Evidence map›Paper›PMID 41203624›Full record

ArticleNature communications2025

A simulation study of the impact of population-wide lifestyle modifications on life expectancy in the Chinese population.

Qiufen Sun, Liyun Zhao, Yuxiang Yang, Yinqi Ding, Canqing Yu, Dianjianyi Sun, Yuanjie Pang, Pei Pei, Ling Yang, Yiping Chen and 9 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

19 authors.

Qiufen Sun *Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Liyun Zhao *National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuxiang YangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID http://orcid.org/0000-0001-5491-4789
Yinqi DingDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Canqing YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.ORCID http://orcid.org/0000-0002-0019-0014
Dianjianyi SunDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.ORCID http://orcid.org/0000-0003-3651-6693
Yuanjie PangDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Pei PeiPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
Ling YangClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0001-5750-6588
Yiping ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-4973-0296
Huaidong DuClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-9814-0049
Ranran DuQingdao Center for Disease Control and Prevention, Qingdao, Shandong, China.
Maxim BarnardClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Junshi ChenChina National Center for Food Safety Risk Assessment, Beijing, 100022, China.
Zhengming ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0001-6423-105X
Dongmei YuNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China. yudm@ninh.chinacdc.cn.
Liming LiDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. lmlee@vip.163.com.ORCID http://orcid.org/0000-0001-5873-7089
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. lvjun@bjmu.edu.cn.ORCID http://orcid.org/0000-0001-7916-3870
China Kadoorie Biobank Collaborative Group

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81390540National Natural Science Foundation of China (National Science Foundation of China) 81941018National Natural Science Foundation of China (National Science Foundation of China) 82192900National Natural Science Foundation of China (National Science Foundation of China) 91846303Wellcome Trust
6 · The paper itself

Abstract

It is uncertain how much life expectancy of the Chinese population would improve under current and greater policy targets on lifestyle-based risk factors for chronic diseases and mortality. Here we report a simulation of how improvements in four risk factors, namely smoking, alcohol use, physical activity and diet, could affect mortality. We show that in the ideal scenario, that is, all people who currently smoke quit smoking, excessive alcohol use was reduced to moderate intake, people under 65 increased moderate physical activity by one hour and those aged 65 and older increased by half an hour per day, and all participants ate 200 g more fresh fruits and 50 g more fish/seafood per day, life expectancy at age 30 would increase by 4.83 and 5.39 years for men and women, respectively. In a more moderate risk reduction scenario referred to as the practical scenario, where improvements in each lifestyle factor were approximately halved, the gains in life expectancy at age 30 could be half those of the ideal scenario. However, the possibility to realize these estimates in practise may be influenced by population-wide adherence to lifestyle recommendations.

Indexed as

East Asian PeopleLife ExpectancyLife StyleAdultAgedAlcohol DrinkingChinaComputer SimulationDietExerciseFemaleHumansMaleMiddle AgedRisk FactorsRisk Reduction Behavior

Identifiers

PMID41203624
PMCPMC12594753

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.