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Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents

Authors :
Gladi Thng
Xueyi Shen
Aleks Stolicyn
Mathew A. Harris
Mark J. Adams
Miruna C. Barbu
Alex S. F. Kwong
Sophia Frangou
Stephen M. Lawrie
Andrew M. McIntosh
Liana Romaniuk
Heather C. Whalley
Source :
European Psychiatry, Vol 65 (2022)
Publication Year :
2022
Publisher :
Cambridge University Press, 2022.

Abstract

Abstract Background Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1). Methods MDD-RVIs quantify the correlation of the individual’s corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed. Results In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099–0.281, PFDR = 0.001–0.043) than MDD-PRS (β = 0.056–0.152, PFDR = 0.140–0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084–0.086, p = 1.38 × 10−4−4.77 × 10−4) but not with any MDD-RVIs (β 0.05). Conclusions Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.

Details

Language :
English
ISSN :
09249338 and 17783585
Volume :
65
Database :
Directory of Open Access Journals
Journal :
European Psychiatry
Publication Type :
Academic Journal
Accession number :
edsdoj.50eb08abd1764d5fadc0c0b451a255a7
Document Type :
article
Full Text :
https://doi.org/10.1192/j.eurpsy.2022.2301