{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T11:31:56Z","timestamp":1792927916032,"version":"4.5.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Metabolic dysfunction-associated steatotic liver disease (MASLD) shares metabolic pathways with cardiovascular-kidney-metabolic (CKM) syndrome, but prospective community-based evidence linking CKM metabolic risk burden to incident MASLD remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analysed longitudinal health examination data from a community cohort in Guangxi, southern China. Because subclinical cardiovascular and kidney assessments were unavailable, CKM staging was operationalised as a metabolic-dimension staging system based on five cardiometabolic (CM) components. Cross-sectional analyses included 3,315 participants. The primary forward cohort included 2,055 participants free of MASLD at baseline, with 294 incident events over a median 2.61 years. An exploratory reverse cohort included 648 participants at CKM early stage, with 407 progression events over a median 1.77 years. Ultrasonographic fatty liver alone was used as a sensitivity outcome to address definitional overlap between MASLD and CM components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Each additional CM component was associated with higher odds of prevalent MASLD (OR\u00a0=\u00a01.94, 95% CI: 1.79\u20132.10), with a similar estimate for fatty liver alone (OR\u00a0=\u00a01.90, 95% CI: 1.75\u20132.05). In the forward cohort, metabolic-dimension CKM Stage 2 was associated with higher incident MASLD risk compared with Stage 0 (HR\u00a0=\u00a02.33, 95% CI: 1.20\u20134.54), and the fatty-liver-alone sensitivity outcome yielded a consistent estimate (HR\u00a0=\u00a02.95, 95% CI: 1.39\u20136.26). Among individual CM components, excess adiposity was the strongest predictor (mutually adjusted HR\u00a0=\u00a02.97, 95% CI: 2.00\u20134.41), followed by hypertriglyceridaemia (HR\u00a0=\u00a01.63, 95% CI: 1.23\u20132.18). In the exploratory reverse cohort, baseline MASLD was associated with CKM stage progression in the age- and sex-adjusted model (HR\u00a0=\u00a02.13, 95% CI: 1.49\u20133.03), but this association attenuated after adjustment for baseline BMI (HR\u00a0=\u00a01.18, 95% CI: 0.81\u20131.71).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Metabolic-dimension CKM staging and CM component burden predicted incident MASLD, with excess adiposity and\n                      <jats:bold>hypertriglyceridaemia<\/jats:bold>\n                      as dominant component-level predictors. The forward association was robust to removing MASLD-CM definitional overlap. In contrast, baseline MASLD was not clearly associated with CKM stage progression after accounting for baseline adiposity and related metabolic severity.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1847134","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T05:01:22Z","timestamp":1779426082000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Metabolic-dimension CKM staging and cardiometabolic components predict incident MASLD: a longitudinal community-based cohort study"],"prefix":"10.3389","volume":"17","author":[{"given":"Xinyang","family":"Long","sequence":"first","affiliation":[{"name":"School of Public Health, Guangxi Medical University","place":["Nanning, Guangxi, China"]},{"name":"Centre for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Centre 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China"]},{"name":"Centre for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Centre for Genomic and Personalized Medicine, Guangxi Medical University","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuling","family":"Zhang","sequence":"additional","affiliation":[{"name":"The First People\u2019s Hospital of Yulin","place":["Yulin, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengnan","family":"Mo","sequence":"additional","affiliation":[{"name":"Centre for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Centre for Genomic and Personalized Medicine, Guangxi Medical University","place":["Nanning, Guangxi, China"]},{"name":"Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Guangxi Medical 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