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We aimed to develop a body composition\u2010based obesity phenotyping framework and examine its associations with cardiometabolic risk factor transitions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Utilising nine body composition metrics (whole\u2010body and regional fat\/muscle mass index, visceral fat area) from 2262 children with obesity in a training cohort, we applied the Discriminative Dimensionality Reduction Tree algorithm to construct a continuous two\u2010dimensional phenotypic manifold. Modified Poisson regression and spatial autocorrelation analyses were used to evaluate associations between the mapped spatial dimensions and 2\u2010year cardiometabolic risk factors transitions (progression and recovery). The topological framework was externally validated in an independent cohort of 330 children with obesity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Obesity phenotypes were mapped onto two principal axes to define three clinical profiles (mixed fat\u2013muscle elevation, adiposity\u2010dominant, and muscle\u2010dominant), with their underlying structure consistently reproduced across external replication cohort. Dimension 1 was positively associated with progression to hypertension (\n                      <jats:italic>RR<\/jats:italic>\n                      \u2009=\u20091.18, 95% CI 1.09\u20131.28), high LDL\u2010C (1.29, 1.13\u20131.49), and hyperuricemia (1.22, 1.13\u20131.30) and inversely with hypertension recovery (0.84, 0.76\u20130.92). Dimension 2 showed positive association with hypertension progression (1.27, 1.03\u20131.58) and inverse association with high LDL\u2010C recovery (0.48, 0.23\u20130.96). DDRTree\u2010derived dimensions showed no clear predictive advantage over BMI or body composition metrics in most analyses, suggesting the DDRTree manifold should be used for exploratory visualisation rather than clinical prediction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Phenotypic manifold mapping of childhood obesity identifies body composition subtypes with divergent short\u2010term cardiometabolic risk trajectories.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70998","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:40:59Z","timestamp":1781494859000},"page":"7931-7942","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phenotypic Heterogeneity of Obesity and Short\u2010Term Cardiometabolic Risk Factors Transitions: A Population\u2010Based Cohort Study"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9976-7655","authenticated-orcid":false,"given":"Pei","family":"Xiao","sequence":"first","affiliation":[{"name":"Center for Non\u2010Communicable Disease Management, Beijing Children's Hospital Capital Medical University, National Center for Children's Health  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Capital Institute of Pediatrics  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongqing","family":"Hou","sequence":"additional","affiliation":[{"name":"Child Health Big Data Research Center, Capital Center for Children's Health Capital Medical University, Capital Institute of Pediatrics  Beijing 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