Metabolic syndrome and epigenetic aging: a twin study

• DOI: 10.1038/s41366-024-01466-x
• Publication Date: 2024-01-25T12:09:39Z
• Authors: Tiina Föhr; Arne Hendrix; Anna Kankaanpää; Eija K. Laakkonen; Urho Kujala; Kirsi H. Pietiläinen; Terho Lehtimäki; Mika Kähönen; Olli Raitakari; Xiaoling Wang; Jaakko Kaprio; Miina Ollikainen; Elina Sillanpää
• Keywords: Male; Adult; elintavat; Aging; Fyysinen aktiivisuus eliniän aikana; ympäristötekijät; 610; vanheneminen; Associations; 1184; epigenetic aging; Article; 3121; Epigenesis, Genetic; Young Adult; Age; Hyvinvoinnin tutkimuksen yhteisö; Humans; genetics; Obesity; Physical activity through life span; Sports and Exercise Medicine; aineenvaihdunta; metabolinen oireyhtymä; Life Style; Adiposity; Aged; School of Wellbeing; kaksostutkimus; Metabolic Syndrome; Gerontologia ja kansanterveys; Cohort; Aging, Premature; Metaanalysis; Liikuntalääketiede; Middle Aged; DNA Methylation; Cardiovascular risk; General medicine, internal medicine and other clinical medicine; signs and symptoms; epigeneettinen ikääntyminen; epigenetiikka; perimä; Physical-activity; Female; Involvement; ikä; Gerontology and Public Health

Abstract Background Metabolic syndrome (MetS) is associated with premature aging, but whether this association is driven by genetic or lifestyle factors remains unclear.

Methods Two independent discovery cohorts, consisting of twins and unrelated individuals, were examined (N = 268, aged 23–69 years). The findings were replicated in two cohorts from the same base population. One consisted of unrelated individuals (N = 1 564), and the other of twins (N = 293). Participants’ epigenetic age, estimated using blood DNA methylation data, was determined using the epigenetic clocks GrimAge and DunedinPACE. The individual-level linear regression models for investigating the associations of MetS and its components with epigenetic aging were followed by within-twin-pair analyses using fixed-effects regression models to account for genetic factors.

Results In individual-level analyses, GrimAge age acceleration was higher among participants with MetS (N = 56) compared to participants without MetS (N = 212) (mean 2.078 [95% CI = 0.996,3.160] years vs. −0.549 [−1.053,−0.045] years, between-group p = 3.5E-5). Likewise, the DunedinPACE estimate was higher among the participants with MetS compared to the participants without MetS (1.032 [1.002,1.063] years/calendar year vs. 0.911 [0.896,0.927] years/calendar year, p = 4.8E-11). An adverse profile in terms of specific MetS components was associated with accelerated aging. However, adjustments for lifestyle attenuated these associations; nevertheless, for DunedinPACE, they remained statistically significant. The within-twin-pair analyses suggested that genetics explains these associations fully for GrimAge and partly for DunedinPACE. The replication analyses provided additional evidence that the association between MetS components and accelerated aging is independent of the lifestyle factors considered in this study, however, suggesting that genetics is a significant confounder in this association.

Conclusions The results of this study suggests that MetS is associated with accelerated epigenetic aging, independent of physical activity, smoking or alcohol consumption, and that the association may be explained by genetics.