Epigenetic clock analysis of diet, exercise, education, and lifestyle factors

Results

Sample characteristics

The WHI sample consisted of 4,173 postmenopausal women including 2,045 Caucasians, 1,192 African Americans, and 717 Hispanics. Chronological age ranged from 50-82 years (mean=64, s.d.=7.1). The InCHIANTI sample was composed of 402 participants from a European (Italian) population, including 178 men (44%) and 229 women (56%). We used the most current cross-sectional wave for this cohort, and at that time-point participants ranged in age from 30 to 100 years (mean=71, s.d.=16). Additional details on participant characteristics can be found in the Methods and in Table 1.

Table 1. Characteristics of the WHI and InCHIANTI samples.

			WHI		InCHIANTI
			Count	Percent	Count	Percent
Ethnic	American Indian or Alaskan Native		56	1%
	Asian or Pacific Islander		140	3%
	Black or African-American		1277	28%
	Hispanic/Latino		784	17%
	White (not of Hispanic origin)		2196	49%
	Other		37	1%
WHI data set	BA23		2098	47%
	AS315		2392	53%
Sex	Male				178	44%
	Female				229	56%
Current smoker	Nonsmoker		4027	90%	367	90%
	Smoker		439	10%	40	10%
Education	< Primary		43	1%	80	20%
	> Primary		154	3%	154	38%
	> Lower secondary		293	7%	91	22%
	> Upper secondary		2588	58%	62	15%
	> Higher		1393	31%	20	5%
Physical activity	Active		894	20%	329	81%
	Inactive		3572	80%	78	19%
			Mean	SD	Mean	SD
Diet	Total energy, kcal	kcal/day	1641	777	2069	573
	Carbohydrate	% kcal	49.0	9.1	52.4	6.9
	Protein	% kcal	16.5	3.3	15.8	2.0
	Fat	% kcal	34.6	8.1	30.9	5.5
	Red meat	serv/day	0.8	0.7	1.1	0.5
	Poultry	serv/day	0.4	0.3	0.2	0.2
	Fish	serv/day	0.3	0.3	0.2	0.2
	Dairy	serv/day	1.6	1.3	2.8	1.8
	Whole grains	serv/day	1.2	0.9
	Nuts	serv/day	0.2	0.3	0.0	0.1
	Fruits	serv/day	1.7	1.3	1.9	0.9
	Vegetables	serv/day	1.9	1.3	1.6	0.8
	Alcohol	g/day	3.6	9.6	12.7	14.9
	Measurements	IEAA	years	0.0	4.7	0.2	4.6
EEAA		years	0.0	6.0	-0.2	6.5
C-reactive protein		mg/L	5.2	6.6	3.9	7.4
Insulin		mg/dL	57.1	115.3
Glucose		mg/dL	106.3	38.0	93.0	21.3
Triglycerides		mg/dL	146.4	85.6	122.7	81.5
Total cholesterol		mg/dL	228.4	42.7	207.2	36.6
LDL cholesterol		mg/dL	144.9	39.7	125.5	32.1
HDL cholesterol		mg/dL	54.0	14.3	57.6	15.7
Creatinine		mg/dL	0.8	0.2	0.9	0.4
Systolic blood pressure		mmHg	130.0	18.0	129.3	19.8
Diastolic blood pressure		mmHg	75.8	9.4	77.2	10.3
Waist / hip ratio		cm/cm	0.8	0.1	0.9	0.1
BMI		cm/m2	29.7	6.0	27.0	4.3
The cohort samples are listed for each column and variables of interest are listed for each row. The upper portion of the table correspond to categorical variables and are described using counts and percentages; the lower portion of the table displays continuous variables which are described using means and standard deviations (SD).

Dietary and metabolic associations with measures of age acceleration

Here we leverage two distinct measures of epigenetic age acceleration which are based on different sets of CpGs: intrinsic epigenetic age acceleration (IEAA), and extrinsic epigenetic age acceleration (EEAA) (Methods). Epigenetic age acceleration is broadly defined as the epigenetic age left unexplained by chronological age, where intrinsic and extrinsic denote additional modifications to this concept. In addition to adjusting for chronologic age, IEAA also adjusts the epigenetic clock for blood cell count estimates, arriving at a measure that is unaffected by both variation in chronologic age and blood cell composition. EEAA, on the other hand, integrates known age-related changes in blood cell counts with a blood-based measure of epigenetic age [37] before adjusting for chronologic age, making EEAA dependent on age-related changes in blood cell composition. In essence, IEAA can be interpreted as a measure of cell-intrinsic aging and EEAA as a measure of immune system aging, where for both, a positive value indicates that the epigenetic age of an individual (organ or tissue) is higher than expected based on their chronological age—or that the individual is exhibiting accelerated epigenetic aging.

We find that IEAA is only moderately correlated with EEAA (r=0.37), and that measurements on the same individuals at different time points (mean difference 3.0 years between visit dates) showed moderately strong correlations (IEAA r=0.70, EEAA r=0.66).

We first used a robust correlation test to relate our two measures of epigenetic aging (IEAA and EEAA) to select reported dietary exposures, blood nutrient levels, cardiometabolic plasma biomarkers, and lifestyle factors, designating a Bonferroni-corrected significance threshold of α=7x10^-4 (Figure 1). The correlation test results for specific racial/ethnic groups are presented in Supplementary Figure 1 and select marginal associations are shown as bar plots in Supplementary Figure 2. Pairwise correlations between dietary variables, metabolic biomarkers, and lifestyle factors are presented in Supplementary Figure 3.

Figure 1. Marginal correlations with epigenetic age acceleration in the WHI. Correlations (bicor, biweight midcorrelation) between select variables and the two measures of epigenetic age acceleration are colored according to their magnitude with positive correlations in red, negative correlations in blue, and statistical significance (p-values) in green. Blood biomarkers were measured from fasting plasma collected at baseline. Food groups and nutrients are inclusive, including all types and all preparation methods, e.g. folic acid includes synthetic and natural, dairy includes cheese and all types of milk, etc. Variables are adjusted for ethnicity and dataset (BA23 or AS315).

