Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-24T02:42:27.642Z Has data issue: false hasContentIssue false
  • English
  • Français

Symptom dimensions of major depression in a large community-based cohort

Published online by Cambridge University Press:  19 May 2021

Michael Wainberg Open the ORCID record for Michael Wainberg [Opens in a new window] ,
Peter Zhukovsky ,
Sean L. Hill ,
Daniel Felsky ,
Aristotle Voineskos ,
Sidney Kennedy ,
Colin Hawco  and
Shreejoy J. Tripathy
Michael Wainberg*
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
Peter Zhukovsky
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
Sean L. Hill
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada Department of Physiology, University of Toronto, Toronto, Canada
Daniel Felsky
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada
Aristotle Voineskos
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada
Sidney Kennedy
Affiliation:
Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada Krembil Research Institute, University Health Network, Toronto, Canada Li Ka Shing Knowledge Institute, Saint Michael's Hospital, Toronto, Canada
Colin Hawco
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada
Shreejoy J. Tripathy
Affiliation:
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, Canada Institute of Medical Sciences, University of Toronto, Toronto, Canada Department of Physiology, University of Toronto, Toronto, Canada
*
Author for correspondence: Shreejoy J. Tripathy, E-mail: shreejoy.tripathy@camh.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community.

Methods

This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or ‘symptom dimensions’ via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records.

Results

Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations.

