Association of lifestyle and cardiometabolic risk factors with epigenetic age acceleration in adults in China
Chen Lu, Si Jiahui, Sun Dianjianyi, Yu Canqing, Guo Yu, Pei Pei, Chen Junshi, Chen Zhengming, Lyu Jun, Li Liming
Abstract
ObjectiveTo explore the association of lifestyle and cardiometabolic risk factors with five epigenetic age acceleration (AA) indices.
MethodsThis study included 980 participants of China Kadoorie Biobank, for whom genome-wide DNA methylation of peripheral blood cells had been detected in baseline survey. Five indices of DNA methylation age (DNAm age) were calculated, i.e. Horvath clock, Hannum clock, DNAm PhenoAge, GrimAge and Li clock. Epigenetic AA was defined as the residual of regressing DNAm age on chronological age. Lifestyle factors studied included smoking status, alcohol consumption, eating habits, physical activity level and body shape defined by a combination of BMI and waist circumference. Cardiometabolic risk factors included blood pressure, blood glucose level and total cholesterol level. Linear regression model was used to analyze the association of lifestyle and cardiometabolic risk factors with AA (β).
ResultsGrimAge_AA was associated with smoking status, alcohol consumption, physical activity level and BMI. Compared with non-smokers, non-drinkers, or participants with BMI of 18.5- 23.9 kg/m2, the smokers who smoked 1-14 cigarettes/day (β=0.71, 95%CI: 0.57-0.86), 15-24 cigarettes/day (β=0.88, 95%CI: 0.73-1.03), and ≥25 cigarettes/day (β=0.99, 95%CI: 0.81-1.18), respectively, heavy drinkers with daily pure alcohol consumption ≥60 g (β=0.33, 95%CI: 0.11-0.55) and participants with BMI<18.5 kg/m2 (β=0.23, 95%CI: 0.03-0.43) showed accelerated aging. Compared with those in the lowest quintile of physical activity level, participants in the top three quintile of physical activity level showed decelerated aging (β=-0.13, 95%CI: -0.26-0.01, β=-0.12, 95%CI: -0.26-0.02, and β=-0.14, 95%CI: -0.27- -0.00, respectively). GrimAge_AA decreased with the increase of the number of healthy lifestyle factors (P<0.001). Compared with the participants with 0 to 1 healthy lifestyle factor, the β of those with 2, 3, or 4 to 5 healthy lifestyle factors were -0.30 (95%CI: -0.47- -0.12), -0.47 (95%CI: -0.65- -0.30) and -0.72 (95%CI: -0.90- -0.53), respectively. The other four indices were not statistically significantly associated with most lifestyle factors. None of the five indices of AA was associated with blood pressure, blood glucose level or total cholesterol level.
ConclusionPeople with unhealthy lifestyle showed accelerated epigenetic aging, that is, the predicted DNAm age is older than their own chronological age.
Key words:
Epigenetic age acceleration; Lifestyle; Cardiometabolic risk factors
Contributor Information
Chen Lu
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Si Jiahui
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Sun Dianjianyi
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Yu Canqing
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Peking University Center for Public Health and Epidemic Preparedness &
Response, Beijing 100191, China
Guo Yu
Fuwai Hospital, Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing 100037, China
Pei Pei
Chinese Academy of Medical Sciences, Beijing 100730, China
Chen Junshi
China National Center for Food Safety Risk Assessment, Beijing 100022, China
Chen Zhengming
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
Lyu Jun
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Peking University Center for Public Health and Epidemic Preparedness &
Response, Beijing 100191, China
Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing 100191, China
Li Liming
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Peking University Center for Public Health and Epidemic Preparedness &
Response, Beijing 100191, China