EEAA exhibits weak but statistically significant correlations with fish intake (r=-0.07, p=2x10^-5), alcohol consumption (r=-0.07, p=3x10^-5, Supplementary Figure 4), plasma levels of mean carotenoids (r=-0.13, p=2x10^-9), alpha-carotene (r=-0.11, p=9x10^-8), beta-carotene (r=-0.11, p=3x10^-7), lutein+zeaxanthin (r=-0.9, p=1x10^-5), beta-cryptoxanthin (r=-0.11, p=3x10^-7), gamma-tocopherol (r=0.09, p=9x10^-6), triglyceride (r=0.7, p=6x10^-6), C-reactive protein (CRP, r=0.12, p=2x10^-10), insulin (r=0.11, p=3x10^-12), HDL cholesterol (r=-0.09, p=2x10^-8), glucose (r=0.06, p=2x10^-4), systolic blood pressure (r=0.07, p=4x10^-6), waist-to-hip ratio (WHR, r=0.09, p=2x10^-8), BMI (r=0.09, p=2x10^-8), education (r=-0.10, p=3x10^-10), income (r=-0.06, p=1x10^-4), and exercise (r=-0.07, p=2x10^-5, Figure 1). In contrast, the intrinsic epigenetic aging rate exhibits weaker correlations with dietary variables and lifestyle factors: IEAA is only associated with BMI (r=0.08, p=1x10^-6), and plasma levels of gamma-tocopherol (r=0.08, p=2x10^-4), CRP (r=0.08, p=6x10^-5), insulin (r=0.07, p=2x10^-5), glucose (r=0.06, p=8x10^-5), and triglyceride levels (r=0.05, p=5x10^-4, Figure 1).

Meta-analysis of multivariable linear models link epigenetic age acceleration to diet

Associations with EEAA

We have recently shown that ethnicity relates to epigenetic aging rates: e.g. Hispanics have lower levels of IEAA compared to other ethnic groups [59]. Given the potential for confounding by sociodemographic and lifestyle factors, we used Stouffer's method to meta-analyze multivariate linear models, stratified by racial/ethnic group, in order to re-examine the suggestive associations from our marginal correlation analysis. After adjusting for sex and dataset (Figure 2A), we find that lower EEAA is significantly associated with greater intake of fish (t_meta=-2.92, p_meta=0.003), higher education (t_meta=-4.14, p_meta=3x10^-5), lower BMI (t_meta=4.86, p_meta=1x10^-6), and current drinker status (t_meta=-3.23, p_meta=0.001). However, we find no association for current smoking status, and poultry intake, and only a trend toward association with physical activity (t_meta=-1.70, p_meta=0.09). In the subset of WHI participants with circulating carotenoid measurements, we also find that mean carotenoid levels are associated with EEAA (t_meta=-4.34, p_meta=1x10^-5, Supplementary Figure 5A).

Figure 2. Meta-analysis of multivariable linear models of EEAA and IEAA in the WHI and InCHIANTI. EEAA (panel A) and IEAA (panel B) were regressed on potential confounding factors, fish and poultry intake, and current drinker status for the ethnic strata with sufficient sample sizes (n>100). Individual columns correspond to coefficient estimates (β) colored blue or red for negative and positive values respectively, and p-values (p) colored in green according to magnitude of significance, with the exception of the last two columns which denote Stouffer's method meta-t and meta-p values. Models are adjusted for originating dataset (WHI BA23 or AS315) and for sex (InCHIANTI) .

Multivariate linear models were used to examine whether variations in cardiometabolic biomarkers and/or the number of symptoms for metabolic syndrome accounted for any of the associations between EEAA and lifestyle factors. The inclusion of biomarkers in an unstratified model shows that EEAA positively relates to CRP (log2, β=0.31, p=3x10^-4, Figure 3A, model 3) and that this is accompanied by a concomitant diminishing in the effect size of BMI (67% decrease in coefficient magnitude, Figure 3A, model 2 vs. model 5), suggesting that higher CRP may partially explain the positive association between BMI and EEAA. When metabolic syndrome (MetS) was included in the model, results showed that higher EEAA is positively associated with the number of metabolic syndrome symptoms (β=0.29, p=0.002, Figure 3A, model 4). In the subset of participants with both biomarker and carotenoid measurements, EEAA was negatively associated with mean carotenoid levels (β=-1.10, p=1x10^-4) while appearing to diminish associations with biomarkers (Supplementary Figure 6A, model 5).

Figure 3. Multivariate linear models of EEAA and IEAA with and without biomarkers in the WHI. EEAA (panel A) and IEAA (panel B) were regressed on potential confounding factors, fish and poultry intake and current drinker status, and select biomarkers. Individual columns list the corresponding coefficient estimates (β) and p-values (p) for each fitting. Coefficients are colored according to sign (positive = red, negative = blue) and significance according to magnitude (green). Models 1 through 5 correspond to a minimal model, a model including dietary intake variables, a model including potential explanatory biomarkers, a model including number of metabolic syndrome symptoms and a complete model with all of the variables above, respectively. Models are adjusted for originating dataset (BA23 or AS315).

Additionally, we find that for the small subset of individuals for whom we have EEAA measurements at two time points (n=239, mean time interval = 2.7 years), increase in BMI (β=0.40, p=0.002) but not initial BMI (β=-0.01, p=0.81) is significantly associated with increased EEAA (higher follow-up EEAA after adjusting for the initial EEAA, dataset, and ethnicity).

Associations with IEAA

We conducted an analogous meta-analysis of ethnically-stratified linear models of IEAA and found that lower IEAA was significantly associated with poultry intake (t_meta=-3.30, p_meta=0.001) and lower BMI (t_meta=4.14, p_meta=4x10^-5), after adjusting for potential confounders (Figure 2B). In the subset of participants with measured carotenoids, IEAA was significantly associated with mean carotenoid levels (t_meta=-2.47, p_meta=0.01, Supplementary Figure 5B). When regressed on clinical biomarkers IEAA was significantly associated with triglycerides (log2, β=0.40, p=0.02, Figure 3B, model 3). Their inclusion diminished the association between IEAA and BMI (60% decrease in coefficient magnitude, Figure 3B, model 2 vs. model 5). Number of metabolic syndrome symptoms was also significantly associated with IEAA (β=0.27, p=4x10^-4, Figure 3B, model 4), and diminished the association between IEAA and BMI by 50%. In the subset of WHI participants with circulating carotenoid measurements, we find a trend toward association between IEAA and mean carotenoid levels (β=-0.40, p=0.07, Supplementary Figure 6B, model 5). Finally, in the participants with epigenetic profiling at two time points, increase in BMI (β=0.22, p=0.03) but not initial BMI (β=-0.22, p=0.44) is significantly associated with increased IEAA (higher follow-up IEAA after adjusting for the initial IEAA, dataset, and ethnicity).

