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 equallyGenome Biology201617:171
© The Author(s). 2016
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 . 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 , 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 , and even prenatal brain samples . 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 . Third, the epigenetic age of blood relates to frailty  and cognitive/physical fitness in the elderly . The utility of the epigenetic clock method has been demonstrated in applications surrounding obesity , Down’s syndrome , HIV infection , Parkinson’s disease , Alzheimer’s disease-related neuropathologies , lung cancer , and lifetime stress . 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 groupsAn 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.
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
African Ancestry, Caucasian, Hispanic, Tsimane, East Asian (n)
Mean age (years) (range)
1. Women’s Health Initiative (blood)
676, 353, 433, 0, 0
2. Bogalusa (blood)
288, 681, 0, 0, 0
3. PEG (blood)
0, 289, 46, 0, 0
4. Saliva from PEG
0, 166, 93, 0, 0
5. Older Tsimane and others
0, 235, 38, 37, 0
6. Younger Tsimane and Caucasians
0, 24, 0, 22, 0
7. East Asians vs. Caucasians (PSP samples removed)
0, 279, 0, 0, 33
Li, 2014 
8. African populations
256, 0, 0, 0, 0
Fagny, 2015 
9. Cord blood
92, 70, 0, 0, 0
Adkins, 2011 
10. Male saliva
0, 59, 32, 0, 0
Liu, 2010 
11. Female saliva
0, 27, 15, 0, 0
Liu, 2010 
12. Lymphoblastoid cell lines
75, 68, 0, 0, 94
Heyn, 2013 
Additional file 1
Description of brain datasets for evaluating the effect of gender. Additional details can be found in “Methods”
Age mean ± SE [min, max]
Brain tissue samples (n)
84.0 ± 9.8 [40, 105]
48.0 ± 23.2 [16, 96]
44.3 ± 9.6 [19, 68]
64.4 ± 17.4 [25, 96]
52.3 ± 29.8 [1, 102]
88.5 ± 6.6 [66, 108]
Accuracy of the epigenetic clockDNAm 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 . 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 . 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.
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 IEAAWe 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).
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
Hispanic vs. African American
White vs. African American
1.6 × 10–7
High school (HS) vs. no HS
Some college vs. no HS
College vs. no HS
Grad school vs. no HS
Past drinker vs. Never
Light drinker vs. Never
Moderate vs. Never
Heavy vs. Never
Former vs. Current
Never vs. Current
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 . 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 . 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 WHIBased 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).
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
Epig. Age Accel
4.3 × 10-4
Hispanic vs. African American
White vs. African American
1.8 × 10–5
1.5 × 10-5
High school (HS) vs. no HS
Some College vs. no HS
College vs. no HS
Grad school vs. no HS
Past drinker vs. Never
Light drinker vs. Never
Moderate vs. Never
Heavy vs. Never
Former vs. Current
Never vs. Current
3.0 × 10-4
3.4 × 10-4
4.8 × 10-8
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 AmericansIn 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.
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
Caucasian vs. African American
Female vs. Male
Grade 8–9 vs. < Grade 8
Grade 10–12 vs. < Grade 8
Vocat/Tech vs. < Grade 8
College vs. < Grade 8
Graduate vs. < Grade 8
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 . 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).
Sex effects in brain tissueWe 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 . 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).
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 , 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. . Again, these results corroborate our findings (Additional file 8D, E). Third, we validated our findings using the original blood-based aging measure by Hannum  (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.
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  and that Hispanics have a lower relative CD4+ T cell percentage than Caucasians . 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 . 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 . High levels of physical activity are maintained well into late adulthood .
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 . 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 .
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 , 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 . Mathematical details and software tutorials for the epigenetic clock can be found in the Additional files of . An online age calculator can be found at our webpage (https://dnamage.genetics.ucla.edu).
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 , 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.  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 . 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 . Published results in Marioni et al.  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  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 . 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 . 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 (www.whi.org/researchers/data/WHIStudies/StudySites/M5) 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 https://www.whi.org/researchers/Stories/June%202015%20WHI%20Investigators’%20Datasets%20Released.aspx, see the link https://www.whi.org/researchers/data/Documents/WHI%20Data%20Preparation%20and%20Use.pdf.
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  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” , generating quality control report, finding sample outliers, cell counts estimation, and annotation accessing. The R package wateRmelon  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  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 https://biolincc.nhlbi.nih.gov/studies/bhs/.
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 . 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 . 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 . 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. , 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 .
Datasets 10 and 11
Saliva samples from Caucasians and Hispanics. The data were generated by splitting the data from  by sex, which reflected the use of these data in the development of the epigenetic clock software . 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 datasetsWe 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 , 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 , 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 .
