Evidence map›Paper›PMID 40413504›Full record

ArticleCardiovascular diabetology2025

Metabolic profiling of frailty, associations with type 2 diabetes and interaction with genetic susceptibility.

Yuxiang Wang, Shuang Chen, Yue Li, Qi Lu, Rui Li, Jun-Xiang Chen, Ji-Juan Zhang, Hancheng Yu, Hanrui Xu, Jinchi Xie and 5 more

Abstract read
In one paragraph

Article in Cardiovascular diabetology, 2025. 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.

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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

15 authors.

Yuxiang WangDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Shuang ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Yue LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Qi LuDepartment of Chronic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan, 430079, China.
Rui LiDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Jun-Xiang ChenDepartment of Public Health and Medical Technology, Xiamen Medical College, Xiamen, 361023, China.
Ji-Juan ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hancheng YuDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hanrui XuDepartment of Information Security, College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Jinchi XieDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Xianli LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Gang LiuDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
An PanDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Tingting GengDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. geng_tingting@hust.edu.cn.
Yun-Fei LiaoDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. 2009xh0824@hust.edu.cn.

Funding

National Key Research and Development Program of Hubei province 2022BCA036National Natural Science Foundation of China 82325043National Nature Science Foundation of China 82470896
6 · The paper itself

Abstract

backgroundIndividuals with frailty are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We aimed to investigate whether the frailty-T2D association is mediated by alterations in the metabolome and assess potential interaction with genetic susceptibility to diabetes.

methodsThis retrospective analysis, using data from a large prospective population-based cohort, included a total of 197,502 adults with baseline metabolomics data from the UK Biobank. Frailty was defined using the Fried frailty phenotype according to five components. Elastic net regression was applied to create a frailty-related metabolic signature. We assessed hazard ratios (HR) and its 95% confidence interval (CI) of incident T2D in relation to the baseline metabolic signature of frailty and examined the mediating role of the metabolic signature in the effect of frailty on T2D. Additive interaction between the metabolic signature and polygenic risk score for T2D (PRS-T2D) on the incidence of T2D was assessed as relative excess risk due to interaction (RERI).

resultsCompared with non-frailty, the HR (95% CI) of incident T2D in pre-frailty and frailty was 1.33 (1.26, 1.40) and 1.59 (1.46, 1.74), respectively. The metabolic signature of frailty (comprised of 53 metabolites) was positively associated with T2D risk (HR per standard deviation increment: 1.45; 95% CI: 1.42, 1.48), and explained 31.0% (95% CI: 25.8, 36.8) of the association between frailty and T2D. An additive interaction between metabolic signature of frailty and PRS-T2D was found (RERI: 9.43; 95% CI: 6.06, 12.80).

conclusionsThe increased risk of T2D in individuals with frailty may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D.

Indexed as

Diabetes Mellitus, Type 2FrailtyMetabolomeMetabolomicsAgedBiomarkersFemaleFrail ElderlyGenetic Predisposition to DiseaseHumansIncidenceMaleMiddle AgedMultifactorial InheritancePhenotypeRetrospective StudiesBiomarkersFrailtyGenetic risk scoreMetabolomicsType 2 diabetes

Identifiers

PMID40413504
PMCPMC12102881

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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.