Menopause accelerates biological aging

Abstract

Although epigenetic processes have been linked to aging and disease in other systems, it is not yet known whether they relate to reproductive aging. Recently, we developed a highly accurate epigenetic biomarker of age (known as the "epigenetic clock"), which is based on DNA methylation levels. Here we carry out an epigenetic clock analysis of blood, saliva, and buccal epithelium using data from four large studies: the Women's Health Initiative (n = 1,864); Invecchiare nel Chianti (n = 200); Parkinson's disease, Environment, and Genes (n = 256); and the United Kingdom Medical Research Council National Survey of Health and Development (n = 790). We find that increased epigenetic age acceleration in blood is significantly associated with earlier menopause (P = 0.00091), bilateral oophorectomy (P = 0.0018), and a longer time since menopause (P = 0.017). Conversely, epigenetic age acceleration in buccal epithelium and saliva do not relate to age at menopause; however, a higher epigenetic age in saliva is exhibited in women who undergo bilateral oophorectomy (P = 0.0079), while a lower epigenetic age in buccal epithelium was found for women who underwent menopausal hormone therapy (P = 0.00078). Using genetic data, we find evidence of coheritability between age at menopause and epigenetic age acceleration in blood. Using Mendelian randomization analysis, we find that two SNPs that are highly associated with age at menopause exhibit a significant association with epigenetic age acceleration. Overall, our Mendelian randomization approach and other lines of evidence suggest that menopause accelerates epigenetic aging of blood, but mechanistic studies will be needed to dissect cause-and-effect relationships further.

Keywords: DNA methylation; WHI; aging; epigenetic clock; menopause.

Fig. S1.

Epigenetic age versus age at menopause. (A–E) DNA methylation age (y axis) versus chronological age (x axis) in the WHI (white) (A), WHI (black) (B), WHI (Hispanic) (C), InCHIANTI (D), and PEG (E) studies. (F–J) Correlations between epigenetic AgeAccel (y axis) and age at menopause (x axis) in the WHI (white) (F), WHI (black) (G), WHI (Hispanic) (H), InCHIANTI (I), and PEG (J) studies. Each panel reports a Pearson correlation coefficient and a corresponding P value. The analysis was restricted to women whose age at menopause was >30 y. For all five samples, DNAm age and chronological age are highly correlated. We also find significant inverse associations between epigenetic AgeAccel and age of menopause in the WHI white (F) and WHI black (G) cohorts and a marginally significant inverse association in the PEG cohort (J). Metaanalysis for these and other strata can be found in Table S2.

Fig. 1.

Epigenetic AgeAccel versus surgical menopause status. The x axis of each plot reports surgical menopause status before age 50 y; i.e., “yes” denotes the group of women who experienced surgical menopause before age 50 y, and “no” corresponds to the group of women who did not undergo bilateral oophorectomy at any age before the blood draw. The bar plots report mean values of AgeAccel (± 1 SE) and the P value from a Student t test. The six panels show data from WHI (white) (A), WHI (black) (B), WHI (Hispanic) (C), InCHIANTI (D), and PEG (E) samples and all samples combined (F). A metaanalysis based on Stouffer's Z method indicates that AgeAccel is significantly positively associated with surgical menopause status (P = 0.0018).

Fig. 2.

Epigenetic age analysis of buccal samples from the NSHD. The measure of AgeAccel was defined as the difference between DNAm age and the mean DNAm age in this birth cohort. The scatter plot (B) reports Pearson correlation coefficients and corresponding P values, and the bar plots (A, C, and D) report the P value (±1 SE) from a nonparametric group comparison test (Kruskal–Wallis test). Epigenetic AgeAccel (y axis) is associated with MHT (P = 0.00078) (A) but not with age at menopause (B), menopausal status (C), or surgical menopause (D).

Fig. 3.

Epigenetic age analysis of saliva samples from PEG. The measure of AgeAccel in saliva was defined in the same way as in blood. The scatter plots (A and B) report biweight midcorrelation coefficients and corresponding P values, and the bar plots (C and D) report the P value (±1 SE) from a nonparametric group comparison test (Kruskal–Wallis test). (A) There is a strong correlation between epigenetic age (y axis) and chronological age. (B) Although the correlation between epigenetic AgeAccel and age at menopause in saliva is about twice that of the association in blood, the finding is not significant. (C) However, we do observe an association between epigenetic AgeAccel in saliva and surgical menopause (P = 0.0079). (D) No association was observed between epigenetic AgeAccel and MHT (P = 0.4).

Fig. S2.

Potential causal relationships between age at menopause and epigenetic age. (A) One causal model assumes that both age at menopause and epigenetic AgeAccel reflect biological aging, which is a latent construct. (B) Another potential causal model assumes that menopause leads to an increase of epigenetic age via an acceleration of the biological aging process.

Fig. S3.

Correlation between epigenetic AgeAccel in blood and saliva. Using data from women in the PEG study for whom blood and saliva measures were available, we examined the association between epigenetic AgeAccel in the two tissues. We found that epigenetic AgeAccel in saliva may be a good proxy for measures in blood, given that the two are correlated at r = 0.70 (P = 1.4E-12).

Fig. S4.

Correlation between epigenetic AgeAccel in blood and buccal cells. Using data from women in the NSHD study for whom data from both blood and buccal epithelium were available, we found that the correlation of epigenetic AgeAccel in these two tissues is relatively weak (r = 0.20, P = 0.013), suggesting they may capture distinct phenomena.