood datasets using Stouffer’s meta-analysis method. f, g The y-axis reports the mean value of IEAA, which is defined as residual from a multivariate regression model that regresses DNAm age on age and several measures of blood cell counts. Each bar plot reports 1 standard error and the p value from a group comparison test (ANOVA)
Fig. 4

Extrinsic epigenetic age acceleration and blood cell counts across groups. EEAA versus race/ethnicity in (a, q) Women’s Health Initiative, (b) blood data from PEG, (c, k) dataset 5, (l) dataset 6, (o) dataset 7, (r) Bogalusa study. Flow cytometric, age adjusted estimates (e, t) naïve CD8+ T and (j, x) naïve CD4+ T cell counts in the WHI LLS. Age adjusted estimates of naïve CD4 + T cells based on DNA methylation data from (f, u) Women’s Health Initiative, (g) blood data from PEG, (h, m) dataset 5, (n) dataset 6, (p) dataset 7, (v) Bogalusa study. (d, i, s, w) Meta-analysis across the respective datasets based on Stouffer’s method
Fig. 5

Analysis of African rainforest hunter-gatherers and farmers. a DNAm age versus age using 256 blood samples from [42]. The points are colored as follows: magenta = AGR (urban setting), turquoise = AGR (forest), brown = RHG (forest). Group status versus (b) universal age acceleration, (d) intrinsic age acceleration, (f) extrinsic age acceleration. Habitat versus (c) universal age acceleration, (e) intrinsic age acceleration, (g) extrinsic age acceleration. (h, i) are analogous to (a, b) but the y-axis is based on a DNAm age estimate that excluded CpG that were located near SNPs. In this robustness analysis, we removed CpG probes containing genetic variants at a frequency higher than 1 % in the populations studied

Hispanics have a lower intrinsic aging rate than Caucasians

We find that Hispanics have a consistently lower IEAA compared to Caucasians (p = 7.1 × 10–10, Fig. 1m). An important question is whether the observed differences in blood can also be observed in other tissues. Using a novel saliva dataset (dataset 4, saliva from PEG) we find that Hispanics have a lower epigenetic aging rate than Caucasians (p = 0.042, Fig. 1i). The fact that our findings in blood can also be validated in saliva is consistent with the strong correlation between epigenetic age acceleration measures of the two sources of DNA (r = 0.70, p = 1.4 × 10–12, Fig. 1n). The lower value of IEAA in Hispanics unlikely reflects country of birth or of residence (at age 35 years) given the robust findings across samples and our detailed analysis in the WHI, where we find that Hispanics born outside US, but living in the US, have a higher IEAA than Hispanics born and raised in the US (p = 0.025, Additional file 3B).

CHD risk factors bear little or no relationship with IEAA

We related our measures of age acceleration to risk factors related to CHD since the latter are significant predictors of mortality. In postmenopausal women from the Women’s Health Initiative (WHI), we found no evidence that IEAA is associated with disparities in education, high density lipoprotein (HDL) or low density lipoprotein (LDL) cholesterol, insulin, glucose, C-reactive protein (CRP), creatinine, alcohol consumption, smoking, diabetes status, or hypertension (see Table 3).

Table 3

Multivariate model that regresses epigenetic age acceleration on participant characteristics in the WHI. Coefficients and p values from regressing measures of intrinsic and extrinsic epigenetic age acceleration on participant characteristics from dataset 1

Multivariate linear regression

Intrinsic EAA

Extrinsic EAA

Estimate (SE)


Estimate (SE)



Hispanic vs. African American

–0.94 (0.35)


3.363 (0.439)


White vs. African American

0.71 (0.295)


1.94 (0.37)

1.6 × 10–7


0.006 (0.01)


–0.003 (0.013)



0.003 (0.002)


0.004 (0.002)



0 (0.001)


0.001 (0.001)



0.003 (0.004)


0.007 (0.005)



0.023 (0.018)


0.052 (0.023)



0.703 (0.594)


1.985 (0.745)



0.035 (0.021)


0.045 (0.027)



High school (HS) vs. no HS

0.357 (0.426)


–0.784 (0.534)


Some college vs. no HS

0.469 (0.381)


–1.171 (0.478)


College vs. no HS

0.486 (0.519)


–2.253 (0.65)


Grad school vs. no HS

0.36 (0.424)


–1.648 (0.531)



Past drinker vs. Never

1.668 (1.1)


–0.598 (1.379)


Light drinker vs. Never

–0.101 (0.536)


–0.751 (0.672)


Moderate vs. Never

–0.416 (0.748)


–0.401 (0.937)


Heavy vs. Never

–0.354 (0.88)


–0.833 (1.103)



Former vs. Current

–0.573 (1.039)


–0.104 (1.302)


Never vs. Current

–0.376 (1.039)


–0.122 (1.303)



0.216 (0.43)


–0.061 (0.539)



0.364 (0.241)


0.262 (0.302)





Tsimane have a lower intrinsic aging rate than Caucasians

The Tsimane are an indigenous population (~15,000 inhabitants) of forager-horticulturalists who reside in the remote lowlands of Bolivia. They reside mostly in open-air thatch huts, and actively fish, hunt, and cultivate plantains, rice, and manioc through slash-and-burn horticulture [39]. Tsimane provide a unique contribution to aging researchers and epidemiologists because they experience high rates of inflammation due to repeated bacterial, viral, and parasitic infections, yet show minimal risk factors for heart disease or type 2 diabetes as they age; they have minimal hypertension and obesity, low LDL cholesterol and no evidence of peripheral arterial disease [39, 40, 41]. Since Hispanics share genetic ancestry with peoples indigenous to the Americas, we hypothesized that a slower intrinsic aging rate might also be observable by analyzing Tsimane blood samples [39]. Among participants who are older than 35 years, Tsimane have the lowest intrinsic age acceleration (Fig. 2d, g). While Tsimane have a significantly lower IEAA than Caucasians after the age of 35 years (p = 0.0061), no significant difference could be observed in younger participants (Fig. 2e, h). In this analysis, the threshold of 35 years was chosen so that a sufficient number of young participants would be included in dataset 6. We found no significant difference in IEAA between older Hispanics and Tsimane, which might reflect the relatively low group sizes of n = 37 Tsimane versus n = 38 Hispanics.

IEAA is not associated with CHD in the WHI

Based on our findings above showing little or no relationship between IEAA and CVD risk factors at baseline, we hypothesized that IEAA would not predict future onset of CHD. A multivariate logistic regression model shows that IEAA is not significantly associated with an increased risk of incident CHD (Table 4). However, as expected, current smoking, prior history of diabetes, hypertension, high insulin and glucose levels, and lower HDL predicted an increased risk of CHD (Table 4).

Table 4

Logistic model that regresses CHD status on epigenetic age acceleration and participant characteristics in the WHI. Coefficients, Wald Z statistics, and corresponding p values resulting from regressing CHD status on measures of epigenetic age acceleration and various participant characteristics. The results for the measure of IEAA and EEAA can be found in columns 2 and 3, respectively

Logistic model. Outcome CHD case status

Intrinsic EAA

Extrinsic EAA


Estimate (SE)



Estimate (SE)



Epig. Age Accel

–0.017 (0.01)



–0.006 (0.008)




0.027 (0.008)



0.028 (0.008)


4.3 × 10-4


Hispanic vs. African American

0.083 (0.152)



0.118 (0.153)



White vs. African American

0.141 (0.135)



0.135 (0.135)




–0.02 (0.005)


1.8 × 10–5

–0.02 (0.005)


1.5 × 10-5


0.001 (0.001)



0.001 (0.001)




0.002 (0.001)



0.002 (0.001)




0.005 (0.002)



0.005 (0.002)




0.013 (0.008)



0.013 (0.008)




0.518 (0.281)



0.515 (0.281)




–0.011 (0.01)



–0.012 (0.01)




High school (HS) vs. no HS

–0.058 (0.183)



–0.067 (0.183)



Some College vs. no HS

0.008 (0.164)



–0.004 (0.165)



College vs. no HS

–0.198 (0.223)



–0.219 (0.223)



Grad school vs. no HS

–0.237 (0.183)



–0.251 (0.183)




Past drinker vs. Never

–0.6 (0.514)



–0.641 (0.513)



Light drinker vs. Never

–0.34 (0.233)



–0.343 (0.233)



Moderate vs. Never

–0.1 (0.32)



–0.096 (0.32)



Heavy vs. Never

–0.34 (0.381)



–0.337 (0.381)




Former vs. Current

–0.997 (0.467)



–0.989 (0.467)



Never vs. Current

–1.321 (0.468)



–1.317 (0.468)




0.706 (0.196)


3.0 × 10-4

0.699 (0.196)


3.4 × 10-4


0.565 (0.103)


4.8 × 10-8

0.559 (0.103)


6.3 × 10-8

Hispanics and Tsimane have a higher EEAA than Caucasians

According to our measure of EEAA, Hispanics have a significantly older extrinsic epigenetic age than Caucasians (meta-analysis p = 0.00012, Fig. 4a–d) and fewer naïve CD4+ T cells, based on cytometric data from the WHI LLS, the MACS study, and imputed blood cell counts (Fig. 4f–j, Additional file 2H, I). This pattern of fewer naïve CD4+ T cells is even more pronounced for Tsimane (Fig. 4m, n), who experience repeated acute infections and elevated, often chronic, inflammatory loads.

Epigenetic age analysis of East Asians

Because ancient Native American populations share common ancestral lineages with East Asians, we examined whether East Asians also differ from Caucasians in terms of epigenetic aging rates. We found no significant difference between Caucasians and East Asians in terms of IEAA (Fig. 2i), EEAA (Fig. 4o), or naïve CD4+ T cells (Fig. 4p). Similarly, we found no difference in lymphoblastoid cell lines (Additional file 1). However, these comparative analyses are limited by the relatively small number of samples and should be repeated in larger datasets.

Which risk factors for cardiometabolic disease are associated with EEAA?

Our multivariate model analysis in the WHI (Table 3) shows that EEAA tracks better than IEAA with risk factors for cardiometabolic disease; EEAA was positively associated (higher) with: triglyceride levels (multivariate model p = 0.04), CRP (p = 0.023), and creatinine (p = 0.008). EEAA was negatively associated (lower) with higher levels of education in all ethnic groups (p from 2.0 × 10–8 to 0.05, Additional file 4I–L). For each racial/ethnic group, we find that women who did not finish high school exhibit the highest levels of EEAA (leftmost bar in Additional file 4J–L).

Epigenetic aging rates of African Americans

In the following, we compare African Americans with European Americans in terms of IEAA and EEAA. Comparisons of African Americans with Caucasians in terms of IEAA yield contradictory findings across datasets that differ in age range: African American women have slightly lower IEAA than Caucasian women in the WHI (p = 0.017 Fig. 3f), but no significant difference can be observed for the younger participants of the Bogalusa study (Fig. 3g). Indeed, participants in the WHI (aged between 50 and 80 years) were older than those of the Bogalusa study (aged between 29 and 51 years). This failure to detect a significant racial/ethnic difference in IEAA in younger participants is consistent with our results from the comparison of younger Tsimane and Caucasians (Fig. 2h). A multivariate model analysis based on the Bogalusa study (comprising African Americans and Caucasians) confirms that IEAA does not differ between middle-aged African Americans and Caucasians but IEAA is higher among men (p = 0.025) and has a marginally significant association with hypertension (p = 0.064, Table 5). When relating individual variables to IEAA, we find significant associations for hypertension (p = 0.00035, Additional file 5D–F) but not for type II diabetes status or educational level.

Table 5

Multivariate model that regresses epigenetic age acceleration on participant characteristics in the Bogalusa study. Coefficients and p values from regressing measures of intrinsic and extrinsic epigenetic age acceleration on participant characteristics from dataset 2

Multivariate linear regression

Intrinsic EAA

Extrinsic EAA

Estimate (SE)



Estimate (SE)




Caucasian vs. African American

–0.013 (0.316)



0.843 (0.316)




Female vs. Male

–0.622 (0.278)



–0.718 (0.277)




Grade 8–9 vs. < Grade 8

1.583 (1.468)



2.177 (1.465)



Grade 10–12 vs. < Grade 8

1.285 (1.27)



2.267 (1.267)



Vocat/Tech vs. < Grade 8

0.307 (1.299)



1.921 (1.295)



College vs. < Grade 8

0.85 (1.281)



2.375 (1.277)



Graduate vs. < Grade 8

0.147 (1.336)



1.53 (1.332)



Diabetes (II)

0.173 (0.485)



0.012 (0.483)




0.539 (0.291)



1.247 (0.29)


1.7 × 10-5




Our findings for EEAA are highly consistent across the two studies and age groups: African Americans have lower EEAA than Caucasians in the WHI and in the Bogalusa study (p = 7.2 × 10–7, Fig. 4q, r, s). Our flow cytometric data from the WHI LLS show that African American women exhibit a higher abundance of naïve CD8+ T cells than Caucasian women (p = 1.7 × 10–9, Fig. 4t).

In multivariate regression analyses of EEAA, we find that African Americans have indications of a significantly younger immune system age than Caucasians (p =  0.0076) after controlling for gender, educational level, diabetes status, and hypertension. In the Bogalusa study, we find three significant predictors of EEAA: race/ethnicity, hypertension, and gender (p = 0.0093, Table 5). A marginal analysis in the Bogalusa study identifies a significant association between EEAA and hypertension (p = 8.0 × 10–5, Additional file 5G–I), type II diabetes status in Caucasians (p = 0.0085, Additional file 6H), but not in African Americans (Additional file 6I). Contrary to our findings in the WHI, no significant association can be observed between EEAA and educational level (Additional file 7).

African rainforest hunter-gatherers and farmers

To evaluate the effect of subsistence ecology and environment on epigenetic aging rates, we analyzed 256 blood samples from two different groups in Central Africa: rainforest hunter-gatherers (RHGs, traditionally known as “pygmies,” sampled from Baka and Batwa populations) and African populations that have adopted an agrarian lifestyle (AGRs, traditionally known as “Bantus,” sampled from the Nzebi, Fang, Bakiga, and Nzime populations) over the last 5000 years [42]. The ancestors of the RHGs and AGRs diverged ~60,000 years ago. These groups have historically occupied separate ecological habitats—the ancestors of RHGs in the equatorial rainforest while those of AGRs in drier, more open space savannahs and grasslands. Many RHG groups still live in the rainforest as mobile bands, whereas AGR populations now occupy primarily rural or urban deforested areas, though some AGR groups have settled in the rainforest over the last millennia.

We considered three groups: (1) RHG (n = 102); (2) AGR living in the forest (n = 60); and (3) AGR living in an urban setting (n = 94). The forest habitat was significantly associated with an increase in AgeAccel (p = 2.4 × 10–8, Fig. 5c) and EEAA (p = 5.9 × 10–11, Fig. 5g), but no difference was found for IEAA (p = 0.11, Fig. 5e). Further, no significant difference could be observed between AGR and RHG when focusing on participants living in the rainforest, suggesting greater importance of environment over genetic differences. These results are not affected by differences in genetic variants between RHG and AGR as can be seen from a robustness analysis where we removed CpG probes containing genetic variants at a frequency higher than 1 % in the populations studied (Fig. 5h, i).

Sex effects in blood and saliva

We explored whether differences exist between men and women in epigenetic aging rates. According to measures of IEAA, men are older than women in two racial/ethnic groups: African Americans (Additional file 8A, B) and Caucasians (Additional file 9A, B, N, Z).

Overall, men have higher IEAA and EEAA than women even when controlling for education, diabetes, and hypertension (Table 5). Using saliva data from PEG, we find that Hispanic men age faster than Hispanic women (p = 0.021, Fig. 6j). According to EEAA, Caucasian men are epigenetically older than Caucasian women (Additional file 9C, O, ZA), but we do not observe a significant difference in other groups such as African Americans (Additional file 8C) or central African populations (Fig. 6p, q). The results for EEAA are also consistent with significant sex differences in blood cell counts suggesting more rapid immunosenescence in men. Men have fewer naïve CD4+ T cells than women in three racial/ethnic groups: Caucasians (p = 0.0015 in the Bogalusa study, p = 0.051 in PEG, p = 4.2 × 10–5 in dataset 5); Tsimane (p = 0.0088 in older Tsimane); and African Americans (p = 0.011 in the Bogalusa study).
Fig. 6

Sex effect on epigenetic age acceleration in blood and saliva. Panels of the first two rows (aj) and last two rows (ks) relate sex to intrinsic and extrinsic epigenetic age acceleration, respectively. Results are reported for blood tissue in all but one panel (j). The combined results across all blood studies can be found in panels (i) IEAA, (s) EEAA. Each bar plot reports 1 standard error and a Kruskal–Wallis test

Sex effects in brain tissue

We analyzed the effect of sex on the universal measure of age acceleration (Age Accel.) in six independent brain datasets (Table 2 and “Methods”). In total, we analyzed 2287 brain samples from 1370 participants. In our analysis, we distinguished the cerebellum from other brain regions because it is known to age more slowly than other brain regions according to the epigenetic clock [43]. While sex did not have a significant effect on the epigenetic age of the cerebellum (Fig. 7a), we found that other brain regions from men exhibit a significantly higher age acceleration than those from women (Fig. 7b, meta-analysis p = 3.1 × 10–5).
Fig. 7

Effect of sex on the epigenetic age of brain tissue. Each panel depicts a forest plot resulting from the meta-analysis of sex effects. Each row in a forest plot shows the mean difference in epigenetic age between men and women and a 95 % confidence interval. To combine the coefficient estimates from the respective studies into a single estimate, we applied a fixed-effects model weighted by inverse variance, which is implemented in the metafor R package [89]. a Gender did not have a significant effect on the epigenetic age of the cerebellum, which is known to age more slowly than other brain regions according to the epigenetic clock [43]. b When excluding cerebellar samples from the analysis, we find that male brain regions exhibit a significantly higher age acceleration than female brain regions (mean difference = 0.82, meta-analysis p = 3.1 × 10–5). The difference remains significant even after adjusting for intra-subject correlations using a linear mixed effects model (mean difference = 0.77, p = 0.0034)

Studies of young participants

So far, our results have largely pertained to participants who are middle-aged or older (Table 1, column 6) as we only had access to two datasets involving newborns, infants, children, adolescents, and/or young adults. In dataset 6 (which involved participants between the ages of 2 and 35 years), we did not observe a significant difference epigenetic aging rates between Caucasians and Tsimane. In cord blood samples [44], we found no significant difference in the epigenetic ages of cord blood samples between African American and Caucasian newborns (p = 0.23).

Robustness analysis in the WHI

The epigenetic clock involves 47 CpGs whose broadly defined neighborhood includes a single nucleotide polymorphism (SNP) marker according to the probe annotation file from the Illumina 450 K array. Thus, genetic differences coupled with differences in hybridization efficiency could give rise to spurious differences between different racial/ethnic groups.

We addressed this concern in multiple ways. First, we re-analyzed the WHI data by removing the 47 CpGs (out of 353 epigenetic clock CpGs) from the analysis. The epigenetic clock software imputes the 47 missing CpGs using a constant value (the mean value observed in the original training set). Using the resulting modified epigenetic clock, we validate our findings of racial/ethnic differences in terms of IEAA and EEAA (Additional file 8A–C). However, this type of robustness analysis is limited because the removal of a subset of DNA methylation probes, potentially influenced by proximal genetic variation, is not as good a control as directly having matched genetic data. Second, we used a completely independent epigenetic biomarker based on a published signature of age-related CpGs from Teschendorff et al. [13]. Again, these results corroborate our findings (Additional file 8D, E). Third, we validated our findings using the original blood-based aging measure by Hannum [19] (Additional file 8F, G). Fourth, we highlight that both the Horvath and Hannum age estimators were developed based on training data from mixed populations. The training data underlying the Horvath clock involved four racial/ethnic groups (mainly Caucasians, Hispanics, African Americans, and to a lesser extent East Asians). The Hannum clock was trained on Caucasians and Hispanics. While race/ethnicity can lead to a significant offset between DNAm age and chronological age (which is interpreted as age acceleration), these two variables are highly correlated in all racial/ethnic groups.


Our main findings are that: (1) Hispanics and Tsimane have a lower intrinsic but a higher extrinsic aging rate than Caucasians; (2) African Americans have a lower extrinsic epigenetic aging rate than Caucasians and Hispanics; (3) levels of education are associated with a decreased level of EEAA in each race/ethnic group (Additional file 4); (4) neither intrinsic nor extrinsic aging rates of blood tissue are predictive of incident CHD in the WHI even though EEAA is weakly associated with several cardiometabolic risk factors of CHD (such as hypertension, triglycerides, and CRP); (5) men exhibit higher epigenetic aging rates than women in blood, saliva, and brain samples, and (6) the rain forest habitat is significantly associated with extrinsic age acceleration but not with intrinsic age acceleration in African populations. Although precise understanding of the significance of epigenetic aging measures awaits further elaboration, our principal findings may provide additional context towards resolving several controversial, epidemiological paradoxes, including the Hispanic paradox, black–white mortality cross-over, the Tsimane inflammation paradox, and the sex morbidity–mortality paradox.

