Genetic Evidence for Causal Effects of Socioeconomic, Lifestyle, and Cardiometabolic Factors on Epigenetic-Age Acceleration

Author:

Kong Lijie12ORCID,Ye Chaojie12,Wang Yiying12,Hou Tianzhichao12,Zheng Jie12ORCID,Zhao Zhiyun12ORCID,Li Mian12,Xu Yu12ORCID,Lu Jieli12ORCID,Chen Yuhong12,Xu Min12ORCID,Wang Weiqing12ORCID,Ning Guang12ORCID,Bi Yufang12,Wang Tiange12ORCID

Affiliation:

1. Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China

2. Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China

Abstract

Abstract GrimAge acceleration (GrimAgeAccel) and PhenoAge acceleration (PhenoAgeAccel) are DNA methylation-based markers of accelerated biological aging, standing out in predicting mortality and age-related cardiometabolic morbidities. Causal risk factors for GrimAgeAccel and PhenoAgeAccel are unclear. In this study, we performed 2-sample univariable and multivariable Mendelian randomization (MR) to investigate causal associations of 19 modifiable socioeconomic, lifestyle, and cardiometabolic factors with GrimAgeAccel and PhenoAgeAccel. Instrument variants representing 19 modifiable factors were extracted from genome-wide association studies (GWASs) with up to 1 million Europeans. Summary statistics for GrimAgeAccel and PhenoAgeAccel were derived from a GWAS of 34 710 Europeans. We identified 12 and 8 factors causally associated with GrimAgeAccel and PhenoAgeAccel, respectively. Smoking was the strongest risk factor (β [standard error {SE}]: 1.299 [0.107] year) for GrimAgeAccel, followed by higher alcohol intake, higher waist circumference, daytime napping, higher body fat percentage, higher body mass index, higher C-reactive protein, higher triglycerides, childhood obesity, and type 2 diabetes; whereas education was the strongest protective factor (β [SE]: −1.143 [0.121] year), followed by household income. Furthermore, higher waist circumference (β [SE]: 0.850 [0.269] year) and education (β [SE]: −0.718 [0.151] year) were the leading causal risk and protective factors for PhenoAgeAccel, respectively. Sensitivity analyses strengthened the robustness of these causal associations. Multivariable MR analyses further demonstrated independent effects of the strongest risk and protective factors on GrimAgeAccel and PhenoAgeAccel, respectively. In conclusion, our findings provide novel quantitative evidence on modifiable causal risk factors for accelerated epigenetic aging, suggesting promising intervention targets against age-related morbidity and improving healthy longevity.

Funder

National Natural Science Foundation of China

Shanghai Jiao Tong University

Shanghai Municipal Education Commission

Ruijin Hospital

Publisher

Oxford University Press (OUP)

Subject

Geriatrics and Gerontology,Aging

Reference50 articles.

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