Methylation-Based Biological Age and Hypertension Prevalence and Incidence

Abstract

Background: Hypertension is common in older individuals and is a major risk factor for cardiovascular disease. Blood DNA methylation profiles have been used to derive metrics of biological age that capture age-related physiological change, disease risk, and mortality. The relationships between hypertension and DNA methylation-based biological age metrics have yet to be carefully described. Methods: Among 4419 women enrolled in the prospective Sister Study cohort, DNA methylation data generated from whole blood samples collected at baseline were used to calculate 3 biological age metrics (PhenoAgeAccel, GrimAgeAccel, DunedinPACE). Women were classified as hypertensive at baseline if they had high blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg) or reported current use of antihypertensive medication. New incident cases of hypertension during follow-up were identified via self-report on annual health questionnaires. Results: All 3 DNA methylation metrics of biological age were positively associated with prevalent hypertension at baseline (per 1-SD increase; PhenoAgeAccel, adjusted odds ratio, 1.16 [95% CI, 1.05–1.28]; GrimAgeAccel, adjusted odds ratio, 1.28 [95% CI, 1.14–1.45]; DunedinPACE, adjusted odds ratio, 1.16 [95% CI, 1.03–1.30]). Among 2610 women who were normotensive at baseline, women with higher biological age were more likely to be diagnosed with incident hypertension (per 1-SD increase; PhenoAgeAccel, adjusted hazard ratio, 1.09 [95% CI, 0.97–1.23]; GrimAgeAccel, adjusted hazard ratio, 1.16 [95% CI, 0.99–1.36]; DunedinPACE, adjusted hazard ratio, 1.16 [95% CI, 1.01–1.33]). Conclusions: Methylation-based biological age metrics increase before a hypertension diagnosis and appear to remain elevated in the years after clinical diagnosis and treatment.

Educational Mobility, Pace of Aging, and Lifespan Among Participants in the Framingham Heart Study

“…First, the DunedinPACE algorithm is predictive of diverse aging-related outcomes, including disease, disability, and mortality. 14 , 25 , 26 , 27 , 29 Second, the algorithm is associated with social determinants of healthy aging in young, midlife, and older adults. 14 , 15 , 28 , 30 , 31 , 56 Third, the algorithm shows evidence of being modified by calorie restriction, 57 an intervention that modifies the basic biology of aging in animal experiments.…”

“… 24 In diverse cohorts in the US and around the globe, this algorithm has been associated with incident morbidity and disability, survival, and a range of socioenvironmental exposures including educational attainment. 14 , 15 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 We calculated participants’ clock values from their DNAm data calculated using code available on GitHub. 33…”

Circulating Leukocyte Subsets Before and After a Breast Cancer Diagnosis and Therapy

“…Genomic DNA was extracted from whole blood aliquots as previously described . One microgram of extracted DNA was bisulfite converted using a commercially available kit (EZ DNA Methylation Kit; Zymo Research) in 96-well plates.…”

“…Genomic DNA was extracted from whole blood aliquots as previously described. 25 One microgram of extracted DNA was bisulfite converted using a commercially available kit (EZ DNA Methylation Kit; Zymo Research) in 96-well plates. Samples were tested for completeness of bisulfite conversion, and converted DNA was assayed using a genome-wide methylation screening tool (Infinium MethylationEPIC v1 BeadChip Array; Illumina) at the Cancer Genomics Research Laboratory of the National Cancer Institute.…”