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However, the stage\u2010specific metabolic heterogeneity underlying this clinical framework remains incompletely characterised.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      We performed plasma metabolomic profiling in 1374 participants from the China Multi\u2010Ethnic Cohort across CKM stages 0\u20134. Participants from Chengdu and Chongqing provinces comprised the discovery cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0969), whereas those from Yunnan province comprised the validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0405). Stage\u2010associated metabolites were identified using prespecified pairwise comparisons with OPLS\u2010DA and covariate\u2010adjusted limma analyses. Weighted correlation network analysis identified coordinated metabolic modules. Machine\u2010learning models were developed to evaluate discrimination of advanced CKM (stages 3\u20134) using metabolite signatures independent of conventional CKM\u2010defining variables.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 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\u2010 and bile acid\u2010related metabolites, stage 2 by broader lipid and amino\u2010acid remodelling, and stage 3 by additional carbohydrate, aromatic amino\u2010acid, secondary bile acid, host\u2013microbial, and renal\u2010handling signals. Metabolic separation between stages 3 and 4 was comparatively weak. WGCNA identified a CKM\u2010associated turquoise module, with \u03b3\u2010glutamylvaline 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\u20130.931) in the validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CKM stages exhibit distinct and non\u2010linear metabolic signatures. A compact metabolomic panel independent of conventional CKM\u2010defining variables may provide complementary molecular information for advanced CKM phenotyping and future risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dmrr.70230","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:53:18Z","timestamp":1790250798000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolomic Profiling Delineates Stage\u2010Associated Metabolic Remodelling in Cardiovascular\u2010Kidney\u2010Metabolic Syndrome"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8513-5112","authenticated-orcid":false,"given":"Xuemei","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Nephrology Institute of Kidney Diseases West China Hospital of Sichuan University  Chengdu 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