A Direct Comparison of the Relationship of Epigenetic Aging and Epigenetic Substance Consumption Markers to Mortality in the Framingham Heart Study

Abstract

A number of studies have examined the relationship of indices of epigenetic aging (EA) to key health outcomes. Unfortunately, our understanding of the relationship of EA to mortality and substance use-related health variables is unclear. In order to clarify these interpretations, we analyzed the relationship of the Levine EA index (LEA), as well as established epigenetic indices of cigarette (cg05575921) and alcohol consumption (cg04987734), to all-cause mortality in the Framingham Heart Study Offspring Cohort (n = 2256) Cox proportional hazards regression. We found that cg05575921 and cg04987734 had an independent effect relative to LEA and vice versa, with the model including all the predictors having better performance than models with either LEA or cg05575921 and cg04987734 alone. After correction for multiple comparisons, 195 and 327, respectively, of the 513 markers in the LEA index, as well as the overall index itself, were significantly associated with cg05575921 and cg04987734 methylation status. We conclude that the epigenetic indices of substance use have an independent effect over and above LEA, and are slightly stronger predictors of mortality in head-to-head comparisons. We also conclude that the majority of the strength of association conveyed by the LEA is secondary to smoking and drinking behaviors, and that efforts to promote healthy aging should continue to focus on addressing substance use.

A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD

“…This observation suggests that the methylation sites that are nominally associated with smoking are also associated with additional aspects of aging [15]. For example, methylation of one of the genes associated with this surrogate, cg05575921, which codes for aryl hydrocarbon receptor repressor (AHRR), is not solely a function of smoking, but also reflects exposure to various environmental exposures and xenobiotics, including polycyclic hydrocarbons, airborne particulate matter and certain pharmaceuticals [35]. Of potential importance, the aryl hydrocarbon receptor also plays important roles in inflammation, cell differentiation and cell cycle control [36].…”

Childhood adversity is linked to adult health among African Americans via adolescent weight gain and effects are genetically moderated

“…Specifically, all analyses statistically controlled for participants’ annual income at age 29 and educational level at age 29, to control for potential effects of adult SES on health outcomes (see Cockerham, Hamby, & Oates, 2017; Minkler, Fuller-Thompson, & Guralnik, 2006). Similarly, all analyses controlled for sex (1 = male ), and health behaviors assessed at age 29 (including substance use, diet, and exercise) to control for factors in adulthood that might influence health outcomes (Beach et al, 2015; Mills et al, 2019; Rezende, Rodrigues Lopes, Rey-López, Matsudo, & Luiz, 2014; Schwingshackl & Hoffmann, 2015). Items assessing level of substance use at age 29 included alcohol consumption (“How many alcoholic drinks have you consumed during the past month?”), cigarette use (“How many cigarettes have you smoked in the last 3 months?”), and marijuana use (“How many times have you used marijuana during the past month?”).…”

Array-Based Epigenetic Aging Indices May Be Racially Biased

“…Such differences have the potential to yield scales that reflect different influences across racial/ethnic groups. For example, in the FHS (White) sample, the DNAm PhenoAge EA index largely loaded on cigarette and alcohol consumption and this was not the case in the FACHS (AA) sample [ 24 , 25 ]. Since the rate of smoking and alcohol consumption are roughly equal in Whites and AAs [ 38 , 39 ], it is unlikely that differences in habits affected the methylation outcomes, suggesting that the DNAm PhenoAge index is operating somewhat differently across ethnic groups.…”

“…For example, with respect to smoking, we have shown that almost 90% of the top ranked CpG sites for predicting are affected by ethnic specific genetic variation [ 23 ]. In the Framingham Heart Study (FHS) Offspring Cohort, which consists of individuals of European ancestry, 195 of the 513 CpG sites in the DNAm PhenoAge EA Index are significantly associated with methylation at the well-established indicator of smoking, cg05575921 [ 24 ]. However, in AA samples, the Levine PhenoAge Index is only modestly associated with cotinine seropositivity and cg05575921 inferred smoking intensity [ 25 ].…”