Evidence map›Paper›PMID 42781965›Full record

ArticleDiabetes/metabolism research and reviews2026

Metabolomic Profiling Delineates Stage-Associated Metabolic Remodelling in Cardiovascular-Kidney-Metabolic Syndrome.

Xuemei Gong, Chunyang Li, Jing Chen, Yujiao Wang, Wenge Tang, Xuehui Zhang, Jianzhong Yin, Xing Zhao, Haopeng Yu, Ping Fu and 1 more

Abstract read
In one paragraph

Article in Diabetes/metabolism research and reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Xuemei GongDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0001-8513-5112
Chunyang LiWest China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0001-9676-8898
Jing ChenDepartment of Nephrology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Yujiao WangDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, China.
Wenge TangChongqing Municipal Center for Disease Control and Prevention, Chongqing, China.
Xuehui ZhangSchool of Public Health, Kunming Medical University, Kunming, China.
Jianzhong YinSchool of Public Health, Kunming Medical University, Kunming, China.ORCID https://orcid.org/0000-0002-1876-387X
Xing ZhaoWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0001-5713-3603
Haopeng YuWest China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.
Ping FuDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-3061-5925
Xiaoxi ZengDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0001-9946-4291

Funding

General Program of the National Natural Science Foundation of China 72574155National Key Research and Development Program of China 2017YFC0907300
6 · The paper itself

Abstract

aimsCardiovascular-kidney-metabolic (CKM) syndrome represents an integrated continuum of metabolic, kidney, and cardiovascular abnormalities. However, the stage-specific metabolic heterogeneity underlying this clinical framework remains incompletely characterised. MATERIALS AND

methodsWe performed plasma metabolomic profiling in 1374 participants from the China Multi-Ethnic Cohort across CKM stages 0-4. Participants from Chengdu and Chongqing provinces comprised the discovery cohort (n = 969), whereas those from Yunnan province comprised the validation cohort (n = 405). Stage-associated metabolites were identified using prespecified pairwise comparisons with OPLS-DA and covariate-adjusted limma analyses. Weighted correlation network analysis identified coordinated metabolic modules. Machine-learning models were developed to evaluate discrimination of advanced CKM (stages 3-4) using metabolite signatures independent of conventional CKM-defining variables.

resultsAmong 858 endogenous metabolites, 261 unique metabolites were associated with CKM stages, revealing distinct metabolic patterns from early to advanced CKM. Stage 1 was characterised by altered lipid- and bile acid-related metabolites, stage 2 by broader lipid and amino-acid remodelling, and stage 3 by additional carbohydrate, aromatic amino-acid, secondary bile acid, host-microbial, and renal-handling signals. Metabolic separation between stages 3 and 4 was comparatively weak. WGCNA identified a CKM-associated turquoise module, with γ-glutamylvaline as a hub metabolite. A model incorporating age, sex, and eight metabolites derived from the discovery cohort showed favourable performance for distinguishing advanced CKM from earlier stages and achieved an AUROC of 0.874 (95% CI, 0.802-0.931) in the validation cohort.

conclusionsCKM stages exhibit distinct and non-linear metabolic signatures. A compact metabolomic panel independent of conventional CKM-defining variables may provide complementary molecular information for advanced CKM phenotyping and future risk stratification.

Indexed as

BiomarkersCardio-Renal SyndromeCardiovascular DiseasesMetabolic SyndromeMetabolomeMetabolomicsChinaCohort StudiesFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisBiomarkersbiomarkerscardiovascular‐kidney‐metabolic syndromemetabolic remodellingmetabolomics

Identifiers

PMID42781965
PMCPMC13602901

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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.