Online phenotypes of depression symptoms are associated with polygenic risk scores of somatic diseases in a population-based cohort

Kibitov AO, Rakitko AS, Kasyanov ED, et al. . Online phenotypes of depression symptoms are associated with polygenic risk scores of somatic diseases in a population-based cohort. S.S. Korsakov Journal of Neurology and Psychiatry. 2024;124(11):122‑131. (In Russ.)
https://doi.org/10.17116/jnevro2024124111122

Подписка 2026 Журнал неврологии и психиатрии им. С.С. Корсакова (S.S. Korsakov Journal of Neurology and Psychiatry)

Использование инфографики авторами научных работ

Close metadata

OBJECTIVE

To test an associations of online phenotypes of depressive symptoms with polygenic risk scores (PRS) for selected somatic diseases in a population-based cohort.

MATERIAL AND METHODS

Participants in a Russian population-based cohort (n=4520) underwent online phenotyping based on the originally developed questionnaire using DSM-5 criteria (DSM phenotypes) and the Hospital Anxiety and Depression Scale (HADS) questionnaire (HADS phenotypes). After DNA genotyping with microarrays, PRS were calculated using summary statistics from large-scale GWASs (mostly from UK Biobank) for irritable bowel syndrome (IBS), coronary heart disease (narrow and broad phenotypes) (CHD), ischemic stroke (IS),diabetes mellitus type 2 (DT2) and migraine (MG). Then we assessed associations of PRS for somatic diseases with online phenotypes of depressive symptoms in a population-based cohort.

RESULTS

Highly specific associations of PRS for somatic diseases with online phenotypes of depressive symptoms in a population cohort were identified: positive associations of PRS IBS with «DSM depression» (p=0.0035) and PRS CHD (narrow phenotype) with «DSM bipolar disorder» (p=0.0207), as well as a negative association of PRS IS with the symptomatic DSM phenotype «Hyperphagia» (p=0.0262). All the HADS phenotypes (clinical depression, subclinical depression, and HADS-D total score) showed a positive association with PRS for IBS and DT2, and at the same time were negatively associated with PRS IS. The DSM phenotype «Hypersomnia and hyperphagia» was positively associated with PRS for DT2 and IS, and negatively associated with PRS CHD (narrow phenotype). The DSM phenotypes «Subclinical Depression», «Hypersomnia», and «Anhedonia» showed exclusively negative associations with PRSs for DT2, IS, and IBS. Three maximum informative PRS by explained variance (PRS.R2) were found for the single DSM phenotype «Hypersomnia and hyperphagia»: PRS for IS and DT2 are positively associated and increase the risk of this phenotype, and PRS CHD (narrow phenotype) has a negative association and reduces the risk of this phenotype.

CONCLUSION

Our study confirmed the presence of possible genetic comorbidity of depression and chronic somatic diseases and showed different effectiveness of online phenotyping of depressive symptoms as markers of genetic risk of somatic diseases in the population cohort. Further research is needed to construct various versions of online phenotyping test systems for population-based screening of cohorts at high genetic risk for genetically comorbid diseases.

Keywords:

depression

chronic somatic diseases

polygenic risk scores

Authors:

Kibitov A.O.

Bekhterev National Medical Research Center for Psychiatry and Neurology;
Pavlov First Saint-Petersburg State Medical University

ORCID: 0000-0002-8771-625X

Rakitko A.S.

Bekhterev National Medical Research Center for Psychiatry and Neurology;
Genotek ltd.

ORCID: 0000-0003-0567-7734

Kasyanov E.D.

Bekhterev National Medical Research Center for Psychiatry and Neurology

ORCID: 0000-0002-4658-2195

Rukavishnikov G.V.

Bekhterev National Medical Research Center for Psychiatry and Neurology

ORCID: 0000-0002-5282-2036

Shumskaia D.S.

Bekhterev National Medical Research Center for Psychiatry and Neurology

ORCID: 0009-0009-2859-8626

Ilinsky V.V.

Bekhterev National Medical Research Center for Psychiatry and Neurology

ORCID: 0000-0003-4377-2759

Neznanov N.G.

