{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:18:08Z","timestamp":1787807888432,"version":"build-2784847793"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009433","name":"Istituto Auxologico Italiano","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009433","id-type":"DOI","asserted-by":"publisher"}]}],"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>Pediatric obesity is characterized by substantial metabolic heterogeneity, including adiposity, insulin resistance, inflammation, and energy metabolism. Traditional categorical classifications may inadequately capture this complexity. Objective: To identify clinically meaningful metabolic phenotypes using unsupervised clustering and to compare the statistical performance and clinical interpretability of two- and three-cluster solutions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this cross-sectional study, 758 children and adolescents with obesity underwent anthropometric, biochemical, and indirect calorimetry assessments. Fat-free mass was estimated using validated bioimpedance-based equations, and fat-free mass index (FFMI) was standardized before clustering. K-means clustering (k = 2 and k = 3) was performed using FFMI z-score, BMI-SDS, HOMA-IR, triglycerides, and HDL-cholesterol. Cluster validity was assessed using the Silhouette, Elbow, Davies\u2013Bouldin index, Calinski\u2013Harabasz index, and Gap statistic methods. Validation employed variables not included in clustering, such as glucose-insulin dynamics, inflammatory and hepatic markers, blood pressure, respiratory quotient, and resting energy expenditure (REE). Group comparisons were performed using Kruskal\u2013Wallis and Dunn\u2019s\n                      <jats:italic>post-hoc<\/jats:italic>\n                      tests with false discovery rate correction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Both clustering solutions demonstrated significant separation across metabolic domains. The two-cluster model identified metabolically favourable and unfavourable phenotypes differing in adiposity, insulin resistance, inflammatory burden, hepatic markers, blood pressure, and REE. The three-cluster solution revealed a more granular metabolic stratification with an intermediate phenotype characterized by partial metabolic impairment. Validation confirmed robust differences across physiological variables (all FDR-adjusted p &amp;lt; 0.05). Although the two-cluster solution showed slightly superior internal validity, the three-cluster model provided greater clinical granularity and phenotypic resolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Unsupervised clustering identified biologically coherent metabolic phenotypes in pediatric obesity. While the two-cluster solution provided statistically robust stratification, the three-cluster configuration captured a broader spectrum of metabolic heterogeneity and risk profiles, supporting the application of higher-resolution phenotyping approaches in pediatric endocrinology and metabolic risk assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1875870","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T04:27:06Z","timestamp":1787804826000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distinct metabolic phenotypes in adolescents with obesity identified by unsupervised learning: associations with insulin resistance and resting energy expenditure"],"prefix":"10.3389","volume":"17","author":[{"given":"Anelise","family":"Sonza","sequence":"first","affiliation":[{"name":"Centro de Ci\u00eancias da Sa\u00fade e do Esporte (CEFID), Department of Physical Therapy, Programa de P\u00f3s-Gradua\u00e7\u00e3o em Ci\u00eancias do Movimento Humano (PPGCMH), Universidade do Estado de Santa Catarina (UDESC)","place":["Florian\u00f3polis, Brazil"]},{"name":"Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia","family":"Tamini","sequence":"additional","affiliation":[{"name":"Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adele","family":"Bondesan","sequence":"additional","affiliation":[{"name":"Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana","family":"Caroli","sequence":"additional","affiliation":[{"name":"Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Abbruzzese","sequence":"additional","affiliation":[{"name":"Division of Auxology, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Sartorio","sequence":"additional","affiliation":[{"name":"Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]},{"name":"Division of Auxology, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)","place":["Piancavallo-Verbania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/s2213-8587(22)00047-x","article-title":"Obesity in children and adolescents: epidemiology, causes, assessment, and management","volume":"10","author":"Jebeile","year":"2022","journal-title":"Lancet Diabetes Endocrinol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2024.1576","article-title":"Global prevalence of overweight and obesity in children and adolescents","volume":"178","author":"Zhang","year":"2024","journal-title":"JAMA Pediatr"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.clnesp.2025.11.162","article-title":"Multiple regression models to predict electrical bioimpedance variables through anthropometric and simple measures in children aged 6\u201312 years","volume":"71","author":"Lopes","year":"2026","journal-title":"Clin Nutr ESPEN"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1017\/s0007114519002344","article-title":"Association between obesity phenotypes in adolescents and adult metabolic syndrome: Tehran Lipid and Glucose Study","volume":"122","author":"Asghari","year":"2019","journal-title":"Br J Nutr"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics15233025","article-title":"Beyond BMI. 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