Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function
BackgroundIncreasing evidences suggests that depression is a heterogeneous clinical syndrome. Cognitive deficits in depression are associated with poor psychosocial functioning and worse response to conventional antidepressants. However, a consistent profile of neurocognitive abnormalities in depres...
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| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1537331/full |
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| author | Chenyang Xu Yanbao Tao Yunhan Lin Jiahui Zhu Zhuoran Li Jiayi Li Mingqia Wang Tao Huang Chuan Shi |
| author_facet | Chenyang Xu Yanbao Tao Yunhan Lin Jiahui Zhu Zhuoran Li Jiayi Li Mingqia Wang Tao Huang Chuan Shi |
| author_sort | Chenyang Xu |
| collection | DOAJ |
| description | BackgroundIncreasing evidences suggests that depression is a heterogeneous clinical syndrome. Cognitive deficits in depression are associated with poor psychosocial functioning and worse response to conventional antidepressants. However, a consistent profile of neurocognitive abnormalities in depression remains unclear.ObjectiveWe used data-driven parsing of cognitive performance to reveal subgroups present across depressed individuals and then investigate the change pattern of cognitive subgroups across the course in follow-up.MethodWe assessed cognition in 163 patients with depression using The Chinese Brief Cognitive Test(C-BCT) and the scores were compared with those of 196 healthy controls (HCs). 58 patients were reassessed after 8 weeks. We used K-means cluster analysis to identify cognitive subgroups, and compared clinical variables among these subgroups. A linear mixed-effects model, incorporating time and group (with interaction term: time × group) as fixed effects, was used to assess cognitive changes over time. Stepwise logistic regression analysis was conducted to identify risk factors associated with these subgroups.ResultsTwo distinct neurocognitive subgroups were identified: (1) a cognitive-impaired subgroup with global impairment across all domains assessed by the C-BCT, and (2) a cognitive-preserved subgroup, exhibited intact cognitive function, with performance well within the healthy range. The cognitive-impaired subgroup presented with more severe baseline symptoms, including depressed mood, guilt, suicidality, and poorer work performance. Significant group × time interactions were observed in the Trail Making Test Part A (TMT-A) and Continuous Performance Test (CPT), but not in Symbol Coding or Digit Span tests. Despite partial improvement in TMT-A and CPT tests, the cognitive-impaired subgroup's scores remained lower than those of the cognitive-preserved subgroup across all tests at the study endpoint. Multiple regression analysis indicated that longer illness duration, lower educational levels, and antipsychotic medication use may be risk factors for cognitive impairment.ConclusionThis study identifies distinguishable cognitive subgroups in acute depression, thereby confirming the presence of cognitive heterogeneity. The cognitive-impaired subgroup exhibits distinct symptoms and persistent cognitive deficits even after treatment. Screening for cognitive dysfunction may facilitate more targeted interventions.Clinical Trial Registrationhttps://www.chictr.org, identifier ChiCTR2400092796. |
| format | Article |
| id | doaj-art-bc886186ada04fe8a43acd7977bd9d23 |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-bc886186ada04fe8a43acd7977bd9d232025-01-30T06:22:17ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011610.3389/fpsyt.2025.15373311537331Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive functionChenyang Xu0Yanbao Tao1Yunhan Lin2Jiahui Zhu3Zhuoran Li4Jiayi Li5Mingqia Wang6Tao Huang7Chuan Shi8Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaThe First Affiliated Hospital of Xinxiang Medical College, Xinxiang, Henan, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, ChinaOntario Institute for Studies in Education, University of Toronto, Toronto, ON, CanadaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, ChinaBackgroundIncreasing evidences suggests that depression is a heterogeneous clinical syndrome. Cognitive deficits in depression are associated with poor psychosocial functioning and worse response to conventional antidepressants. However, a consistent profile of neurocognitive abnormalities in depression remains unclear.ObjectiveWe used data-driven parsing of cognitive performance to reveal subgroups present across depressed individuals and then investigate the change pattern of cognitive subgroups across the course in follow-up.MethodWe assessed cognition in 163 patients with depression using The Chinese Brief Cognitive Test(C-BCT) and the scores were compared with those of 196 healthy controls (HCs). 58 patients were reassessed after 8 weeks. We used K-means cluster analysis to identify cognitive subgroups, and compared clinical variables among these subgroups. A linear mixed-effects model, incorporating time and group (with interaction term: time × group) as fixed effects, was used to assess cognitive changes over time. Stepwise logistic regression analysis was conducted to identify risk factors associated with these subgroups.ResultsTwo distinct neurocognitive subgroups were identified: (1) a cognitive-impaired subgroup with global impairment across all domains assessed by the C-BCT, and (2) a cognitive-preserved subgroup, exhibited intact cognitive function, with performance well within the healthy range. The cognitive-impaired subgroup presented with more severe baseline symptoms, including depressed mood, guilt, suicidality, and poorer work performance. Significant group × time interactions were observed in the Trail Making Test Part A (TMT-A) and Continuous Performance Test (CPT), but not in Symbol Coding or Digit Span tests. Despite partial improvement in TMT-A and CPT tests, the cognitive-impaired subgroup's scores remained lower than those of the cognitive-preserved subgroup across all tests at the study endpoint. Multiple regression analysis indicated that longer illness duration, lower educational levels, and antipsychotic medication use may be risk factors for cognitive impairment.ConclusionThis study identifies distinguishable cognitive subgroups in acute depression, thereby confirming the presence of cognitive heterogeneity. The cognitive-impaired subgroup exhibits distinct symptoms and persistent cognitive deficits even after treatment. Screening for cognitive dysfunction may facilitate more targeted interventions.Clinical Trial Registrationhttps://www.chictr.org, identifier ChiCTR2400092796.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1537331/fulldepressioncognitive subtypecluster analysisheterogeneitylongitudinal study |
| spellingShingle | Chenyang Xu Yanbao Tao Yunhan Lin Jiahui Zhu Zhuoran Li Jiayi Li Mingqia Wang Tao Huang Chuan Shi Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function Frontiers in Psychiatry depression cognitive subtype cluster analysis heterogeneity longitudinal study |
| title | Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function |
| title_full | Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function |
| title_fullStr | Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function |
| title_full_unstemmed | Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function |
| title_short | Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function |
| title_sort | parsing the heterogeneity of depression a data driven subgroup derived from cognitive function |
| topic | depression cognitive subtype cluster analysis heterogeneity longitudinal study |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1537331/full |
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