Evidence map›Paper›PMID 42824864›Full record

ArticleFrontiers in psychiatry2026

Exploratory cluster analysis of self-reported adverse events associated with antidepressants using Gower distance and partitioning around medoids: a single-center cross-sectional study.

Qian Zhai, Fang Yan, Han Qi, Ling Zhang, Gang Wang, Lei Feng

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Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qian ZhaiBeijing Anding Hospital, Capital Medical University, Beijing, China.
Fang YanBeijing Anding Hospital, Capital Medical University, Beijing, China.
Han QiBeijing Anding Hospital, Capital Medical University, Beijing, China.
Ling ZhangBeijing Anding Hospital, Capital Medical University, Beijing, China.
Gang WangBeijing Anding Hospital, Capital Medical University, Beijing, China.
Lei FengBeijing Anding Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background​: 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. Objective: This study aims to overcome the limitations of traditional single-dimensional analyses by integrating multidimensional data-including demographic and sociological​ characteristics, clinical disease severity, and AE-related information-to conduct exploratory cluster subtyping at the patient level. Methods: 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 AE episodes) self-reporting AEs within the past year at Beijing Anding Hospital (April 2025-January 2026). Unlike the original survey's 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. Results: K = 2 was the optimal clustering solution (silhouette coefficient = 0.219; mean ARI = 0.916). Cluster 1 (42.2%) was characterized by early onset, high burden, and discontinuation-prone​ 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. Conclusions​: This study identified two AE subtypes, revealing a concomitant pattern of "disease burden-psychosocial resources-AE tolerance," 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. Clinical trial registration: https://www.chictr.org.cn/bin/home, identifier ChiCTR2500111836.

Indexed as

adverse eventsantidepressantscluster analysisGower distancepartitioning around medoids

Identifiers

PMID42824864
PMCPMC13628165

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.