Metformin and epigenetic age acceleration

Considering the significant association between our measures of epigenetic aging and markers of diabetes, we investigated the potential of metformin to modulate epigenetic aging. As shown in Supplementary Figure 7, women in the No Metformin group were the oldest, with mean ages around 65 years, compared to mean ages around 64 years for the Metformin Now group and 62.5 years for the Metformin Future group. Those in the No Metformin group also appeared to have a higher proportion of college graduates compared to the other two groups, whose members tended to have lower educational attainment. The two Metformin groups had much higher proportions of Non-Hispanic blacks, and somewhat higher proportions of Hispanics. As expected, women who were prescribed Metformin had the highest fasting insulin, fasting glucose, and HOMA-IR levels, with current Metformin users being the highest of all. Similarly, among Metformin users, fasting insulin, fasting glucose, and HOMA-IR levels were inversely associated with timing of Metformin initiation. This suggests that 1) women who had been on Metformin the longest at the time of blood draw were the most insulin resistant—arguably because they were long-time diabetics—and 2) among women who would use Metformin in the future, those who would receive prescriptions sooner—who may already have prediabetes or diabetes at the time of blood draw—were more insulin resistant than women who were prescribed Metformin later.

As shown in Supplementary Figure 8, when examining the full sample, IEAA and EEAA were highest for women in the Metformin Now group, although differences between Metformin Now and Metformin Future were not significant. Further, the timing of Metformin prescription relative to the timing of blood draw was not associated with either IEAA or EEAA. Overall, these results held for IEAA when restricting the sample to only Non-Hispanic whites, Non-Hispanic blacks, or women at least 65 years of age. Results for EEAA held when restricting the sample to only Hispanics, women less than 65 years of age, or those with fasting glucose levels under 126 mg/dL. Nevertheless, there were no strata in which women in the Metformin Now group had significantly lower IEAA or EEAA than women in either the No Metformin or the Metformin Future group.

Given that Metformin may not be effective for all or that not all women prescribed Metformin may comply with their physician’s recommendations, we compared future Metformin users to current Metformin users who had fasting glucose levels under 140 mg/dL (Supplementary Figure 9). Similar to Supplementary Figure 8, no significant differences were found for the two metformin groups, and in most cases those currently on Metformin had somewhat higher IEAA and EEAA than those who would be prescribed Metformin in the future.

Finally, when using women with repeat blood draws to compare the change in IEAA and EEAA for those who started Metformin between the two visits to those who didn’t (Supplementary Table 1), we find that, while those who start Metformin appear to have lower IEAA (β=-0.79), the difference is not significant (P=0.46). Results for EEAA show very little difference between those who start Metformin between first and second blood draws, and those who don’t (β=-0.003, P=0.99). Given that we only have 11 women who start Metformin, our null results, particularly for IEAA, may be due to a lack a power. Thus, in moving forward, this analysis may be worth revisiting in a randomized control trial or after acquiring larger sample sizes.

Discussion

To our knowledge, this is the first study to examine associations between lifestyle factors and measures of epigenetic age acceleration in blood. Our main findings are summarized graphically in Figure 4. Overall, our dietary results are consistent with some of the current Dietary Guidelines for Americans [60,61], reflecting potential health benefits associated with higher intake of fish, poultry, and fruits and vegetables. The weak correlations between dietary factors and epigenetic aging rates probably reflect that a relatively large proportion of the variance in aging rates (around 40 percent) is explained by genetic factors [38,59,62]. We find that education, physical activity, low body mass index are associated with a slow extrinsic age acceleration both in univariate correlation tests (Figure 1) and in multivariate regression models (Figure 2A, 3A). However, consistent with our previous work, smoking status was not associated with epigenetic age acceleration [45], which highlights that not every poor lifestyle choice is associated with an increased epigenetic aging effect in blood tissue.

Figure 4. Pictorial summary of our main findings. The blue and red arrows depict anti-aging and pro-aging effects in blood respectively. The two clocks symbolize the extrinsic epigenetic clock (enhanced version of the Hannum estimate) and the intrinsic epigenetic clock (Horvath 2013) which are dependent and independent of blood cell counts, respectively.

EEAA, inflammation, and metabolic functioning

The age-related changes in immune functioning and inflammation are believed to contribute to increased susceptibility of a wide range of diseases later in life, including diabetes, some cancers, cardiovascular, neurodegenerative, auto-immune, and infectious diseases [63,64]. In our analysis, EEAA, a biomarker which explicitly incorporates aspects of immune system aging such as age-related changes in blood cell counts, was associated with cardiometabolic biomarkers, fish, fruit, vegetable, and alcohol intake.

Our finding that fish intake was negatively associated with EEAA is consistent with prospective studies suggesting that fish consumption is protective against various age-related diseases [65–67]. The benefits of fish intake may be mediated in part through the omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which stimulate the synthesis of anti-inflammatory cytokines [68]. This is further supported by our finding that CRP—a well-known marker of inflammation—was the most significant explanatory biomarker of EEAA. This suggests that one reason higher fish consumption may lower EEAA is because it has beneficial anti-inflammatory or metabolic effects. The consensus between these associations also appears to converge on MetS as a potential mediating factor; this was further supported by our results showing that the number of MetS characteristics significantly relates to EEAA. Though CRP is not included in most MetS diagnostic criteria, the association between the two has been previously established [69].

We also find that alcohol consumption was negatively associated with EEAA even after adjusting for potential confounders such as socioeconomic status; this is consistent with prospective studies which have identified light to moderate alcohol intake as a protective factor against all-cause and CHD-related mortality [70,71] and is supported by a recent publication that also found an inverse association between epigenetic age and alcohol intake in Caucasian and African American individuals (n=656, n=180, respectively) [72]. In our study, we find that the potential benefits of alcohol consumption are observed using a threshold of more than one serving per month, though the effect size of this variable was also stable when adding weekly and daily intake levels (Supplementary Figure 4A-D). The association appears to be driven by wine consumption though there is also a trend towards association with beer (Supplementary Figures 4E-H). This is consistent with other studies have suggested that wine may have added benefits compared to light alcohol consumption [73]. This finding may also be related to the anti-inflammatory effects of light alcohol consumption, which are associated with decreased circulating levels of inflammatory markers such as IL-6 and CRP [74]. Alternatively, this may be the result of reverse causation, whereby individuals suffering from health issues abstain from alcohol consumption [75], though interventional studies support a causal protective effect of moderated alcohol intake on cardiovascular blood biomarkers [76].

