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This study refines the subtyping of overweight and obesity using metabolic and inflammatory markers to enhance risk assessment and personalized prevention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      Based on the UK Biobank, this retrospective study included participants classified as overweight or obese (BMI \u226525\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ). K\u2010means clustering was performed using metabolic and inflammatory biomarkers. Multivariate Cox regression analysis assessed the risk of complications and mortality over a follow\u2010up period of 13.5\u2009years. Genome\u2010Wide Association Studies (GWAS) and Phenome\u2010Wide Association Studies (PheWAS) explored cluster\u2010specific genetic traits.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 126\u2009145 participants (mean [IQR] age: 55.0 [14.0] years; 61\u2009983 males [49.1%]), five clusters were identified: (1) Low Metabolic Risk\u2010related, (2) Hypertension\u2010Related, (3) Mixed Hyperlipidemia\u2010Related, (4) Elevated Lipoprotein(a)\u2010Related and (5) High BMI and Inflammation\u2010Related. Cluster 1 exhibited a lower risk of complications than other clusters. Cluster 2 had the highest incidence of stroke, linked to variants affecting blood circulation. Cluster 3 showed the highest risks for ischaemic heart disease, characterized by variants enriched in cholesterol metabolism pathways. Cluster 4 was associated with high cardiovascular risks. Cluster 5 had the highest risks for diabetes, asthma, chronic obstructive pulmonary disease, osteoarthritis and mortality, linked to obesity\u2010related genetic variants. We also proposed a method for applying this classification in clinical settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This classification provides insights into the heterogeneity of individuals with overweight and obesity, aiding in the identification of high\u2010risk patients who may benefit from targeted interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.16262","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T23:20:23Z","timestamp":1740007223000},"page":"2613-2625","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Novel metabolic and inflammatory stratification of overweight\/obesity to characterize risks of adverse outcomes: A large population\u2010based cohort study"],"prefix":"10.1111","volume":"27","author":[{"given":"Hang","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Shandong Provincial Hospital Shandong University; 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