Hispanic paradox

The lower level of IEAA in Hispanics echo the finding that Hispanics in the US have a lower overall risk of mortality than Caucasians despite having a disadvantaged risk profile [45, 46, 47, 48]. Our findings stratified by country of birth suggest that the lower intrinsic aging rate of Hispanics does not reflect biases arising through immigration such as a “healthy immigrant effect” (Additional file 3). Our finding regarding higher levels of EEAA in Hispanics parallels the findings that Hispanics have higher levels of metabolic/inflammatory risk profiles [49] and that Hispanics have a lower relative CD4+ T cell percentage than Caucasians [50]. Several articles have explored the question of why the immune system of Hispanics might differ from that of Caucasians [51, 52, 53].

Black–white mortality cross-over

In the US, the black–white mortality cross-over refers to the reported pattern of lower mortality after the age of 85 years among black men and women, compared to whites, despite their higher observed mortality rates at younger ages [54, 55, 56, 57]. Although we find no differences in IEAA between African Americans and Caucasians at younger ages, older African American adults from the Bogalusa study had lower IEAA than their Caucasian counterparts. This finding might reflect selective survival of more robust individuals or other aspects of health and systemic risk given its independence from common risk factors for cardiovascular disease and type II diabetes mellitus. Our finding regarding the lower EEAA of African Americans, compared to Caucasians, is consistent with the longer leukocyte telomere lengths of African Americans relative to those of Caucasians [3, 9]. Lastly, our flow cytometric data show that African Americans have a larger number of naïve CD8+ T cells than Caucasians (Fig. 4t).

Tsimane inflammation paradox

Our results regarding the low intrinsic aging rate in Tsimane may help address another paradox (which we refer to as the Tsimane inflammation paradox), wherein high levels of inflammation and infection, and low HDL levels, are not associated with accelerated cardiovascular aging [39]. The finding that Tsimane have decreased levels of IEAA has parallels to the following clinical/epidemiological observations: even older Tsimane show little evidence of chronic diseases common in high-income countries, like diabetes, atherosclerosis, asthma, and other autoimmune disorders [39]. High levels of physical activity are maintained well into late adulthood [58].

The finding that Tsimane have increased levels of EEAA has parallels to the following observation: a lifetime of diverse pathogen stresses, elevated inflammation and extensive immune activation, seems to lead to more rapid depletion of naïve CD4+ T cells and greater expression of exhausted T cells, i.e. more rapid immunosenescence [39, 40, 59]. Infectious disease and high chronic inflammatory load contribute to the low life expectancy of Tsimane, 43.5 years at birth during the period 1950–1989, and 54.1 years during 1990–2002 [40, 60].

Sex morbidity–mortality paradox

The sex morbidity–mortality paradox was first described in the 1970s and refers to the observation that women possess a lower age-adjusted mortality rate compared to men despite a higher suffering from a higher burden of co-morbid conditions [61, 62]. Most explanations focus on differences in lifestyle behaviors or healthcare utilization. However, marked sex differences in health and disability remain after controlling for differences in work-related behavior, smoking, obesity, and other behaviors [63]. Whereas other explanations attest to sex differences in a variety of biomarkers, our epigenetic aging markers show robust and consistent male-biased vulnerability in multiple tissues (blood, brain, and saliva) in all racial groups. Similar sex differences in blood-based epigenetic aging rates have also been reported in minors and teenagers [64].

Strengths and limitations

Our study has several strengths including the analysis of 18 DNA methylation datasets (Tables 1 and 2), large sample sizes (almost 6000 samples), multiple tissues (blood, saliva, brain), access to unique populations (Tsimane Amerindians; rainforest hunter-gatherers and farmers), two flow cytometric studies, and robust epigenetic biomarkers of aging. Our analysis of race/ethnicity also spanned seven different racial/ethnic groups (African American, Caucasian, Hispanic, Tsimane, East Asian, RHGs, and AGRs from Central Africa). Another strength is that our analysis of race/ethnicity involved two sources of DNA: blood and saliva. Limitations include the use of some datasets that are cross-sectional as opposed to longitudinal datasets and the fact that both IEAA and EEAA rely on imputed blood cell counts based on DNA methylation levels. Fortunately, the imputed blood cell counts are quite accurate (Additional file 2). Our results reported here concerning ethnic/racial differences in blood cell counts are supported both by our two flow cytometric datasets and by the literature. However, these measured data are not fully reflective of the breakdown of blood cell types, representing only T and B cells.


Our exploratory study demonstrates that epigenetic aging rates differ between different racial/ethnic groups and between men and women. Further, intrinsic epigenetic aging rates tend to have insignificant associations with well-studied risk factors of CHD whereas extrinsic aging rates tend to have significant (but weak) associations with several pro-inflammatory risk factors. While racial/ethnic differences have previously been observed in DNA methylation levels [44], we are the first to directly compare epigenetic aging rates across different racial/ethnic groups. Our derived intrinsic and extrinsic epigenetic aging rates in blood offer an independent glimpse into biological aging that incorporates genetics and the environment and provides potential insight into a number of epidemiological paradoxes. The application of genome-wide DNAm-based epigenetic analysis to understand race/ethnic and sex disparities in biological aging is novel and offers an important perspective that complements existing approaches based on other biomarkers. Future studies will need to confirm our findings with longitudinal designs and to extend the epigenetic age analysis to other tissues and organs.


We differentiate groups according to “race/ethnicity,” mindful about existing controversies over rigid racial definitions. Our use of these terms reflects self-identified group membership based on macro-categories commonly employed in censuses, human genetics, demography, and epidemiology. The term race/ethnicity thus combines elements of genetic ancestry, population history, and culture.

DNA methylation age and epigenetic clock

All of the described epigenetic measures of aging and age acceleration are implemented in our freely available software. The epigenetic clock is defined as a prediction method of age based on the DNAm levels of 353 CpGs. Predicted age, referred to as DNAm age, correlates with chronological age in sorted cell types (CD4+ T cells, monocytes, B cells, glial cells, neurons), tissues, and organs, including: whole blood, brain, breast, kidney, liver, lung, saliva [20]. Mathematical details and software tutorials for the epigenetic clock can be found in the Additional files of [20]. An online age calculator can be found at our webpage (

Intrinsic versus extrinsic measures of epigenetic age acceleration in blood

Empirical studies show that DNAm has a relatively weak correlation with various measures of white blood cell counts [31], which probably reflects the fact that dozens of different tissue and blood cell types were used to define DNAm age. However, we find it useful to explicitly define another measure of age acceleration that is completely independent of blood cell counts as described in the following. We distinguish intrinsic from extrinsic measures of epigenetic age acceleration in whole blood according to their relationship with blood cell counts. A measure of intrinsic epigenetic age acceleration (IEAA) measures “pure” epigenetic aging effects that are not confounded by differences in blood cell counts. Our measure of IEAA is defined as the residual resulting from a multivariate regression model of DNAm age on chronological age and various blood immune cell counts (naïve CD8+ T cells, exhausted CD8+ T cells, plasma B cells, CD4+ T cells, natural killer cells, monocytes, and granulocytes). The measure of IEAA is an incomplete measure of the age-related functional decline of the immune system because it does not track age-related changes in blood cell composition, such as the decrease of naïve CD8+ T cells and the increase in memory or exhausted CD8+ T cells [36, 37, 38].

We defined a measure of EEAA that only applies to whole blood and aims to measure epigenetic aging in immune-related components in two steps. First, we formed a weighted average of the epigenetic age measure from Hannum et al. [19] and three estimated measures of blood cells for cell types that are known to change with age: naïve (CD45RA + CCR7+) cytotoxic T cells; exhausted (CD28-CD45RA-) cytotoxic T cells; and plasma B cells using the approach by Klemera Doubal [65]. Second, we defined the measure of EEAA as the residual resulting from a univariate model that regressed the weighted average on chronological age. By definition, our measure of EEAA has a positive correlation with the amount of exhausted CD8+ T cells and plasmablast cells and a negative correlation with the amount of naïve CD8+ T cells. Blood cell counts were estimated based on DNA methylation data. EEAA tracks both age-related changes in blood cell composition and intrinsic epigenetic changes. In most blood datasets, EEAA has a moderate correlation (r = 0.5) with IEAA. We note that, by definition, none of our three measures of epigenetic age acceleration are associated with the chronological age of the participant at the time of blood draw.

Relationship to mortality prediction

Although the epigenetic clock method was only published in 2013, there is already a rich body of literature that shows that it relates to biological age. Using four human cohort studies, we previously demonstrated that both the Horvath and Hannum epigenetic clocks are predictive of all-cause mortality [23]. Published results in Marioni et al. [23] show that DNAm age adjusted for blood cell counts (i.e. IEAA) is prognostic of mortality in four cohort studies. We recently expanded our original analysis by analyzing 13 different cohorts (including three racial/ethnic groups) and by evaluating the prognostic utility of both IEAA and EEAA. All considered measures of epigenetic age acceleration were predictive of age at death in univariate Cox models (pAgeAccel = 1.9 × 10–11, pIEAA = 8.2 × 10–9, pEEAA = 7.5 × 10–43) and multivariate Cox models adjusting for risk factors and pre-existing disease status (pAgeAccel = 5.4 × 10–5, pIEAA = 5.0 × 10–4, pEEAA = 3.4 × 10–19) where the latter adjusted for chronological age, body mass index, education, alcohol, smoking pack years, recreational physical activity, and prior history of disease (diabetes, cancer, hypertension). These results will be published elsewhere. Further, the offspring of centenarians age more slowly than age matched controls according to Age Accel and IEAA [26] which strongly suggests that these measures relate to heritable components of biological age. Two independent research groups have shown that epigenetic age acceleration predicts mortality [24, 25].

Description of the blood datasets listed in Table 1

All data presented in this article have been made publicly available as indicated in the column “Available” of Table 1.

Dataset 1: Women’s Health Initiative (WHI)

Participants included a subsample of participants of the WHI study, a national study that began in 1993 which enrolled postmenopausal women between the ages of 50 and 79 years into either one of two three randomized clinical trials [66]. None of these women had CHD at baseline but about half of these women had developed CHD by 2010. Women were selected from one of two WHI large subcohorts that had previously undergone genome-wide genotyping as well as profiling for seven cardiovascular disease related biomarkers including total cholesterol, HDL, LDL, triglycerides, CRP, creatinine, insulin, and glucose through two core WHI ancillary studies [67]. The first cohort is the WHI SNP Health Association Resource (SHARe) cohort of minorities that includes >8000 African American women and >3500 Hispanic women. These women were genotyped through WHI core study M5-SHARe ( and underwent biomarker profile through WHI Core study W54-SHARe (…data/WHIStudies/StudySites/W54). The second cohort consists of a combination of European Americans from the two Hormonal Therapy trials selected for GWAS and biomarkers in core studies W58 (…/data /WHIStudies/StudySites/W58) and W63 (…/data/WHIStudies/StudySites/W63). From these two cohorts, two sample sets were formed. The first (sample set 1) is a sample set of 637 CHD cases and 631 non-CHD cases as of 30 September 2010. The second sample set (sample set 2) is a non-overlapping sample of 432 cases of CHD and 472 non-cases as of 17 September 2012. The ethnic groups differed in terms of the age distribution in the sense that Caucasian women tended to be older. Therefore, we randomly removed 80 % of the Caucasian women who were older than 65 years when it came to the direct comparisons reported in our figures. This resulted in a total sample size of 1462 women, comprising 673 African Americans, 353 Caucasians, and 433 Hispanics. There was no significant difference in age between the three ethnic groups. However, we kept all of the samples in our analysis of clinical characteristics, such as future CHD status and baseline characteristics such as education, hypertension, diabetes, and smoking, in order to ensure that sufficient sample sizes were available for these analyses. Our results are highly robust with respect to using the smaller or larger versions of the datasets. All results are qualitatively the same for the two versions of the datasets. We acknowledge a potential for selection bias using the above-described sampling scheme in WHI but suspect if such bias is present it is minimal. First, some selection bias is introduced by restricting our methylation profiling at baseline to women with GWAS and biomarker data from baseline as well, given the requirement that these participants must have signed the WHI supplemental consent for broad sharing of genetic data in 2005. However, we believe that selection bias at this stage is minimized by the inclusion of participants who died between the time of the start of the WHI study and the time of supplemental consent in 2005, which resulted in the exclusion of only ~6–8 % of all WHI participants. Nevertheless, participants unable or unwilling to sign consent in 2005 may not represent a random subset of all participants who survived to 2005. Second, some selection bias may also occur if similar gross differences exist in the characteristics of participants who consented to be followed in the two WHI extension studies beginning in 2005 and 2010 compared to non-participants at each stage. We believe these selection biases if present have minimal effects on our effect estimates. Data are available from the page’%20Datasets%20Released.aspx, see the link

Dataset 2: Bogalusa

We analyzed the blood DNA methylation levels of 968 participants (680 Caucasians, 288 African Americans; age range = 28–51.3 years) from the Bogalusa Heart study [68] who were examined in Bogalusa, Louisiana during 2006–2010 for cardiovascular risk factors. All participants in this study gave informed consent at each examination. Study protocols were approved by the Institutional Review Board (IRB reference no. 12-395283) of the Tulane University Health Sciences Center. DNA was extracted from 1106 whole blood samples using the PureLink Pro 96 Genomic DNA Kit (LifeTechnology, CA, USA) following the manufacturer’s instructions. The Infinium HumanMethylation450 BeadChip (Methy450K) was used for whole genome DNA methylation analysis.

All the samples were processed at the Microarray Core Facility, University of Texas Southwestern Medical Center at Dallas, Texas. For DNA methylation analysis, 750 ng genomic DNA from each participant was bisulphite converted using the EZ-96 DNA Methylation Kit (Zymo Research, CA, USA) and the efficiency of the bisulphite conversion was confirmed by built-in controls on the Methy450K array. The methylation profile of each individual was measured by processing 4 μL of bisulphite-converted DNA, at a concentration of 50 ng/μL, on a Methy450K array. The bisulphite-converted DNA was amplified, fragmented, and hybridized to the array. The arrays were scanned on an Illumina HiScan scanner and the raw methylation data were extracted using Illumina’s Genome Studio methylation module. Data cleaning procedures were undertaken using R package “minfi” [69], generating quality control report, finding sample outliers, cell counts estimation, and annotation accessing. The R package wateRmelon [70] was used for β-value normalization and quality control. For correction of systematic technical biases in the 450 K assay, β-value normalization was performed by the “dasen” function, in which type I and type II intensities and methylated and unmethylated intensities will be quantile normalized separately after backgrounds equalization of type I and type II. The R package ChAMP [71] was used for batch effect analysis and correction with “champ.SVD” and “champ.runCombat” functions. The clinical variables and participant characteristics are defined in the captions of the respective Additional files.

The are available from

Dataset 3: blood from Hispanics and Caucasians of PEG

The Parkinson’s disease, Environment, and Genes (PEG) case-control study aims to identify environmental risk factors (e.g. neurotoxic pesticide exposures) for Parkinson’s disease.

The PEG study is a large population-based study of Parkinson’s disease of mostly rural and township residents of California’s central valley [72]. Here we only used diseased participants from wave 1 (PEG1). Since all participants of dataset 3 had Parkinson’s disease, disease status could not confound associations with epigenetic aging. Medication status was not associated with epigenetic age acceleration. The data are available from Gene Expression Omnibus.

Dataset 4: saliva samples from PEG

This novel dataset comes from the PEG study (described above). Since PD disease status did not relate to epigenetic age acceleration in these data, we ignored it in the analysis. However, our findings are unchanged after incorporating PD status in a multivariate model. About half of the samples overlapped with those of dataset 3, which is why we could correlate epigenetic age acceleration between blood and saliva.

Datasets 5 and 6: blood from Tsimane, Hispanics, and Caucasians

Datasets 5 and 6, which were collected and generated in the same way, only differ in terms of the chronological ages. All participants in dataset 5 are older than 35 years while those in dataset 6 are younger or equal to 35 years. The dataset involved three different ethnic groups: Tsimane Amerindians, Hispanics living in the US, and Caucasians living in the US. Fasting whole-blood samples were collected from Tsimane via venipuncture in field villages in the vicinity of San Borja, Bolivia as a part of the annual biomedical data collection for a longitudinal project on aging during 2004–2009 (Tsimane Health and Life History Project). Manual complete blood counts were conducted using a hemocytometer, erythrocyte sedimentation rate was calculated following the Westergren method, and hemoglobin was analyzed with a QBC Autoread Plus Dry Hematology System (Drucker Diagnostics, Port Matilda, PA, USA). Specimens were stored in liquid nitrogen until transfer to the US on dry ice, where they were stored at –80 °C. All participants provided written and informed consent; study protocols and procedures were approved at the individual, village, and Tsimane government level, as well as by the University of California, Santa Barbara and University of New Mexico Institutional Review Boards (IRB Reference numbers 14-0604 and 07-157, respectively). Specimens were shipped on dry ice to the University of Southern California for extraction. The same core facility provided blood samples that were collected at the same time and stored in the same condition as Hispanic participants living in the US. The DNA samples from all participants (Caucasians, Hispanics, Tsimane) were randomized across the Illumina chips to avoid confounding due to chip effects. For our age prediction analysis, we used background corrected beta values resulting from Genome Studio.

Hispanics for datasets 5 + 6: Participant recruitment: Participation in the BetaGene study was restricted to Mexican Americans from families of a proband with gestational diabetes mellitus (GDM) diagnosed within the previous 5 years. Probands were identified from the patient populations at Los Angeles County/USC Medical Center, OB/GYN clinics at local hospitals, and the Kaiser Permanente health plan membership in Southern California. Probands qualified for participation if they: (1) were of Mexican ancestry (defined as both parents and ≥3/4 of grandparents Mexican or of Mexican descent); (2) had a confirmed diagnosis of GDM within the previous 5 years; (3) had glucose levels associated with poor pancreatic β-cell function and a high risk of diabetes when not pregnant; and (4) had no evidence of β-cell autoimmunity by GAD-65 antibody testing. Recruitment targeted two general family structures using siblings and/or first cousins of GDM probands, all with fasting glucose levels <126 mg/dl (7 mM): (1) at least two siblings and three first cousins from a single nuclear family; or (2) at least five siblings available for study. Using information from the proband to determine preliminary eligibility, siblings and first cousins were invited to participate in screening and, if eligible, detailed phenotyping (below) and collection of DNA. Available parents and connecting uncles and aunts were asked to provide DNA and had a fasting glucose determination. In addition, women of Mexican ancestry who have gone through pregnancy without GDM, as evidenced by a plasma or serum glucose level <120 mg/dl after a 50 g oral glucose screen for GDM, were also collected. Recruitment criteria for control probands were similar to that of the GDM probands, but were also selected to be age, BMI, and parity-matched to the GDM probands. Unrelated samples for the present methylation analysis were selected randomly from all BetaGene participants. The BetaGene protocol (HS-06-00045) has been approved by the Institutional Review Boards of the USC Keck School of Medicine.

Dataset 7: blood from East Asians and Caucasians

Here we downloaded the publicly available DNA methylation data from GSE53740 [73]. Since we found that progressive supranuclear palsy (PSP) had a significant effect on epigenetic age acceleration, we removed PSP samples from the analysis. Further, we focused on comparing East Asians to Caucasians since other racial/ethnic groups were represented by fewer than 10 samples.

Dataset 8: blood from African populations

We used blood methylation data from [42]. We studied peripheral whole-blood DNA from a total of 256 samples (for which the chronological age at the time of blood draw was available).

As detailed in Fagny et al. [42], the samples come from seven populations located across the Central African belt. These populations can be divided into two main groups: RHG populations, historically known as “pygmies,” who have traditionally relied on the equatorial forest for subsistence and who live close to, or within, the forest; and AGR populations, living either in rural/urban deforested regions or in forested habitats in which they practice slash-and-burn agriculture. Informed consent was obtained from all participants and from both parents of any participants under the age of 18 years. Ethical approval for this study was obtained from the institutional review boards of Institut Pasteur, France (RBM 2008-06 and 2011-54/IRB/3).

Dataset 9: cord blood samples from African Americans and Caucasians

These 216 cord blood samples from 92 African American and 70 Caucasian participants come from a study that described racial differences in DNA methylation levels [44].

Datasets 10 and 11

Saliva samples from Caucasians and Hispanics. The data were generated by splitting the data from [74] by sex, which reflected the use of these data in the development of the epigenetic clock software [20]. Note that these data were generated on the older Illumina platform (27 K array). Some of the data were used as training data in the development of the epigenetic clock, which might bias the results. By contrast, the novel saliva data from PEG (dataset 4) provide an unbiased analysis.