Though EEAA only trended toward association with reported fruit and vegetable intake, we find significant associations with blood carotenoid levels, which are quantitative surrogates of fruit and vegetable intake; this is likely a reflection of the bias and inaccuracy of self-reported diet. This is in agreement with the wide range of literature supporting the protective effects of high fruit and vegetable intake against age-related diseases CHD [77,78], stroke [79], type-2 diabetes [80], breast cancer [81], and all-cause mortality [82]. The association between fruit and vegetables with aging of the blood immune system may be partially mediated by anti-inflammatory [83,84] and cardiometabolic effects, however it is interesting to note that the explanatory power of mean carotenoid levels remained even after including the other explanatory factors into the model, suggesting the possibility of independent anti-aging mechanisms (Supplementary Figure 6A, model 5).

Our results for EEAA also share similarities with previously reported findings showing that LTL relates to BMI [22], metabolic factors, vegetable consumption [85], and dietary intake of foods high in omega-3 fatty acids [86]. This agreement is likely a reflection of the shared immunological basis, which is supported by the weak negative correlation between EEAA and age-adjusted LTL. In contrast, IEAA is not significantly associated with LTL, supporting the idea that these measures represent different aspects of aging.

Intrinsic epigenetic aging and metabolic health

Results showed that BMI has a positive association with IEAA (Figure 3B, model 2). The statistically significant but weak correlations between BMI and epigenetic age acceleration in blood (r<0.10) are much smaller than those we recently reported for human liver (r=0.42) [54], suggesting that associations between aging signatures and risk factors may vary in strength depending on the tissue, and may be stronger in organs/tissues most affected by the risk. Interestingly, IEAA was also associated with number of metabolic syndrome characteristics, suggesting a role in tracking metabolic aging processes (Figure 3B, model 4).

We did find that reported poultry intake was negatively associated with IEAA, even after adjusting for potential confounders and explanatory factors (Figure 3B, model 5). Given the relative inert behavior of IEAA, the mechanism by which poultry may affect aging is unclear.

Lack of an effect of metformin

Metformin, which is a first-line medication for the treatment of type 2 diabetes, has garnered substantial interest by aging researchers [87]. The fact that we did not detect an anti-aging effect of metformin in our study could be due to the following factors: a) it could reflect the limitations/biases of an observational study, b) it could reflect the limited sample size, or c) that metformin does not affect epigenetic aging rates of blood tissue. Future randomized controlled trials should revisit the question whether metformin affects epigenetic aging rates in blood or other tissues.

Generalization to the InCHIANTI

Our results from the InCHIANTI show some validation of our findings from the WHI: fish intake was related to EEAA, and poultry was related to IEAA. Associations with available biomarkers of cardiometabolic health, however, were not found to be validated, and in a few cases were reversed in directions, within the InCHIANTI (data not shown).

The discrepancies between the WHI and InCHIANTI cohorts may be due to numerous differences in the study population (cultural, demographic, genetic, health status, Table 1) and data collection methodology (dietary assessment). Despite being younger, on average, US participants from the WHI had higher body mass indexes (BMI), and worse metabolic health than their Italian counterparts—as indicated by their greater prevalence of metabolic syndrome (23% in the WHI versus 7.6% in the InCHIANTI).

The InCHIANTI study is also arguably underpowered (n=402) when it comes to detecting the weak associations with epigenetic age acceleration. According to sample size calculations (PASS software), we find that n=1820 samples are needed to provide 80% power to detect a correlation of r=0.08 at a two-sided significance level of α=0.01. Similarly, n=1163 samples are needed to detect a correlation of r=0.10.

Limitations

While our study of the WHI benefits from having a relatively large sample size, associations with epigenetic aging may not be detectable in smaller studies given the weak effect sizes observed here. This situation is exacerbated by self-reported lifestyle habits which are notorious for bias and inaccuracy, limiting their ability to represent true lifestyle habits and potentially producing false negative results. Further, as evidenced by our results from the ethnic strata and the InCHIANTI, studies conducted in different ethnic populations may not be entirely consistent due to fundamental differences in age, diet, culture, and demographics. There were several potential limitations to this study, which include the assumption of non-confounding from unmeasured variables such as existing patient co-morbidities and the assumption of accuracy and long-term consistency of reported dietary habits. This is the first longitudinal study to show that an increase in BMI is associated with an increase in epigenetic age acceleration but larger longitudinal studies will be needed to dissect causal relationships between epigenetic aging rates and dietary measures, education, exercise, and lifestyle factors.

Conclusions about Epigenetic Age Acceleration

Our large sample size (n>4500) provides sufficient statistical power for one of our main conclusions: diet has only a weak effect on epigenetic aging rates in blood. These findings will be valuable for researchers who plan to use epigenetic biomarkers in dietary intervention studies. The wide range of associations found with EEAA suggest that immune system aging may be closely linked to conventional notions of metabolic health and may be sensitive to variations in environment and lifestyle. In contrast, IEAA has few associations, which is consistent with the hypothesis that cell-intrinsic aging remains relatively stable, more likely being determined by an intrinsic aging or developmental process under genetic control. Further, using longitudinal data in the WHI, we found that change in both EEAA and IEAA are significantly associated with change in BMI, suggesting that both modes of epigenetic aging may respond to changes in lifestyle, at least with respect to change in obesity. Overall, our results are consistent with previous literature supporting the protective effects of fish, poultry and alcohol consumption, exercise, education, as well as the risk of obesity and dyslipidemia.

Author Contributions

Austin Quach, Morgan E. Levine, Toshiko Tanaka, Brian H. Chen, and Steve Horvath carried out the analysis. Devin Absher, Themistocles L. Assimes, Phil Tsao, Steve Horvath, Beate Ritz, Luigi Ferrucci, Stefania Bandinelli, Eric A. Whitsel, Lifang Hou, Andrea Baccerelli, Yun Li, James Stewart contributed data. Marian L. Neuhouser, Jeannette M. Beasley, Linda Snetselaar, Robert B. Wallace, Ake Lu helped with the interpretation of the findings and drafting of the article. Steve Horvath designed the study.

Acknowledgements

We would like to acknowledge The WHI Investigators listed below:

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller.

Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg.

Investigators and Academic Centers: (Brigham and Women's Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA)

Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY)

Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker.

Women’s Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker.

Conflicts of Interest

The Regents of the University of California is the sole owner of a provisional patent application directed at the invention of measures of epigenetic age acceleration for which SH is a named inventor. The other authors declare no conflict of interest.

