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Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping
Open accessAbstract
Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.
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Metadata
- Authors
- Beijers, L., Wardenaar, K. J., van Loo, H. M., & Schoevers, R. A.
- Affiliations
- University Medical Center Groningen (NL)
- Corresponding author
- Beijers, Lian
- Venue
- Molecular Psychiatry
- Published
- 2019-03-01
- Volume
- 24
- Issue
- 6
- Pages
- 888-900
- ISSN
- 1359-4184 (ISSN-L)
- 1476-5578
- OpenAlex
- W2919937325
- Cited by
- 240
- References
- 99
- Citations by year
- 2026: 29 · 2025: 48 · 2024: 34 · 2023: 36 · 2022: 32 · 2021: 22 · 2020: 31 · 2019: 7
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Molecular Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- major depressive disorder, depression subtypes, biological heterogeneity, neurotransmitter levels, inflammatory depression, biomarker profiles, functional connectivity, neuroimaging, genetic subtypes, childhood abuse, treatment response, weight gain and loss
- MeSH terms
- Adult, Depression, Major Depressive Disorder, Female, Humans, Male, Middle Aged, Psychometrics, Genetic Association Studies, Neuroimaging
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