Accelerated DNA methylation age and the use of antihypertensive medication among older adults

Participant characteristics

Characteristics of the 546 participants at each visit are shown in Table 1. Overall, the average age at first and second visits was 72 and 75 years, respectively. More than 60% of the participants were former smokers and less than 5% were current smokers, and the majority of participants were overweight or obese, consumed no or low amounts of alcohol, reported low physical activity and had less than 16 years of education. During a median follow-up of 3.86 years, the prevalence of hypertension increased from about 68% to 75%, stroke from nearly 6.0% to 8.6%, coronary heart disease (CHD) from 26% to 34%, diabetes from 12% to 16% and cancer from 50% to 59%. In particular, 313/374 and 383/411 participants with hypertension took the medication for elevated blood pressure at each visit, respectively. ACE inhibitors and beta blockers were the two most widely used medications. According to our definition of hypertension, the participants using AHM were considered to be have hypertension. Among the 546 participants, after the first visit, 13 stopped using any AHM and 83 started to use.

Our main analyses based on the DNAmAge estimated by Horvath’s algorithm. Figure 1 shows that the estimates were highly correlated with chronological age at each visit (Spearman coefficients >0.6). While DNAmAge increased between the first and second visit, the overall AA of the second visit was lower than that of the first visit (Table 1), showing declining trajectories with a crude average Δ_AA of about -0.03 year/chronological year. The trajectories of AA and distributions of Δ_AA were illustrated in Figure 2. Figure S1 showed strong correlations between the AAs at both visits among those with or without hypertension at both visits (Spearman coefficients ~0.7), while for those who had hypertension or hypertension was controlled after the first visit, their correlation was slightly attenuated (Spearman coefficients = 0.53).

Associations of hypertension with age acceleration and the change rate of age acceleration

We evaluated the relationship between hypertension and AA at each visit. Participants with hypertension had higher AA (years) than those without (first visit: 0.18 vs. 0.09; second visit: -0.01 vs. -0.18; p-value=0.016). The similar pattern was also observed for Δ_AA that participants with hypertension had a higher Δ_AA (year/chronological year) compared to those without (first visit: 0.03 vs. -0.14; second visit: 0.01 vs. -0.12; p-value=0.007). We further adjusted for other potential covariates at each visit in a mixed linear model to validate the observed patterns, including age, body max index (BMI), smoking status, alcohol consumption, years of education, physical activity, leukocyte distribution and the batch of microarray experiments in DNA methylation measurement. As shown in Table S1, the correlations of hypertension with AA and Δ_AA remained positive, albeit not statistically significant. We also tested the associations of diabetes, metabolic syndrome and commonly measured clinical biomarkers with AA and Δ_AA (Table S1 and S2). Despite the statistically significant association between diabetes and Δ_AA, neither metabolic syndrome nor any biomarkers were strongly associated with AA or Δ_AA.

Cross-sectional associations of antihypertensive medication with age acceleration and the change rate of age acceleration

We first investigated the cross-sectional associations of AHM use with AA and Δ_AA at each visit. Overall, taking any AHM was significantly associated with higher AA and Δ_AA (Table 2). After adjusting for potential covariates at each visit [age, BMI, smoking status, alcohol consumption, years of education, physical activity, leukocyte distribution, total cholesterol, high-density lipoprotein (HDL), triglycerides, fasting glucose, systolic blood pressure (SBP), hypertension, stroke, CHD, diabetes, cancer and the batch of microarray experiments in DNA methylation measurement], people who took any AHM at each visit were about 2.3 years older than the controls in terms of AA, and their AA increased significantly during follow-up at a rate of more than 0.4 year/chronological year compared to the people who did not take any AHMs. However, the effects of specific classes of medications varied. While taking diuretics was negatively associated with AA and Δ_AA, calcium channel blockers and alpha and beta blockers were positively correlated with AA and Δ_AA. Angiotensin-converting enzyme inhibitors (ACE inhibitors) and angiotensin receptor antagonists (ARBs) both had negative associations with Δ_AA, but their relationships with AA were in different directions. An additional subgroup analysis was carried out among people with hypertension at each visit (Table 3), and the use of any AHM remained robustly associated with increased AA and Δ_AA.

