Evidence map›Paper›PMID 35417546›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2022

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations.

Kai Wei, Shanshan Peng, Na Liu, Guyanan Li, Jiangjing Wang, Xiaotong Chen, Leqi He, Qiudan Chen, Yuan Lv, Huan Guo and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.2field-weighted citation impact, top 7% of its field
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

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

11 authors at 3 institutions in 1 country.

Kai WeiDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China.ORCID 0000-0001-7984-1758
Shanshan PengDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Na LiuDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Guyanan LiDepartment of Clinical Laboratory Medicine, Fifth People's Hospital of Shanghai Fudan University, Shanghai, China.
Jiangjing WangShanghai Advanced Institute of Finance, Shanghai Jiao Tong University, Shanghai, China.
Xiaotong ChenDepartment of Clinical Laboratory, Central Laboratory, Jing'an District Central Hospital of Shanghai, Fudan University, Shanghai, China.
Leqi HeDepartment of Clinical Laboratory Medicine, Fifth People's Hospital of Shanghai Fudan University, Shanghai, China.
Qiudan ChenDepartment of Clinical Laboratory, Central Laboratory, Jing'an District Central Hospital of Shanghai, Fudan University, Shanghai, China.
Yuan LvDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Huan GuoDepartment of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yong LinDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Fudan University · CNHuazhong University of Science and Technology · CNShanghai Jiao Tong University · CN

Funding

MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Emergence of Cardiometabolic Risk Across the Lifecycle in ChinaR01HL108427 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2012 to 2015
$2.7M
Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiomeR01DK104371 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2015 to 2018
$2.5M
Nutrition-Related NCD Prevention Training in ChinaD43TW009077 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2011 to 2015
$1.1M
FIC NIH HHS D43 TW009077National Key Research and Development Program of China 2018YFC2000200NHLBI NIH HHS R01 HL108427NICHD NIH HHS R01 HD030880NIDDK NIH HHS R01 DK104371
6 · The paper itself

Abstract

backgroundKlemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

methodsClinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

resultsA total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

conclusionAll-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Indexed as

LongevityBiomarkersNutrition SurveysProportional Hazards ModelsBiomarkersAll-cause mortalityAll-subsetBiological age

Identifiers

PMID35417546
PMCPMC9923798
OpenAlexW4224072706

What OpenQuestion holds

Textmetadata
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.