Literature DB >> 30824865

Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping.

Lian Beijers1, Klaas J Wardenaar2, Hanna M van Loo2, Robert A Schoevers2,3.   

Abstract

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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Year:  2019        PMID: 30824865     DOI: 10.1038/s41380-019-0385-5

Source DB:  PubMed          Journal:  Mol Psychiatry        ISSN: 1359-4184            Impact factor:   15.992


  43 in total

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2.  We need an operational framework for heterogeneity in psychiatric research

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3.  Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies.

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Review 4.  Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards.

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6.  Allostatic load in the association of depressive symptoms with incident coronary heart disease: The Jackson Heart Study.

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Review 8.  Structural and functional neuroimaging of late-life depression: a coordinate-based meta-analysis.

Authors:  Amin Saberi; Esmaeil Mohammadi; Mojtaba Zarei; Simon B Eickhoff; Masoud Tahmasian
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9.  Coupling of spatial and directional functional network connectivity reveals a physiological basis for salience network hubs in asthma.

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Review 10.  Depression and prostate cancer: implications for urologists and oncologists.

Authors:  Christopher F Sharpley; David R H Christie; Vicki Bitsika
Journal:  Nat Rev Urol       Date:  2020-07-30       Impact factor: 14.432

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