{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:05:58Z","timestamp":1789639558903,"version":"4.0.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"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. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Background\u200b<\/jats:title>\n                    <jats:p>Antidepressants are first-line pharmacological interventions for depressive disorders, with globally increasing prescription volumes. Associated adverse events (AEs) represent the primary reason for treatment discontinuation. Existing studies mostly limit analysis to simple stratification by organ system or severity, while exploratory subtyping grounded in multidimensional clinical features of AEs remains scarce.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to overcome the limitations of traditional single-dimensional analyses by integrating multidimensional data\u2014including demographic and sociological\u200b characteristics, clinical disease severity, and AE-related information\u2014to conduct exploratory cluster subtyping at the patient level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study is an exploratory secondary analysis of a single-center, retrospective, recall-based cross-sectional survey on antidepressant adverse events registered with the Chinese Clinical Trial Registry (ChiCTR2500111836). It included 500 patients (905\u00a0AE episodes) self-reporting AEs within the past year at Beijing Anding Hospital (April 2025\u2013January 2026). Unlike the original survey\u2019s descriptive aim, this analysis identified heterogeneous patient subtypes: one AE per patient ID ensured independence; the Gower-PAM algorithm clustered 23 variables; and bootstrap-derived Adjusted Rand Index (ARI) values plus an all-event sensitivity analysis confirmed robust subtyping stability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>K\u00a0=\u00a02 was the optimal clustering solution (silhouette coefficient = 0.219; mean ARI\u00a0=\u00a00.916). Cluster 1 (42.2%) was characterized by early onset, high burden, and discontinuation-prone\u200b features, with moderate-to-severe AEs in 65.9% and a discontinuation rate of 75.8%. Cluster 2 (57.8%) was late-onset, low-burden, and high-tolerance, with mild AEs in 75.4% and a discontinuation rate of 16.6%. Significant differences were observed between groups in social support, work stress, and improvement of depressive symptoms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions\u200b<\/jats:title>\n                    <jats:p>This study identified two AE subtypes, revealing a concomitant pattern of \u201cdisease burden\u2013psychosocial resources\u2013AE tolerance,\u201d providing exploratory evidence for risk stratification. Limited by the single-center retrospective design and self-report bias, validation in future prospective multicenter studies is needed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>\n                      <jats:uri>https:\/\/www.chictr.org.cn\/bin\/home<\/jats:uri>\n                      , identifier ChiCTR2500111836.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1916987","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T09:55:27Z","timestamp":1789638927000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory cluster analysis of self-reported adverse events associated with antidepressants using Gower distance and partitioning around medoids: a single-center cross-sectional 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