Wenjing Gao

Background Traditional twin studies have reported high heritability estimates for common cardiometabolic traits, yet discrepancies compared to genome-wide association studies (GWAS) derived estimates underscore the effects of “missing heritability”, potentially attributed to genetic interactions and environmental influences. However, research exploring potential contributors to missing heritability in cardiometabolic phenotypes remains scarce and largely confined to European populations. Methods We analyzed twin data (n = 2548) from the Chinese National Twin Registry (CNTR) and GWAS summary-level data (n = 92,615) from Asian cohorts to estimate the heritability of 10 cardiometabolic traits: BMI, waist-to-hip ratio (WHR), hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Using a twin study design, we quantified dominance genetic effects and explored gene-environment interactions involving age, lifestyle, and psychosocial factors on these traits. Additionally, we leveraged both twin and GWAS data to investigate genetic correlations between cardiometabolic traits, revealing shared genetic underpinnings. Results Twin data revealed moderate to high heritability estimates (0.34–0.72) for cardiometabolic traits, whereas GWAS data yielded lower estimates (0.10–0.22). Dominant genetic variance was statistically significant only for BMI (0.42), suggesting a limited role for dominance across most cardiometabolic traits examined in this study. No evidence of non-additive effects was observed in GWAS summary statistics. Gene-environment interaction analyses identified 12 small yet significant interaction effects involving age, smoking, drinking, education, and mental health. Finally, genetic correlations between pairs of cardiometabolic traits ranged from low to high (0.09–0.90), with broadly consistent patterns observed across twin and GWAS approaches. Conclusions These findings collectively advance our understanding of the genetic architecture underlying cardiometabolic traits and their interrelationships in Asian populations.

The objective of this study was to investigate the gene-breastfeeding interaction on BMI based on the Chinese National Twin Register (CNTR). The study included 4,573 pairs of same-sex twins aged 2–18 from CNTR. Data were collected using a self-reported questionnaire, and a structural equation model was used to analyze the gene-environment interaction of breastfeeding with BMI in six age groups. Our findings indicate that as age increases, the heritability of BMI shows an increasing trend, being the lowest (h ² : 0.08; 95% CI [0.00, 0.19]) in the 6- to 8-year age group and the highest (h ² : 0.57, 95% CI [0.44, 0.72]) in the 12- to 14-year age group. Additionally, breastfeeding significantly modified the additive genetic component of BMI in the 6- to 8-year age group and 12- to 14-year age group. In the 6- to 8-year age group, breastfeeding decreased the impact of genes on BMI, with a genetic effect modification coefficient (βa) of −0.19 (−0.25, −0.13). In the 12- to 14-year age group, breastfeeding increased the impact of genes on BMI, with a genetic effect modification coefficient (βa) of 0.08 (0.02, 0.15). In conclusion, as age increases, the genetic influence on children’s BMI becomes more pronounced. Breastfeeding may modulate genetic effects at the ages of 6–8 and 12–14. Given the metabolic diversity of obesity, our findings offer insight into how breastfeeding interacts with genetic background, helping to unravel the complex gene–environment interplay influencing obesity.

Objective: To explore the association between life’s essential 8 and epigenetic age based on twins population. Methods: This study included 1030 twins (515 pairs) for cross-sectional analysis and conducted cross-lagged analysis among 294 twins (147 pairs) who participated in both the baseline and follow-up surveys from the Chinese National Twin Registry. LE8 scores were obtained from measurements based on American Heart Association definitions. DNA methylation data were used to calculate epigenetic age metrics, including GrimAA, DamAA and DunedinPACE. Linear mixed-effect models were applied for cross-twin analyses and within-monozygotic-pair analyses. Results: In the cross-sectional analysis, higher LE8 score was associated with slower epigenetic aging (DunedinPACE and DamAA) in both across-twin analyses and within-monozygotic-pair analyses. In stratified analyses, the association between LE8 score and epigenetic age appeared more significant in males and in individuals aged 50 years older. The cross-lagged analysis further revealed significant temporal associations between LE8, health factor, and DunedinPACE. Conclusion: Higher LE8 scores were associated with a deceleration in biological aging.