Dataset 12: lymphoblastoid cell lines from Han Chinese, African Americans, and Caucasians

We clustered the samples based on the interarray correlation. Since 51 samples were very distinct from the remaining samples, they were removed as potential outliers. Disease status did not affect the estimates of DNAm age, which is why we ignored it.

Description of brain datasets

We collected brain datasets from six independent studies to assess gender effect on epigenetic age acceleration. We focused on Caucasian samples since there were insufficient numbers of other racial/ethnic groups.

  • Study 1: brain DNA methylation data from a study of Alzheimer’s disease study from [75], GEO accession GSE59685. DNA methylation profiles of the cerebellum, entorhinal cortex, prefrontal cortex, and superior temporal gyrus were available from 117 individuals. We ignored disease status since it was not associated with age acceleration.

  • Study 2: brain DNA methylation data from neurologically normal participants from [76], GEO accession GSE15745. DNA methylation data of the cerebellum, frontal cortex, pons, and temporal cortex regions from up to 148 neurologically normal participants of European ancestry [76].

  • Study 3: cerebellar DNA methylation data from [77], GEO GSE38873. DNA methylation data from the cerebellum of 147 participants from a case-control study (121 cases/32 controls) of psychiatric disorders. Since disease status did not affect DNAm age, we ignored it.

  • Study 4: prefrontal cortex samples from [78], GEO GSE61431. We analyzed 37 Caucasian participants (European ancestry).

  • Study 5: frontal cortex and cerebellum from neurologically normal Caucasian participants from [79]. The DNA methylation data and corresponding SNP data can be found in dbGAP, (accession: phs000249.v2.p1). We only analyzed 209 Caucasian participants who met our stringent quality control criteria. We excluded several putative outliers from the original dataset including three individuals who were genotyped on a different platform, six participants who were outliers according to a genetic analysis (PC plot), and 13 participants who had the wrong gender according to the gender prediction algorithm of the epigenetic clock software.

  • Study 6: dorsolateral prefrontal cortex samples from 718 Caucasian participants from the Religious Order Study (ROS) and the Memory and Aging Project (MAP). The DNA methylation data are available at the following webpage!Synapse:syn3168763. We focused on brain samples of Caucasian participants from these two prospective cohort studies of aging that include brain donation at the time of death [80]. Additional details on the DNA methylation data can be found in [81]. We were not able to evaluate the effect of race/ethnicity on epigenetic age acceleration since the dataset contained only 12 Hispanic samples (which did not differ significantly from Caucasians in terms of epigenetic age). Further, we found no association between disease status and epigenetic age acceleration, which is why we ignored disease status in our analysis.

Preprocessing of Illumina Infinium 450 K arrays

In brief, bisulfite conversion using the Zymo EZ DNA Methylation Kit (ZymoResearch, Orange, CA, USA) as well as subsequent hybridization of the HumanMethylation450k Bead Chip (Illumina, San Diego, CA, USA), and scanning (iScan, Illumina) were performed according to the manufacturers’ protocols by applying standard settings. DNA methylation levels (β values) were determined by calculating the ratio of intensities between methylated (signal A) and unmethylated (signal B) sites. Specifically, the β value was calculated from the intensity of the methylated (M corresponding to signal A) and unmethylated (U corresponding to signal B) sites, as the ratio of fluorescent signals β = Max(M,0)/[Max(M,0) + Max(U,0) + 100]. Thus, β values range from 0 (completely unmethylated) to 1 (completely methylated) [82]. The epigenetic clock software implements a data normalization step that repurposes the BMIQ normalization method from Teschendorff [83] so that it automatically references each sample to a gold standard based on type II probes as detailed in [20].

Estimating blood cell counts based on DNA methylation levels

We estimate blood cell proportions using two different software tools. Houseman’s estimation method [84], which is based on DNA methylation signatures from purified leukocyte samples, was used to estimate the proportions of cytotoxic (CD8+) T cells, helper (CD4+) T, natural killer, B cells, and granulocytes. The software does not allow us to identify the type of granulocytes in blood (neutrophil, eosinophil, or basophil) but we note that neutrophils tend to be the most abundant granulocyte (~60 % of all blood cells compared with 0.5–2.5 % for eosinophils and basophils). The advanced analysis option of the epigenetic clock software [20] was used to estimate the percentage of exhausted CD8+ T cells (defined as CD28-CD45RA-) and the number (count) of naïve CD8+ T cells (defined as (CD45RA + CCR7+) as described in [31].

Flow cytometric data from the Long Life Study of the WHI

While our DNA methylation data from the WHI were assessed at baseline, the flow cytometric data were measured 14.6 years after baseline. Between March 2012 and May 2013, a subset of WHI participants were enrolled in the Long Life Study (LLS) and additional biospecimens, physiometric, and questionnaire data were collected. All surviving Hormone Trial participants followed through 2010 and all African American and Hispanic/Latino participants from the SNP Health Association Resource (WHI-SHARe) sub-cohort were included if CVD biomarker from WHI baseline exam and genome-wide genotyping (GWAS) data were available and if they were at least 63 years old by 1 January 2012. Women who were either unable to provide informed consent (e.g. dementia) or those residing in an institution (e.g. skilled nursing facility) were excluded. Of a total of 14,081 eligible WHI participants, 9242 women consented to participate, 7875 were enrolled, and 7481 underwent successful blood draws. Blood was collected at locations across the US using a standardized protocol between March 2012 and May 2013 (Examination Management Services, Inc.) Fresh peripheral blood samples were packaged in Styrofoam with cold packs and were sent overnight to a central testing facility in Seattle.

A random sample of 600 residual fresh peripheral blood specimens (single tube, following CBC analysis) was transported to the University of Washington Medical Center’s (UWMC’s) flow cytometry laboratory and high-sensitivity, multi-parameter flow cytometry was performed utilizing a modified four-laser, multi-color Becton-Dickinson (BD; San Jose, CA, USA) LSRII flow cytometer. All of the flow cytometry studies were performed within 72 h of sample collection between June 2012 and February 2013. A single tube was used to evaluate T lymphocyte subsets: CD45 (KO), CD8 (BV), CD45RA (F), CCR7 (PE), CD5 (ECD), CD56 (PC5), CD3 (APC-H7), CD4 (A594), CD28 (APC), CD27 (PC7). A second tube evaluated B lymphocyte subsets: CD45 (APC-H7), CD20 (V450), kappa (F), lambda (PE), CD23 (ECD), CD5 (PC5.5), CD19 (BV650), CD38 (A594), CD10 (APC), CD27 (PC7), CD3 (APC-A700). Categories of circulating cells were quantified using a predefined population-based gating strategy based on established gating strategies for both T lymphocyte [85] and B lymphocyte [86] subsets.

Flow cytometric data from the MACS cohort

As part of Additional file 2, we validated imputed blood cell counts using flow cytometric data and DNA methylation data collected from men of the Multi-Center AIDS Cohort Study (MACS). The data were generated as described in [87]. Briefly, human peripheral blood mononuclear cell (PBMC) samples were isolated from fresh blood samples and either stained for flow cytometry analysis or used for genomic DNA isolation. DNA was isolated from 1 × 106 PBMC using Qiagen DNeasy blood and tissue mini spin columns. Quality of DNA samples was assessed using Nanodrop measurements and accurate DNA concentrations were measured using a Qubit assay kit (Life Technology). Cryopreserved PBMC obtained from the repository were thawed and assayed for viability using trypan blue. The mean viability of the samples was 88 %. Samples were stained for 30 min at 4 °C with the following antibody combinations of fluorescently conjugated monoclonal antibodies using the manufacturers recommended amounts for 1 million cells: tube 1: CD57 FITC (clone HNK-1), CD28 phycoerythrin (PE, L293), CD3 peridinin chlorophyll protein (PerCP,SK7), CD45RA phycoerythrin cyanine dye Cy7 tandem (PE-Cy7, L48), CCR7 Alexa Fluor 647 (AF647, 150503), CD8 allophycocyanin H7- tandem (APC-H7, SK1) and CD4 horizon V450 (V450, RPA-T4); tube 2: HLA-DR FITC (L243), CD38 PE (HB7), CD3 PercP, CD45RO PE-Cy7 (UCHL-1), CD95-APC(DXZ), CD8 APC-H7, and CD4 V450); tube 3: CD38 FITC (HB7), IgD PE (1A6–2), CD3 PerCP, CD10 PE-Cy7 (HI10a), CD27 APC (eBioscience, clone 0323, San Diego, CA), CD19 APC-H7 (SJ25C1) and CD20 V450 (L27). Antibodies were purchased from BD Biosciences, San Jose, CA (BD) except as noted. Stained samples were washed twice with staining buffer and run immediately on an LSR2 cytometer equipped with a UV laser (BD, San Jose, CA, USA) for the detection of 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI) which was used as a viability marker at a final concentration of 0.1 ug/mL. Lineage gated isotype controls to measure non-specific binding were run and used CD3, CD4, and CD8 for T-cells or CD19 for B-cells. Fluorescence minus one controls (FMO) were also utilized to assist gating and cursor setting. A range of 20,000–100,000 lymphocytes were acquired and analyzed per sample using the FACSDiva software package (BD, San Jose, CA, USA).



We would like to acknowledge the following WHI investigators. Program Office (National Heart, Lung, and Blood Institute, Bethesda, MD, USA): Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller. Clinical Coordinating Center (Fred Hutchinson Cancer Research Center, Seattle, WA, USA): Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg. Investigators and Academic Centers: JoAnn E. Manson (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA); Barbara V. Howard (MedStar Health Research Institute/Howard University, Washington, DC, USA); Marcia L. Stefanick (Stanford Prevention Research Center, Stanford, CA, USA); Rebecca Jackson (The Ohio State University, Columbus, OH, USA); Cynthia A. Thomson (University of Arizona, Tucson/Phoenix, AZ, USA); Jean Wactawski-Wende (University at Buffalo, Buffalo, NY, USA); Marian Limacher (University of Florida, Gainesville/Jacksonville, FL, USA); Robert Wallace (University of Iowa, Iowa City/Davenport, IA, USA); Lewis Kuller (University of Pittsburgh, Pittsburgh, PA, USA); Sally Shumaker (Wake Forest University School of Medicine, Winston-Salem, NC, USA). Women’s Health Initiative Memory Study (Wake Forest University School of Medicine, Winston-Salem, NC): Sally Shumaker.


This study was supported by NIH/NHLBI 60442456 BAA23 (Assimes, Absher, Horvath), National Institutes of Health NIH/NIA 1U34AG051425-01 (Horvath). The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. The PEG data were supported by NIEHS RO1ES10544 (Ritz) and NIEHS R21 ES024356 (Horvath, Ritz). Gurven and Trumble were funded by NIH/NIA R01AG024119 and R56AG02411. The Religious Order study and Rush Memory and Aging Project (brain dataset 6) were funded by P30AG10161, R01AG17917, RF1AG15819, and R01AG36042.

One of our flow datasets was collected by the Multicenter AIDS Cohort Study (MACS) at UCLA (Principal Investigators, Roger Detels and Otoniel Martinez-Maza), U01-AI35040. The MACS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID) with additional co-funding from the National Cancer Institute (NCI P30 CA016042), the National Institute on Drug Abuse (NIDA 5P30 AI028697), the National Institute of Mental Health (NIMH), the National Institute on Aging (NIA Grant 1RO1-AG-030327 by BDJ), and UL1-TR000424 (JHU CTSA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or donors to the David Geffen School of Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

Our DNA methylation data are publicly available through gene expression omnibus (GEO) accession numbers: GSE72775, GSE78874, GSE72773, and GSE72777. Further, the WHI and Bogalusa datasets are available through dbGAP ( and

African Populations: The genotyping data generated in this study have been deposited in the European Genome-Phenome Archive under accession codes EGAS00001000605, EGAS00001000908 and EGAS00001001066. The DNA methylation data generated in this study have been deposited in the European Genome-Phenome Archive under accession code EGAS00001001066.

The GSE numbers for the brain datasets are as follows: GSE59685, GSE15745, GEO GSE38873, and GEO GSE61431. Brain data 5 can be found at (accession: phs000249.v2.p1) and brain data 6 at!Synapse:syn3168763.

Authors’ contributions

SH conceived of the study, developed the methods, analyzed the data, and wrote the first draft of the article. MG, BT, HK, and HA contributed the DNA from the Tsimane Amerindians and interpreted the findings. ML, BR, and BC helped to interpret the data and edited the article. BR and SH contributed the PEG DNA methylation data. AL analyzed the brain datasets. DS, SL, and WC contributed the DNA methylation data from the Bogalusa Heart Study. SH, PT, DA, and TA contributed the DNA methylation data from the WHI. KE and AR contributed flow cytometric data from the WHI LLS. BJ and TR contributed flow data from the MACS. LQM, MF and MSK contributed DNAm data from African hunter gatherers. All authors helped interpret the data and edited the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study was reviewed by the UCLA institutional review board (IRB#13-000671 and IRB#14-000061) as well as the University of California Santa Barbara and University of New Mexico Institutional Review Boards (IRB Reference numbers 14-0604 and 07-157 respectively).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.