Funding

This WHI study was supported by National Institutes of Health NIH/NHLBI 60442456 BAA23 (Assimes, Absher, Horvath) and the National Institute of Environmental Health Sciences R01ES020836 WHI-EMPC (Whitsel, Baccarelli, Hou),R01ES021733 (Baccarelli), and R01ES025225 (Baccarelli). 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 authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf.

Additional support came from NIH/NIA 5R01AG042511-02 (Horvath), NIH/NIA U34AG051425-01 (Horvath), NIH/NINDS T32NS048004 (Levine), and the Burroughs Wellcome Fund Inter-school Training Program in Chronic Diseases (BWF-CHIP, Quach). The funding bodies played no role in the design, the collection, analysis, or interpretation of the data.

The InCHIANTI study baseline (1998-2000) was supported as a "targeted project" (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336).

References

• 1. Kaczmarczyk MM, Miller MJ, Freund GG. The health benefits of dietary fiber: beyond the usual suspects of type 2 diabetes mellitus, cardiovascular disease and colon cancer. Metabolism. 2012; 61:1058–66. https://doi.org/10.1016/j.metabol.2012.01.017 [PubMed]
• 2. Kris-Etherton PM, Harris WS, Appel LJ, and American Heart Association. Nutrition Committee. Fish consumption, fish oil, omega-3 fatty acids, and cardiovascular disease. Circulation. 2002; 106:2747–57. https://doi.org/10.1161/01.CIR.0000038493.65177.94 [PubMed]
• 3. van’t Veer P, Jansen MC, Klerk M, Kok FJ. Fruits and vegetables in the prevention of cancer and cardiovascular disease. Public Health Nutr. 2000; 3:103–07. https://doi.org/10.1017/S1368980000000136 [PubMed]
• 4. Giugliano D, Ceriello A, Esposito K. The effects of diet on inflammation: emphasis on the metabolic syndrome. J Am Coll Cardiol. 2006; 48:677–85. https://doi.org/10.1016/j.jacc.2006.03.052 [PubMed]
• 5. Mozaffarian D. Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity. 2016; 133:187–225.
• 6. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SCJr, and International Diabetes Federation Task Force on Epidemiology and Prevention, and Hational Heart, Lung, and Blood Institute, and American Heart Association, and World Heart Federation, and International Atherosclerosis Society, and International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009; 120:1640–45. https://doi.org/10.1161/CIRCULATIONAHA.109.192644 [PubMed]
• 7. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SCJr, Spertus JA, Costa F, and American Heart Association, and National Heart, Lung, and Blood Institute. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005; 112:2735–52. https://doi.org/10.1161/CIRCULATIONAHA.105.169404 [PubMed]
• 8. Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005; 112:3066–72. https://doi.org/10.1161/CIRCULATIONAHA.105.539528 [PubMed]
• 9. Thun MJ, Peto R, Lopez AD, Monaco JH, Henley SJ, Heath CWJr, Doll R. Alcohol consumption and mortality among middle-aged and elderly U.S. adults. N Engl J Med. 1997; 337:1705–14. https://doi.org/10.1056/NEJM199712113372401 [PubMed]
• 10. Rimm EB, Williams P, Fosher K, Criqui M, Stampfer MJ. Moderate alcohol intake and lower risk of coronary heart disease: meta-analysis of effects on lipids and haemostatic factors. BMJ. 1999; 319:1523–28. https://doi.org/10.1136/bmj.319.7224.1523 [PubMed]
• 11. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006; 174:801–09. https://doi.org/10.1503/cmaj.051351 [PubMed]
• 12. González MA, Rodríguez Artalejo F, Calero JR. Relationship between socioeconomic status and ischaemic heart disease in cohort and case-control studies: 1960-1993. Int J Epidemiol. 1998; 27:350–58. https://doi.org/10.1093/ije/27.3.350 [PubMed]
• 13. Madsen M, Andersen AM, Christensen K, Andersen PK, Osler M. Does educational status impact adult mortality in Denmark? A twin approach. Am J Epidemiol. 2010; 172:225–34. https://doi.org/10.1093/aje/kwq072 [PubMed]
• 14. Conti G, Heckman J, Urzua S. THE EDUCATION-HEALTH GRADIENT. Am Econ Rev. 2010; 100:234–38. https://doi.org/10.1257/aer.100.2.234 [PubMed]
• 15. Currie J. Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development. National Bureau of Economic Research Working Paper Series. 2008; No. 13987.
• 16. Cutler DM, Lleras-Muney A. Understanding differences in health behaviors by education. J Health Econ. 2010; 29:1–28. https://doi.org/10.1016/j.jhealeco.2009.10.003 [PubMed]
• 17. Mirabello L, Huang WY, Wong JY, Chatterjee N, Reding D, Crawford ED, De Vivo I, Hayes RB, Savage SA. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell. 2009; 8:405–13. https://doi.org/10.1111/j.1474-9726.2009.00485.x [PubMed]
• 18. Boccardi V, Esposito A, Rizzo MR, Marfella R, Barbieri M, Paolisso G. Mediterranean diet, telomere maintenance and health status among elderly. PLoS One. 2013; 8:e62781. https://doi.org/10.1371/journal.pone.0062781 [PubMed]
• 19. García-Calzón S, Gea A, Razquin C, Corella D, Lamuela-Raventós RM, Martínez JA, Martínez-González MA, Zalba G, Marti A. Longitudinal association of telomere length and obesity indices in an intervention study with a Mediterranean diet: the PREDIMED-NAVARRA trial. Int J Obes. 2014; 38:177–82. https://doi.org/10.1038/ijo.2013.68 [PubMed]
• 20. Brouilette SW, Moore JS, McMahon AD, Thompson JR, Ford I, Shepherd J, Packard CJ, Samani NJ, and West of Scotland Coronary Prevention Study Group. Telomere length, risk of coronary heart disease, and statin treatment in the West of Scotland Primary Prevention Study: a nested case-control study. Lancet. 2007; 369:107–14. https://doi.org/10.1016/S0140-6736(07)60071-3 [PubMed]
• 21. Kiecolt-Glaser JK, Epel ES, Belury MA, Andridge R, Lin J, Glaser R, Malarkey WB, Hwang BS, Blackburn E. Omega-3 fatty acids, oxidative stress, and leukocyte telomere length: A randomized controlled trial. Brain Behav Immun. 2013; 28:16–24. https://doi.org/10.1016/j.bbi.2012.09.004 [PubMed]