The reproducibility of obesity-related DNA methylation (DNAm) sites (CpGs) remains low across studies, and the underlying mechanisms involving genetic and environmental contributions require further investigation. In this study, we systematically searched PubMed, EMBASE, and EWAS catalogue for CpGs associated with BMI, waist circumference (WC), and waist-to-hip ratio (WHR) from previous publications, identifying 29 studies with 2,603/892/28 CpGs for BMI/WC/WHR, respectively. Then, based on 1,074 twins from Chinese National Twin Registry, 493/149/8 CpGs were validated for BMI/WC/WHR, controlling for genetic factors. Following this, within the full twin population and a subsample of 308 twins with longitudinal data collected over a 5-year period, structural equation models (SEMs) were used to assess the heritability of CpGs and how it changes over time. The overall heritability of these obesity-related CpGs was relatively high, averaging 0.34 in full twin population, which decreased from 0.38 at baseline to 0.31 at follow-up. Bivariate SEMs were employed to investigate the genetic/environmental effects behind the longitudinal stability of DNAm. Genetic correlations between baseline and follow-up DNAm levels were high (mean = 0.74), whereas environmental correlations were relatively low (mean = 0.19), suggesting the role of genetic influence on the stability of DNAm at these CpGs. For longitudinal cross-relationships between obesity indicators and DNAm, considerably higher genetic contributions from baseline obesity traits to follow-up DNAm (mean = 0.15) was observed, compared to the baseline DNAm’s genetic influences on follow-up obesity indices (mean = 0.09, P < 0.01). These findings provide genetic evidence for obesity-related DNAm and demonstrate that the genetic effects of obesity have a persistent longitudinal influence on DNAm.

Background: Both obesity and DNA methylation (DNAm) are influenced by genetic factors. Despite more than a thousand of obesity-related DNAm sites (CpGs) being identified, studies that account for genetic influences in these associations are limited. Results: Using data from 1,074 twins in the Chinese National Twin Registry and bivariate structural equation models (SEMs), we investigated the phenotypic (Rph), genetic (Ra), and environmental (Re) correlations between genome-wide DNAm and three obesity indices: BMI, waist circumference (WC), and waist-to-hip ratio (WHR). Genome-wide, correlations between DNAm and obesity were small (Rph = 0.04, Ra = 0.08–0.09, Re = 0.02–0.03). For CpGs with high phenotypic correlation (Rph > 0.1), the mean genetic and environmental correlations were 0.23–0.24 and 0.03–0.05, respectively, indicating significant genetic influence on the DNAm-obesity associations. To further investigate the role of genetic influences, we then categorized the CpGs into different groups: high phenotypic correlation (Rph ≥ 0.2); high phenotypic and genetic correlations (Rph > 0.1 and Ra > 0.5); high phenotypic and low genetic correlations (Rph > 0.1 and Ra < 0.5). Association studies were conducted in the full population and in the monozygotic (MZ) twin-paired design, where genetic influences were controlled. For CpGs with Rph ≥ 0.2, 9, 8, and 22 were associated with BMI, WC, and WHR in the full population, but only 6, 1, and 1 CpGs remained significant after controlling for genetic effects in MZ twin-pair analyses. For CpGs with Rph > 0.1 and Ra > 0.5, genetic factors predominantly drove the association, and none of the 155/155/189 CpGs associated with BMI/WC/WHR in the full population were significant in MZ-paired analyses. For CpGs with Rph > 0.1 and Ra < 0.1, genetic effects were minimal or confounding, with 89, 4, and 17 significant in both full population and MZ-paired analyses. Conclusions: Our results highlight the significant genetic influences on the DNAm-obesity relationships, which may explain the low replicability of obesity-related DNAm markers. This indicates that genetic influences should be carefully considered in DNAm-related studies.

