Evidence map›Paper›PMID 39309670›Full record

ArticlePrecision clinical medicine2024

Phenotypic age mediates effects of Life's Essential 8 on reduced mortality risk in US adults.

Yuxuan Zhao, Haiming Yang, Rong Jiao, Yueqing Wang, Meng Xiao, Mingyu Song, Huan Yu, Chunxiao Liao, Yuanjie Pang, Wenjing Gao and 7 more

Abstract read
In one paragraph

Article in Precision clinical medicine, 2024. 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

17 authors.

Yuxuan ZhaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.ORCID https://orcid.org/0009-0003-9190-3095
Haiming YangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Rong JiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Yueqing WangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Meng XiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Mingyu SongDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Huan YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Chunxiao LiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Yuanjie PangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.ORCID https://orcid.org/0000-0002-4826-8861
Wenjing GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Tao HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.ORCID https://orcid.org/0000-0002-0019-0014
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.ORCID https://orcid.org/0000-0001-7916-3870
Shengxu LiChildren's Minnesota Research Institute, Children's Minnesota, Minneapolis, MN 55404, USA.
Lu QiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, USA.ORCID https://orcid.org/0000-0002-6637-6428
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to find out whether phenotypic age could mediate the protective effects of a healthy lifestyle on mortality. Methods: We included adult participants with available data for individual phenotypic age (PhenoAge) and Life's Essential 8 (LE8) scores from the National Health and Nutrition Examination Survey 2005-2010 (three cycles) and linked mortality records until 31 December 2019. Adjusted hazard ratios (HR) were estimated to evaluate the associations of PhenoAge and LE8 scores with all-cause and cardiovascular mortality risk. Mediation analyses were performed to estimate the proportional contribution of PhenoAge to the effect of LE8 on mortality risks. Results: A 1-year increment in PhenoAge was associated with a higher risk of all-cause (HR = 1.04 [95% confidence interval, 1.04-1.05]) and cardiovascular (HR = 1.04 [95% confidence interval, 1.04-1.05]) mortality, independent of chronological age, demographic characteristics, and disease history. High level of LE8 (score: 80-100) was associated with a 3.30-year younger PhenoAge. PhenoAge was estimated to mediate 36 and 22% of the effect of LE8 on all-cause and cardiovascular mortality, respectively (all Conclusion: Adherence to LE8 recommendations slows phenotypic aging. PhenoAge could mediate the effect of LE8 on mortality risk.

Indexed as

all-cause mortalitycardiovascular mortalityLife's Essential 8phenotypic age

Identifiers

PMID39309670
PMCPMC11413533

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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.