• 22. Cassidy A, De Vivo I, Liu Y, Han J, Prescott J, Hunter DJ, Rimm EB. Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr. 2010; 91:1273–80. https://doi.org/10.3945/ajcn.2009.28947 [PubMed]
• 23. Latifovic L, Peacock SD, Massey TE, King WD. The Influence of Alcohol Consumption, Cigarette Smoking, and Physical Activity on Leukocyte Telomere Length. Cancer Epidemiol Biomarkers Prev. 2016; 25:374–80. https://doi.org/10.1158/1055-9965.EPI-14-1364 [PubMed]
• 24. Cherkas LF, Hunkin JL, Kato BS, Richards JB, Gardner JP, Surdulescu GL, Kimura M, Lu X, Spector TD, Aviv A. The association between physical activity in leisure time and leukocyte telomere length. Arch Intern Med. 2008; 168:154–58. https://doi.org/10.1001/archinternmed.2007.39 [PubMed]
• 25. Adler N, Pantell MS, O’Donovan A, Blackburn E, Cawthon R, Koster A, Opresko P, Newman A, Harris TB, Epel E. Educational attainment and late life telomere length in the Health, Aging and Body Composition Study. Brain Behav Immun. 2013; 27:15–21. https://doi.org/10.1016/j.bbi.2012.08.014 [PubMed]
• 26. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, Nelson HH, Karagas MR, Padbury JF, Bueno R, Sugarbaker DJ, Yeh RF, Wiencke JK, Kelsey KT. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009; 5:e1000602. https://doi.org/10.1371/journal.pgen.1000602 [PubMed]
• 27. Bollati V, Schwartz J, Wright R, Litonjua A, Tarantini L, Suh H, Sparrow D, Vokonas P, Baccarelli A. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech Ageing Dev. 2009; 130:234–39. https://doi.org/10.1016/j.mad.2008.12.003 [PubMed]
• 28. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, Leslie RD, Deloukas P, Spector TD. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010; 20:434–39. https://doi.org/10.1101/gr.103101.109 [PubMed]
• 29. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, Savage DA, Mueller-Holzner E, Marth C, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010; 20:440–46. https://doi.org/10.1101/gr.103606.109 [PubMed]
• 30. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, van den Berg LH, Ophoff RA. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012; 13:R97. https://doi.org/10.1186/gb-2012-13-10-r97 [PubMed]
• 31. Numata S, Ye T, Hyde TM, Guitart-Navarro X, Tao R, Wininger M, Colantuoni C, Weinberger DR, Kleinman JE, Lipska BK. DNA methylation signatures in development and aging of the human prefrontal cortex. Am J Hum Genet. 2012; 90:260–72. https://doi.org/10.1016/j.ajhg.2011.12.020 [PubMed]
• 32. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST. Age-associated DNA methylation in pediatric populations. Genome Res. 2012; 22:623–32. https://doi.org/10.1101/gr.125187.111 [PubMed]
• 33. Johansson A, Enroth S, Gyllensten U. Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan. PLoS One. 2013; 8:e67378. https://doi.org/10.1371/journal.pone.0067378 [PubMed]
• 34. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, Myers RM, Absher D. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 2013; 14:R102. https://doi.org/10.1186/gb-2013-14-9-r102 [PubMed]
• 35. Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011; 6:e14821. https://doi.org/10.1371/journal.pone.0014821 [PubMed]
• 36. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, Di Blasio AM, Gentilini D, Vitale G, Collino S, Rezzi S, Castellani G, Capri M, et al. Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012; 11:1132–34. https://doi.org/10.1111/acel.12005 [PubMed]
• 37. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49:359–67. https://doi.org/10.1016/j.molcel.2012.10.016 [PubMed]
• 38. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14:R115. https://doi.org/10.1186/gb-2013-14-10-r115 [PubMed]
• 39. Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MR, Deary IJ, Wagner W. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY). 2016; 8:394–401. https://doi.org/10.18632/aging.100908 [PubMed]
• 40. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, Gibson J, Henders AK, Redmond P, Cox SR, Pattie A, Corley J, Murphy L, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015; 16:25. https://doi.org/10.1186/s13059-015-0584-6 [PubMed]
• 41. Christiansen L, Lenart A, Tan Q, Vaupel JW, Aviv A, McGue M, Christensen K. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell. 2015. [PubMed]
• 42. Perna L, Zhang Y, Mons U, Holleczek B, Saum K-U, Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin Epigenetics. 2016; 8:64. https://doi.org/10.1186/s13148-016-0228-z [PubMed]
• 43. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC, Roetker NS, Just AC, Demerath EW, Guan W, Bressler J, Fornage M, Studenski S, et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY). 2016; 8:1844–65. [PubMed]
• 44. Breitling LP, Saum KU, Perna L, Schöttker B, Holleczek B, Brenner H. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin Epigenetics. 2016; 8:21. https://doi.org/10.1186/s13148-016-0186-5 [PubMed]
• 45. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging (Albany NY). 2015; 7:690–700. https://doi.org/10.18632/aging.100809 [PubMed]
• 46. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, Gibson J, Redmond P, Cox SR, Pattie A, Corley J, Taylor A, Murphy L, et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol. 2015; 44:1388–96. https://doi.org/10.1093/ije/dyu277 [PubMed]
• 47. Horvath S, Pirazzini C, Bacalini MG, Gentilini D, Di Blasio AM, Delledonne M, Mari D, Arosio B, Monti D, Passarino G, De Rango F, D’Aquila P, Giuliani C, et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging (Albany NY). 2015; 7:1159–70. https://doi.org/10.18632/aging.100861 [PubMed]
• 48. Levine ME, Lu AT, Bennett DA, Horvath S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY). 2015; 7:1198–211. https://doi.org/10.18632/aging.100864 [PubMed]