To explore the relationship between obesity indicators and DNA methylation clocks acceleration, and to analyze their temporal sequence. Data were obtained from two surveys conducted in 2013 and 2017-2018 by the Chinese National Twin Registry. Peripheral blood DNA methylation data were measured using the Illumina Infinium Human Methylation 450K BeadChip and EPIC BeadChip. DNA methylation clocks/acceleration metrics (GrimAA, PCGrimAA and DunedinPACE) were calculated using the DNA methylation online tool (https://dnamage. ucla.edu/) or R code provided by researchers. Obesity indicators included weight, body mass index (BMI), waist circumference, waist-hip ratio, and waist-height ratio. A total of 1 070 twin individuals were included in the cross-sectional analysis, comprising 378 monozygotic (MZ) twin pairs and 155 dizygotic (DZ) twin pairs for within-pair analysis. Mixed-effects models were used to examine the associations between obesity indicators and DNA methylation clocks, as well as their acceleration measures. The longitudinal analysis included 314 twin individuals, comprising 95 MZ twin pairs and 62 DZ twin pairs for within-pair analysis. Cross-lagged panel models were applied to further explore the temporal relationships between obesity and DNA methylation clock indicators. All analyses were conducted both in the full twin sample and separately within MZ and DZ twin pairs. In the cross-sectional analysis population, monozygotic twins accounted for 71.0%, males for 68.0%, and the mean chronological age was (49.9±12.1) years. In the longitudinal analysis population, monozygotic twins accounted for 60.5%, males for 60.8%, with a mean baseline chronological age of (50.4±10.2) years and a mean follow-up duration of (4.6±0.6) years. Except for the waist-to-hip ratio, which was significantly higher at follow-up compared with baseline, no statistically significant differences were observed in the means of other obesity indicators between baseline and follow-up. Correlation analysis revealed that weight, BMI, waist circumfe-rence, waist-hip ratio (WHR), and waist-height ratio (WHtR) were positively correlated with DunedinPACE in all the twins, with WHtR showing the strongest association (β=0.21, 95%CI: 0.11 to 0.31). Weight and BMI were negatively associated with GrimAA (β=-0.03, 95%CI: -0.05 to -0.01; β=-0.07, 95%CI: -0.12 to -0.02), while weight was negatively associated with PCGrim- AA (β=-0.02, 95%CI: -0.03 to 0.00). However, within-twin-pair analyses showed no statistically significant correlations. Cross-lagged panel model analysis indicated that higher baseline weight might lead to increased GrimAA at follow-up, while elevated baseline weight, BMI, and waist circumference might increase PCGrimAA. Higher baseline WHR was associated with increased DunedinPACE at follow-up. Obesity indicators correlate with DNA methylation clock acceleration metrics. Baseline obesity may influence changes in certain DNA methylation clock indicators over time, suggesting that obesity could exert long-term health effects by accelerating DNA methylation aging. However, these associations may be confounded by shared genetic or environmental factors among the twins.

Several crucial acceleration periods exist during aging process. Epigenetic clocks, serving as indicators of aging, are influenced by genetic factors. Investigating how the genetic contributions on these clocks change with age may provide novel insights into the aging process. In this study, based on 1084 adult twins from the Chinese National Twin Registry (CNTR), we established structural equation models (SEMs) to evaluate the trends in genetic influence with aging for epigenetic clocks, which include PC‐Horvath, PC‐Hannum, PC‐PhenoAge, PC‐GrimAge, and DunedinPACE. A decline in overall heritability was observed for all five clocks from ages 31 to 70, with a relatively stable trend at first. Subsequently, apart from PC‐GrimAge, the other four clocks displayed a more evident drop in heritability: DunedinPACE and PC‐PhenoAge experienced a clear decline between 55 and 65 years, while PC‐Horvath and PC‐Hannum showed a similar decrease between 60 and 70 years. In contrast, the heritability of PC‐GrimAge remained stable throughout. An analysis of methylation sites (CpGs) from these clocks identified 41, 26, 4, and 36 CpG sites potentially underlying heritability changes in DunedinPACE, PC‐Horvath, PC‐Hannum, and PC‐PhenoAge, respectively. Data from the CNTR were collected through two surveys in 2013 and 2018. Based on 308 twins with longitudinal data, declines in genetic components were observed at follow‐up compared to baseline, with significant decreases in the four PC‐clocks. DunedinPACE peaked in 5‐year longitudinal genetic contribution changes at age 55–60, while PC‐clocks consistently peaked at age 50–55. These findings may offer novel insights into the role of genetic variations in aging.

Background: Epigenetic clocks were known as promising biomarkers of aging, including original clocks trained by individual CpG sites and principal component (PC) clocks trained by PCs of CpG sites. The effects of genetic and environmental factors on epigenetic clocks are still unclear, especially for PC clocks. Methods: We constructed univariate twin models in 477 same-sex twin pairs from the Chinese National Twin Registry (CNTR) to estimate the heritability of five epigenetic clocks (GrimAge, PhenoAge, DunedinPACE, PCGrimAge, and PCPhenoAge). Besides, we investigated the longitudinal changes of genetic and environmental influences on epigenetic clocks across 5 years in 134 same-sex twin pairs. Results: Heritability of epigenetic clocks ranged from 0.45 to 0.70, and those for PC clocks were higher than those for original clocks. For five epigenetic clocks, the longitudinal stability was moderate to high and was largely due to genetic effects. The genetic correlations between baseline and follow-up epigenetic clocks were moderate to high. Special unique environmental factors emerged both at baseline and at follow-up. PC clocks showed higher longitudinal stability and unique environmental correlations than original clocks. Conclusions: For five epigenetic clocks, they have the potential to identify aging interventions. High longitudinal stability is mainly due to genetic factors, and changes of epigenetic clocks over time are primarily due to changes in unique environmental factors. Given the disparities in genetic and environmental factors as well as longitudinal stability between PC and original clocks, the results of studies with original clocks need to be further verified with PC clocks.

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.