The use of antihypertension medication (AHM) reduces the risk of adverse age-related health outcomes caused by hypertension. Specifically, observational studies, clinical trials, and systematic reviews mostly suggested that effective antihypertensive therapy greatly reduces the risk of CVD in patients with hypertension [ 18 , 19 ], and may also be associated with a decreased risk of cognitive decline and incident dementia [ 20 ]. As DNA methylation is a durable and reversible modification, we hypothesized that the use of AHMs might also be able to influence the biological aging reflected by the epigenetic AA. Therefore, we assessed the associations of AHM use with AA and further determined whether the change of AHM use could modify the change rate of AA (Δ AA ) during a median follow-up of 3.86 years. This investigation was carried out in the Normative Aging Study (NAS), which is an all-male longitudinal study of a cohort of older veterans living in the Greater Boston area.

Several aging-related factors, including inflammation, neurohormonal disorder and endothelial dysfunction, have been found to play key mechanistic roles in the development of hypertension [ 14 – 16 ], the most common long-term medical condition among older adults that could lead to various forms of age-related health outcomes, such as cardiovascular diseases (CVD), kidney failure and dementia [ 17 ]. Relationships of hypertension and blood pressure with biological aging have also been studied since the introduction of DNAmAge. In 2016, Horvath et al. found that people with hypertension had a higher AA (0.5 – 1.2 years) in comparison to controls in the Bogalusa Heart study [ 7 ] and a more recent study from Quach et al. showed that elevated blood pressure was also correlated with higher extrinsic and intrinsic DNAmAge [ 10 ].

DNA methylation, a major form of epigenetic modification, is known to play an important role in aging and the development of age-related health outcomes [ 1 , 2 ]. Recently, a DNA methylation-based biological age predictor, “DNA methylation age (DNAmAge)”, has been established and found to be highly associated with chronological age [ 3 ]. The discrepancy between this epigenetic-based indicator and the chronological age has been termed age acceleration (AA), which was found to be heritable and has been used as an index of accelerated biological aging. Follow-up investigations have linked AA to lifestyle factors, environmental hazards, stressful life events, as well as all-cause mortality [ 4 – 13 ].

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.

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.

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.

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 .

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 .

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).

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.

Discussion

In the present study, we investigated the cross-sectional and longitudinal associations of AHM use with the AA of Horvath DNAmAge in a longitudinal study of older male participants examined over two visits. Even though a general decrease of AA was observed between the two visits, after fully adjusting for hypertension and other potential covariates, any AHM use showed a cross-sectional significant association with higher AA at each visit, as well as a longitudinal association with increased Δ AA between visits. Particularly, relative to participants who never took any AHM, individuals with continuous AHM use had a higher Δ AA of 0.6 year/chronological year. Additional sensitivity analyses on another two DNA methylation-based biomarkers (Hannum DNAmAge & DNAmPhenoAge) showed the similar patterns with the use of AHM as the Horvath DNAmAge.

Recently, Marioni et al. found that DNAmAge increases at a slower rate than chronological age across the life course in five independent cohorts, especially in the older population [21], which suggested that, overall, AA declines as people get older. The global decreasing pattern of AA across the two visits observed in our study is in line with this finding. This pattern may also be explained by survival bias, due to the higher probability that healthier participants with relatively lower AA may stay longer in longitudinal studies. After adjusting for hypertension and other potential covariates that might affect the DNAmAge, we surprisingly found that taking AHMs was associated with faster AA. This finding is at variance with the declining pattern of DNAmAge across the lifespan and is not consistent with the preventive effects of AHMs against age-related health outcomes caused by hypertension [18,19]. Several explanations may account for this discrepancy.