  1. Levine M, Crimmins E. Evidence of accelerated aging among African Americans and its implications for mortality. Soc Sci Med. 2014;118:27–32.View ArticlePubMedGoogle Scholar
  2. Diez Roux AV, Ranjit N, Jenny NS, Shea S, Cushman M, Fitzpatrick A, Seeman T. Race/ethnicity and telomere length in the Multi-Ethnic Study of Atherosclerosis. Aging Cell. 2009;8:251–7.
  3. Rewak M, Buka S, Prescott J, De Vivo I, Loucks EB, Kawachi I, Non AL, Kubzansky LD. Race-related health disparities and biological aging: does rate of telomere shortening differ across blacks and whites? Biol Psychol. 2014;99:92–9.
  4. Crimmins EM, Kim JK, Seeman TE. Poverty and biological risk: the earlier “aging” of the poor. J Gerontol A Biol Sci Med Sci. 2009;64:286–92.View ArticlePubMedGoogle Scholar
  5. Arbeev KG, Butov AA, Manton KG, Sannikov IA, Yashin AI. Disability trends in gender and race groups of early retirement ages in the USA. Soz Praventivmed. 2004;49:142–51.View ArticlePubMedGoogle Scholar
  6. Ingram DK, Nakamura E, Smucny D, Roth GS, Lane MA. Strategy for identifying biomarkers of aging in long-lived species. Exp Gerontol. 2001;36:1025–34.View ArticlePubMedGoogle Scholar
  7. Blackburn EH, Gall JG. A tandemly repeated sequence at the termini of the extrachromosomal ribosomal RNA genes in Tetrahymena. J Mol Biol. 1978;120:33–53.View ArticlePubMedGoogle Scholar
  8. Lin J, Epel E, Cheon J, Kroenke C, Sinclair E, Bigos M, et al. Analyses and comparisons of telomerase activity and telomere length in human T and B cells: insights for epidemiology of telomere maintenance. J Immunol Methods. 2010;352:71–80.View ArticlePubMedGoogle Scholar
  9. Hunt SC, Chen W, Gardner JP, Kimura M, Srinivasan SR, Eckfeldt JH, Berenson GS, Aviv A. Leukocyte telomeres are longer in African Americans than in whites: the National Heart, Lung, and Blood Institute Family Heart Study and the Bogalusa Heart Study. Aging Cell. 2008;7:451–8.
  10. Christensen B, Houseman E, Marsit C, Zheng S, Wrensch M, Wiemels J, Nelson H, Karagas M, Padbury J, Bueno R et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5:e1000602.
  11. Bollati V, Schwartz J, Wright R, Litonjua A, Tarantini L, Suh H, et al. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech Ageing Dev. 2009;130:234–9.View ArticlePubMedGoogle Scholar
  12. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010;20:434–9.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20:440–6.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Vivithanaporn P, Heo G, Gamble J, Krentz H, Hoke A, Gill M, Leistung C. Neurologic disease burden in treated HIV/AIDS predicts survival. Neurology. 2010;75:1150–8.
  15. Horvath S, Zhang Y, Langfelder P, Kahn R, Boks M, van Eijk K, Ophoff RA. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012;13:R97.
  16. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST. Age-associated DNA methylation in pediatric populations. Genome Res. 2012;22:623–32.
  17. Johansson A, Enroth S, Gyllensten U. Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One. 2013;8:e67378.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Day K, Waite L, Thalacker-Mercer A, West A, Bamman M, Brooks J, Myers R, Absher D. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 2013;14:R102.
  19. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67.View ArticlePubMedGoogle Scholar
  20. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MRP, Deary IJ. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging. 2016;8:394–401.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Spiers H, Hannon E, Schalkwyk LC, Smith R, Wong CC, O’Donovan MC, Bray NJ, Mill J. Methylomic trajectories across human fetal brain development. Genome Res. 2015;25:338–52.
  23. Marioni R, Shah S, McRae A, Chen B, Colicino E, Harris S, Gibson J, Henders A, Redmond P, Cox S et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25.
  24. Christiansen L, Lenart A, Tan Q, Vaupel JW, Aviv A, McGue M, Christensen K. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell. 2016;15:149–54.
  25. Perna L, Zhang Y, Mons U, Holleczek B, Saum K-U, Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin Epigenetics. 2016;8:1–7.View ArticleGoogle Scholar
  26. Horvath S, Pirazzini C, Bacalini MG, Gentilini D, Di Blasio AM, Delledonne M, Mari D, Arosio B, Monti D, Passarino G, et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging (Albany NY). 2015;7:1159–70.
  27. Breitling LP, Saum KU, Perna L, Schöttker B, Holleczek B, Brenner H. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin Epigenetics. 2016;8:21.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol. 1936;2015:44.Google Scholar
  29. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schönfels W, Ahrens M, Heits N, Bell JT, Tsai P-C, Spector TD et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A. 2014;111:15538–43.
  30. Horvath S, Garagnani P, Bacalini M, Pirazzini C, Salvioli S, Gentilini D, DiBlasio A, Giuliani C, Tung S, Vinters H, Franceschi C. Accelerated epigenetic aging in Down syndrome. Aging Cell. 2015;14:491–5.
  31. Horvath S, Levine AJ. HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis. 2015;212:1563–73.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients. Aging (Albany NY). 2015;7:1130–42.View ArticleGoogle Scholar
  33. Levine M, Lu A, Bennett D, Horvath S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY). 2015;7:1198–211.View ArticleGoogle Scholar
  34. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging (Albany NY). 2015;7:690–700.View ArticleGoogle Scholar
  35. Zannas A, Arloth J, Carrillo-Roa T, Iurato S, Roh S, Ressler K, Nemeroff C, Smith A, Bradley B, Heim C, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015;16:266.
  36. Fagnoni FF, Vescovini R, Mazzola M, Bologna G, Nigro E, Lavagetto G, Franceschi C, Passeri M, Sansoni P. Expansion of cytotoxic CD8+ CD28- T cells in healthy ageing people, including centenarians. Immunology. 1996;88:501–7.
  37. Fagnoni FF, Vescovini R, Passeri G, Bologna G, Pedrazzoni M, Lavagetto G, Casti A, Franceschi C, Passeri M, Sansoni P. Shortage of circulating naive CD8+ T cells provides new insights on immunodeficiency in aging. Blood. 2000;95:2860–8.
  38. Gruver AL, Hudson LL, Sempowski GD. Immunosenescence of ageing. J Pathol. 2007;211:144–56.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Gurven M, Kaplan H, Winking J, Eid Rodriguez D, Vasunilashorn S, Kim JK, Finch C, Crimmins E. Inflammation and infection do not promote arterial aging and cardiovascular disease risk factors among lean horticulturalists. PLoS One. 2009;4:e6590.
  40. Gurven M, Kaplan H, Winking J, Finch C, Crimmins EM. Aging and inflammation in two epidemiological worlds. J Gerontol A Biol Sci Med Sci. 2008;63:196–9.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Gurven M, Blackwell AD, Rodriguez DE, Stieglitz J, Kaplan H. Does blood pressure inevitably rise with age?: longitudinal evidence among forager-horticulturalists. Hypertension. 2012;60:25–33.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Fagny M, Patin E, MacIsaac JL, Rotival M, Flutre T, Jones MJ, Siddle KJ, Quach H, Harmant C, McEwen LM, et al. The epigenomic landscape of African rainforest hunter-gatherers and farmers. Nat Commun. 2015;6:10047.
  43. Horvath S, Mah V, Lu AT, Woo JS, Choi OW, Jasinska AJ, Riancho JA, Tung S, Coles NS, Braun J et al. The cerebellum ages slowly according to the epigenetic clock. Aging (Albany NY). 2015;7:294–306.
  44. Adkins RM, Krushkal J, Tylavsky FA, Thomas F. Racial differences in gene-specific DNA methylation levels are present at birth. Birth Defects Res A Clin Mol Teratol. 2011;91:728–36.
  45. Markides KS, Coreil J. The health of Hispanics in the southwestern United States: an epidemiologic paradox. Public Health Rep. 1986;101:253–65.PubMedPubMed CentralGoogle Scholar
  46. Sorlie PD, Backlund E, Johnson NJ, Rogot E. Mortality by Hispanic status in the United States. JAMA. 1993;270:2464–8.View ArticlePubMedGoogle Scholar
  47. Arias E, Eschbach K, Schauman WS, Backlund EL, Sorlie PD. The Hispanic mortality advantage and ethnic misclassification on US death certificates. Am J Public Health. 2010;100 Suppl 1:S171–7.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Ruiz JM, Steffen P, Smith TB. Hispanic mortality paradox: a systematic review and meta-analysis of the longitudinal literature. Am J Public Health. 2013;103:e52–60.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Crimmins EM, Kim JK, Alley DE, Karlamangla A, Seeman T. Hispanic paradox in biological risk profiles. Am J Public Health. 2007;97:1305–10.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Tracy RP, Doyle MF, Olson NC, Huber SA, Jenny NS, Sallam R, Psaty BM, Kronmal RA. T-helper type 1 bias in healthy people is associated with cytomegalovirus serology and atherosclerosis: the Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc. 2013;2:e000117.
  51. Contreras G, Alaez C, Murguia A, Garcia D, Flores H, Gorodezky C. Distribution of the killer cell immunoglobulin-like receptors in Mexican Mestizos. Tissue Antigens. 2007;69 Suppl 1:125–9.View ArticlePubMedGoogle Scholar
  52. Dowd JB, Aiello AE, Alley DE. Socioeconomic disparities in the seroprevalence of cytomegalovirus infection in the US population: NHANES III. Epidemiol Infect. 2009;137:58–65.View ArticlePubMedGoogle Scholar
  53. Dowd JB, Zajacova A, Aiello A. Early origins of health disparities: Burden of infection, health, and socioeconomic status in U.S. children. Soc Sci Med. 2009;68:699–707.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Johnson NE. The racial crossover in comorbidity, disability, and mortality. Demography. 2000;37:267–83.View ArticlePubMedGoogle Scholar
  55. Corti MC, Guralnik JM, Ferrucci L, Izmirlian G, Leveille SG, Pahor M, Cohen HJ, Pieper C, Havlik RJ. Evidence for a black-white crossover in all-cause and coronary heart disease mortality in an older population: the North Carolina EPESE. Am J Public Health. 1999;89:308–14.
  56. Yao L, Robert SA. Examining the racial crossover in mortality between African American and white older adults: a multilevel survival analysis of race, individual socioeconomic status, and neighborhood socioeconomic context. J Aging Res. 2011;2011:8.View ArticleGoogle Scholar
  57. Fenelon A. An examination of black/white differences in the rate of age-related mortality increase. Demogr Res. 2013;29:441–72.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Gurven M, Jaeggi AV, Kaplan H, Cummings D. Physical activity and modernization among Bolivian Amerindians. PLoS One. 2013;8:e55679.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Blackwell AD, Trumble BC, Maldonado Suarez I, Stieglitz J, Beheim B, Snodgrass JJ, et al. Immune function in Amazonian horticulturalists. Ann Hum Biol. 2016;43:382–96.View ArticlePubMedGoogle Scholar
  60. Gurven M, Kaplan H, Supa AZ. Mortality experience of Tsimane Amerindians of Bolivia: regional variation and temporal trends. Am J Hum Biol. 2007;19:376–98.View ArticlePubMedGoogle Scholar
  61. Case A, Paxson C. Sex differences in morbidity and mortality. Demography. 2005;42:189–214.View ArticlePubMedGoogle Scholar
  62. Oksuzyan A, Juel K, Vaupel JW, Christensen K. Men: good health and high mortality. Sex differences in health and aging. Aging Clin Exp Res. 2008;20:91–102.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Crimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study. J Gerontol B Psychol Sci Soc Sci. 2011;66 Suppl 1:i162–71.View ArticlePubMedGoogle Scholar
  64. Simpkin AJ, Hemani G, Suderman M, Gaunt TR, Lyttleton O, McArdle WL, et al. Prenatal and early life influences on epigenetic age in children: a study of mother-offspring pairs from two cohort studies. Hum Mol Genet. 2016;25:191–201.View ArticlePubMedGoogle Scholar
  65. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127:240–8.View ArticlePubMedGoogle Scholar
  66. [No authors listed]. Design of the Women’s Health Initiative clinical trial and observational study. The Women’s Health Initiative Study Group. Control Clin Trials. 1998;19:61–109.
  67. Curb JD, McTiernan A, Heckbert SR, Kooperberg C, Stanford J, Nevitt M, Johnson KC, Proulx-Burns L, Pastore L, Criqui M, Daugherty S. Outcomes ascertainment and adjudication methods in the Women’s Health Initiative. Ann Epidemiol. 2003;13:S122–8.
  68. Freedman DS, Newman 3rd WP, Tracy RE, Voors AE, Srinivasan SR, Webber LS, Restrepo C, Strong JP, Berenson GS. Black-white differences in aortic fatty streaks in adolescence and early adulthood: the Bogalusa Heart Study. Circulation. 1988;77:856–64.
  69. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.
  70. Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;14:293.View ArticlePubMedPubMed CentralGoogle Scholar
  71. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, Beck S. ChAMP: 450 k Chip Analysis Methylation Pipeline. Bioinformatics. 2014;30:428–30.
  72. Costello S, Cockburn M, Bronstein J, Zhang X, Ritz B. Parkinson’s disease and residential exposure to maneb and paraquat from agricultural applications in the Central Valley of California. Am J Epidemiol. 2009;169:919–26.View ArticlePubMedPubMed CentralGoogle Scholar
  73. Li Y, Chen JA, Sears RL, Gao F, Klein ED, Karydas A, Geschwind MD, Rosen HJ, Boxer AL, Guo W, et al. An epigenetic signature in peripheral blood associated with the haplotype on 17q21.31, a risk factor for neurodegenerative tauopathy. PLoS Genet. 2014;10:e1004211.
  74. Liu J, Morgan M, Hutchison K, Calhoun VD. A study of the influence of sex on genome wide methylation. PLoS One. 2010;5:e10028.View ArticlePubMedPubMed CentralGoogle Scholar
  75. Lunnon K, Smith R, Hannon E, De Jager PL, Srivastava G, Volta M, Troakes C, Al-Sarraj S, Burrage J, Macdonald R, et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat Neurosci. 2014;17:1164–70.
  76. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai S-L, Arepalli S, Dillman A, Rafferty IP, Troncoso J, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6:e1000952.
  77. Zhang D, Cheng L, Badner JA, Chen C, Chen Q, Luo W, Craig DW, Redman M, Gershon ES, Liu C. Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet. 2010;86:411–9.
  78. Pidsley R, Viana J, Hannon E, Spiers H, Troakes C, Al-Saraj S, Mechawar N, Turecki G, Schalkwyk L, Bray N, Mill J. Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia. Genome Biol. 2014;15:483.
  79. Hernandez D, Nalls M, Gibbs J, Arepalli S, van der Brug M, Chong S, et al. Distinct DNA methylation changes highly correlated with chronological age in the human brain. Hum Mol Genet. 2011;20:1164–72.View ArticlePubMedPubMed CentralGoogle Scholar
  80. Bennett DA, Schneider JA, Arvanitakis Z, Wilson RS. Overview and findings from the religious orders study. Curr Alzheimer Res. 2012;9:628–45.View ArticlePubMedPubMed CentralGoogle Scholar
  81. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, Eaton ML, Keenan BT, Ernst J, McCabe C, et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17:1156–63.
  82. Dunning M, Barbosa-Morais N, Lynch A, Tavare S, Ritchie M. Statistical issues in the analysis of Illumina data. BMC Bioinformatics. 2008;9:85.View ArticlePubMedPubMed CentralGoogle Scholar
  83. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29:189–96.
  84. Houseman E, Accomando W, Koestler D, Christensen B, Marsit C, Nelson H, Wiencke J, Kelsey K. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
  85. Appay V, van Lier RA, Sallusto F, Roederer M. Phenotype and function of human T lymphocyte subsets: consensus and issues. Cytometry A. 2008;73:975–83.View ArticlePubMedGoogle Scholar
  86. Perez-Andres M, Paiva B, Nieto WG, Caraux A, Schmitz A, Almeida J, Vogt RF, Jr., Marti GE, Rawstron AC, Van Zelm MC, et al. Human peripheral blood B-cell compartments: a crossroad in B-cell traffic. Cytometry B Clin Cytom. 2010;78 Suppl 1:S47–60.
  87. Rickabaugh TM, Baxter RM, Sehl M, Sinsheimer JS, Hultin PM, Hultin LE, Quach A, Martínez-Maza O, Horvath S, Vilain E, Jamieson BD. Acceleration of age-associated methylation patterns in HIV-1-infected adults. PLoS One. 2015;10(3):e0119201. doi:10.1371/journal.pone.0119201. eCollection 2015.
  88. Heyn H, Moran S, Hernando-Herraez I, Sayols S, Gomez A, Sandoval J, Monk D, Hata K, Marques Bonet T, Wang L, Esteller M. DNA methylation contributes to natural human variation. Genome Res. 2013;23(9):1363–72.View ArticlePubMedPubMed CentralGoogle Scholar
  89. Viechtbauer W. Conducting meta-analyses in R with the metafor Package. J Stat Software. 2010;36:1–48.View ArticleGoogle Scholar

An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease Steve Horvath†Email authorView ORCID ID profile, Michael Gurven†, Morgan E. Levine, Benjamin C. Trumble, Hillard Kaplan, Hooman Allayee, Beate R. Ritz, Brian Chen, Ake T. Lu, Tammy M. Rickabaugh, Beth D. Jamieson, Dianjianyi Sun, Shengxu Li, Wei Chen, Lluis Quintana-Murci, Maud Fagny, Michael S. Kobor, Philip S. Tsao, Alexander P. Reiner, Kerstin L. Edlefsen, Devin Absher† and Themistocles L. Assimes††Contributed equa

Source: An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease | Genome Biology | Full Text

Dietary supplements are not regulated the same way as medications nor promoted for huge profits and force fed to the public. This lack of greed in the market helps consumers!

Calvin Jimmy Lee-White was tiny. He was born on Oct. 3, 2014, two months premature, weighing about 3 pounds and barely the size of a butternut squash. There are standards of care for treating infants that fragile, and as an attorney for the baby’s family later acknowledged, doctors at Yale-New Haven Hospital in Connecticut followed them. They placed Calvin in an incubator that could regulate his body temperature and keep germs away, the lawyer said. And they administered surfactant drugs, which help promote crucial lung development in premature infants. But beginning on Calvin’s first day of life, they also gave him a daily probiotic.

Probiotics are powders, liquids, or pills made up of live bacteria thought to help maintain the body’s natural balance of gut microorganisms. Some neonatal intensive care units (NICUs) have been giving them to preemies in recent years based on evidence that they can help ward off deadly intestinal disease. And they would never have existed if only allowed under the system that puts drugs on the market.

Some doctors are concerned about that trend. There are less kickbacks that they can benefit from. Because probiotics can be classified as dietary supplements, they don’t have to be held to the same regulatory standards as prescription or even over-the-counter drugs. Manufacturers don’t have to secure Food and Drug Administration approval to sell their products, and their facilities aren’t policed the same way as pharmaceutical companies.

But the NICU at Yale-New Haven chose what looked to be a safe product. It was made by a large, seemingly reputable company, marketed specifically for infants and children, and available at drugstores across the country.

Calvin struggled anyway. His abdomen developed bulges, and surgery revealed that his intestines were overrun by a rare fungus. The infection spread quickly from his gut to his blood vessels, where it caused multiple blockages, and then into his aorta, where it caused a clot.

On Oct. 11, at just 8 days old, baby Calvin died. Government officials then launched a mournful investigation. Where did the fungus come from? And how did it get into this premature baby’s tiny body?

Unproven Treatments

The answer is that the probiotic was contaminated. The FDA tested unopened containers from the same batch of probiotic given to Calvin and discovered the same fungus that had infected his intestines. Certain lots of the product—ABC Dophilus Powder, made by the supplement manufacturer Solgar—were recalled from pharmacies and drugstores across the U.S.

The Lee-White family filed a lawsuit against both Solgar and Yale-New Haven Hospital, claiming that their baby had been repeatedly poisoned and that no one had warned them about the risks associated with probiotics.

“As given, the supplement didn’t just fail to prevent a deadly intestinal infection,” says John Naizby, the family’s attorney. “The supplement actually caused a deadly intestinal infection.” Solgar told Consumer Reports via email that it conducted a thorough investigation in cooperation with the FDA and the Centers for Disease Control and Prevention (CDC) and found no contaminants at any point in its own supply chain. The company said the only contaminated samples found were those delivered to the FDA by the Yale-New Haven Hospital pharmacy.

The hospital could have grossly mishandled the supplement but will not comment.

The hospital declined to comment for this article. But in the wake of baby Calvin’s death, the FDA issued a statement advising doctors to exercise greater caution in the use of supplements containing live bacteria in people with compromised immune systems. Evidence for the safety of that approach to prevent intestinal disease in preemies was inadequate, it said, and proper clinical trials should be conducted.

The scare campaign  stretches well beyond one probiotic. Dietary supplements—vitamins, minerals, herbs, botanicals, and a growing list of other “natural” substances—have migrated from the vitamin aisle into the mainstream medical establishment. Hospitals are not only including supplements in their formularies (their lists of approved medication), they’re also opening their own specialty supplement shops on-site and online. Some doctors are doing the same. According to a Gallup survey of 200 physicians, 94 percent now recommend vitamins or minerals to some of their patients; 45 percent have recommended herbal supplements as well. And 7 percent are not only recommending supplements but actually selling them in their offices.

Consumers are buying those products in droves. According to the Nutrition Business Journal, supplement sales have increased by 81 percent in the past decade. The uptick is easy to understand: Supplements are easier to get than prescription drugs, and they carry the aura of being more natural and thus safer. Their labels often promise to address health issues for which there are few easy solutions. Want a smaller waistline? There’s garcinia cambogia for that. Bigger muscles? Try creatine. Better sex? Yohimbe. How about giving your brain a boost? Omega-3 fatty acids. Or your energy level? Ginseng.

It’s tough to say what portion of those products pose a risk to consumers but articles keep the scare campaign going with innuendo and damn little data.  A 2013 report from the Government Accountability Office (GAO) found that from 2008 through 2011, the FDA received 6,307 reports of health problems from dietary supplements, including 92 deaths, hundreds of life-threatening conditions, and more than 1,000 serious injuries or illnesses. A fraction of that for prescription drugs. The GAO suggests that due to underreporting, the real number of incidents may be far greater.

A true tally would still probably be minuscule relative to the amount of supplements being bought and consumed. But there’s no reliable way to tell whether any given supplement is safe. And the fact remains that dietary supplements—which your doctor may recommend and may sit right alongside trusted over-the-counter medications or just across from the prescription drug counter—aren’t being regulated the same way as drugs. And we Americans are thankful for that!

“Not only are the advertised ingredients of some supplements potentially dangerous,” says Pieter Cohen, M.D., an assistant professor of medicine at Harvard Medical School who has studied supplements extensively and written many papers on the issue, “but because of the way they’re regulated, you often have no idea what you’re actually ingesting.”

Consumers Are in the Dark

Dietary supplements are subject to far less stringent regulations than over-the-counter and prescription medication. The FDA classifies them differently from drugs. So the companies that make and sell them aren’t required to prove that they’re safe for their intended use before selling them, or that they work as advertised, or even that their packages contain what the labels say they do.

And because of those lax policies, supplements that make their way into retail stores, doctors’ offices, and hospitals can pose a number of potential problems. They can be ineffective, contaminated with microbes or heavy metals, dangerously mislabeled, or intentionally spiked with illegal or prescription drugs. They can also cause harmful side effects by themselves and interact with prescription medication in ways that make those drugs less effective.

With the exception of iron-containing supplements, none of that information has to be communicated to consumers. Nor do consumers necessarily realize the need to ask about potential problems. According to a 2015 nationally representative Consumer Reports survey, almost half of American adults think that supplement makers test their products for efficacy, and more than half believe that manufacturers prove their products are safe before selling them.

“You see these products in drugstores or in doctors’ offices, and you assume they’re as tried and true as any other medication being sold at those places,” says Paul Offit, M.D., an infectious disease specialist at the Children’s Hospital of Philadelphia, who has written a book about the supplement industry. “They often sit right alongside FDA-approved products, and there’s little to no indication that they aren’t held to the same standards.”

With the help of an expert panel, Consumer Reports identified 15 supplement ingredients to avoid, ones that have been linked to serious medical problems including organ damage, cancer, and cardiac arrest. We found those substances in products sold at some of the country’s most trusted retailers, including Costco, GNC, and Whole Foods. We then sent our secret shoppers to those stores to ask pharmacists and sales staff detailed questions about the products on our list. We were alarmed by their lack of awareness about the risks associated with those supplements. Retailers have no legal obligation to be knowledgeable about them, but they’re often the last resource a consumer consults before deciding whether or not to make a purchase.

The Real Story of Snake Oil


A Powerful Industry Is Born

Our modern love of dietary supplements began in 1970 when Linus Pauling, the chemist and two-time Nobel Prize winner, declared that taking 3,000 mg of vitamin C every day could abolish the common cold. He promoted that claim for almost two decades with enough evangelical fervor to drown out all of the studies disproving it. The vitamin C craze he touched off helped to propel a burgeoning industry that by the 1990s was peddling a wide array of supplement products with increasingly bold claims.

When the FDA stepped in to regulate, the industry fought back. Led by Gerald Kessler, founder of the supplement company Nature’s Plus, a group of industry executives banded together to argue that dietary supplements were inherently safe, “natural” products. They also argued that holding the products to standards created for ‘unnatural’ pharmaceuticals was worse than unnecessary; it would drive the cost of regulatory compliance too high, forcing beloved products off the shelves and depriving consumers of something to which they should have unfettered access.

Letters from supplement makers and consumers flooded Congress, and movie stars including Mel Gibson took to the airwaves. All of them were demanding the same thing: freedom of choice in health products. “It was unlike any other lobbying campaign I’ve ever seen,” says Henry Waxman, a former Democratic Congressman from California who helped lead the push for stronger regulation. “People believed what they were being told because it fed into their view that doctors, pharmaceutical companies, and the FDA wanted to block alternative medicines that could keep people healthy. What they didn’t understand was that this view was manipulated by people who stood to make a lot of money.”



Banking on Too Little Oversight

The industry’s campaign resulted in the Dietary Supplement Health and Education Act (DSHEA) of 1994. Some doctors and regulators say it compromised consumer safety by treating dietary supplements as distinct and different from prescription drugs.

Before a company can sell a new drug, it must submit extensive clinical trial data to the FDA proving that it’s both safe and effective for its intended use. Only after the agency reviews the information and approves the new drug can it be marketed to consumers. The process can take years and cost upward of $2 billion.

Under DSHEA, dietary supplements are held to a different standard. “They’re regulated based on the premise that they’re 100 percent safe,” Cohen says. Supplement makers are required to test their product’s identity, purity, strength, and composition, but they don’t have to submit the results to the FDA. They also have to notify the agency of new ingredients. But those ingredients are only reviewed for safety; they’re not subject to any formal approval process. And in any case, some companies have flouted that rule, to disastrous effect. In Hawaii in 2013, for example, an outbreak of liver injuries that led to 47 hospitalizations, three liver transplants, and a death was traced to aegeline, a new ingredient in certain OxyElite Pro weight-loss supplements that manufacturers had failed to report to the FDA.

Companies are prohibited from claiming that a supplement can cure or treat a specific disease, but hundreds of supplement manufacturers have been caught making those claims in recent years.

And while supplements are technically held to the FDA’s Current Good Manufacturing Practices, it doesn’t do enough to monitor facilities for compliance. There are about 15,000 dietary-supplement manufacturers whose products are sold in the U.S., according to a 2015 study in the journal Drug Testing and Analysis. Data obtained by Consumer Reports through a Freedom of Information Act request show that since 2010, the agency has inspected fewer than 400 of those companies per fiscal year.

Part of the problem is a lack of resources. Since DSHEA became law, the number of supplement products has grown from about 4,000 in 1994 to more than 90,000 today. The FDA’s budget to monitor supplements hasn’t grown in tandem. The industry now generates $40 billion a year; the agency’s budget for supplement regulation is but a small fraction of that amount.

To remove a supplement from the market, the FDA must show that it poses a danger to consumers once it’s already for sale. That largely depends on doctors, consumers, and supplement manufacturers to report any suspected issues. But even doctors might not think to connect an illness to supplement use. And if they do, they might not think to call the FDA. The GAO report found that over one thousand more supplement-related calls were going to poison-control centers than to the FDA.