Table S3a showed sensitivity analyses for the cross-sectional associations of any AHM use with the AA and Δ_AA of Hannum DNAmAge and DNA methylation Phenotypic age (DNAmPhenoAge). Taking any AHMs showed positive correlations with the AA of each biomarker and significantly associated with both forms of AA at the first visit. People who took medications were about 2.2 years/1.7 years older in people compared to the controls, respectively. Even though taking any AHMs was not robustly associated with the Δ_AA of neither biomarker, the effects were in the same direction as we identified for the Horvath DNAmAge.

Longitudinal associations of antihypertensive medication with age acceleration and the change rate of age acceleration

We further examined the longitudinal association between AHM use and Δ_AA (Table 4). People that with continuous medication use were older than that never used (76.6 vs. 73.7 years). After controlling for the potential covariates at the first visit, taking AHM showed positive correlations with the AA and Δ_AA. Compared to people who never took any AHM, individuals who started to take AHM after the first visit had an increased Δ_AA of about 0.3 year/chronological year, and individuals with continuous AHM use had an increased Δ_AA of about 0.6 year/chronological year. Consistent with this finding, stopping taking ACE inhibitors, ARBs, and alpha and beta blockers showed negative correlations with Δ_AA, compared to continuous use. A subgroup analysis among 313 participants with hypertension at first visit yielded a similar pattern (Table 5). After fully controlling for potential covariates, stopped taking AHM was correlated with a declining Δ_AA compared to the continued AHM use after the first visit albeit not statistically significant.

Same longitudinal tests were also performed for the Δ_AA of Hannum DNAmAge and DNAmPhenoAge as sensitivity analyses in Table S3b. The Δ_AA of neither biomarker showed significant associations with the change of AHM use, but an increasing pattern was still observed for the people with continuous AHMs use in comparison to the people who never used the AHM for each biomarker.

Study design and population

The NAS study is an ongoing longitudinal study of aging, established by the U.S. Department of Veterans Affairs in 1963. Details of this study have been published previously [33]. Briefly, the NAS is a closed cohort of 2,280 male veterans from the Greater Boston area. They were enrolled after an initial health screening that determined that they were free of known chronic medical conditions. Blood samples were collected from 657 participants, most of whom were visited up to 4 times between 1999 and 2013. Participants have been reevaluated every 3–5 years on a continuous rolling basis using detailed on-site physical examinations and questionnaires. We restricted the current analysis to the data of the first two visits of 546 Caucasian participants (aged 55-85 years) who had been visited twice at least, in order to control for the heterogeneity of race and to analyze the change of AA longitudinally. The NAS study was approved by the Department of Veterans Affairs Boston Healthcare System, and written informed consent was obtained from each subject prior to participation.

Data collection

As previously described [34], at each visit, participants were asked to provide detailed information about their lifestyles, dietary habits, activity levels, and demographic factors. Height and weight were measured and were used to calculate body mass index (BMI, in kg/m²). Blood samples were collected for assessing blood-based biomarkers. Participants’ status of major diseases was assessed based on the medical history and physicians’ diagnosis. In particular, hypertension was defined as a measured SBP of ≥140 mmHg, a measured diastolic blood pressure (DBP) of ≥90 mm Hg, or participants’ use of AHMs [35]. SBP and DBP were measured by a physician, and the AHMs included calcium channel blockers, ACE inhibitors, ARBs, alpha blockers, beta blockers and diuretics.

DNA methylation data

DNA of whole blood samples were collected between 1999 and 2013. As previously described [36,37], we used the QIAamp DNA Blood Kit (Qiagen, CA, USA) to extract DNA from buffy coat, and performed bisulfite conversion with the EZ-96 DNA Methylation Kit (Zymo Research, CA, USA). To minimize batch effects, we randomized chips across plates and randomized samples based on a 2-stage age-stratified algorithm so that age distributed similarly across chips and plates. We measured DNA methylation of CpG probes using Illumina HumanMethylation450 BeadChip for all the samples from both visits. After quality control, the remaining samples were preprocessed using the Illumina-type background correction, dye-bias adjustment and BMIQ normalization [38], which were used to generate methylation status. The methylation status of a specific CpG site was quantified as a β value ranging from 0 (no methylation) to 1 (full methylation).

