Association between epigenetic age and type 2 diabetes mellitus or glycemic traits: A longitudinal twin study

Epigenetic clocks based on DNA methylation have been known as biomarkers of aging, including principal component (PC) clocks representing the degree of aging and DunedinPACE representing the pace of aging. Prior studies have shown the associations between epigenetic aging and T2DM, but the results vary by epigenetic age metrics and people. This study explored the associations between epigenetic age metrics and T2DM or glycemic traits, based on 1070 twins (535 twin pairs) from the Chinese National Twin Registry. It also explored the temporal relationships of epigenetic age metrics and glycemic traits in 314 twins (157 twin pairs) who participated in baseline and follow-up visits after a mean of 4.6 years. DNA methylation data were used to calculate epigenetic age metrics, including PCGrimAge acceleration (PCGrimAA), PCPhenoAge acceleration (PCPhenoAA), DunedinPACE, and the longitudinal change rate of PCGrimAge/PCPhenoAge. Mixed-effects and cross-lagged modelling assessed the cross-sectional and temporal relationships between epigenetic age metrics and T2DM or glycemic traits, respectively. In the cross-sectional analysis, positive associations were identified between DunedinPACE and glycemic traits, as well as between PCPhenoAA and fasting plasma glucose, which may be not confounded by shared genetic factors. Cross-lagged models revealed that glycemic traits (fasting plasma glucose, HbA1c, and TyG index) preceded DunedinPACE increases, and TyG index preceded PCGrimAA increases. Glycemic traits are positively associated with epigenetic age metrics, especially DunedinPACE. Glycemic traits preceded the increases in DunedinPACE and PCGrimAA. Lowering the levels of glycemic traits may reduce DunedinPACE and PCGrimAA, thereby mitigating age-related comorbidities.

Age effect on the shared etiology of glycemic traits and serum lipids: evidence from a Chinese twin study

PURPOSE

Diabetes and dyslipidemia are among the most common chronic diseases with increasing global disease burdens, and they frequently occur together. The study aimed to investigate differences in the heritability of glycemic traits and serum lipid indicators and differences in overlapping genetic and environmental influences between them across age groups.

METHODS

This study included 1189 twin pairs from the Chinese National Twin Registry and divided them into three groups: aged ≤ 40, 41-50, and > 50 years old. Univariate and bivariate structural equation models (SEMs) were conducted on glycemic indicators and serum lipid indicators, including blood glucose (GLU), glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), in the total sample and three age groups.

RESULTS

All phenotypes showed moderate to high heritability (0.37-0.64). The heritability of HbA1c demonstrated a downward trend with age (HbA1c: 0.50-0.79), while others remained relatively stable (GLU: 0.55-0.62, TC: 0.58-0.66, TG: 0.50-0.63, LDL-C: 0.24-0.58, HDL-C: 0.31-0.57). The bivariate SEMs demonstrated that GLU and HbA1c were correlated with each serum lipid indicator (0.10-0.17), except HDL-C. Except for HbA1c and LDL-C, as well as HbA1c and HDL-C, differences in genetic correlations underlying glycemic traits and serum lipids between age groups were observed, with the youngest group showing a significantly higher genetic correlation than the oldest group.

CONCLUSION

Across the whole adulthood, genetic influences were consistently important for GLU, TC, TG, LDL-C and HDL-C, and age may affect the shared genetic influences between glycemic traits and serum lipids. Further studies are needed to elucidate the role of age in the interactions of genes related to glycemic traits and serum lipids.

[A descriptive analysis of hyperlipidemia in adult twins in China]

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.

[A descriptive analysis on hypertension in adult twins in China]

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.