Evidence mapPaperPMID 41350804Full record

ArticleNPJ digital medicine2025

Metabolomic characterization of frailty identifies subtype-specific management strategies.

Lushan Xiao, Qijie Deng, Jiaren Wang, Shengxing Liang, Ruining Li, Hao Cui, Yan Li, Pu Jiang, Rongfeng Zhang, Lin Zeng and 3 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Lushan Xiao *Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Qijie Deng *Nanfang Hospital, Southern Medical University, Guangzhou, China.
Jiaren Wang *Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Shengxing Liang *Nanfang Hospital, Southern Medical University, Guangzhou, China.
Ruining Li *Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Hao CuiDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Yan LiDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Pu JiangDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Rongfeng ZhangNanfang Hospital, Southern Medical University, Guangzhou, China.
Lin ZengDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China. lin_zeng1126@163.com.
Chang HongDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China. shanoicy@163.com.
Weinan LaiDepartment of Rheumatology and Immunology, Nanfang Hospital, Southern Medical University, Guangzhou, China. Laiwn123@smu.edu.cn.
Li LiuDepartment of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China. liuli@i.smu.edu.cn.

Funding

Guangdong Natural Science Foundation 2022A1515110656National Key Research and Development Program of China 2023YFC2308500National Nature Science Foundation of China 81972897National Nature Science Foundation of China 82400664National Nature Science Foundation of China 82404077the Postdoctoral Fellowship Program of CPSF GZC20240663
6 · The paper itself

Abstract

The clinical application of the frailty phenotype and frailty index still has some limitations, and whether the classification of frailty based on metabolites is beneficial to the management of the frailty population remains unclear. This study analyzed 160,407 UK Biobank participants to define frailty subtypes using metabolic profiles. Based on 251 biomarkers, machine learning identified 11 key metabolites, leading to four novel frailty subtypes. Subtypes III and IV, characterized by adverse metabolic features such as high GlycA and low LA/FA, were designated as high-risk groups. These subtypes showed significantly increased risks for 13 chronic diseases and all-cause mortality compared to lower-risk subtypes. Adherence to a healthy diet was associated with risk reduction in the high-risk groups. These findings demonstrate the heterogeneity of frailty and suggest that metabolite-based subtyping could improve prognostic precision and guide targeted dietary interventions in clinical practice.

Identifiers

PMID41350804
PMCPMC12680709

What Socratic 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 Socratic graph.