A total of 353 CpG sites were retrieved from the methylation profiles for the estimation of DNAmAge for each participant based on the algorithm proposed by Horvath [3]. This algorithm was derived from a range of tissues and cell types using 353 probes targeted in the Illumina 27k and 450k methylation arrays. In this study, we performed the estimation by using the online calculator, where background-corrected beta values were pre-processed by the calculator’s internal normalization method [3]. AA was determined as discrepancies between methylation and chronological age in the form of residuals, which have a mean of 0 and represent positive and negative deviations from chronological age in years. The residuals were calculated by a linear regression procedure in which DNA methylation age was the outcome and chronological age was the independent variable. The Δ_AA for each participant between the two visits was determined as:

$${\Delta }_{\text{AA}}=\frac{\text{AA of second visit (year) - AA of first visit (year)}}{\text{Time between first and second visit (year)}}$$(1)

with a unit of year/chronological year.

Statistical analysis

Study population of each visit was described with respect to major socio-demographic characteristics, lifestyle factors and detailed AHM use. The correlation between estimated DNAmAge and chronological age at each visit was evaluated by Spearman correlation coefficient.

We first tested the cross-sectional association of hypertension with AA at each visit and the Δ_AA between the two visits. A linear mixed model with AA (Δ_AA) as outcome was employed, controlling for covariates that have been reported to be associated with DNA methylation changes or with the use of AHM, including age (years), BMI [kg/m², underweight (<18.5, < 1% of the study population) or normal weight (18.5 to <25), overweight (25 to <30), obese (≥30)], alcohol consumption [abstainer, low (0 to <40 g/d), intermediate (40 to <60 g/d), high (≥60 g/d)], smoking (current/ former/ never smoker), education (≤12 years, 13 – 16 years, >16 years), physical activity [metabolic equivalent of task (MET), low (≤12 kcal/kg hours/week), median (12–30 kcal/kg hours/week), high (≥30 kcal/kg hours/week)] and leukocyte distribution (Houseman algorithm [32]). The batch of microarray experiments in DNA methylation measurement was controlled for as the random effect. Corresponding AA was additionally adjusted for in the analyses of Δ_AA at each visit.

We then investigated the cross-sectional associations of AHM use with AA and Δ_AA at each visit in two mixed linear regression models. Model 1 adjusted for age, leukocyte distribution and the random batch effect, and Model 2 further adjusted for BMI, smoking status, alcohol consumption, physical activity, education, total cholesterol (mg/dL), HDL (mg/dL), triglycerides (mg/dL), fasting glucose (mg/dL), SBP (mm Hg), hypertension, stroke, coronary heart disease, diabetes and cancer (yes/no). AA was additionally adjusted for in the analyses of Δ_AA at each visit.

Finally, we examined the longitudinal association between AHM use and the Δ_AA. AHM use in the longitudinal analysis was classified as: never used, stopped use after first visit, started use after first visit and continuous use according to the reports of AHM use between the two visits, and was treated as a predictor in the analysis models. Two mixed linear regression models were used, adjusting for the same covariates described above.

Sensitivity analyses

We further performed sensitivity analyses for the main results by using another two DNA methylation based aging biomarkers: DNAmAge estimated by Hanumm et al.’s algorithm [39], and DNAmPhenoAge recently developed by Morgan et al. as an “update” of the Horvath DNAmAge for the lifespan [40].

Hanumm DNAmAge was estimated based on 71 age-related CpG sites reported in 2013 [39] and determined as the sum of the methylation beta values multiplied by the reported effect sizes of the predictors. AA of Hannum’s DNAmAge was also determined as discrepancies between DNA methylation and chronological age in the form of residuals.

Another batch of 513 CpG sites was retrieved for the estimation of DNAmPhenoAge for each participant based on the algorithm proposed by Levine et al. [40]. With the coefficient and intercept values provided by the authors, we estimated the DNAmPhenoAge as:

DNAmPhenoAge = 𝐶𝑝𝐺₁ × 𝛽₁ + 𝐶𝑝𝐺₂ × 𝛽₂ +⋯ 𝐶𝑝𝐺₅₁₃ × 𝛽₅₁₃ + intercept.

As defined by the authors [40], difference between Phenotypic and chronological age (DNAmPhenoAge – chronological age) was defined as the AA of DNAmPhenoAge. The Δ_AA for the two aging biomarkers between the two visits was also determined by the formula (1).

We tested the cross-sectional and longitudinal associations of the AA and Δ_AA of the two biomarkers with the use of any AHM using the same analysis models employed in the main analyses for Horvath DNAmAge.

Data cleaning and all aforementioned analyses were performed by SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and all statistical tests were two-sided with p-values of <0.05.