Study 3: cerebellar DNA methylation data from , 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 , GEO GSE61431. We analyzed 37 Caucasian participants (European ancestry).
Study 5: frontal cortex and cerebellum from neurologically normal Caucasian participants from . The DNA methylation data and corresponding SNP data can be found in dbGAP, http://www.ncbi.nlm.nih.gov/gap (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 https://www.synapse.org/#!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 . Additional details on the DNA methylation data can be found in . 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) . The epigenetic clock software implements a data normalization step that repurposes the BMIQ normalization method from Teschendorff  so that it automatically references each sample to a gold standard based on type II probes as detailed in .
Estimating blood cell counts based on DNA methylation levels
We estimate blood cell proportions using two different software tools. Houseman’s estimation method , 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  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 .
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  and B lymphocyte  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 . 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 (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000200.v10.p3 and https://biolincc.nhlbi.nih.gov/studies/bhs/).
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 http://www.ncbi.nlm.nih.gov/gap (accession: phs000249.v2.p1) and brain data 6 at https://www.synapse.org/#!Synapse:syn3168763.
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.
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 (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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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
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An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease | Genome Biology | Full TextAuthor: SupremePundit
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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?
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.”
More on Dietary Supplements
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 OilPlay0:00/1:40Fullscreen
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.
Just what is SEX?
The internet got hot and bothered earlier this week following media reports of research that revealed millennials, the so-called “hook up” generation, aren’t having all that much sex after all.
The only problem: That’s not how the study authors, who published their work in the journal Archives of Sexual Behavior, intended for their research to be interpreted.
“Just like it’s not true that millennials are all promiscuous people who are on Tinder all the time, it’s also not true that all millennials are sexless and just watching porn in their moms’ basements,” lead author Jean Twenge, a psychology professor at San Diego State University, told The New York Times.
So what do we know about millennials’ sexual habits? Here’s the lay of the land, according to the latest research:
1. Millennials ARE having sex.
While it’s true that there’s a slightly higher number of millennials who aren’t having sex than there were in older generations, the vast majority of millennials (a full 85 percent) are sexually active, which for the purposes of the study means they’ve had sex in the last 12 months.
As for those who aren’t having sex, delayed adulthood ― the idea that major life events like marriage and parenthood are now occurring later in life ― could have something to do with it.
“For late millennials and iGen [the generation after millennials, roughly those born after 1996], sex is now joining the later to adulthood party. Sex has caught up to other adult milestones and is being delayed,” Twenge told Science of Us.
2. But teen pregnancy plummeted under millennials’ watch.
Let’s give millennials a well-deserved pat on the back, shall we? Teen pregnancy has plummeted to a historic low over the last 30 years, according to the U.S. Centers for Disease Control and Prevention.
Experts aren’t sure exactly who or what we have to thank for lower teen pregnancy rates. But according to a 2014 Brookings report, there’s evidence that reality shows featuring teenaged pregnant millennials, such as MTV’s “Teen Mom,” may have contributed to a third of the decline in teen births between 2009, when the shows began airing, and 2010.
3. They aren’t ready for marriage.
A 2015 Pew study defined millennials as between 18 and 33 that year. Seven in 10 of them had never married, and those who did marry were waiting longer. In 1960, the average age of first marriage was 23 for men and 20 for women; in 2011, according to Pew, it was 27 for women and 29 for men.
And in 2013, only 26 percent of millennials got married between the ages of 18 and 32, compared to 36 percent of Gen Xers 1997 or 48 percent of baby boomers in 1980, Pew reported.
While the changing roles of women, access to reliable birth control and a greater acceptance of premarital sex and cohabitation can’t be discounted, economics also plays a role in young people’s decision to delay marriage.
“When there’s rough economic times, marriage rates go down,” sociologist Eric Klinenberg, who co-authored Aziz Ansari’s book Modern Romance: An Investigation, told the Washington Post. “People don’t feel comfortable committing to someone during hardships.”
4. But they do enjoy a good hookup.
“With more Americans spending more of their young adulthood unmarried, they have more opportunities to engage in sex with more partners and less reason to disapprove of nonmarital sex,” Twenge and her colleagues wrote in a previous study published in the Archives of Sexual Behavior in May 2015. Both Twenge’s studies relied on data from the General Social Survey, a project that has been collecting data on American behavior for decades.
And indeed, 45 percent of millennials in the 2015 study reported they’d had casual sex with someone who wasn’t their boyfriend, girlfriend or spouse in their late teens or 20s, compared to 35 percent of Gen X at that age. (Data wasn’t available for baby boomers.)
5. And when they do tie the knot, it sticks.
While millennials’ marriage delays might not please grandma, marrying later in life does come with benefits ― including including a greater likelihood of staying together.
