Evidence map›Paper›PMID 40598362›Full record

ArticleBMC medicine2025

Physical activities, longevity gene, and all-cause mortality among older adults: a prospective community-based cohort study.

Lanjing Xu, Jun Wang, Xinwei Li, Yang Li, Chen Chen, Zinan Xu, Yuan Xia, Zihan Lu, Jinghui Zhou, Min Zhang and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Lanjing Xu *Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Jun Wang *Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Xinwei LiKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Yang LiKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Chen ChenKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Zinan XuKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Yuan XiaKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Zihan LuKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Jinghui ZhouKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Min ZhangKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Yuan WeiKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Bing WuKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Zheng ZhangKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Fangyu LiKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Yufei LuoKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China.
Guangdi ChenDepartment of Public Health, and Key Laboratory of Reproductive Genetics (Ministry of Education), Women's Hospital, Zhejiang University, Hangzhou, 310058, China.
Yuebin LvKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China. lvyuebin@nieh.chinacdc.cn.
Xiaoming ShiKey Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 100021, China. shixm@chinacdc.cn.

Funding

National Natural Science Foundation of China 82025030National Natural Science Foundation of China 82222063
6 · The paper itself

Abstract

backgroundThe health benefits of physical activity (PA) have been well recognized, while which types of PA are most beneficial are still unclear, especially for older adults. The study aimed to explore associations of different PAs (physical work, regular exercise, and leisure activities) with mortality among Chinese older adults, considering genetic risk.

methodsA total of 9690 older adults from the Chinese Longitudinal Health Longevity Survey (CLHLS, 1998-2018) were included. Self-reported PAs information on physical work, regular exercise, and leisure activities were collected through face-to-face interviews. Leisure activities were interviewed about their engagement in 6 typical activities (i.e., housework tasks, personal outdoor activities, gardening, rearing domestic animals/pets, playing cards/mahjong, and attending in social activities). A weighted genetic risk score (GRS) was constructed based on 11 lifespan-related loci and divided into two groups according to the median scores (0.21). The Cox proportional risk model was used to assess the association between different types of PAs and genetic risk with all-cause mortality.

resultsDuring 63,832 person-years of follow-up, 5678 deaths were documented. The hazard ratios (HRs) for all-cause mortality between different PAs (lowest activity vs highest activity) were 0.85 (95% CI 0.79-0.92) for leisure activities, 0.93 (95% CI 0.87-0.99) for regular exercise, and 0.93 (95% CI 0.86-1.01) for physical work, respectively. Compared with low leisure activities, high leisure activities were associated with 16% reduction in all-cause mortality for individuals with low longevity GRS (HR 0.84, 95% CI 0.76-0.93), and 14% reduction in all-cause mortality for individuals with high longevity GRS (HR 0.86, 95% CI 0.78-0.96). Adherence to regular exercise was associated with 11% reduction in all-cause mortality for individuals with high longevity GRS (HR 0.89, 95% CI 0.81-0.97), while there was no statistically significance for those with low longevity GRS (HR 0.97, 95% CI 0.89-1.06) compared with those without regular exercise. There was no additive or multiplicative interaction between PAs and longevity genetics (P

conclusionsLeisure activities, as a low-risk, low-intensity, simple and inexpensive PA, rather than regular exercise, might bring the greatest health benefits, even for individuals with less longevity genes, highlighting the importance of providing individualized PA recommendations for older adults.

Indexed as

ExerciseLongevityMortalityAgedAged, 80 and overChinaFemaleHumansLeisure ActivitiesLongitudinal StudiesMaleMiddle AgedProspective StudiesAll-cause mortalityGenetic riskOlder adultsPhysical activities

Identifiers

PMID40598362
PMCPMC12220457

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.