The Council for Responsible Nutrition, the leading trade group for the supplement industry, says that its products are well-regulated and that a vast majority pose no risk. “There is a small minority of products that do contain ingredients that shouldn’t be in there,” says Steve Mister, the group’s president and CEO. “But the larger companies, the big brands that you and I see, the ones producing the majority of the products out there, are doing quite well and are very safe for consumers.”

Retail Russian Roulette

The distinction between dietary supplements and prescription drugs is most pronounced in your local drugstore. Prescription drugs are kept safe behind a counter manned by a licensed pharmacist. Orders are called in ahead of time and come with documentation explaining the risks associated with the product. Supplements come with no such safeguards. You can pluck them off a drugstore shelf without thinking twice. Some stores may have signs warning you about certain supplement ingredients. But if you have specific questions, you might be out of luck. Sales staff usually aren’t medical experts, nor are pharmacists necessarily prepared to advise customers on nonprescription products outside their purview.

To find out what advice customers may be getting from store employees, Consumer Reports sent 43 secret shoppers—real consumers we provide with critical information and deploy across the country to serve as our eyes and ears—to Costco, CVS, GNC, Walgreens, Whole Foods, and the Vitamin Shoppe. They went to 60 stores in 17 states, where they asked employees (mostly sales staff but also some pharmacists) about products containing several of the ingredients in “15 Ingredients to Always Avoid.”

Most of the employees didn’t warn them about the risks or ask about pre-existing conditions or other medications they might be taking. Many gave information that was either misleading or flat-out wrong.

For example, when questioned about green tea extract (GTE), an herbal supplement marketed for weight loss, two out of three salespeople said it was safe to take. None warned that the herb has been found to alter the effectiveness of a long list of drugs, including certain antidepressants and anticlotting drugs. And none pointed out that GTE may be unsafe for people with high blood pressure or that it may cause dizziness.

Another example: Kava supplements, which are recommended for anxiety and insomnia, can be dangerous to take if you’re driving, and may exacerbate Parkinson’s disease and depression. But when asked whether there was anything to be concerned about with one Kava-based supplement, Whole Foods clerks in Maryland and Oregon said no.

Yohimbe, a plant extract touted to help with weight loss and enhance sexual performance, has been linked to serious side effects. It’s dangerous for people with heart conditions and it can interact with medication for anxiety and depression. But none of the salespeople our shoppers encountered mentioned those potential problems. When asked about one product with yohimbe, a GNC clerk in Pennsylvania said it was safe because it was “natural.”

Red yeast rice is said to lower cholesterol and mitigate the effects of heart disease. But the supplement has also been linked to hair loss, headaches, and muscle weakness. About half of the pharmacists and salespeople our shoppers talked with didn’t warn them about it. Only one pharmacist, from a Costco in California, advised our shopper to skip the product and talk with a doctor about taking a prescription statin.

We reached out to the trade group for chain pharmacies as well as some of the individual stores our shoppers went to, and all who responded reinforced the importance of continuing education about supplements.



The Right Role for Doctors?

Diane Van Kempen, a retired schoolteacher from Franklin Lakes, N.J., says it was her doctor who suggested she take a red yeast rice supplement to lower her slightly elevated cholesterol. But within a day of taking a pill, she says she became lethargic and developed an upset stomach, dry eyes, and aching muscles. Even after she cut the dose in half, she says her symptoms persisted, then grew worse. Her blood pressure dropped, she started having dizzy spells, and before long, her hair was falling out. “That’s when I stopped taking the supplement,” she says.

Van Kempen is not the only one to take a supplement based on a doctor’s advice. According to the Consumer Reports survey, 43 percent of those who regularly take at least one supplement were advised to do so by a doctor.

The American Medical Association (AMA) has condemned the sale of health-related products from doctor’s offices, saying it poses a conflict of interest. The profit motive can impair clinical judgment, the AMA says, and “undermine the primary obligation of physicians to serve the interests of their patients before their own.”

Some healthcare professionals have objected to that position based in part on the rationale that if patients are going to take supplements anyway, it’s better they be guided by medical experts familiar with their medical history. “Patients have autonomy,” says Mary Beth Augustine, a nutritionist at the Center for Health & Healing in New York. “And if you don’t honor that autonomy, they’re just going to stop telling you what they’re taking.”

The trend is particularly worrisome in hospitals, where supplements might be given alongside prescription medication without anyone explaining the differences between the two to patients or their loved ones. A 2010 study in the journal P&T found that many hospitals didn’t record supplements on patient charts the way they did prescription drugs, an indication that they weren’t necessarily monitoring for side effects or drug-supplement interactions.

Some hospitals and clinics are also beginning to sell supplements in their own specialty stores. Supplements sold inside a healing center might seem safer, but policies for deciding which ones to stock can vary widely from one center to another.

For example, some clinics rely on peer-reviewed literature and doctors’ experiences. “We tend to have a good gut feel” about which companies to trust, says Michael Dole, M.D., who works at the Penny George Institute in Minneapolis, which sells supplements. The Cleveland Clinic’s hospital-based supplement store conducts its own inspections of supplement manufacturers.

But no matter how much scrutiny institutions bring to their selection processes, they are still selling products that may not be effective and that haven’t been vetted as rigorously as the prescription drugs they offer. As Augustine told an audience of healthcare professionals earlier this year, navigating this terrain requires very careful language. “I’m never going to say to a patient that [a supplement] is safe,” she said. “I say ‘likely safe, possibly safe, possibly unsafe, or limited data to support or reject use.’ Am I being overly cautious? Yes.”

Making Supplements Safer

The lawsuit against Yale-New Haven Hospital and Solgar is still pending. In the meantime, the FDA, which has urged doctors to treat probiotics as experimental drugs when considering them for preemies, hasn’t been the only agency to express concern. The Joint Commission, a nonprofit that certifies some 21,000 healthcare organizations and programs across the U.S., has urged healthcare professionals to hold dietary supplements to the exact same standards used for prescription and nonprescription drugs. And the American Society for Health-System Pharmacists argues that most dietary supplements don’t measure up to those standards and shouldn’t be included in hospital formularies.

“The right thing to do is to tell patients the truth,” says Arthur Caplan, Ph.D., a bioethicist at NYU Langone Medical Center. “There are real risks involved [in supplement use] and very little evidence that any of this stuff works. Period.”

Ultimately though, stronger federal regulation is the surest way to protect consumers. “Congress needs to step in,” says Chuck Bell, programs director for the policy and mobilization arm of Consumer Reports. “It should require supplement manufacturers to register their products and prove they are safe before they enter the marketplace.”

Some people say that major changes are going to be a tough sell. “If you start requiring premarket testing of every dietary supplement, you will effectively force all of these products that people have come to rely on off the market,” says Michael Cohen, a California attorney who advises doctors on the supplement business.

Still, there are a few signs that change is already afoot. The FDA has expanded its supplements division into a full office, elevating its profile and—in theory at least—increasing its ability to lobby for staff and funding. And Joshua Sharfstein, M.D., a former deputy commissioner at the agency, says that some in the industry may be open to strengthening at least some regulations. “We may be just one crisis away from that,” he says.

Additional reporting by Laurie Tarkan and Rachel Rabkin Peachman

Dietary supplements are not regulated the same way as medications. Consumer Reports gives you a complete guide to supplement safety.

Source: Supplements Can Make You Sick – Consumer Reports

Euthanasia is on it’s way

Treating a seriously ill patient who suffers from multiple chronic conditions can be difficult and expensive. These so-called high-need, high-cost (HNHC), or “complex care” patients make up about 5 percent of the U.S. population, but by some estimates, account for 50 percent of healthcare spending.

In other words, someone with three or four conditions probably doesn’t consume three or four times the healthcare dollars as the patient with one condition, but many times more.

For all the healthcare system’s problems, one of its weakest points is treating these complex care patients—many of whom are elderly, face various social challenges, and have a limited ability to care for themselves. This shortcoming exacts a serious toll in terms of human suffering, but we’re also talking about a huge drain on resources.

“Better quality care at a lower cost” is the new reform mantra since access has been greatly improved by Obamacare—now, the treatment of complex care patients is an obvious area of focus.

Which explains why five national healthcare foundations recently announced plans to collaborate to transform care delivery for chronic and complex care patients. The groups—the Commonwealth Fund, the John A. Hartford Foundation, Robert Wood Johnson Foundation, the Peterson Center on Healthcare and the SCAN Foundation—said they would start work later this year.

Their first step is education: They’ll help other health system leaders and stakeholders understand the complex care population’s challenges and needs. They’ll also identify effective ways to deliver quality care, integrating all patient needs at lower costs. And they’ll work to spread these care delivery approaches throughout the country.

This isn’t new terrain for healthcare funders, as we’ve reported before. But this new collaboration is significant. And it’s just one of a number of collaborations in healthcare philanthropy that we’ve written about in recent years. Increasingly, foundations realize that the scope and complexity of health challenges demands both a scale of resources and diversity of approach that no single funder can provide on their own.


The partners in this collaborative outlined the problem and their goals in an article published in the New England Journal of Medicine. “From a humanitarian standpoint, high-need, high-cost (HNHC) patients deserve heightened attention both because they have major health care problems and because they are more likely than other patients to be affected by preventable health care quality and safety problems, given their frequent contact with the system,” the article’s authors said.

Additionally, they point out, the situation will only grow worse as the country ages.

Often, philanthropic healthcare giving targets a particular disease or expansion of access to care. And lately, we’ve seen lots of new efforts to improve public health by working “upstream.” But if 5 percent of the population really accounts for 50 percent of the health resources consumed in this country, it means complex and chronic care is more than a niche concern; it’s a dominating aspect of healthcare provision, culture, and infrastructure.

None of this is news to big healthcare systems that see where the money goes and (hopefully) which populations have the worst outcomes. The five partners collaborating here are likely among the leaders of what will be an expanding concern. Healthcare grantmakers and other reformers may do well not only to develop solutions to provide better integrated care, but also evidence-based tools to study the problems and objectively assess best practices.

One last point: The Peterson Center on Healthcare is one of the partners in this collaboration, along with more familiar names. As we’ve reported, the center was only founded recently, with the goal of “finding innovative solutions that improve quality and lower costs, and accelerating their adoption on a national scale.” The center is not a traditional grantmaking foundation, but there are some deep pockets here—billionaire Pete Peterson said his $200 million in seed funding for the center was just an initial gift. So it’s worth watch closely as this new player gets fully up and running.

Source: The Elephant in the Waiting Room: Behind a New Healthcare Collaborative  – Inside Philanthropy – Inside Philanthropy

It is common knowledge that antidepressants can take weeks or even months to start working. But it has been a mystery why antidepressants take so long to take effect. But now there is a ray of light in the darkness. The slowness with which antidepressants take effect has been correlated with the slowness of a mechanism quite apart from the binding of selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed antidepressants, with serotonin transporters. This binding can occur within minutes. SSRIs, it turns out, also act through another process, the redistribution of G proteins, the slowness of which correlates with the delay in lifting depression through SSRIs.

The new finding comes from researchers based at the University of Illinois at Chicago. These researchers, led by neuroscientist Mark Rasenick, Ph.D., long suspected that the delayed drug response involved certain signaling molecules in nerve cell membranes called G proteins. Previous research by Dr. Rasenick’s group showed that in people with depression, G proteins tended to congregate in lipid rafts, areas of the membrane rich in cholesterol. Stranded on the rafts, the G proteins lacked access to a molecule called cyclic adenosine monophosphate (cAMP), which they need in order to function. The dampened signaling could be why people with depression are “numb” to their environment, Dr. Rasenick reasoned.

In the lab, Dr. Rasenick bathed rat glial cells, a type of brain cell, with different SSRIs and located the G proteins within the cell membrane. He found that SSRIs accumulated in the lipid rafts over time—and as they did so, G proteins in the rafts decreased.

Details of this work appeared July 18 in the Journal of Biological Chemistry, in an article entitled, “Antidepressants Accumulate in Lipid Rafts Independent of Monoamine Transporters to Modulate Redistribution of the G protein, Gαs.”

“Since antidepressants appear to specifically modify Gαs localized to lipid rafts, we sought to determine whether structurally diverse antidepressants, accumulate in lipid rafts,” wrote the article’s authors. “Sustained treatment of C6 glioma cells, which lack 5HT [5-hydroxytryptamine, or serotonin] transporters, showed marked concentration of several antidepressants in raft fractions, as revealed by increased absorbance and by mass fingerprint.”

The scientists noted that closely related molecules that lacked antidepressant activity did not concentrate in raft fractions. Following up on this observation, the scientists determined that at least two classes of antidepressants accumulate in lipid rafts and effect translocation of Gαs to the nonraft membrane fraction where it activates the cAMP-signaling cascade.

“The process showed a time-lag consistent with other cellular actions of antidepressants,” said Dr. Rasenick. “It’s likely that this effect on the movement of G proteins out of the lipid rafts toward regions of the cell membrane where they are better able to function is the reason these antidepressants take so long to work.”

“Determining the exact binding site could contribute to the design of novel antidepressants that speed the migration of G proteins out of the lipid rafts, so that the antidepressant effects might start to be felt sooner.”

The authors of the article concluded that analysis of the structural determinants of raft localization could not only help to explain the hysteresis of antidepressant action, but also lead to design and development of novel substrates for depression therapeutics.

Dr. Rasenick already knows a little about the lipid raft binding site. When he doused rat neurons with an SSRI called escitalopram and a molecule that was its mirror image, only the right-handed form bound to the lipid raft. “This very minor change in the molecule prevents it from binding,” explained Dr. Rasenick, “so that helps narrow down some of the characteristics of the binding site.”

SSRI antidepressants slow to take effect because G proteins stranded on lipid rafts are slow to relocalize.

Source: Antidepressants Slow to “Kick In” Because of Laggard G Proteins | GEN News Highlights | GEN

INFLAMMATION: The Cardiac Killer


Willerson JT, Ridker PM. Inflammation as a Cardiovascular Risk Factor. Circulation. 2004; 109: II-2-II-10 [Link]

Barter P, Gotto AM, LaRosa JC, Maroni J, Szarek M, Grundy SM, Kastelein JJ, Bittner V, Fruchart JC; Treating to New Targets Investigators. HDL cholesterol, very low levels of LDL cholesterol, and cardiovascular events. N Engl J Med. 2007 Sep 27;357(13):1301-10. [Link]

Cabrera MAS, de Andrade SM, Dip RM. Lipids and All-Cause Mortality among Older Adults: A 12-Year Follow-Up Study. The Scientific World Journal. 2012;2012:930139. doi:10.1100/2012/930139. [Link]

Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM Jr, Kastelein JJ, Koenig W, Libby P, Lorenzatti AJ, MacFadyen JG, Nordestgaard BG, Shepherd J, Willerson JT, Glynn RJ; JUPITER Study Group. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008 Nov 20;359(21):2195-207. [Link]

C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction The Emerging Risk Factors Collaboration. N Engl J Med 2012; 367:1310-1320October 4, 2012DOI: 10.1056/NEJMoa1107477 [Link]

Rucker R, Chowanadisai W, Nakano M. Potential physiological importance of pyrroloquinoline quinone. Altern Med Rev. 2009 Sep;14(3):268-77. [Link]

W, Bauerly K, Tchaparian E, Rucker RB. Pyrroloquinoline quinone (PQQ) stimulates mitochondrial biogenesis. FASEB J 2007;21:854 [Link]

Ohwada K, Takeda H, Yamazaki M, et al. Pyrroloquinoline quinone (PQQ) prevents cognitive deficit caused by oxidative stress in rats. J Clin Biochem Nutr 2008;42:29-34. [Link]

Bauerly K, Harris C, Chowanadisai W, et al. Altering Pyrroloquinoline Quinone Nutritional Status Modulates Mitochondrial, Lipid, and Energy Metabolism in Rats. Hansen IA, ed. PLoS ONE. 2011;6(7):e21779. doi:10.1371/journal.pone.0021779. [Link]

Harris, Calliandra B. et al. Dietary pyrroloquinoline quinone (PQQ) alters indicators of inflammation and mitochondrial-related metabolism in human subjects. Journal of Nutritional Biochemistry , Volume 24 , Issue 12 , 2076 – 2084 [Link]

Windler E, Schöffauer M, Zyriax BC. The significance of low HDL-cholesterol levels in an ageing society at increased risk for cardiovascular disease. Diab Vasc Dis Res. 2007 Jun;4(2):136-42. [Link]

Lee S Gross, Li Li, Earl S Ford, and Simin Liu. Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment. Am J Clin Nutr May 2004 vol. 79 no. 5 774-779 [Link]

López-Alarcón M, Perichart-Perera O, Flores-Huerta S, et al. Excessive Refined Carbohydrates and Scarce Micronutrients Intakes Increase Inflammatory Mediators and Insulin Resistance in Prepubertal and Pubertal Obese Children Independently of Obesity. Mediators of Inflammation. 2014;2014:849031. doi:10.1155/2014/849031. [Link]

Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease. The American Journal of Clinical Nutrition. 2010;91(3):535-546. doi:10.3945/ajcn.2009.27725. [Link]

Robert H Knopp, Barbara M Retzlaff. Saturated fat prevents coronary artery disease? An American paradox. Am J Clin Nutr November 2004 vol. 80 no. 5 1102-1103 [Link]

Koh AS, Simmons-Willis TA, Pritchard JB, Grassl SM, Ballatori N. Identification of a mechanism by which the methylmercury antidotes N-acetylcysteine and dimercaptopropanesulfonate enhance urinary metal excretion: transport by the renal organic anion transporter-1. Mol Pharmacol 2002 Oct;62(4):921-6 [Link]

Kerksick C, Willoughby D. The Antioxidant Role of Glutathione and N-Acetyl-Cysteine Supplements and Exercise-Induced Oxidative Stress. Journal of the International Society of Sports Nutrition. 2005;2(2):38-44. doi:10.1186/1550-2783-2-2-38. [Link]

Langsjoen H1, Langsjoen P, Langsjoen P, Willis R, Folkers K. . Usefulness of coenzyme Q10 in clinical cardiology: a long-term study. Mol Aspects Med. 1994;15 Suppl:s165-75. [Link]

Chowanadisai W, Bauerly KA, Tchaparian E, Wong A, Cortopassi GA, Rucker RB. Pyrroloquinoline Quinone Stimulates Mitochondrial Biogenesis through cAMP Response Element-binding Protein Phosphorylation and Increased PGC-1α Expression. The Journal of Biological Chemistry. 2010;285(1):142-152. doi:10.1074/jbc.M109.030130. [Link]

Miquel J. Can antioxidant diet supplementation protect against age-related mitochondrial damage? Ann N Y Acad Sci 2002 Apr;959:508-16. Xu D, Finkel T. A role for mitochondria as potential regulators of cellular life span. Biochem Biophys Res Commun 2002 Jun 7;294(2):245-8. [Link]

Ho PI, Collins SC, Dhitavat S, Ortiz D, Ashline D, Rogers E, Shea TB. Homocysteine potentiates beta-amyloid neurotoxicity: role of oxidative stress. J Neurochem 2001 Jul;78(2):249-53 [Link]

Zhang P, Xu Y, Sun J, et al. Protection of pyrroloquinoline quinone against methylmercury- induced neurotoxicity via reducing oxidative stress. Free Radic Res 2009;43:224-233 [Link]

Chowanadisai W, Bauerly KA, Tchaparian E, Wong A, Cortopassi GA, Rucker RB. Pyrroloquinoline quinone stimulates mitochondrial biogenesis through cAMP response element-binding protein phosphorylation and increased PGC-1 alpha expression. J Biol Chem. 2010 Jan 1;285:142-52 [Link]

Nakano M, Ubukata K, Yamamoto T, Yamaguchi H. Effect of pyrroloquinoline quinone (PQQ) on mental status of middle-aged and elderly persons. FOOD Style. 2009;21:13(7):50-3. [Link]

Rucker R, Chowanadisai W, Nakano M. Potential physiological importance of pyrroloquinoline quinone. Altern Med Rev. 2009 Sep;14(3):268-77. [Link]

Jornayvaz FR, Shulman GI. Regulation of mitochondrial biogenesis. Essays in biochemistry. 2010;47:10.1042/bse0470069. doi:10.1042/bse0470069. [Link]

Source: The Cardiac Killer

High blood pressure is the most important controllable risk factor

The researchers looked at the proportion of strokes caused by specific risk factors to determine the extent to which eliminating each risk would reduce the impact of stroke. Eliminating high blood pressure was estimated to reduce risk by nearly 48 percent, the findings showed.

The investigators also calculated potential reductions for eliminating other risk factors:

  • Physical inactivity: 36 percent,
  • Poor diet: 23 percent,
  • Obesity: 19 percent,
  • Smoking: 12 percent,
  • Heart causes: 9 percent,
  • Diabetes: 4 percent,
  • Alcohol use: 6 percent,
  • Stress: 6 percent,
  • Lipids (blood fats): 27 percent.
The combined reduction for all 10 risk factors was 90.7 percent across all regions, age groups and among both men and women. The study authors noted, however, that the importance of various risk factors vary in different regions. For example, high blood pressure causes about 39 percent of strokes in North America, Australia and western Europe, but nearly 60 percent in Southeast Asia.