Couples who marry in their early 20s have a higher risk for divorce than those who wait a few years, although that rate starts to creep up again for couples in their mid 30s. Happy unions even come with an added bonus: the so-called “marriage advantage,” which includes health benefits like a decreased risk of cancer and heart attack, all worth waiting for.
First Generic Version of Tamiflu Approved by FDA
The Elephant in the Waiting Room: Behind a New Healthcare Collaborative – Inside Philanthropy – Inside PhilanthropyAuthor: SupremePundit
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.
- What This New Funder Collaborative Says About the State of Public Healthin the U.S.
- Come On, People, Talk to Each Other: The Latest Effort to Foster Collaboration on Health
- Lower Costs Yet Better Care? Funders See a Key in Helping High-Need Patients
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.
Singer Toni Braxton Boldly Claims That Her Son Has Been ‘Cured’ Of Autism: ‘He Is Off The Spectrum’ [Video]Author: SupremePundit
Braxton partially attributes the cure to the help and influence of the late Susan Wright, a co-founder of Autism Speaks, who died on July 29.
“When she found out about my son, she called me immediately and said ‘Get him in this program. Do this, do that,’” Toni shared. “She’s been an advocate in helping me so much. I miss her already. I mean, I can’t believe she’s gone.”
Autism, and autism spectrum disorder, is an impairment of a brain’s natural development that can lead to trouble with social interaction, verbal communication, and repetitive behaviors. An early diagnosis can greatly affect how a child develops, and with the right treatment, they can go on to live natural and normal lives. However, there is no known cure for autism, and despite what Braxton is claiming, many have expressed that her rhetoric of Diezel being “off the spectrum” doesn’t help the cause.
Initially mentioned by Madame Noire, several readers of the site have already taken Toni to task for her thoughts. What a bunch of pricks!
“Being the mother of an autistic child myself, [I know that] it cannot be cured,” says Marsha Shand. “If your child is on the lighter end of the spectrum, it is much like putting glasses on a near or far sighted child. Can the child see correctly now? Yes. Is his/her condition cured? No. Same with the lighter end of the spectrum. If the condition is caught early, and the person is worked with, it can be controlled, and maybe even corrected, but it cannot be cured.”
Despite Braxton’s intent for sharing such news, this is not the first time that her comments on Diezel’s autism diagnosis have been questioned. In her memoir Unbreak My Heart, which was named after her signature tune, Toni stated that she initially believed that Diezel’s autism was a punishment from God after she aborted a previous pregnancy.
“In my heart, I believed I had taken a life,” she wrote, as shared by Us Weekly, “an action that I thought God might one day punish me for. My initial rage was quickly followed by another strong emotion: guilt. I knew I’d taken a life [and] I believed God’s payback was to give my son autism.”
As for how Diezel’s doing now, Toni marveled that he’s just like any other teenager nowadays.
“He’s our social butterfly. He’s the one who plays with friends and hangs out all the time. [I’m] very, very fortunate. I don’t like to think there’s anything wrong with our babies. I just think they learn differently.”
Toni Braxton is currently gearing up for her upcoming “Hits” tour, which kicks off in Oakland, California, this October. Braxton Family Values, which centers on Toni’s relationship with her family, airs Thursday nights on WE-TV. Feel free to check out Toni’s Access Hollywood interview above (the comments on autism begin around the 2:20 mark).
Read more at http://www.inquisitr.com/3386813/toni-braxton-autism-cure-controversy/#BYkhXP1Ul0q2Cj7l.99
https://www.youtube.com/watch?v=D7qdlWS59JQ Along with being known for her voice, songstress Toni Braxton has also been long recognized as a strong advocate
Sexting is supposed to be a wonderful thing that spices up your relationship and keeps things fresh. It’s also, for the most part, meant to be private (for your partner’s eyes only), especially if photos are involved. A new study, however, suggests that our sexts are probably being shared around
Sexting is supposed to be a wonderful thing that spices up your relationship and keeps things fresh. It’s also, for the most part, meant to be private (for your partner’s eyes only), especially if photos are involved. A new study, however, suggests that our sexts are probably being shared around more often than we’d like.
According to research conducted by the Kinsey Institute at Indiana University and published in the journal Sexual Health, one in four people share sexts they receive with their friends. After surveying 5,805 single adults between the ages of 21 and 75, researchers found that although 73% of participants said they were uncomfortable with their sexts being shared, 23% reported having shared sexy photos and texts with an average of over three different friends.“That finding suggests that the real risk of sexting is the potential for nonconsensual sharing of sext messages,” Justin Garcia, the study’s author, a research scientist at the Kinsey Institute, said in a press release. “It raises the question that if someone sends something to you with the presumption that it’s private, and then you share it with others — which, when it comes to sexting, nearly one out of every four single Americans are doing, what do we want to consider that type of violation? Is it just bad taste? Is it criminal?”
