{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T03:58:56Z","timestamp":1790308736519,"version":"4.1.0"},"reference-count":61,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Eating Disorders"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify data\u2010driven body composition phenotypes in patients with moderate\u2010to\u2010extreme anorexia nervosa (AN) using machine learning (ML) clustering of bioelectrical impedance analysis (BIA) parameters, and to test whether these phenotypes have specific psychopathological and childhood trauma profiles while correlating with early weight\u2010recovery trajectories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      In a prospective observational cohort, 100 females (18\u201340\u2009years) with AN and body mass index (BMI)\u2009&lt;\u200917\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      were enrolled. Baseline BIA\u2010derived measures (phase angle, body cell mass, fat mass, fat\u2010free mass, total body water, and extracellular water) were clustered using Unsupervised Random Forest algorithms and consensus\u2010based selection of the optimal number of clusters. Between\u2010cluster differences were tested, and longitudinal BMI trajectories were modeled with generalized additive mixed models across weekly follow\u2010ups up to 3\u2009months.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three clusters emerged (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200923, 53, 24) with separation driven primarily by hydration and lean\u2010mass indices (total body water, fat\u2010free mass, body cell mass), yielding phenotypes consistent with \u201cPreserved Body Cell Mass\u201d, \u201cSevere Depletion\u201d, and \u201cFluid Redistribution\u201d (elevated extracellular water with low body cell mass and phase angle), despite overlapping BMI. Clusters 1 and 2 showed higher childhood trauma exposure and specific trait differences. At 3\u2009months, Cluster 3 showed a more pronounced BMI increase over time than Clusters 1 and 2.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>BIA\u2010based ML phenotyping may complement current BMI\u2010centric severity staging by capturing distinct morpho\u2010functional patterns associated with differential early weight\u2010recovery trajectories. BIA provides a noninvasive low\u2010cost assessment, supporting more personalized nutritional and psychotherapeutic planning and informing future precision\u2010staging research in AN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/eat.70224","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T03:16:07Z","timestamp":1790306167000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond\n                    <scp>BMI<\/scp>\n                    : Unsupervised Machine Learning Clustering of Bioelectrical Impedance Phenotypes Is Associated With Weight\u2010Recovery Trajectories in Anorexia Nervosa"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3623-7096","authenticated-orcid":false,"given":"Emanuele","family":"Cassioli","sequence":"first","affiliation":[{"name":"Psychiatry Unit, Department of Health Sciences University of Florence  Florence 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