• 49. Horvath S, Mah V, Lu AT, Woo JS, Choi OW, Jasinska AJ, Riancho JA, Tung S, Coles NS, Braun J, Vinters HV, Coles LS. The cerebellum ages slowly according to the epigenetic clock. Aging (Albany NY). 2015; 7:294–306. https://doi.org/10.18632/aging.100742 [PubMed]
• 50. Walker RF, Liu JS, Peters BA, Ritz BR, Wu T, Ophoff RA, Horvath S. Epigenetic age analysis of children who seem to evade aging. Aging (Albany NY). 2015; 7:334–39. https://doi.org/10.18632/aging.100744 [PubMed]
• 51. Horvath S, Garagnani P, Bacalini MG, Pirazzini C, Salvioli S, Gentilini D, Di Blasio AM, Giuliani C, Tung S, Vinters HV, Franceschi C. Accelerated epigenetic aging in Down syndrome. Aging Cell. 2015; 14:491–95. https://doi.org/10.1111/acel.12325 [PubMed]
• 52. Horvath S, Levine AJ. HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis. 2015; 212:1563–73. https://doi.org/10.1093/infdis/jiv277 [PubMed]
• 53. Horvath S, Langfelder P, Kwak S, Aaronson J, Rosinski J, Vogt TF, Eszes M, Faull RL, Curtis MA, Waldvogel HJ, Choi OW, Tung S, Vinters HV, et al. Huntington’s disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels. Aging (Albany NY). 2016; 8:1485–512. https://doi.org/10.18632/aging.101005 [PubMed]
• 54. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schönfels W, Ahrens M, Heits N, Bell JT, Tsai PC, Spector TD, Deloukas P, Siebert R, Sipos B, et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci USA. 2014; 111:15538–43. https://doi.org/10.1073/pnas.1412759111 [PubMed]
• 55. Zannas AS, Arloth J, Carrillo-Roa T, Iurato S, Röh S, Ressler KJ, Nemeroff CB, Smith AK, Bradley B, Heim C, Menke A, Lange JF, Brückl T, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015; 16:266. https://doi.org/10.1186/s13059-015-0828-5 [PubMed]
• 56. Levine ME, Lu AT, Chen BH, Hernandez DG, Singleton AB, Ferrucci L, Bandinelli S, Salfati E, Manson JE, Quach A, Kusters CD, Kuh D, Wong A, et al. Menopause accelerates biological aging. Proc Natl Acad Sci USA. 2016; 113:9327–32. https://doi.org/10.1073/pnas.1604558113 [PubMed]
• 57. Vidal L, Lopez-Golan Y, Rego-Perez I, Horvath S, Blanco FJ, Riancho JA, Gomez-Reino JJ, Gonzalez A. Specific increase of methylation age in osteoarthritis cartilage. Osteoarthritis Cartilage. 2016; 24:S63. https://doi.org/10.1016/j.joca.2016.01.140
• 58. Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients. Aging (Albany NY). 2015; 7:1130–42. https://doi.org/10.18632/aging.100859 [PubMed]
• 59. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT, Rickabaugh TM, Jamieson BD, Sun D, Li S, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016; 17:171. https://doi.org/10.1186/s13059-016-1030-0 [PubMed]
• 60. McGuire S. US Department of Agriculture and US Department of Health and Human Services, Dietary Guidelines for Americans, 2010. Washington, DC: US Government Printing Office, January 2011. Advances in Nutrition: An International Review Journal. 2011; 2(3):293-294.
• 61. Committee DGA. Scientific Report of the 2015 Dietary Guidelines Advisory Committee. Washington (DC): USDA and US Department of Health and Human Services. 2015.
• 62. Lu AT, Hannon E, Levine ME, Hao K, Crimmins EM, Lunnon K, Kozlenkov A, Mill J, Dracheva S, Horvath S. Genetic variants near MLST8 and DHX57 affect the epigenetic age of the cerebellum. Nat Commun. 2016; 7:10561. https://doi.org/10.1038/ncomms10561 [PubMed]
• 63. Castelo-Branco C, Soveral I. The immune system and aging: a review. Gynecol Endocrinol. 2014; 30:16–22. https://doi.org/10.3109/09513590.2013.852531 [PubMed]
• 64. Finch CE. Evolution in health and medicine Sackler colloquium: Evolution of the human lifespan and diseases of aging: roles of infection, inflammation, and nutrition. Proc Natl Acad Sci USA. 2010 (Suppl 1); 107:1718–24. https://doi.org/10.1073/pnas.0909606106 [PubMed]
• 65. Chowdhury R, Stevens S, Gorman D, Pan A, Warnakula S, Chowdhury S, Ward H, Johnson L, Crowe F, Hu FB, Franco OH. Association between fish consumption, long chain omega 3 fatty acids, and risk of cerebrovascular disease: systematic review and meta-analysis. BMJ. 2012; 345:e6698. https://doi.org/10.1136/bmj.e6698 [PubMed]
• 66. Zheng J-S, Hu X-J, Zhao Y-M, Yang J, Li D. Intake of fish and marine n-3 polyunsaturated fatty acids and risk of breast cancer: meta-analysis of data from 21 independent prospective cohort studies. BMJ. 2013; 346:f3706. https://doi.org/10.1136/bmj.f3706 [PubMed]
• 67. Farina EK, Kiel DP, Roubenoff R, Schaefer EJ, Cupples LA, Tucker KL. Protective effects of fish intake and interactive effects of long-chain polyunsaturated fatty acid intakes on hip bone mineral density in older adults: the Framingham Osteoporosis Study. Am J Clin Nutr. 2011; 93:1142–51. https://doi.org/10.3945/ajcn.110.005926 [PubMed]
• 68. Bannenberg GL, Chiang N, Ariel A, Arita M, Tjonahen E, Gotlinger KH, Hong S, Serhan CN. Molecular circuits of resolution: formation and actions of resolvins and protectins. J Immunol. 2005; 174:4345–55. https://doi.org/10.4049/jimmunol.174.7.4345 [PubMed]
• 69. Ridker PM, Buring JE, Cook NR, Rifai N. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation. 2003; 107:391–97. https://doi.org/10.1161/01.CIR.0000055014.62083.05 [PubMed]
• 70. de Labry LO, Glynn RJ, Levenson MR, Hermos JA, LoCastro JS, Vokonas PS. Alcohol consumption and mortality in an American male population: recovering the U-shaped curve--findings from the normative Aging Study. J Stud Alcohol. 1992; 53:25–32. https://doi.org/10.15288/jsa.1992.53.25 [PubMed]
• 71. Knott CS, Coombs N, Stamatakis E, Biddulph JP. All cause mortality and the case for age specific alcohol consumption guidelines: pooled analyses of up to 10 population based cohorts. BMJ. 2015; 350:h384. https://doi.org/10.1136/bmj.h384 [PubMed]
• 72. Beach SR, Dogan MV, Lei MK, Cutrona CE, Gerrard M, Gibbons FX, Simons RL, Brody GH, Philibert RA. Methylomic Aging as a Window onto the Influence of Lifestyle: Tobacco and Alcohol Use Alter the Rate of Biological Aging. J Am Geriatr Soc. 2015; 63:2519–25. https://doi.org/10.1111/jgs.13830 [PubMed]