These, of course, are all valid questions, and well worth asking, considering that 60% to 74% of respondents reported believing that sexting could damage their reputation, career, self-esteem, or current relationships or friendships. Interestingly, the study also found that men were twice as likely as women to share sexy texts with others, and women were more likely to be upset about their sexts being shared.
As Garcia says, it’s not really the sexting itself that presents an issue: It’s the risk of a violation of privacy.
“The real risk is not the sending of sexual messages and images per se, but rather the nonconsensual distribution of those materials to other parties,” he said. “As sexting becomes more common and normative, we’re seeing a contemporary struggle as men and women attempt to reconcile digital eroticism with real-world consequences.”
The bottom line is, as fun and exciting as sexting may be, keep in mind that your sexy messages might not always be for your partner’s eyes only.
Investigators identify the bad lines of genetic code that may lead to the disease
On the off-chance you’re alive in 150 years, you could be in for a very bad day, when the asteroid Bennu collides with Earth, unleashing a blast 200 times more powerful than that of the bomb that destroyed Hiroshima. OK, the odds are pretty good you won’t be around in 150 years, and they’re only 1 in 2,700 at the very likeliest that Bennu will pay us such a nasty house call. But the space rock is making a lot of news and getting a lot of attention from NASA all the same—and it should.
Discovered in 1999, Bennu measures 1,614 ft. (492 m) across and checks two worrisome boxes on the asteroid danger list. It is what astronomers all a near-Earth object (NEO), which is any comet or asteroid that approaches the sun at 1.3 Earth’s distance or closer. Bennu not only gets that close but actually crosses Earth’s orbit every six years as it makes its own circuit through the solar system. It is also what is known as a potentially hazardous asteroid (PHA), which is any asteroid that measures 460 ft. (140 m) or more, is close enough to be an NEO and poses a risk of doing serious global or regional damage if it strikes Earth. A blast equivalent to 200 Hiroshimas is serious indeed.
But Bennu is less worrisome than it seems too. Here’s why.
For starters, in order to reach that 1 in 2,700 risk level in 150 years or so, the asteroid first has to be gravitationally nudged from its current course when it passes between Earth and the moon on an earlier approach it will make in 2135. That could happen: Gravity plays unpredictable tricks on moving bodies, and a body that passes between the gravitational fields of both Earth and the moon can be jostled in innumerable ways. Of course that also means Bennu might be pushed in a direction that makes it less rather than more likely to hit Earth in the future.
What’s more, asteroid tracking has become a very precise science—one that is led by NASA’s Near-Earth Objects Program Office at the Jet Propulsion Laboratory (JPL) in Pasadena. With the help of astronomers around the world, the JPL team has identified and mapped the route of 95% of the dangerous rocks in the solar system measuring 0.62 miles (1 km) or more, and 40% of those in the 460-ft. class. With the help of a bump in funding from Congress that was approved in 2012, JPL expects to get that second number up to 90%.
Knowing the course the space rocks take as they make their periodic swings through our cosmic neighborhood does a lot more than just let us know how long our species has to live before an incoming bit of ordnance sends us the way of the dinosaurs. It also means we can do something to prevent the disaster from happening at all.
Both NASA and the European Space Agency have gotten very good at visiting asteroids and comets. The ESA’s Rosetta spacecraft and Philae lander arrived at comet 67P in 2014 and NASA’s Dawn spacecraft is currently orbiting the dwarf planet Ceres after having already orbited the asteroid Vesta. Similar navigational skills could be used to launch interceptors to asteroids when they are still years away from reaching Earth. Once the spacecraft arrived at the target it could either break it apart with an explosive or, more prudently, simply push it off course either with an engine or simply by crashing into it.
NASA will get some practice soon with Bennu itself when it launches the OSIRIS-REx spacecraft on Sept. 8. Over the course of its seven-year mission, the probe will fly to Bennu, map its surface and return a small sample of its dust and other material to Earth for study. The mission should reveal more about the chemistry, history and the organic potential of the solar system generally and about Bennu’s composition specifically. The precise makeup of the asteroid could help scientists determine what it would take to destroy or deflect it if the need ever arises.
Until then though, as you were. Bennu may or may not be coming our way, but if it is, we’ve got plenty of time to prepare our hello.
At least not much
Others more strongly criticized the new explanation. Two behavioral neuroendocrinologists, Michael Baum from Boston University and Kim Wallen from Emory University in Atlanta, tell Science that Pavlićev and Warner misinterpret some previously published results and do not have the details about the hormonal changes during ovulation and orgasm correct. “Their hypothesis remains a good hypothesis,” Wallen says. “But I’m not very convinced by the data they marshal.”
The mosquito-borne transmissions detected so far are occurring in a Miami neighborhood.