Conclusions

An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

Keywords

Major depressionsymptom dimensionscomorbiditypolygenic risk scoresUK Biobank
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 2 , January 2023 , pp. 438 - 445
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbasi, J. (2017). 23andMe, Big data, and the genetics of depression. JAMA: The Journal of the American Medical Association, 317(1), 14–16.CrossRefGoogle ScholarPubMed
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Pub.Google Scholar
Brainstorm Consortium, Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., … Murray, R. (2018). Analysis of shared heritability in common disorders of the brain. Science (New York, N.Y.), 360(6395). https://doi.org/10.1126/science.aap8757.Google ScholarPubMed
Ballard, E. D., Yarrington, J. S., Farmer, C. A., Lener, M. S., Kadriu, B., Lally, N., … Zarate, C. A. Jr. (2018). Parsing the heterogeneity of depression: An exploratory factor analysis across commonly used depression rating scales. Journal of Affective Disorders, 231, 51–57.CrossRefGoogle ScholarPubMed
Beijers, L., Wardenaar, K. J., van Loo, H. M., & Schoevers, R. A. (2019). Data-driven biological subtypes of depression: Systematic review of biological approaches to depression subtyping. Molecular Psychiatry, 24(6), 888–900.CrossRefGoogle ScholarPubMed
Blumenthal, M. D. (1971). Heterogeneity and research on depressive disorders. Archives of General Psychiatry, 24, 524. https://doi.org/10.1001/archpsyc.1971.01750120040007.CrossRefGoogle ScholarPubMed
Boerema, A. M., Kleiboer, A., Beekman, A. T. F., van Zoonen, K., Dijkshoorn, H., & Cuijpers, P. (2016). Determinants of help-seeking behavior in depression: A cross-sectional study. BMC Psychiatry, 16, 78.CrossRefGoogle ScholarPubMed
Brailean, A., Curtis, J., Davis, K., Dregan, A., & Hotopf, M. (2020). Characteristics, comorbidities, and correlates of atypical depression: Evidence from the UK biobank mental health survey. Psychological Medicine, 50(7), 1129–1138.CrossRefGoogle ScholarPubMed
Bulik-Sullivan, B. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., … Neale, B. M. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47, pp. 291–295. https://doi.org/10.1038/ng.3211.CrossRefGoogle ScholarPubMed
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., … Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209.CrossRefGoogle ScholarPubMed
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., … Moffitt, T. E. (2014). The p factor. Clinical Psychological Science, 2, 119–137. https://doi.org/10.1177/2167702613497473.CrossRefGoogle ScholarPubMed
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience, 4, 7.CrossRefGoogle Scholar
Davis, K. A. S., Coleman, J. R. I., Adams, M., Allen, N., Breen, G., Cullen, B., … Hotopf, M. (2020). Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: A reanalysis. BJPsych Open, 6(2), E18. https://doi.org/10.1192/bjo.2019.100.CrossRefGoogle ScholarPubMed
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., … Liston, C. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23(1), 28–38.CrossRefGoogle ScholarPubMed
Ehring, T., & Watkins, E. R. (2008). Repetitive negative thinking as a transdiagnostic process. International Journal of Cognitive Therapy, 1, 192–205. https://doi.org/10.1521/ijct.2008.1.3.192.CrossRefGoogle Scholar
Fried, E. I. (2017). The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. Journal of Affective Disorders, 208, 191–197.CrossRefGoogle ScholarPubMed
Grisanzio, K. A., Goldstein-Piekarski, A. N., Wang, M. Y., Rashed Ahmed, A. P., Samara, Z., & Williams, L. M. (2018). Transdiagnostic symptom clusters and associations with brain, behavior, and daily function in mood, anxiety, and trauma disorders. JAMA Psychiatry, 75(2), 201–209.CrossRefGoogle ScholarPubMed
Häfner, H., Maurer, K., Trendler, G., Heiden, W an der, Schmidt, M., & Könnecke, R. (2005). Schizophrenia and depression: Challenging the paradigm of two separate diseases – a controlled study of schizophrenia, depression and healthy controls. Schizophrenia Research, 77(1). https://doi.org/10.1016/j.schres.2005.01.004.CrossRefGoogle Scholar
Hamilton, M. (1960). A RATING SCALE FOR DEPRESSION. Journal of Neurology, Neurosurgery & Psychiatry, 23, 56–62. https://doi.org/10.1136/jnnp.23.1.56.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., … McIntosh, A. M. (2018). Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature Communications, 9(1), 1470.CrossRefGoogle ScholarPubMed
Howard, D. M., Folkersen, L., Coleman, J. R. I., Adams, M. J., Glanville, K., Werge, T., … McIntosh, A. M. (2020). Genetic stratification of depression in UK Biobank. Translational Psychiatry, 10(1), 163.CrossRefGoogle ScholarPubMed
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, pp. 748–751. https://doi.org/10.1176/appi.ajp.2010.09091379.CrossRefGoogle Scholar
Kessler, R. C., Andrews, G., Mroczek, D., Ustun, B., & Wittchen, H.-U. (1998). The world health organization composite international diagnostic interview short-form (CIDI-SF). International Journal of Methods in Psychiatric Research, 7, pp. 171–185. https://doi.org/10.1002/mpr.47.CrossRefGoogle Scholar
Kohn, R., Saxena, S., Levav, I., & Saraceno, B. (2004). The treatment gap in mental health care. Bulletin of the World Health Organization, 82(11), 858.Google ScholarPubMed
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2003). The patient health questionnaire-2: Validity of a two-item depression screener. Medical Care, 41(11), 1284–1292.CrossRefGoogle ScholarPubMed
Lawley, D. N. (1940). VI.—The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh, 60, pp. 64–82. https://doi.org/10.1017/s037016460002006x.CrossRefGoogle Scholar
Levis, B., Sun, Y., He, C., Wu, Y., Krishnan, A., & Bhandari, P. M., … Depression Screening Data (DEPRESSD) PHQ Collaboration. (2020). Accuracy of the PHQ-2 alone and in combination With the PHQ-9 for screening to detect Major depression: Systematic review and meta-analysis. JAMA: The Journal of the American Medical Association, 323(22), 2290–2300.CrossRefGoogle ScholarPubMed
Marshall, M. (2020). The hidden links between mental disorders. Nature, 581(7806), 19–21.CrossRefGoogle ScholarPubMed
McIntosh, A. M., Sullivan, P. F., & Lewis, C. M. (2019). Uncovering the genetic architecture of major depression. Neuron, 102(1), 91–103.CrossRefGoogle ScholarPubMed
Pardiñas, A. F., Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., … Walters, J. T. R. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics, 50(3), 381–389.CrossRefGoogle ScholarPubMed
Plana-Ripoll, O., Pedersen, C. B., Holtz, Y., Benros, M. E., Dalsgaard, S., de Jonge, P., … McGrath, J. J. (2019). Exploring comorbidity within mental disorders Among a danish national population. JAMA Psychiatry, 76(3), 259–270.CrossRefGoogle ScholarPubMed
Rosseel, Y. (2012). lavaan: An RPackage for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02.CrossRefGoogle Scholar
Shanmugan, S., Wolf, D. H., Calkins, M. E., Moore, T. M., Ruparel, K., Hopson, R. D., … Satterthwaite, T. D. (2016). Common and dissociable mechanisms of executive system dysfunction across psychiatric disorders in youth. The American Journal of Psychiatry, 173(5), 517–526.CrossRefGoogle ScholarPubMed
Smart, A., & Harrison, E. (2017). The under-representation of minority ethnic groups in UK medical research. Ethnicity & Health, 22(1), 65–82.CrossRefGoogle ScholarPubMed
Stahl, E. A., Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., & Trubetskoy, V., … Bipolar Disorder Working Group of the Psychiatric Genomics Consortium. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature Genetics, 51(5), 793–803.CrossRefGoogle ScholarPubMed
Stein, D. J., Szatmari, P., Gaebel, W., Berk, M., Vieta, E., Maj, M., … Reed, G. M. (2020). Mental, behavioral and neurodevelopmental disorders in the ICD-11: An international perspective on key changes and controversies. BMC Medicine, 18(1), 21.CrossRefGoogle ScholarPubMed
ten Berge, J. M. F., Krijnen, W. P., Wansbeek, T., & Shapiro, A. (1999). Some new results on correlation-preserving factor scores prediction methods. Linear Algebra and its Applications, 289, 311–318. https://doi.org/10.1016/s0024-3795(97)10007-6.CrossRefGoogle Scholar
Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. https://doi.org/10.1007/bf02291170.CrossRefGoogle Scholar
van Loo, H. M., de Jonge, P., Romeijn, J.-W., Kessler, R. C., & Schoevers, R. A. (2012). Data-driven subtypes of major depressive disorder: A systematic review. BMC Medicine, 10, 156.CrossRefGoogle ScholarPubMed
Weissman, M. M. (2020). Big data begin in psychiatry. JAMA Psychiatry, 77(9), 967–973. https://doi.org/10.1001/jamapsychiatry.2020.0954.CrossRefGoogle ScholarPubMed
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., & Abdellaoui, A., … Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681.CrossRefGoogle ScholarPubMed