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Impact of physical activity structure on phenotypic age acceleration in older adults using compositional data analysis
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Shuang JIANG1, Lin HU1, Hua-ying CHENG1, Xin YAO2, Lu LONG1, Jia-qiang LIAO1, Xia JIANG1, Tao ZHANG1, Jia-yuan LI1
Modern Preventive Medicine | 2025, 52(24) : 4436 - 4442
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Modern Preventive Medicine | 2025, 52(24): 4436-4442
• Epidemiology and Statistical Methods Advances •
Impact of physical activity structure on phenotypic age acceleration in older adults using compositional data analysis
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Shuang JIANG1, Lin HU1, Hua-ying CHENG1, Xin YAO2, Lu LONG1, Jia-qiang LIAO1, Xia JIANG1, Tao ZHANG1, Jia-yuan LI1
Affiliations
  • West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
Published: 2025-12-25 doi: 10.20043/j.cnki.MPM.202505185
Outline
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Objective

This study aims to explore the effects of total physical activity (PA) and its structural composition on phenotypic age acceleration (PhenoAgeAccel) in older adults, providing a scientific basis for intervening in aging through optimizing PA structure.

Methods

A cross-sectional study was conducted involving 7 016 older adults aged 60 years and above from the West China Health and Aging Cohort Study. Weekly PA levels and their domain-specific proportions (work, transportation, recreation) were assessed using the Global Physical Activity Questionnaire-2 (GPAQ-2). PhenoAgeAccel was calculated as the residual of chronological age regressed on PhenoAge. Multiple linear regression models were employed to analyze the effects of total PA on PhenoAgeAccel, controlling for demographic characteristics, anthropometric indices, lifestyle factors, and chronic diseases, followed by subgroup analyses. Isometric log-ratio transformation (ILR) in compositional data analysis (CoDA) and isotemporal substitution model were applied to analyze the effects of the proportion of different PA types.

Results

Total PA was negatively correlated with PhenoAgeAccel. Each 1-standard deviation increase in total PA was associated with a 0.32-year reduction in PhenoAgeAccel (β=-0.32, 95% CI: -0.46 - -0.18). Subgroup analyses showed that increasing PA could more effectively delay aging in elderly individuals aged 60 - 70 years, those with obesity, non-smokers, and those with baseline hypertension, diabetes mellitus, or dyslipidemia. There were differential contributions among PA types. When 1% of leisure activity replaced work activity, PhenoAgeAccel decreased by 0.031 years (β=-0.031, 95% CI: -0.057 - -0.006). When 1% of leisure activity replaced transport activity, PhenoAgeAccel decreased by 0.034 years (β=-0.034, 95% CI: -0.059 - -0.010). The substitution effect between the proportion of work activity and that of transport activity was not statistically significant.

Conclusion

Increasing total PA and optimizing PA types can slow down PhenoAgeAccel, and recreation activities replacing work or transport activities have prominent effects in delaying aging. Promoting a physical activity pattern centered on recreational activities is recommended to reduce the risk of aging-related diseases.

Physical activity  /  Phenotypic age acceleration  /  Compositional data analysis  /  Older adults
姜双, 胡琳, 程华英, 姚歆, 龙璐, 廖加强, 姜侠, 张韬, 李佳圆. 基于成分数据分析的老年人群体力活动类型对表型生物年龄加速的影响. 现代预防医学, 2025 , 52 (24) : 4436 -4442 . DOI: 10.20043/j.cnki.MPM.202505185
Shuang JIANG, Lin HU, Hua-ying CHENG, Xin YAO, Lu LONG, Jia-qiang LIAO, Xia JIANG, Tao ZHANG, Jia-yuan LI. Impact of physical activity structure on phenotypic age acceleration in older adults using compositional data analysis[J]. Modern Preventive Medicine, 2025 , 52 (24) : 4436 -4442 . DOI: 10.20043/j.cnki.MPM.202505185
Year 2025 volume 52 Issue 24
28
14
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doi: 10.20043/j.cnki.MPM.202505185
  • Receive Date:2025-05-14
  • Online Date:2026-11-02
  • Published:2025-12-25
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  • Received:2025-05-14
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    West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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