Bekhterev National Medical Research Center for Psychiatry and Neurology;
Pavlov First Saint-Petersburg State Medical University

ORCID: 0000-0001-5618-4206

Mazo G.E.

Bekhterev National Medical Research Center for Psychiatry and Neurology

ORCID: 0000-0001-7910-9129

Received:

30.06.2024

Accepted:

08.07.2024

Close metadata

References:

Neznanov NG, Kibitov AO, Rukavisnikov GV, Mazo GE. The Prognostic role of depression as a predictor of chronic somatic diseases manifestation. Terapevticheskii arkhiv. 2018;90(12):122-132. (In Russ.). https://doi.org/10.26442/00403660.2018.12.000019
Lewinsohn PM, Shankman SA, Gau JM, et al. The prevalence and co-morbidity of subthreshold psychiatric conditions. Psychol Med. 2004;34(4):613‐622. https://doi.org/10.1017/S0033291703001466
Chang CK, Hayes RD, Perera G, et al. Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS One. 2011;6(5):e19590. https://doi.org/10.1371/journal.pone.0019590
König H, König HH, Konnopka A. The excess costs of depression: a systematic review and meta-analysis. Epidemiology and Psychiatric Sciences. 2019;29:e30. https://doi.org/10.1017/S2045796019000180
Šprah L, Dernovšek MZ, Wahlbeck K, et al. Psychiatric readmissions and their association with physical comorbidity: a systematic literature review. BMC Psychiatry. 2017;17(1):2. https://doi.org/10.1186/s12888-016-1172-3
Neznanov NG, Kibitov AO, Mazo GE, eds. Depressija i risk razvitija somaticheskih zabolevanij. Moscow: Special’noe izdatel’stvo medicinskih knig; 2018. (In Russ.).
Rukavishnikov GV, Kibitov AO, Mazo GE, et al. Genetic comorbidity of depression and somatic disorders. S.S. Korsakov Journal of Neurology and Psychiatry. 2019;119(1):89-96. (In Russ.). https://doi.org/10.17116/jnevro201911901189
Kibitov AO, Mazo GE, Kasyanov ED, et al. Genetic Comorbidity of Depression and Somatic Diseases: Rationale Study Design in a Population Cohort Using Polygenic Risk Scores. Psychiatry (Moscow)=Psikhiatriya. 2024;22(2):54-65. (In Russ.). https://doi.org/10.30629/2618-6667-2024-22-2-54-65
Fang Y, Fritsche LG, Mukherjee B, et al. Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals. Biological Psychiatry. 2022;92(12):923-931. https://doi.org/10.1016/j.biopsych.2022.06.004
Golimbet VE, Klyushnik TP. Genome-wide studies of comorbidity of somatic and mental diseases. S.S. Korsakov Journal of Neurology and Psychiatry. 2023;123(4 vyp 2):60-64. (In Russ.). https://doi.org/10.17116/jnevro202312304260
Martin AR, Daly MJ, Robinson EB, et al. Predicting Polygenic Risk of Psychiatric Disorders. Biological Psychiatry. 2019;86(2):97-109. https://doi.org/10.1016/j.biopsych.2018.12.015
Polimanti R. Not Only Gene Discovery: Genome-wide Association Studies and Polygenic Risk Scores as Tools to Dissect the Heterogeneity of Major Depressive Disorder. Biological Psychiatry. 2022;92(3):177-178. https://doi.org/10.1016/j.biopsych.2022.05.002
Kibitov AO, Rakitko AS, Kasyanov ED, et al. Results of a pilot genome-wide association study (GWAS) and polygenic depression risk scales in the Russian population using online phenotyping. Russian Journal of Psychiatry=Rossiiskii psikhiatricheskii zhurnal. 2022;(5):13-29. (In Russ.).
Andryushchenko AV, Drobizhev MYu, Dobrovol’skii AV. Comparative evaluation of the CES-B, IVA and HADS scales in the diagnosis of depression in general medical practice. S.S. Korsakov Journal of Neurology and Psychiatry. 2003;5:11-18. (In Russ.).
Kasyanov ED, Verbitskaya EV, Rakitko AS, et al. Validation of a DSM-5-based screening test using digital phenotyping in the Russian population. S.S. Korsakov Journal of Neurology and Psychiatry. 2022;122(6 vyp 2):64-70. (In Russ.). https://doi.org/10.17116/jnevro202212206264