• 73. Grønbaek M, Becker U, Johansen D, Gottschau A, Schnohr P, Hein HO, Jensen G, Sørensen TI. Type of alcohol consumed and mortality from all causes, coronary heart disease, and cancer. Ann Intern Med. 2000; 133:411–19. https://doi.org/10.7326/0003-4819-133-6-200009190-00008 [PubMed]
• 74. Volpato S, Pahor M, Ferrucci L, Simonsick EM, Guralnik JM, Kritchevsky SB, Fellin R, Harris TB. Relationship of alcohol intake with inflammatory markers and plasminogen activator inhibitor-1 in well-functioning older adults: the Health, Aging, and Body Composition study. Circulation. 2004; 109:607–12. https://doi.org/10.1161/01.CIR.0000109503.13955.00 [PubMed]
• 75. Fillmore KM, Kerr WC, Stockwell T, Chikritzhs T, Bostrom A. Moderate alcohol use and reduced mortality risk: systematic error in prospective studies. Addict Res Theory. 2006; 14:101–32. https://doi.org/10.1080/16066350500497983
• 76. Brien SE, Ronksley PE, Turner BJ, Mukamal KJ, Ghali WA. Effect of alcohol consumption on biological markers associated with risk of coronary heart disease: systematic review and meta-analysis of interventional studies. BMJ. 2011; 342:d636. https://doi.org/10.1136/bmj.d636 [PubMed]
• 77. Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr. 2006; 136:2588–93. [PubMed]
• 78. He FJ, Nowson CA, Lucas M, MacGregor GA. Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: meta-analysis of cohort studies. J Hum Hypertens. 2007; 21:717–28. https://doi.org/10.1038/sj.jhh.1002212 [PubMed]
• 79. He FJ, Nowson CA, MacGregor GA. Fruit and vegetable consumption and stroke: meta-analysis of cohort studies. Lancet. 2006; 367:320–26. https://doi.org/10.1016/S0140-6736(06)68069-0 [PubMed]
• 80. Carter P, Gray LJ, Troughton J, Khunti K, Davies MJ. Fruit and vegetable intake and incidence of type 2 diabetes mellitus: systematic review and meta-analysis. BMJ. 2010; 341:c4229. https://doi.org/10.1136/bmj.c4229 [PubMed]
• 81. Gandini S, Merzenich H, Robertson C, Boyle P. Meta-analysis of studies on breast cancer risk and diet: the role of fruit and vegetable consumption and the intake of associated micronutrients. Eur J Cancer. 2000; 36:636–46. https://doi.org/10.1016/S0959-8049(00)00022-8 [PubMed]
• 82. Wang X, Ouyang Y, Liu J, Zhu M, Zhao G, Bao W, Hu FB. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ. 2014; 349:g4490. https://doi.org/10.1136/bmj.g4490 [PubMed]
• 83. Holt EM, Steffen LM, Moran A, Basu S, Steinberger J, Ross JA, Hong C-P, Sinaiko AR. Fruit and vegetable consumption and its relation to markers of inflammation and oxidative stress in adolescents. J Am Diet Assoc. 2009; 109:414–21. https://doi.org/10.1016/j.jada.2008.11.036 [PubMed]
• 84. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Fruit and vegetable intakes, C-reactive protein, and the metabolic syndrome. Am J Clin Nutr. 2006; 84:1489–97. [PubMed]
• 85. Ornish D, Lin J, Chan JM, Epel E, Kemp C, Weidner G, Marlin R, Frenda SJ, Magbanua MJ, Daubenmier J, Estay I, Hills NK, Chainani-Wu N, et al. Effect of comprehensive lifestyle changes on telomerase activity and telomere length in men with biopsy-proven low-risk prostate cancer: 5-year follow-up of a descriptive pilot study. Lancet Oncol. 2013; 14:1112–20. https://doi.org/10.1016/S1470-2045(13)70366-8 [PubMed]
• 86. Farzaneh-Far R, Lin J, Epel ES, Harris WS, Blackburn EH, Whooley MA. Association of marine omega-3 fatty acid levels with telomeric aging in patients with coronary heart disease. JAMA. 2010; 303:250–57. https://doi.org/10.1001/jama.2009.2008 [PubMed]
• 87. Barzilai N, Crandall JP, Kritchevsky SB, Espeland MA. Metformin as a Tool to Target Aging. Cell Metab. 2016; 23:1060–65. https://doi.org/10.1016/j.cmet.2016.05.011 [PubMed]
• 88. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006; 127:240–48. https://doi.org/10.1016/j.mad.2005.10.004 [PubMed]
• 89. The Women’s Health Initiative Study Group. Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials. 1998; 19:61–109. https://doi.org/10.1016/S0197-2456(97)00078-0 [PubMed]
• 90. Patterson RE, Kristal AR, Tinker LF, Carter RA, Bolton MP, Agurs-Collins T. Measurement characteristics of the Women’s Health Initiative food frequency questionnaire. Ann Epidemiol. 1999; 9:178–87. https://doi.org/10.1016/S1047-2797(98)00055-6 [PubMed]
• 91. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012; 13:86. https://doi.org/10.1186/1471-2105-13-86 [PubMed]
• 92. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT, Rickabaugh TM, Jamieson BD, Sun D, Li S, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016; 17:171. https://doi.org/10.1186/s13059-016-1030-0 [PubMed]
• 93. Whitsel EA, Baccarelli A, Hou L. Epigenetic Mechanisms of PM-Mediated CVD. Research Portfo-lio Online Reporting Tools. National Institutes of Health: U.S. Department of Health and Human Services: https://projectreporter.nih.gov/project_info_description.cfm?aid=8369149&icde=27485446).
• 94. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007; 8:118–27. https://doi.org/10.1093/biostatistics/kxj037 [PubMed]
• 95. Pisani P, Faggiano F, Krogh V, Palli D, Vineis P, Berrino F. Relative validity and reproducibility of a food frequency dietary questionnaire for use in the Italian EPIC centres. Int J Epidemiol. 1997 (Suppl 1); 26:S152–60. https://doi.org/10.1093/ije/26.suppl_1.S152 [PubMed]
• 96. Bartali B, Frongillo EA, Bandinelli S, Lauretani F, Semba RD, Fried LP, Ferrucci L. Low nutrient intake is an essential component of frailty in older persons. J Gerontol A Biol Sci Med Sci. 2006; 61:589–93. https://doi.org/10.1093/gerona/61.6.589 [PubMed]
• 97. Ferrucci L, Bandinelli S, Benvenuti E, Di Iorio A, Macchi C, Harris TB, Guralnik JM. Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc. 2000; 48:1618–25. https://doi.org/10.1111/j.1532-5415.2000.tb03873.x [PubMed]