First, measurement bias or inaccuracies may affect the comparability of longitudinal AA and Δ AA estimates. Nevertheless, we have restricted this technical bias to the greatest extent by adjusting for the batch of DNA methylation measurement as a random effect and normalizing the methylation profiles with Horvath’s internal normalization method. Furthermore, selection bias due to differential survival rate may cause an underrepresentation of participants with higher AA [22], and may lead to underestimation or even contradicting results of the effect of AHM use on AA and Δ AA , and distort their associations towards the opposite. In our analyses, we accounted for potential selection bias due to the loss of follow-up using inverse probability weighting (IPW) [23]. Point estimates were similar between models using and not using IPW, indicating that little selection bias was introduced due to the loss to follow-up. Additionally, since the use of AHMs is usually treated as the indicator of hypertension severity, our study might report a proxy outcome that indeed reflected the impact of hypertension on aging (confounding by indication). Due to this concern, despite the weak positive but not significant pattern we observed between hypertension and the AA (Δ AA ), we additionally adjusted for SBP and hypertension in the fully-adjusted analysis model and performed another sensitivity analysis by adding the interaction term of hypertension and AHMs (data not shown). Neither of the two adjustments altered the patterns of taking AHMs with AA and Δ AA in any relevant manner. In the meanwhile, people might stop taking AHMs after reducing the blood pressure through changing lifestyles and exercises, two factors that are likely to decrease the Δ AA . After controlling for the factors, we still observed a declining pattern between stopping taking any AHM and Δ AA , albeit not significant due to the limited sample size.

One biologically plausible explanation for this inconsistent observation is a potential causal connection among aging, epigenetic biomarkers of age, hypertension and AHM use. We speculate that in these four-corner relationships, aging independently leads to the change of epigenetic biomarkers of age (e.g., DNAmAge, DNAmPhenoAge) and hypertension via two separate pathways. Aging could cause the change of epigenetic biomarkers of age by altering the methylation levels of age-related CpG sites. In parallel, aging could prompt hypertension via a “vicious cycle”, which consists of inflammation, oxidative stress and endothelial dysfunction, and might not be closely related to age-related DNA methylation changes [16,17]. Taking AHMs could control blood pressure and reduce the risks of aging-related diseases, such as CVD, kidney failure and dementia, which are caused by hypertension. Nevertheless, as the causation between aging and hypertension is one-way, reducing blood pressure using AHM might not reversely affect biological aging by bringing the aberrant changes of age-related CpG sites back to normal levels. On the contrary, the AHMs’ potential side effects, such as abnormal glucose and lipid metabolism [24] and psychological/cognitive disorders [25,26], might have the potential to accelerate epigenetic biomarkers of age as suggested by previous studies [6,10,27]. However, this hypothesis needs to be evaluated by further research with larger populations and multiple follow-ups along with corresponding causal inferences and functional tests.

It is worth noting that the effects of specific medications on AA and Δ AA were not all unfavorable. In our study, ACE inhibitors, ARBs, and diuretics showed weak negative correlation with AA and Δ AA , while beta blockers showed a strong aging accelerating effect. Beyond the effects from specific medications, we should not ignore the adverse drug reactions (ADRs) from combined and inappropriate medication use [28], given most of the participants with hypertension took multiple medications at the same time. ADRs could become more severe and frequent in the elderly due to age-dependent pharmacodynamics and pharmacokinetic changes promoting drug-drug or drug-disease interactions, and such reactions could directly (or indirectly) facilitate the development of aging-related health outcomes including frailty and all-cause mortality [29,30]. This effect might play another key role in accelerating DNAmAge if the ADRs are not identified and treated with further changes in prescriptions.

Major strengths of the present study include the relatively large sample size and repeated measurements with detailed information on a broad range of covariates in a large cohort study. We also acknowledge several limitations in the interpretation of results. First, shifts of leukocyte distribution might affect the DNA methylation changes in whole blood samples [31]. Hence, we adjusted for leukocyte distribution by the Houseman algorithm to restrict potential confounding from differential blood counts to the greatest possible extent [32]. Nevertheless, residual confounding by this and other factors cannot be excluded entirely for the longitudinal analysis. Furthermore, the selected study participants were Caucasians and all male, which limits the generalizability of our results to other racial/ethnic groups and women. Finally, although our overall sample size was relatively large, some of the nonsignificant results may have been due to the lack of statistical power with the relatively smaller size of subsamples for specific AHMs. And given those subgroup analyses did not appear as robust as the whole population and showed contradicted patterns, there might be a risk of getting false positive findings, further research with a bigger sample size and longer multiple follow-ups are warranted to validate our results by eliminating the possibility of false positives.