A machine learning–based biological aging prediction and its associations with healthy lifestyles: the Dongfeng–Tongji cohort
| Autor: | Yansen Bai, Meian He, Chenming Wang, Jiali Jie, Yue Feng, Wei Wei, Ming Fu, Hang Li, Handong Yang, Mengying Li, Xiaomin Zhang, Xin Guan, Yanjun Lu, Xiulong Wu, Guyanan Li, Huan Guo, Hua Meng |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Aging China Alcohol Drinking Homocysteine Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Cohort Studies Machine Learning Correlation chemistry.chemical_compound History and Philosophy of Science Humans Medicine Healthy Lifestyle Prospective Studies Extreme gradient boosting Exercise Aged Aged 80 and over Principal Component Analysis Absolute threshold of hearing business.industry General Neuroscience Smoking Chronological age Middle Aged Blood pressure chemistry Cohort Female Artificial intelligence business computer Follow-Up Studies Forecasting Cohort study |
| Zdroj: | Annals of the New York Academy of Sciences. 1507:108-120 |
| ISSN: | 1749-6632 0077-8923 |
| DOI: | 10.1111/nyas.14685 |
| Popis: | This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used to estimate BA. We developed a composite BA predictor showing a high correlation with chronological age (CA) (r = 0.82) by using an extreme gradient boosting (XGBoost) algorithm. The average frequency hearing threshold, forced expiratory volume in 1 second (FEV1 ), gender, systolic blood pressure, and homocysteine ranked as the top five important features for the BA predictor. Two aging indexes, recorded as the AgingAccel (the residual from regressing predicted age on CA) and aging rate (the ratio of predicted age to CA), showed positive associations with the risks of all-cause (HR (95% CI) = 1.12 (1.10-1.14) and 1.08 (1.07-1.10), respectively) and cause-specific (HRs ranged from 1.06 to ∼1.15) mortality. Each 1-point increase in healthy lifestyle score (including normal body mass index, never smoking, moderate alcohol drinking, physically active, and sleep 7-9 h/night) was associated with a 0.21-year decrease in the AgingAccel (95% CI: -0.27 to -0.15) and a 0.4% decrease in the aging rate (95% CI: -0.5% to -0.3%). This study developed a machine learning-based BA predictor in a prospective Chinese cohort. Adherence to healthy lifestyles showed associations with delayed biological aging, which highlights potential preventive interventions. |
| Databáze: | OpenAIRE |
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