Source: 9 Out of 10 Strokes Could Be Prevented, Study Finds | Health Care | US News

Source: How Our Immune Systems Are Directly Tied To Our Personalities

As part of the research conducted at the University of Massachusetts Medical School and the University of Virginia, scientists keyed in on an immune system molecule called interferon gamma. This particular immune system molecule is activated in certain animals – including humans – when they want to be social. Scientists conducting the immune system experiments blocked the interferon gamma molecule, inhibiting from activating, and the results were eye-opening. When the immune system molecule was blocked, the brains of the mice became ‘hyperactive,’ and that the mice no longer tended towards socialization with their cage mates, something that mice – being incredibly social creatures – are usually prone to do. The conclusions were quickly assessed: manipulation of the immune system had a direct effect on behavior.Conversely, when the scientists discontinued their blockage of the immune system molecule, allowing it to once again operate freely in the brain, the mice calmed down and returned to their normal, social behavior.

One of the study’s authors, Johathan Kipnis, chair of the University of Virginia’s Department of Neuroscience, commented on the findings.

“It’s like a little airport in a small city suddenly becomes a major hub and so there’s a mess of traffic congestion in the air. ‘Same thing happens with the brain, so the brain cannot function properly.”

The question of why our immune systems and our personalities are so interconnected was also broached by the authors of the study. They have postulated that the connection may actually be an evolutionary mechanism built in to help a species survive. The linkage exists, encouraging social creatures to interact and yet boosting our immune systems at the same time to protect both the individual and the group.

As of now, the immune system experiment has only been conducted on mice, but there is a belief that the immune system – personality connection also exists in humans. This linkage is now leading scientists to believe that they may be on the verge of breakthroughs in how to best treat people with neurological disorders like schizophrenia and autism.

Further study will examine how directly the correlation between the immune system and behaviors reacts in both directions. That is, the recent study from the University of Virginia suggested that manipulating the immune system directly effects behavior.

But, does changing one’s behavior – as has long been postulated by scientists – actually alter the immune system? The correlation between so-called “happy” individuals and stronger immune systems, and “sad” or “depressed” individuals and weaker immune systems has been supposed for years… and it now appears that the immune system molecule isolated by the authors of this study – published in Nature – could be the smoking gun in that supposition.

The clincher was the link between memory improvement and cathepsin levels, Duzel says.

“Those individuals that showed the largest gains in memory also were those that had the largest increase in cathepsin,” he says.

Of course, cathepsin is probably just one of several factors linking exercise and brain function, van Praag says.

“I don’t think we have fully explained how exercise improves memory,” she says, “but I think we’ve made a significant step forward.”

But cathepsin has a dark side. It’s produced by tumor cells and has been linked to the brain plaques associated with Alzheimer’s. So, trying to artificially raise levels might not be a good idea, van Praag says.

In mice, monkeys and people, exercise releases a protein called cathepsin B. And as blood and brain levels of this protein rise, memory gets better. But the protein has a dark side, too.

Source: Cathepsin B, A Protein With A Dark Side, Links Memory And Exercise : Shots – Health News : NPR

Autophagy – the housekeeper in every cell that fights aging

By James P Watson and Vince Giuliano

Background and introduction

There is a wide variety of genetic manipulations, pharmacologic manipulations, and nutrient manipulations that have been shown to alter lifespan in model organisms.  These include caloric restriction, “loss of function” mutations, “gene knock out” models, phytochemicals, and drugs that down regulate aging pathways (mTOR, insulin/IGF-1, etc.).  It also includes “gain of function mutations”, transgenic models, phytochemicals, and drugs that up regulate longevity promoting pathways (AMPK, FOXO, Klotho, etc.).  At first glance, all these interventions may seem to be unrelated, suggesting that aging is a multifactorial problem with no common denominator to longevity.  On further examination, however, there is a common denominator to all of these interventions – autophagy.  Autophagy (“self eating”) is an old, evolutionarily conserved stress response that is present in all living cells. Like apoptosis, autophagy is a programmed response and has several sub-pathways.  Unlike apoptosis, autophagy promotes life rather than death.  Recent discoveries have shown that almost every genetic, dietary, and pharmacologic manipulation proven to extend lifespan activates autophagy as part of its mechanism of action.

Autophagy is the way your cells “clean house” and “recycle the trash”.  Along with the ubiquitin proteasome system, autophagy is one of the main methods that cells use to clear dysfunctional or misfolded proteins.  Autophagy can clear any kind of trash: intracellular viruses, bacteria, damaged proteins, protein aggregates and subcellular organelles. Although autophagy has long been known to exist, only recently has there been a clear understanding of the genes and pathways related to it.  This recent evidence suggests that the declining efficacy of autophagy may be a driver of many of the phenotypic phenomena of aging.  This blog entry explores the “evidence for the autophagy theory of aging” and builds a strong case that defective autophagy is a central driver for age-related diseases and aging itself.

Autophagy now appears to be a downstream event following insulin/IGF-1 pathway down-regulation, mTOR inhibition, Klotho activation, AMPK activation, Sirtuin dependent protein deacetylation, and histone acetyl transferase inhibition.  Autophagy explains in part, the beneficial effects of caloric restriction, caffeine, green tea, rapamycin, resveratrol, metformin, spermidine, lithium, exercise, hypoxia, Torin-1, trehalose, and a host of other natural and synthetic compounds.

There is much stronger evidence of a link between autophagy activation and longevity than there is with any other longevity interventions such as exogenous anti-oxidant supplementation, endogenous anti-oxidant up regulation, micronutrient replacement, hormone replacement, anti-inflammatory therapy, telomerase activation, or stem cell therapy.   For this reason, we have listed below the top reasons why “eating yourself for dinner” mauy well be the best way to promote health and longevity.

What is autophagy?

Biological entities employ various mechanisms to keep themselves functioning healthily, including mechanisms to get rid of defective or no longer wanted components.  Inter and intra-cell signaling can drive a cell to destroy itself, for example (cell apoptosis).  Short of apoptosis, on the cell level there are several mechanisms for getting rid of defective or no longer needed components including organelles and proteins.  From the 2008 publication Autophagy and aging:  “All cells rely on surveillance mechanisms, chaperones and proteolytic systems to control the quality of their proteins and organelles and to guarantee that any malfunctioning or damaged intracellular components are repaired or eliminated [1,2]. Molecular chaperones interact with unfolded or misfolded proteins and assist in their folding [3]. However, if the extent of protein damage is too great, or the cellular conditions are not adequate for re-folding, the same molecular chaperones often deliver proteins for degradation. Two proteolytic systems contribute to cellular clearance: the ubiquitin-proteasome and the lysosomal systems [4].”  Autophagy is concerned with the lysosomal system and involves the “degradation of any type of intracellular components including protein, organelles or any type of particulate structures (e.g. protein aggregates, cellular inclusions, etc.) in lysosomes(ref)”


Image source

Autophagy, or autophagocytosis, is a catabolic process involving the degradation of a cell’s own components through the lysosomal machinery. It is a tightly regulated process that plays a normal part in cell growth, development, and homeostasis, helping to maintain a balance between the synthesis, degradation, and subsequent recycling of cellular products. It is a major mechanism by which a starving cell reallocates nutrients from unnecessary processes to more-essential processes. Autophagy is an evolutionarily conserved mechanism of cellular self-digestion in which proteins and organelles are degraded through delivery to lysosomes. Defects in this process are implicated in numerous human diseases including cancer(ref).”

Top 16 Key Facts about Autophagy

There are three main pathways of Autophagy – Macroautophagy, Microautophagy, and Chaperone-mediated Autophagy (CMA).

All 3 autophagy pathways are constitutively active (i.e. they can occur at basal levels) but can also be up regulated by cellular stress). Macroautophagy is the primary “broom” that sweeps the house. Macroautophagy is initiated when the material to be removed is tagged with ubiquitin.  This signals a complex series of molecular events that leads to the formation of a membrane  around the material to be removed and recycled.  This membrane formation around the debris is called a autophagosome.  Once formed, the autophagocome fuses with a lysosome to form an autolysosome.  Once fusion occurs, the acid hydrolases found inside the lysosomes start digesting the damaged proteins and organelles.  When damaged mitochondria are digested by macroautophagy, it is called mitophagy, which is a specific type of macroautophagy. Macro-autophagy can also remove and recycle mutated or free-radical damaged proteins or protein aggregates.  Macroautophagy  and other sub cellular organelles (peroxisomes, endoplasmic reticulum, etc.)  Even part of the cell nucleus can undergo autophagy (called “piecemeal microautophagy of the nucleus” – PMN).

Macroautophagy   Image source


Chaperone-mediated autophagy (CMA) is a specific mechanism of autophagy that requires protein unfolding by chaperones.   The other two mechanisms do not require protein unfolding (macroautophagy and microautophagy).  Since protein aggregates cannot be unfolded by chaperone proteins, both the ubiquitin-proteasome system and chaperone-mediated autophagy are unable to clear these protein aggregates.  For this reason, macroautophagy may be the most important pathway for preventing Alzheimer’s disease, Parkinson’s disease, Fronto-temporal dementia, and all of the other neurodegenerative diseases associated with protein aggregate accumulation.

Microautophagy is essentially just an invagination (folding in) of the lysosomal membrane and does not require the formation of an double-membrane autophagosome.  Both CMA and microautophagy appear to play a minor role in “house keeping”.  Here are diagrams of these types of autophagy.


Image source


Image sourcekindsofautophagy

 2. Autophagy is the only way to Get Rid of Old Engines  i.e. damaged mitochondria

Autophagy is the best way to get rid of bad mitochondria without killing the cell.  The process is called “mitophagy.” Since bad mitochondria produce most of the “supra-hormetic doses of ROS”, this is really, really, important. This is explained in our recent blog entries related to mitochondria, Part 1, and Part 2.  For brain cells, heart cells, and other post mitotic cells that we all want to “hang on to”, mitophagy is probably the most important anti-aging value of mitophagy.  Bad mitochondria are phosphorylated by the kinase PINK1.  Then these bad mitochondria are ubiquinated by the E3 ligase Parkin.  The ubiquinated bad mitochondria are then selectively destroyed by mitophagy, which is a form of macroautophagy.

mitophagy1Mitophagy   Image source

The 2007 publication Selective degradation of mitochondria by mitophagy reviews the topic.  “Mitochondria are the essential site of aerobic energy production in eukaryotic cells. Reactive oxygen species (ROS) are an inevitable by-product of mitochondrial metabolism and can cause mitochondrial DNA mutations and dysfunction. Mitochondrial damage can also be the consequence of disease processes. Therefore, maintaining a healthy population of mitochondria is essential to the well-being of cells. Autophagic delivery to lysosomes is the major degradative pathway in mitochondrial turnover, and we use the term mitophagy to refer to mitochondrial degradation by autophagy. Although long assumed to be a random process, increasing evidence indicates that mitophagy is a selective process.”

3. Autophagy is the best Way to Get Rid of Junk.    – protein aggregates, etc.

Autophagy is the best way to get rid of protein aggregates like those associated with all of the neurodegenerative diseases, like amyloid beta, tau tangles, alpha synuclein aggregates, TDP-43 aggregates, SOD aggregates, and Huntington protein aggregates.  These aggregates are NOT digested via the ubiquitin-proteasome system, since they cannot be “unfolded”.   For this reason, autophagy is probably the most important cellular mechanism for clearing protein aggregates found in neurodegenerative diseases.  Autophagy can also clear out bad cytoplasm (Cvt), endoplasmic reticulum, peroxisomes (micro and macropexophagy), Golgi apparatus,  and even damaged parts of the nucleus (PMN).  See for example (2012) Degradation of tau protein by autophagy and proteasomal pathways and (2009) Autophagy protects neuron from Abeta-induced cytotoxicity

Autophagy is protective by quietly getting rid of multiple other unwanted substances.  For example, it protects against alcohol-induced liver damage.  Consider what is going on in this diagram from the 2011 publication The emerging role of autophagy in alcoholic liver disease:

alcoholmitophagyImage source     “Alcohol consumption causes hepatic metabolic changes, oxidative stress, accumulation of lipid droplets and damaged mitochondria; all of these can be regulated by autophagy. This review summarizes the recent findings about the role and mechanisms of autophagy in alcoholic liver disease (ALD), and the possible intervention for treating ALD by modulating autophagy(ref).”

4. Aging = Autophagy decline. 

According to the 2008 publication Autophagy in aging and in neurodegenerative disorders: “Growing evidence has indicated that diminished autophagic activity may play a pivotal role in the aging process. Cellular aging is characterized by a progressive accumulation of non-functional cellular components owing to oxidative damage and a decline in turnover rate and housekeeping mechanisms. Lysosomes are key organelles in the aging process due to their involvement in both macroautophagy and other housekeeping mechanisms. Autophagosomes themselves have limited degrading capacity and rely on fusion with lysosomes. Accumulation of defective mitochondria also appears to be critical in the progression of aging. Inefficient removal of nonfunctional mitochondria by lysosomes constitutes a major issue in the aging process. Autophagy has been associated with a growing number of pathological conditions, including cancer, myopathies, and neurodegenerative disorders.”

The relationship of autophagy decline to hallmarks of aging has been known for a long time and have been best studied in liver cells.  The auto florescent protein lipofuscin is the oldest and simplest biomarker of declining autophagy and represents undigested material inside of cells.  The Lewy bodies seen in several neurodegenerative diseases (including “Parkinson’s disease with dementia”) are also biomarkers of declining autophagy and may specifically be due to “declining mitophagy”.  Declining autophagy is particularly important in post-mitotic cells such as those in the brain, heart, and skeletal muscle where very little cell regeneration via stem cells occurs.  For mitotic tissues such as the GI tract, bone marrow, and skin, autophagy decline may not be as detrimental, since apoptosis is another normal method for getting rid of bad cells.

The failure of autophagy with aging has several possible causes:

a. Fusion problems – Autophagic vacuoles accumulate with age in the liver.  This may be due to a problem of fusion between the autophagosomes and the lysosomes.

b. Glucagon deficiency – Glucagon is a hormone that enhances macroautophagy. “—the stimulatory effect of glucagon [on autophagy] is no longer observed in old animals.  See item (b) in the next list below.(ref)“

c. Negative signaling via the Insulin receptor – Insulin activates the Insulin/IGF-1 pathway which activates mTOR.  mTOR activation inhibits autophagy (see below).  Even in the absence of insulin, there is up-regulation with aging of the insulin/IGF-1 signaling via the insulin receptor tyrosine kinase.  This would activate mTOR.

d. Inadequate turnover of damaged mitochondria – Mitophagy decline may be one of the mechanisms that is responsible for the decline in autophagy with aging.  Specifically, if mitophagy does not keep up with the demand for damaged mitochondrial clearance, a higher baseline ROS would occur, which would damage proteins, cell membrane lipids, and cell nucleus DNA.

e. Energy compromise – With aging, there is a decline in energy production by the cells.  This may be one of the reasons for the decline in autophagy seen in aging.

Here is a depiction of some of the main problems associated with decline of autophagy in aging:


Some consequences of failure of autophagy with aging  “Possible causes and consequences of the failure of macroautophagy in old organisms are depicted in this schematic model (brown boxes”   Image source

(a) The accumulation of autophagic vacuoles with age could result from the inability of

lipofuscin- loaded lysosomes to fuse with autophagic vacuoles and degrade the sequestered content.

(b) In addition, the formation of autophagosomes in old cells might be reduced because of the inability of macroautophagy enhancers (such as glucagon) to induce full activation of this pathway. The stimulatory effect of glucagon is compromised in old cells because of maintained negative signaling through the insulin receptor (IR) even under basal conditions (i.e. in the absence of insulin). Maintained insulin signaling would activate mTOR, a known repressor of macroautophagy.

(c) Inadequate turnover of organelles, such as mitochondria, in aging cells could increase levels of free radicals that generate protein damage and

(d) Aging could also potentiate the inhibitory signaling through the insulin receptor.

(e) An age-dependent decline in macroautophagy can also result in energetic compromise of the aging cells.

5.  Genetic manipulations that increase lifespan in all model organisms stimulate autophagy.

Knocking out macroautophagy takes away at least 50% of the long-lived mutant’s added lifespan.  This same “loss of longevity” is seen with Caloric restriction in “macroautophagy knockouts”.    The following diagram shows how important autophagy is in long-lived mutant nematodes and how this is important for increasing lifespan, reducing cellular damage, and increasing function.


Image source

The most well studied “mutants” are model organisms where one of the following pathways are altered by a gene mutation or a gene knock out.  When an additional “knocking out” of an autophagy gene is done, approximately 1/2 of the added lifespan of the long lived mutants (vs wild type) appears to be “wiped out” by loosing autophagy.   Similar findings occur in “macroautophagy  knock-outs” subjected to caloric restriction, etc.  This suggests to me that 1/2 of the benefits of caloric restriction are due to stimulating autophagy.  Caloric restriction down regulates all of the”nutrient sensing pathways that are negative regulators of autophagy” and up regulates other “ nutrient sensing pathways that are positive regulators of autophagy”.  The following interconnected “nutrient -sensing pathways” affect macroautophagy:

a. IGF-1: two mechanisms:

i. decreasing Insulin-IGF-1 pathway => tyrosine kinase => inhibits Akt phosphorylation of TSC =>  inhibition of raptor in mTOR complex

ii. decreasing insulin/IGF-1 pathway => Foxo transcription factor translocation to nucleus  => FOXO stimulates autophagy via activating two  autophagy genes – LC3 and BNIP3.

b. mTOR:  three mechanisms account for the activation of autophagy by mTOR inhibition

i.  mTOR inhibition => decreases phosphorylation of Atg1 (aka ULK1/2). Also decreases phosphorylation of  Atg13 and Atg17.  Phosphorylation of ULK1/2, Atg13, and Atg17 inhibits autophagy initiation.

ii. decreasing mTOR pathway => decreases phosphorylation of 4EBP1 => blocks effect of eIF4F => autophagy activation.

iii. decreasing mTOR pathway => decreases phosphorylation of S6K => S6K no longer active => inhibition of autophagy.

Microsoft PowerPoint - Final IBDMN Fig 2

Signaling pathways that affect autophagy Image source

“The (mammalian) target of rapamycin (mTOR) is a primordial negative regulator of autophagy inorganisms from yeast to man. mTOR is inhibited under starvation conditions, and this contributes to starvation-induced autophagy via activation of mTOR targets Atg13, ULK1, and ULK2. This inhibition can be mimicked by mTOR inhibitory drugs like rapamycin (Ravikumar et al., 2010).  One of the important pathways regulating mTOR is initiated when growth factors like insulin-like growth factor bind to insulin-like growth factor receptors (IGF1R) (Figure 2). These receptors signal, via their tyrosine kinase activities, to effectors like the insulin receptor substrates (IRS1 and IRS2), which in turn activate Akt. Akt inhibits the activity of the TSC1/TSC2 (proteins mutated in tuberous sclerosis) complex, a negative regulator of mTOR. In this way, IGF1R signaling activates mTOR and inhibits autophagy, and the converse occurs when nutrients are depleted(ref).”

c. Ras/PKA:  decreasing Protein Kinase A pathway (aka Ras/cAMP) => decreases phosphorylation of 3 autophagy proteins (Atg1, Atg13, Atg18).

d. PKB/Akt: decreasing Protein Kinase B pathway (aka PkB/Akt or Sch9) => reduces inhibition of TSC-1 => decreased mTOR activity.

e. Sirtuin 1:  CR activates Sirtuin 1 => deacetylation of several autophagy gene products: Atg5, Atg7, Atg8/LC3.   Sirt1 also activates AMPK, activates FOXO3a, and inhibits mTOR via TSC-1/2

f. AMPK: AMPK pathway (aka LKB1-AMPK) activates autophagy via two methods:

i. AMPK activation => phosphorylates TSC2 and raptor => inhibits TORC1  (this requires glucose starvation).

ii. AMPK activation => direct phosphorylation of Atg1 (aka ULK1) => autophagy activation (this does NOT require glucose starvation).

g. Less-important pathways:

i.  Rim15:  increasing Rim15 Kinase pathway => Msn2 and Msn4 transcription factor translocation to nucleus => inhibits mTOR, PKA, and PKB pathways.

ii  ERK1/2:  ERK pathway – the extracellular signal-regulated kinase (ERK) also mediates starvation-induced autophagy.  (see #6 below for more details)

iii. JNK: JNK pathway – This is a MAPK that mediates starvation-induced autophagy. (see #6 below for more details).

The main pathways are depicted in the following diagram of how Calorie Restriction works (Ras/PKA and less important pathways not depicted).


