Evidence map›Paper›PMID 37789775›Full record

ArticleJournal of the American Geriatrics Society2024

Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality.

Xueqin Li, Xingqi Cao, Jingyun Zhang, Jinjing Fu, Mayila Mohedaner, Danzengzhuoga, Xiaoyi Sun, Gan Yang, Zhenqing Yang, Chia-Ling Kuo and 3 more

Abstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

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10 more citing papers are in PubMed but not listed here.

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

13 authors.

Xueqin LiCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Xingqi CaoCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Jingyun ZhangCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Jinjing FuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Mayila MohedanerCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
DanzengzhuogaCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Xiaoyi SunCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Gan YangCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Zhenqing YangCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Chia-Ling KuoDepartment of Community Medicine and Health Care, Connecticut Convergence Institute for Translation in Regenerative Engineering, Institute for Systems Genomics, University of Connecticut Health, Farmington, Connecticut, USA.
Xi ChenDepartment of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA.
Alan A CohenDepartment of Family Medicine, Research Centre on Aging, CHUS Research Centre, University of Sherbrooke, Sherbrooke, Quebec, Canada.
Zuyun LiuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Lauren Ferrante · 2002 to 2026
$37.9M
A Life Course Approach to Understanding Racial and Ethnic Disparities in Alzheimer's Disease and Related Dementias and Health CareR01AG077529 · NIA · YALE UNIVERSITY · PI Xi Chen · 2022 to 2026
$3.6M
Social Pension, Health, and Healthy AgingK01AG053408 · NIA · YALE UNIVERSITY · PI CHEN, XI · 2018 to 2021
$514k
NIA NIH HHS K01 AG053408NIA NIH HHS P30 AG021342NIA NIH HHS P30AG021342NIA NIH HHS R01 AG077529
6 · The paper itself

Abstract

backgroundWith two well-validated aging measures capturing mortality and morbidity risk, this study examined whether and to what extent aging mediates the associations of unhealthy lifestyles with adverse health outcomes.

methodsData were from 405,944 adults (40-69 years) from UK Biobank (UKB) and 9972 adults (20-84 years) from the US National Health and Nutrition Examination Survey (NHANES). An unhealthy lifestyles score (range: 0-5) was constructed based on five factors (smoking, drinking, physical inactivity, unhealthy body mass index, and unhealthy diet). Two aging measures, Phenotypic Age Acceleration (PhenoAgeAccel) and Biological Age Acceleration (BioAgeAccel) were calculated using nine and seven blood biomarkers, respectively, with a higher value indicating the acceleration of aging. The outcomes included incident cardiovascular disease (CVD), incident cancer, and all-cause mortality in UKB; CVD mortality, cancer mortality, and all-cause mortality in NHANES. A general linear regression model, Cox proportional hazards model, and formal mediation analysis were performed.

resultsThe unhealthy lifestyles score was positively associated with PhenoAgeAccel (UKB: β = 0.741; NHANES: β = 0.874, all p < 0.001). We further confirmed the respective associations of PhenoAgeAccel and unhealthy lifestyles with the outcomes in UKB and NHANES. The mediation proportion of PhenoAgeAccel in associations of unhealthy lifestyles with incident CVD, incident cancer, and all-cause mortality were 20.0%, 17.8%, and 26.6% (all p < 0.001) in UKB, respectively. Similar results were found in NHANES. The findings were robust when using another aging measure-BioAgeAccel.

conclusionsAccelerated aging partially mediated the associations of lifestyles with CVD, cancer, and mortality in UK and US populations. The findings reveal a novel pathway and the potential of geroprotective programs in mitigating health inequality in late life beyond lifestyle interventions.

Indexed as

Cardiovascular DiseasesNeoplasmsAgingHealth Status DisparitiesHumansLife StyleNutrition SurveysRisk Factorsadverse health outcomesbiological agelifestylesmediation analysisphenotypic age

Identifiers

PMID37789775
PMCPMC11078652

What Socratic holds

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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 Socratic graph.