Browning SR, Browning BL. Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies by Use of Localized Haplotype Clustering. American Journal of Human Genetics. 2007;81(5):1084-1097. https://doi.org/10.1086/521987
Consortium, The 1000 Genomes Project, and the 1000 Genomes Project Consortium A Global Reference for Human Genetic Variation. Nature. 2015;526:68-74. https://doi.org/10.1038/nature15393
McCarthy S, Das S, Kretzschmar W, et al. A Reference Panel of 64,976 Haplotypes for Genotype Imputation. Nature Genetics. 2016;48(10):1279-1283. https://doi.org/10.1038/ng.3643
Andries TM, de Kluiver H, Stringer S. A Tutorial on Conducting Genome-Wide Association Studies: Quality Control and Statistical Analysis. International Journal of Methods in Psychiatric Research. 2018;27(2):e1608. https://doi.org/10.1002/mpr.1608
Staples J, Qiao D, Cho MH, et al. PRIMUS: Rapid Reconstruction of Pedigrees from Genome-Wide Estimates of Identity by Descent. American Journal of Human Genetics. 2014;95(5):553-564. https://doi.org/10.1016/j.ajhg.2014.10.005
Shing Wan C, O’Reilly PF. PRSice-2: Polygenic Risk Score Software for Biobank-Scale Data. GigaScience. 2019;8(7):giz082. https://doi.org/10.1093/gigascience/giz082
Hagenaars SP, Coleman JRI, Choi SW, et al. Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression. Am J Med Genet B Neuropsychiatr Genet. 2020;183(6):309-330. https://doi.org/10.1002/ajmg.b.32807
Milaneschi Y, Kappelmann N, Ye Z, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Molecular Psychiatry. 2021;26(12):7393-7402. https://doi.org/10.1038/s41380-021-01188-w
Qin X, Pan C, Cai Q, et al. Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression. Brain Behav Immun Health. 2022;26:100557. https://doi.org/10.1016/j.bbih.2022.100557
Pinakhina D, Yermakovich D, Vergasova E, et al. GWAS of depression in 4,520 individuals from the Russian population highlights the role of MAGI2 (S-SCAM) in the gut-brain axis. Front Genet. 2023;13:972196. https://doi.org/10.3389/fgene.2022.972196
Neznanov NG, Rukavishnikov GV, Kasyanov ED, et al. Biopsychosocial model in psychiatry as an optimal paradigm for relevant biomedical research. Obozrenie psikhiatrii i meditsinskoi psikhologii imeni V.M. Bekhtereva. 2020;2:3-15. (In Russ.). https://doi.org/10.31363/2313-7053-2020-2-3-15
Fabbri C, Lewis CM, Serretti A. Polygenic risk scores for mood and related disorders and environmental factors: Interaction effects on wellbeing in the UK biobank. Prog Neuropsychopharmacol Biol Psychiatry. 2024;132:110972. https://doi.org/10.1016/j.pnpbp.2024.110972
Oliva V, Fanelli G, Kasper S, et al. Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns. Journal of Affective Disorders. 2023;320:534-543. https://doi.org/10.1016/j.jad.2022.10.003
Refisch A, Sen ZD, Klassert TE, et al. Microbiome and immuno-metabolic dysregulation in patients with major depressive disorder with atypical clinical presentation. Neuropharmacology. 2023;235:109568. https://doi.org/10.1016/j.neuropharm.2023.109568
Shell AL, Crawford CA, Cyders MA, et al. Depressive disorder subtypes, depressive symptom clusters, and risk of obesity and diabetes: A systematic review. Journal of Affective Disorders. 2024;353:70-89. https://doi.org/10.1016/j.jad.2024.02.051
Meng T, Liu Z, Liu J, et al. Multiple coronary heart diseases are risk factors for mental health disorders: A mendelian randomization study. Heart Lung. 2024;66:86-93. https://doi.org/10.1016/j.hrtlng.2024.04.009