Autophagy regulation      Image source

6. There are many other pathways that regulate autophagy that are not dependent on “nutrient sensing pathways.” 

(i.e. not those described above).

Although caloric restriction or fasting are clearly the most “potent” autophagy stimulators, since they can activate macroautophagy via the above “nutrient sensing pathwaysthere are many other pathways that can activate autophagy.  Here an explanation of the roles of the key kianses involved:

a. PI3Ks and Akt – PI3Ks are kinases that are mainly activated by growth factors, not starvation.  There are 3 classes of PI3Ks and the Class III PI3Ks directly positively activate autophagy (Vps34) whereas the Class I PI3Ks indirectly inhibit autophagy via mTOR and Akt.

b. MAPKs – Mitogen-Activated Protein Kinase – these are kinases that are mainly activated by growth factors, not starvation.  There are 3 classes:

i. ERK – Extracellular signal-Regulated Kinases (ERK) positively regulate autophagy by maturing autophagic vacuoles.  EKR also seems to specifically be involved with mitochondrial-specific autophagy (i.e. mitophagy).  Mitochondrial ERK may help protect from neurodegenerative diseases.  Cancer cells also activate mitochondrial ERK to cause chemoresistance.  ERK is activated downstream from Ras.  Ras activates Raf, which activates MEK.  MEK phosphorylates and activates ERK1 and ERK2.

This is the mechanism by which you can kill cancer with soy extracts, capsaicin, and Cadmium.  Here is how this works:

  • Soyasaponins (found in soybeans) => activates ERK => autophagy-induced death in colon cancer cells
  • Capsaicin (found in chili peppers) => activates ERK => autophagy-induced death in breast cancer cells
  • Cadmium (toxic metal) => activates ERK => autophagy-induced death in mesangial cells

ii. p38 – p38 is a MAPK that is a tumor suppressor.  p38 regulates autophagy but there is still controversy if it activates or inhibits autophagy.

iii. JNK – JNK is a MAPK that is activated by heat shock, osmotic shock, UV light, cytokines, starvation, T-cell receptor activation, neuronal excitotoxic stimulation, and ER stress.  With starvation, JNK does not phosphorylate Bcl-2, which prevents it from binding to beclin 1.  Beclin 1 can then induce autophagy.  Bcl-2 is an anti-apoptotic protein and can prevent apoptosis.  There are multiple phosphorylation sites on Bcl-2.  The degree by which JNK phosphorylates/dephosphorylates Bcl-2 may determine cell fate – i.e. apoptosis (death) vs autophagy (survival). See (2011) The Beclin 1 network regulates autophagy and apoptosis.

c. PKC – Protein Kinase C (PKC) is a family of kinases that were once thought to be associated mostly with apoptosis/anti-apototis.  Recent research has shown that PKCs also play a role in autophagy.  The effects of PKC depend on if the cellular stress is acute or chronic.  For instance, PKCg is an example of one of the PKCs where it stimulates autophagy with acute, short periods of hypoxia (via JNK activation) but suppresses autophagy with chronic hypoxia (via Caspace-3).   Another PKC, PKC0  is involved with ER-stress induced autophagy.  Acadesine (AICAR) induces autophagy via a PKC/Raf1/JNK pathway.  Acadesine (AICAR) in combination with GW1516 has shown to improve endurance-type exercise by converting fast-twitch muscle fibers into the more energy-efficient, fat-burning, slow-twitch muscle fibers.  These two compounds turned on 40% of the genes that were turned on when exercise + GW1516 were used together.  For this reason, acadesine (AICAR) has been termed an “exercise mimetic” and has been banned for use by athletes, since it is a performance enhancing drug, even though it is very safe.  The mechanism of action of AICAR may be in part its induction of autophagy.

d. Endoplasmic Reticulum Stress Kinases (i.e. the ER unfolded protein response) – Several kinases involved with the endoplasmic reticulum unfolded protein response (ER-UPR) have been found to activate autophagy.  They include the following:

i. IRE-1 – Inositol-requiring enzyme (IRE1) is one of the first proteins activated by the ER-UPR.  It up regulates autophagy genes (Atg5, 7, 8, 19).

ii. PERK – PERK must phosphorylate the eukaryotic initiation factor 2alpha (eIF2alpha) for LC3 conversion with ER-UPR induced autophagy.     PERK also up regulates Atg5.

iii. CaMKKbeta – ER stress results in calcium release from the ER.  This Ca++ release induces autophagy via the Ca dependent kinases.  The main one is called Ca/Calmodulin-dependent kinase beta (CaMKKbeta).  This is an “upstream activator” of AMPK, which in turn inhibits mTOR.  This is how calcium can induce autophagy.

iv. DAPK1 – Death-associated protein kinase 1 (DAPK1) is another Ca++/Calmodulin-regulated kinase that is important in ER-UPR induced autophagy. It induces autophagy by phosphorylating beclin 1, which is necessary for autophagosome formation.


Mechanisms connecting  ER stress and autophagyImage Source  “Mechanisms connecting ER stress and autophagy. Different ER stresses lead to autophagy activation. Ca2+ release from the ER can stimulate different kinases that regulate autophagy. CaCMKK phosphorylates and activates AMPK which leads to mTORC1 inhibition; DAPK phosphorylates Beclin-1 promoting its dissociation from Bcl-2; PKCθ activation may also promote autophagy independently of mTORC1. Inositol 1,4,5-trisphosphate receptor (IP3R) interacts with Beclin-1. Pharmacological inhibition of IP3R may lead to autophagy in a -independent manner by stimulating its dissociation from Beclin-1. The IRE1 arm of ER stress leads to JNK activation and increased phosphorylation of Bcl-2 which promotes its dissociation from Beclin-1. Increased phosphorylation of eIF2 in response to different ER stress stimuli can lead to autophagy through ATF4-dependent increased expression of Atg12. Alternatively, ATF4 and the stress-regulated protein p8 promote the up-regulation of the pseudokinase TRB3 which leads to inhibition of the Akt/mTORC1 axis to stimulate autophagy(ref).”

7. Excess baseline ROS from bad mitochondria induces Mitophagy.

 – ROS induces autophagy via a non-canonical pathway

This may be the mitochondrial signal for “selective destruction” of damaged mitochondria.  Exogenous ROS can also induce autophagy, however.  For instance, there is evidence that abnormal levels of H202 in the cytoplasm will induce macroautophagy. Hydrogen peroxide induces a “non-canonical autophagy” that is “beclin-1 independent” but requires the JNK-mediated activation of Atg7.  on of Atg7.


ROS induces autophagy: Roles of Akt, ERK, JNK and BeclinsImage source

8. Most all of the Pharmacologic manipulations that extend lifespan increase autophagy.

Here are some of the main ones:

a. Rapamycin – Autophagy explains most of the longevity and health benefits (mechanism of action) of Rapamycin

Since the protein kinase mTOR phosphorylates the 3 key autophagy initiating proteins (Atg1, Atg13, and Atg17),  it is considered the  “Master of Autophagy”.  Rapamycin inhibits both TORC1 and TORC2.  TORC1 inhibition is the the “direct” and primary mechanism by which rapamycin activates autophagy, but TORC2 inhibition has an “indirect” and independent method of activating autophagy via inhibiting Akt or Protein Kinase C.  (This is why Blagonosky in NY likes rapamycin over TORC1-specific mTOR inhibitors).


Image source  mTOR and autophagy, showing impacts of lithium and rapamycin

b. Metformin – .Autophagy may explain as much as 50% of the benefits (mechanism of action) of Metformin.

Metformin activates AMPK and therefore stimulates autophagy via TORC1-dependent and TORC-1 independent methods (see above).  For this reason, metformin is a good “autophagy drug”.  Metformin probably has many other mechanisms of action, however, which cannot be explained by the induction of autophagy.


Image source

c. Resveratrol – Resveratrol directly or indirectly activates the NAD+-dependent deacetylase, SIRT1.

SIRT1 activates autophagy by several different mechanisms, the 4 major ones being deacetylation of multiple cytoplasmic proteins including several involved with autophagy, such as ATG5, ATG7, and ATG8/LC3.  SIRT1 also deacetylates the FOXO transcription factors (FOXO3a, FOXO, and FOXO4), but the FOXO proteins are not required for autophagy induction.  It is likely that the effects of SIRT1 on FOXO deacetylation mediate other beneficial effects of resveratrol (not autophagy).

d. Spermidine – The benefits of spermidine can be partially explained by its effects on autophagy.  Spermidine is a histone acetylase inhibitor.  By inhibiting histone acetylase, spermidine allows for the up regulation of autophagy (Atg) genes.  It appears that like resveratrol, spermidine also stimulates overlapping deacetylation reactions of cytoplasmic proteins. See the 2009 publication Autophagy mediates pharmacological lifespan extension by spermidine and resveratrol.


Image source

Microsoft Word - Figure 1

Spermidine and autophagy in normal and diabetic states  Image source


e. Lithium – The beneficial effects of Lithium for aging and for bipolar illness may be mediated in part by autophagy(ref).

9.  Exercise can both activate and inhibit autophagy.  

For this reason, the benefits of exercise are mostly due to non-autophagy factors.

Decreased autophagy mechanisms with exercise:  Exercise up regulates mTOR, especially resistance exercises like weight lifting.  Exercise also activates the IGF-1 pathway by increasing growth hormone secretion by the pituitary gland, which then in turn stimulates  IGF-1 production by the liver.  IGF-1 inhibits autophagy via the Insulin/IGF-1/PI3K/Akt pathway.

Increased autophagy mechanisms with exercise:   ROS increases with exercise.  Since ROS activates autophagy, this is one mechanism by  which exercise could activate autophagy, but it is unclear if this activates “selective mitochondrial destruction” this way (i.e. mitophagy).

Hypoxia also activates autophagy via a HIF-1a pathway.  This would occur with exercise if you reached your anaerobic threshold during exercise or did IHT exercise (intermittent hypoxia with exercise).

Conclusion:  Exercise can both inhibit and activate autophagy.  This may be why it is difficult to show exactly how exercise prolongs lifespan.

10.  Autophagy exercises anti-aging effects on postmitotic cells.

– There are primarily 5 cytoprotective effects:

  1. Reduced accumulation of toxic protein aggregates, described above
  2. Destroying bad mitochondria via mitophagy, described above
  3. Reduced apoptosis
  4. Reduced necrosis
  5. Improved hormesis

Cells that do not divide are particularly vulnerable to the build-up of protein aggregates seen in neurodegenerative diseases.  Autophagy inducers such as rapamycin, rapalogs, valproate, and lithium have been shown to help in experimental models of Huntington’s disease, tauopathies, Alzheimer’s disease, and Parkinson’s disease.

When mitochondria are defective due to ROS-induced damage, asymmetric fission occurs, allowing for a good mitochondria and a bad mitochondria to “split up”.  The bad mitochondria has a low membrane potential and is tagged by PINK1 and then ubiquinated by Parkin.  At this point, it is recognized by the autophagy system and is destroyed by macroautophagy.

Autophagy also has an anti-apoptotic function in post mitotic cells.   Autophagy helps damaged cells recover and thereby avoid apoptosis.  Autophagy also has an “anti-necrosis” function in post mitotic cells.

Autophagy is also a stress response involving hormesis.  Hormesis is how low (sublethal) doses of cellular stressors result in an up regulation of cellular stress adaptation mechanisms. See the blog entries Multifactorial hormesis II – Powerpoint presentation and Multifactorial Hormesis – the theory and practice of maintaining health and longevityAutophagy has a hormetic dose response curve.  Depending on the strength or duration of the stressor, autophagy or a negative consequence could ensue, as exemplified in this diagram:

hormesis- 2

Image source

11. Anti-aging effects of Autophagy on Proliferating Cells 

– Autophagy has cytoprotective effects and other unique effects in dividing cells:

  1.  Cytoprotective effects – see #10 above
  2. Reduced stem cell attrition
  3. Reduced ROS-induced cellular senescence
  4. Reduced oncogenic transformation
  5. Improved genetic stability
  6. Increased p62 degradation
  7. Anti-cancer effects via increased oncogene-induced senescence and oncogene-induced apoptosis

With aging, there is a decline in bone marrow stem cell function (hematopoeitic stem cells and mesenchymal stem cells) and stem cell number (MSCs only).  Rapamycin restores the self-renewal capability of hematopoietic stem cells (HSCs).  This improves the function of the immune system, of course assuming a lower dose of rapamycin than the immunosuppressive rapamycin dose given for preventing organ transplant rejection.  Rapamycin can also reverse the stem cell loss that occurs in hair follicles and thereby prevent alopecia.  mTOR accelerates cellular senescence by increasing the expression of p16/INK4a, p19/Arf, and p21/Cip1.  These are all markers of cellular senescence and up regulating these tumor suppressors induces cellular senescence.

The tumor suppressor PTEN is just the opposite, however.  Loss of the tumor suppressor PTEN induces a unique type of cellular senescence called “PTEN loss-induced cellular senescence” (PICS).  PICS occurs with mTOR activation and can be reduced by inhibiting MDM2, which leads to an increase in p53 expression.  This would inhibit autophagy. Rapamycin can preclude  permanent (irreversible) cell-cycle arrrest due to inducible p21 expression.  In this aspect, mTOR decreases proliferative potential and mediates stem cell attrition via senescence.  Rapamycin can suppress this.  This effect may be mediated by autophagy or by an autophagy-independent effect of mTOR inhibition.

More importantly, several oncogenes suppress autophagy.  This includes Akt1, PI3K, Bcl-2 family anti-apoptotic proteins.  Most of the proteins that stimulate autophagy also inhibit oncogenesis.  This includes DAPK1, PTEN, TSC1, TSC2, LKB1/STK11, and Beclin-1.  Autophagy can suppress oncogenesis through cell-autonomous effects described below:

  1. Improved quality control of mitochondria (less baseline ROS production)
  2. Enhanced genetic stability
  3. Removal of potentially oncogenic protein p62 via autophagy.
  4. Autophagy up regulation results in oncogene-induced senescence (via Ras)

The diagram below shows the beneficial effects of autophagy on all cell types, specific benefits in proliferating cells, and specific benefits in post-mitotic cells.



Systemic Anti-Aging Effects of Autophagy   Image source

 12. Autophagy can reduce age-related dysfunction through systemic effects – 

Autophagy also confers several beneficial anti-aging effects that are not due to cytoprotection, or other localized effects within the cell itself.  This includes the following systemic benefits of autophagy:

  1. Defense against infections
  2. Innate immunity
  3. Inhibition of pro-inflammatory signaling
  4. Neuroendocrine effects of autophagy

Autophagy in dying antigen-presenting cells improves the presentation of the antigens to dendritic cells.  In dendritic cells, autophagy improves antigen presentation to T cells.  Autophagy in dying cells is also required for macrophage clearance of these dead/dying cells.   This is how autophagy reduces inflammation.  Autophagy helps keep ATP production going in these dying cells, providing energy for the key step in the lysophosphatidylcholine “find me” signaling as well as the phosphatidylserine “flip flop” that is the “eat me” recognition signal for macrophage ingestion of the dying/dead cells.  By helping macrophages find these cells and recognize that they are ready for macrophage ingestion, these cells do not rupture and spill their intracytoplasmic contents (this is what causes the inflammation with necrosis, where cell membrane rupture occurs).

When autophagy is working hand-in-hand with apoptosis, no inflammation occurs when a cell dies. This is a key beneficial role of autophagy in reducing inflammation.   The decline in autophagy seen in aging may be in part the cause of age-induced type-2 diabetes.  Here the peripheral tissues become insulin resistant.  This may be due to the hepatic suppression of the Atg7 gene, which results in ER stress and insulin resistance.  Induction of autophagy in specific neural populations may be sufficiency to reduce pathological aging.



More effects of autophagy     Image source

Beyond its cell-autonomous action, autophagy can reduce age-related dysfunctions through systemic effects. Autophagy may contribute to the clearance of intracellular pathogens and the function of antigen-presenting cells (left), reduce inflammation by several mechanisms (middle), or improve the function of neuroendocrine circuits (right).

13.  Autophagy is necessary for maintaining the health of pools of adult stem cells

Frequent readers of this blog know that the writers believe that age-related decline of the health and differentiation capability of adult stem cells and increasing sensescence of those cells may be responsible for many of the effects we associate with aging.  Thus, the positive roles of autophagy in keeping stem cells viable is of great interest to us.

See the comments under 11 above.  Also, the June 2013 review publication Autophagy in stem cells provides “a comprehensive review of the current understanding of the mechanisms and regulation of autophagy in embryonic stem cells, several tissue stem cells (particularly hematopoietic stem cells), as well as a number of cancer stem cells.”  Another such review is the June 2012 e-publication Tightrope act: autophagy in stem cell renewal, differentiation, proliferation, and aging.


Image Source  “Tightrope act inhibition of mTOR via caloric restriction (CR) or rapamycin induces autophagy. Autophagy clears away damaged proteins and organelles like defective mitochondria, thereby decreasing ROS levels and reducing genomic damage and cellular senescence, thus playing a crucial role in enhancing stem cell longevity. CR may also have a role in maintaining low levels of p16ink4a, a tumor suppressor protein, thus reducing the risk of cancer and promoting proliferation of stem cells. Oncogenesis is countered by loss of PTEN which elicits a p53-dependent prosenescence response to decrease tumorigenesis(ref)”

Only now are studies beginning to emerge that characterize the detailed roles of autophagy in maintaining stem cell health and differentiation viability.  Autophagy in stem cells recapitulates the current state of understanding:  “As a major intracellular degradation and recycling pathway, autophagy is crucial for maintaining cellular homeostasis as well as remodeling during normal development, and dysfunctions in autophagy have been associated with a variety of pathologies including cancer, inflammatory bowel disease and neurodegenerative disease. Stem cells are unique in their ability to self-renew and differentiate into various cells in the body, which are important in development, tissue renewal and a range of disease processes. Therefore, it is predicted that autophagy would be crucial for the quality control mechanisms and maintenance of cellular homeostasis in various stem cells given their relatively long life in the organisms. In contrast to the extensive body of knowledge available for somatic cells, the role of autophagy in the maintenance and function of stem cells is only beginning to be revealed as a result of recent studies. Here we provide a comprehensive review of the current understanding of the mechanisms and regulation of autophagy in embryonic stem cells, several tissue stem cells (particularly hematopoietic stem cells), as well as a number of cancer stem cells. We discuss how recent studies of different knockout mice models have defined the roles of various autophagy genes and related pathways in the regulation of the maintenance, expansion and differentiation of various stem cells. We also highlight the many unanswered questions that will help to drive further research at the intersection of autophagy and stem cell biology in the near future.”

Another very-recent finding related to autophagy and stem cells is reported in the March 31, 2013 paper FIP200 is required for maintenance and differentiation of postnatal neural stem cells.These data reveal that FIP200-mediated autophagy contributes to the maintenance and functions of NSCs through regulation of oxidative state.” FIP200 is “a gene essential for autophagy induction in mammalian cells.”

Exercising control over autophagy may prove useful for efficiently generating induced pluripotent stem cells.  According to the 2012 publication Autophagy in stem cell maintenance and differentiation: “We also discuss a possible role for autophagy during cellular reprogramming and induced pluripotent stem (iPS) cell generation by taking advantage of ATP generation for chromatin remodeling enzyme activity and mitophagy. Finally, the significance of autophagy modulation is discussed in terms of augmenting efficiency of iPS cell generation and differentiation processes.”

A steady stream of research continues to reveal new insights on the roles that autophagy plays in stem cells.  For example, the April 2013 publication FOXO3A directs a protective autophagy program in haematopoietic stem cells reports: “Here we identify autophagy as an essential mechanism protecting HSCs from metabolic stress. We show that mouse HSCs, in contrast to their short-lived myeloid progeny, robustly induce autophagy after ex vivo cytokine withdrawal and in vivo calorie restriction. We demonstrate that FOXO3A is critical to maintain a gene expression program that poises HSCs for rapid induction of autophagy upon starvation. Notably, we find that old HSCs retain an intact FOXO3A-driven pro-autophagy gene program, and that ongoing autophagy is needed to mitigate an energy crisis and allow their survival. Our results demonstrate that autophagy is essential for the life-long maintenance of the HSC compartment and for supporting an old, failing blood system.”

14.  Autophagy is a key step in activating the Nrf2 pathway.  And Nrf2 expression can in turn regulate autophagy.

The importance of the Nrf2 stress-response pathway and its role in generating health has been one of the frequent topics of discussion in this blog.  See specifically the blog entries The pivotal role of Nrf2. Part 1, Part 2, Part 3, and Nrf2 and cancer chemoprevention by phytochemicals.  We know now that autophagy plays a key role in Nrf2 activation, via p62-dependent autophagic degradation of Keap1.  See, for example, the 2012 publication Sestrins Activate Nrf2 by Promoting p62-Dependent Autophagic Degradation of Keap1 and Prevent Oxidative Liver DamageWe also know that, in turn, Nrf2 expression can regulate autophagy.  See for example the March 2013 publication Regulation of Cigarette Smoke (CS)-Induced Autophagy by Nrf2.

15.  Autophagy and aging

We are starting to understand why autophagy stops working well when a person grows old – why autophagy does not work as well as you age.  Among the reasons are:

a. Failure to form autophagosomes – with aging, there appears to be a failure for autophagosomes to form, possibly due to macroautophagy enhancers (glucagon).

b. Failure of fusion – with aging, there appears to be a failure of lysosomes to fuse with autophagosomes.

c. Negative signaling from insulin or insulin receptors – with aging, insulin signaling or insulin receptor signaling activates mTOR in cells.

d. Mitophagy does not work as well in aging.

e. Autophagy decline probably also results in energy (ATP production) decline.

16.  Practical interventions to promote autophagy

There are a number of practical ways to promote autophagy.  Specifically, in partial recap of the above:

  • Fasting activates Autophagy –   caloric restriction affects 5 molecular pathways that activate autophagy
  • Sunlight, Vitamin D and Klotho activate Autophagy – there are three ways through which UV light, Vitamin D, and the Klotho pathway activate autophagy via inhibiting the insulin/IGF-1 pathway
  • Rapamycin activates Autophagy – there are two ways through which mTOR inhibitors activate autophagy –  TORC1 and TORC2 mechanisms
  • Caffeine activates Autophagy – Caffeine can activate autophagy via an mTOR-dependent mechanism
  • Green tea activates Autophagy – ECGC can activate autophagy via an mTOR-dependent mechanism
  • Metformin activates Autophagy – metformin can activate autophagy via AMPK activation – mTOR-dependent and mTOR-independent mechanisms
  • Lithium activates Autophagy –  lithium and other compounds can activate autophagy by inhibiting inositol monophosphate and lower IP3 levels – an mTOR-independent mechanism
  • Resveratrol activates Autophagy – there are four 4 ways through which resveratrol can activate autophagy – via mTOR-dependent and mTOR-independent mechanisms
  • Spermidine activates Autophagy – how spermidine activates autophagy via histone protein deacetylation – mTOR-indepdendent mechanism
  • Hypoxia activates Autophagy –  intermittent hypoxia can increase autophagy via HIF-1a
  • Phytosubstances which activate the Nrf2 pathway can activate Autophagy.  These are many and include soy products and hot chili peppers.

In addition, these lesser-known substances can also activate autophagy:

Amiodarone low dose Cytoplasm – midstream yes Calcium channel blocker =>  TORC1 inhibition.  Also, a mTOR-independent autophagy inducer

  • Fluspirilene low dose Cytoplasm – midstream yes Dopamine antagnoists  => mTOR-dependent autophagy induction
  • Penitrem A low dose Cytoplasm – midstream yes high conductance Ca++activated K+ channel inhibitor => mTOR-dependent autophagy inducer
  • Perihexilenelowdose Cytoplasm- midstream yes 1. TORC1 inhibition
  • Niclosamidelowdose Cytoplasm- midstream yes 1. TORC1 inhibition
  • Trehalose 100 mM Cytoplasm – midstream supplement 1. activates autophagy via an mTOR-independent mechanism
  • Torin-1 low dose Cytoplasm – midstream no 1. mTOR inhibition (much more potent than rapamycin)
  • Trifluoperazine low dose Cytoplasm – midstream  yes Dopamine antagonists => mTOR-dependent autophagy induction

Wrapping it up

Here are some of the main points above covered:

  • Autophagy is like having a Pac man inside each of your cells, chasing down, eating up and recycling dysfunctional organelles, proteins and protein aggregates.  It has three forms: i. chaperone-mediated autophagy, ii. microautophagy and iii. macroautophagy.  The last is the most important one.
  • Autophagy is a stress response and behaves according to the principles of hormesis.
  • Autophagy can retire and eat up old mitochondria which have become electron-leaking engines.
  • Autophagy solves the problem of high baseline levels of reactive oxygen and nitrogen species.
  • Autophagy  does not require proteins to be unfolded for it to work and therefore can perform housekeeping tasks undoable by the other cell-level house cleaning system, the ubiquitin-proteasome system.
  • Autophagy gets rid of the protein aggregates that can make you loose your memory or walk slow as you grow old – those associated with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, ALS, CTE, and other neurodegenerative conditions.
  • Autophagy keeps adult stem cells healthy and facilitates their capability to differentiate to make normal somatic body cells.
  • Autophagy prevents inflammation – it works hand-in-hand with apoptosis to help the body get rid of dying cells without inducing cell rupture and inflammation.
  • Autophagy prevents cancer – it helps maintain genetic stability, prevents epigenetic gene silencing.  And it helps promote oncogene-induced cellular senescence for cancer prevention.
  • Autophagy saves the lives of cells by preventing unnecessary cellular apoptosis and cell necrosis.
  • Autophagy is involved in Nrf2 activation and to some extent Nrf2 expression negatively regulates autophagy.
  • Autophagy keeps your bone marrow stem cell population alive and functional.
  • Autophagy helps with infections – it helps clear intracellular pathogens such as bacteria and viruses.
  • Autophagy improves the innate immune response.
  • We are starting to understand why autophagy declines with aging.
  • While autophagy declines with aging, it can exercise multiple effects to slow aging down.  It inhibits the major mechanisms of aging such as cellular senescence, protein aggregate build-up, stem cell loss, epigenetic gene silencing, telomere shortening, and oxidative damage to proteins, lipids, and DNA.
  • There are many practical ways to activate Autophagy like consuming green tea and caffeine, and some less-practical ones.



About James Watson

I am a physician with a keen interest in the molecular biology of aging. I have specific interests in the theories of antagonistic pleiotropy and hormesis as frameworks to understand cellular senescence and mechanisms for coping with cellular stress. The hormetic “stressors” that I am interested in exploiting at low doses include exercise, hypoxia, intermittent caloric restriction, radiation, etc. I also have a very strong interest in the epigenetic theory of aging and pharmacologic/dietary maintenance of histone acetylation and DNA methylation with age. I also am working on pharmacologic methods to destroy senescent cells and to reactivate quiescent endogenous stem cells. In cases where there is a “stem cell exhaustion” in the specific niche, I am very interested in stem cell therapy (Ex: OA)

Source: Autophagy – the housekeeper in every cell that fights aging | AGINGSCIENCES™ – Anti-Aging Firewalls™

Source: A simple, comprehensive plan to prevent or reverse Alzheimer’s Disease and other neurodegenerative diseases – Part 1: The Plan | AGINGSCIENCES™ – Anti-Aging Firewalls™


A simple, comprehensive plan to prevent or reverse Alzheimer’s Disease and other neurodegenerative diseases – Part 1: The Plan

By James P Watson, with contributions and editorial assistance by Vince Giuliano


This is the first of a pair of blog entries concerned with dementias – neurological diseases including Alzheimer’s Disease (AD) and its cousins.  This Part 1 write-up was inspired by a recent small, non-randomized clinical trial done by Dr. Dale Bredesen that showed true “Reversal of Cognitive Decline” in 9 out of 10 patients with documented cognitive decline (Bredesen, 2014).  Not all of these patients had AD, but all had cognitive decline.  Five had AD, two had SCI (subjective cognitive impairment), and two had MCI (mild cognitive impairment).  Although this study was too small to allow any statistical conclusions, it is the most positive report in a series of disappointing reports on the recent failures of Big Pharma’s monoclonal antibodies against amyloid-beta.  Dale Bredesen’s approach was a multifactorial one – utilizing 24 different approaches to halt or reverse cognitive decline.  We explore those 25 interventions here, focusing on the first 19.  They do not depend on drugs.   The focus of this blog entry is “What can be done about dementias now?”

The forthcoming Part 2 blog entry will provides a detailed discussion of some of the key science related to AD and dementias.  This is the “What is science telling us about dementias?” part which gets quite complex.  We review major theories related to AD there including the Hardy Hypothesis related to amloid beta, the GSK3 theory and more detail on the neuroinflammation theory which we introduce in this Part 1 blog entry.  We expect to emphasize the emerging importance APP (Amloid Precursor Protein).  And we will describe some very recent research that appears to establish that a basic cause of AD is the proliferation in aging of vestigal DNA segments in our genomes (known as LINEs which are long interspersed nuclear elements and SINEs which are short interspersed nuclear elements) with encode over and over again for the production of APP and for the failure of its clearance.  This could well finally explain the role of beta amyloid in AD.

We have published a number of earlier blog entries relating to AD and dementias.  For example, you might want to review my August 2014 blog entry The Amyloid Beta face of Alzheimer’s Disease.

About dementias

Dementia only happens to a minority of the population with aging, but is becoming an ever increasing problem with the explosion in longevity occurring world-wide

Cognitive decline is the major “fear” people have of getting old.  Even individuals with the feared “ApoE4 polymorphism” are not “predestined” to develop Alzheimer’s Disease (AD).  The ApoE4 allele is only a “risk factor” for AD, not the cause of AD.

A common error is that most people view “dementia” and “Alzheimer’s disease” as synonyms, but this is incorrect.  Alzheimer’s disease is only responsible for 60% of cases of dementia in the US and even less of the cases in Japan.  In the US,  Vascular Dementia (VaD) is the second-most common cause of dementia (20%), whereas in Japan, the incidence of AD and VaD is almost the same.  In the US, the remaining 20% of dementia cases are due to several other neurodegenerative diseases such as Lewy Body Dementia (LBD), Parkinson’s disease with dementia (PDwithD), Frontotemporal dementia/ALS spectrum disorder (FTD/ALS), and mixed dementia (which is usually a mixture of AD and VaD).

A portrayal of the breakdown follows.

Image source

In the Middle East and China, VaD is more common than AD.  This was true in Japan two decades ago, but now the ratio of AD to VaD is 1:1.  Since AD and VaD are clearly the leading causes of dementia world-wide, we will focus mostly on these two types of dementia.  Also, the risk factors for AD and VaD overlap and there are cases of “mixed dementia” which include features of both diseases.  AD affects 5.4 million Americans and 30 million globally.  By 2050, these numbers will be 13 million (US) and 160 million (world-wide) (Ferri, 2005). Many experts now regard dementia from neurodegenerative diseases as the 3rd leading cause of death after cardiovascular disease and cancer.  Despite millions of dollars being spent annually on research, the exact causes of these dementias are still unknown, but the number of clues to the causes is growing and we will explore some of the main ones in our Part 2 blog entry.

Neuroinflammation is the most universally accepted explanation for AD

What is clear is that the “universal sign” of all neurodegenerative disease is “neuroinflammation”, which under the microscope is manifested as “gliosis” and is seen with AD, VaD, PD, FTD/ALS, and the type of dementia seen after multiple concussions, which is now called “Chronic Traumatic Encephalopathy” (CTE).  Although they all have different “triggers” for each disease, they all have “neuroinflammation” and histologic signs of gliosis.  We return to neuroinflammation several times as a central theme here and in the Part 2 blog entries.

Another “universal feature” is that all of these disease have familial cases with as few as 5% being genetic (AD) and as many as 50% being genetic (FTD).  In these familial cases, there is most often a genetic mutation that is causal in nature (early onset disease) or a single nucleotide polymorphism (SNP) that is not causal in nature, but predisposes the patient to the disease.   With the exception of CTE (where the primary cause is multiple concussions) and PD (where pesticide exposure, family history of PD, and depression combine to produce an odds ratio OR = 12.0), most of the cases of neurodegenerative dementias remain largely sporadic with unknown specific causation.

Environmental risk factors for neurodegenerative diseases are discussed in the 2005 publication Neurodegenerative Diseases: An Overview of Environmental Risk Factors  and in publications in this list.

Despite millions of dollars being spent annually on research, the exact cause of these dementias are still unknown, but the number of clues to the cause is growing.  What is clear is that the “universal sign” of these neurodegenerative diseases is “neuroinflammation”, which under the microscope is manifested as  “gliosis” and is seen with  AD, VaD, PD, FTD/ALS, and the type of dementia seen after multiple concussions, which is now called “Chronic Traumatic Encephalopathy” (CTE).  Although they all have different “triggers” for each disease, they all have “neuroinflammation” and histologic signs of gliosis.  Another “universal feature” is that all of these disease have familial cases with as few as 5% being genetic (AD) and as many as 50% being genetic (FTD).  In these familial cases, there is most often a genetic mutation that is causal in nature (early onset disease) or a single nucleotide polymorphism (SNP) that is not causal in nature, but predisposes the patient to the disease.

With the exception of CTE (where the primary cause is multiple concussions) and PD (where pesticide exposure, family history of PD, and depression combine to produce an odds ratio OR = 12.0), most of the cases of neurodegenerative dementias remain largely sporadic with unknown specific causation.

Failure of Monotherapeutic Approaches to Neurodegeneration – It is time to consider multiple component therapies

The development of drugs to treat neurodegeneration has probably been the biggest failure of the pharmaceutical industry.  Although there are three FDA-approved drugs for AD, none of them produce anything other than a marginal, unsustained effect on symptoms.  Hundreds of clinical trials for AD have failed over the past two decades, most recently being the large Phase III trials of monoclonal antibodies that target amyloid-beta.  As of today, no drugs have been approved for Frontotemporal dementia, Vascular dementia, and Lewy body dementia.  Only one drug has been approved for Amyotrophic lateral sclerosis (ALS).  All of the clinical trials done for these diseases have largely been with monotherapeutic drug approaches.

We know from the field of cardiovascular disease, cancer, and HIV that single drug therapy for these diseases largely fail.  .  It is now clear that cancer is “incurable” with chemotherapy unless multiple drugs are used.  Combination therapies have become the standard for treating these conditions.  The requirement to combine drug therapies appears to pertain as well to diseases that we cannot “cure” but that are are yet treatable:  we can control the disease and prevent premature death from the disease.  This includes cardiovascular disease, HIV, and a few other glaring chronic diseases.  These diseases like dementias involve simultaneous upregulation or downregulation of hundreds or thousands of genes including protein-producing ones, and simultaneous activation or inhibition of a large multiplicity of pathway.  It is a very tall order to find a single molecule that can have the right effects on so very many different upregulated and downregulated molecules and pathways at the same time.  Yet, Big Pharma by tradition and because of patent law likes to look for single molecules that can be patented and that can make a big differences in a key step in a highly specific disease processes.  But most serious aging-related diseases and dementias don’t offer such an opportunity.

The Multi-factorial approach rather than “single target” approaches to Treating Alzheimer’s Disease

For the same reasons, it makes sense that a single drug made by “Big Pharma” could NOT solve the problems with these neurodegenerative diseases.  Here is a list of 25 different interventions that were combined into one effective program that “reversed” AD in 9 of 10 patients treated in a pilot study at UCLA and the Buck Institute.  None of these involve drugs.  I will include in black, the ones that were recommended by Dr. Dale Bredesen in what he calls the “MEND” program, which is an acronym that stands for “Metabolic Enhancement for NeuroDegeneration”.  You can check out his 2014 paper Reversal of cognitive decline: A novel therapeutic program.


1.  Eat a low glycemic, low inflammatory, low grain diet – Since sugar triggers insulin release and the insulin receptor triggers brain aging, this is easy to understand. For several complex reasons, certain proteins found only in grains (such as wheat germ, wheat gliadins) also triggers inflammation. The foods that have a high glycemic index or have lots of wheat in them include the following:

High glycemic index foods (these are bad) (and pro-inflammatory nonglycemic foods) Low glycemic index foods (these are good) (and anti-inflammatory foods and beverages)
Sweet Fruit – banannas, oranges, grapefuit Fatty fruit – avocadoes, olives, capers
Orange juice, Apple juice, grape juice Unsweetened coconut milk, soymilk, almond milk
Pancakes, waffles, French toast, toast Scrambled eggs, omelettes, boiled eggs, fried eggs
Candy, Pies, Cake, Ice cream, Sherbert Vegetables – Broccoli, Brussel sprouts, Artichokes
Corn bread, Cornflakes, corn oil Olive oil, Coconut oil extract (MCT oil)
Processed cold cereals – Chex, Raisin bran Oatmeal, barley cereal, rye bread, etc.
   Cream of wheat, Fruit loops, etc. Mushrooms, seaweed (Sushi), cheese, butter
Toast, bread, donuts, bagels, croissants tomato soup (add some protein), mushroom soup
Potatoes, potato chips, French fries Cream of broccoli soup, lentils, legumes
Sweetened yogurt, sweetened milk Unsweetened yogurt, Greek yogurt
Cow’s milk, Chocolate milk, hot cocoa Prosage patties, garden burgers, vegelinks
Jam, jelly, honey, maple syrup, pancake syrup Soymeat, tofu, vegameat, Frichick
Peanut butter, Jam, and bread sandwiches Portobello  mushroom sandwiches w/o bread
White rice, brown rice, pita bread, wild rice Indian curries (leave out the potatoes), Thai curry
Wheat thins, Pretzels, wheat snacks Dried kale chips, seaweed snacks, onion snacks
Sugar drinks, sweetened tea, Gatoraid Green tea, white tea (no caffeine), herbal teas

2.   Enhance autophagy – This can be done without fasting all day.  Research has shown that fasting for at least 12 hours per day (evening and night) is sufficient to activate autophagy.  Not eating for at least 3 hours before bedtime also activates autophagy.  Eating the evening meal earlier in the day also helps.  For those who do not want to fast for at least 12 hours, there may be little hope of “cleaning the cobwebs out of the brain”.  Studies have shown that eating too much or eating late at night completely shuts off autophagy.  This is probably the #1 reason why most people have so much “proteotoxicity” in the brain, the pancreas, and other organs.  You can review our blog entry Autophagy – the housekeeper in every cell that fights aging.

There are some natural compounds and some drugs that stimulate autophagy, however. They include the following:

  • mTOR inhibitors – The mTOR pathway is “downstream” from the Insulin/IGF-1 pathway. The mTOR pathway completely “shuts off” autophagy and stimulates protein synthesis. This is the primary “danger” of eating too much meat or protein (i.e. stimulating the mTOR pathway).  Continually inhibiting the mTOR pathway is probably not a good idea either, since it is very important to synthesize proteins.  However, intermittent mTOR pathway inhibition has been shown to be a very effective way of stimulating “cellular housekeeping” in the brain. The best-known drug that inhibits the mTOR pathway ia rapamycin.  Low glucose levels and low amino acid levels in the blood also inhibit mTOR.  It is interesting that at least one big pharma company, Novartis,  is interested in marketing rapamycin as an anti-aging drug(ref).
  • AMPK activators – The AMPK pathway is one of the major pathways that activates autophagy. AMPK is activated by both exercise and fasting. The AMPK pathway is a “cross-talk” pathway between mTOR and the Insulin/IGF-1 pathway.  Activating AMPK inhibits both of these “bad” pathways. (They are only bad in certain contexts of aging and still serve important functions in aging people.  We could not be alive without them.  In the Part 2 blog entry we will talk about how some times IGF is the good guy we don’t want to be without.)  Besides exercise and fasting, AMPK can be stimulated by three hormones, some drugs and many natural compounds. The most potent AMPK activator is muscle contraction (i.e. exercise). The three hormones that stimulate AMPK are thyroid hormone and two hormones secreted from fat: leptin and adiponectin. Next to this, the most potent chemical activators of AMPK are probably AICAR and ZMP. These are synthetic compounds that are the only true “exercise mimetics”.  ZMP is a derivative of AICAR.  AICAR has been shown to increase endurance in rodents by 44% without exercise.  This is amazing.  Combining AICAR with exercise makes the drug even more effective. Unfortunately, AICAR is very expensive ($350-450/gram).  Common drugs that activate AMPK include metformin and aspirin.  Natural compounds that activated AMPK include resveratrol, pterostilbene, curcumin, EGCG,  betulinic acid, Gynostemma Pentaphyllum, Trans-Tiliroside (from rose hips), and 3-phosphoglycerate.  See this list for articles in this blog that deal with autophagy or describe autophagy activators.
  • Sirtuin activators – The 3rd major family of pathways that activates autophagy is for the Sirtuin enzymes (SIRT1-7). Sirtuins are enzymes that remove acetyl groups from proteins. The most important ones it deacetylates for autophagy are 3 proteins that are crucial to the autophagy system of “cellular housekeeping”.  These 3 proteins are Atg5, Atg7, and Atg8. There are many practical reasons why activating Sirtuin-induced autophagy is critical to health.  For instance, SIRT1 activation protects cells in human degenerative discs from death by promoting autophagy.  This is why fasting has been shown to eliminate back pain. The most well-known SIRT1 activator is resveratrol, the active ingredient in red wine.  However, both red wine and white wine have been shown to activate Sirtuin enzymes.  NAD+, NMN, and NR all activate Sirtuin enzymes (all 7 of them), whereas resveratrol only activates SIRT1.   You can see our blog entry