Multimorbidity concept as integrative research method of mental and somatic disorders mechanisms

The problem of the simultaneous history of several diseases, including somatic ones, in one patient is of high relevance in the context of the prevention, diagnosis and treatment of mental disorders. The complexity and multivariance of the relationships of concomitant diseases does not allow us to evaluate their complex interaction according to the concept of comorbidity. The concept of multimorbidity is convenient and promising for studying various aspects of the combination of both mental disorders and their associations with somatic pathology. However, at present, most studies in the area of multimorbidity are based primarily on epidemiological assessments, while much less attention has been paid to the mechanisms of multimorbid conditions. In this review, we consider the main recent approaches related to the study of the multimorbidity of mental disorders and somatic diseases in order to determine the optimal research strategies in this area. Further studies of the etiopathogenetic aspects of multimorbidity, the mechanisms of multimorbid clusters, and the aspects of the course and prognosis of multimorbid diseases are necessary to optimize the concept for both scientific projects and clinical practice. The most important directions for future research on the phenomenon of multimorbidity of mental disorders and somatic diseases are: 1) the study of their shared genetic and molecular mechanisms of development; 2) clinical and fundamental prospective studies of the manifestation, concomitant course and outcomes of multimorbid diseases; 3) study of the effectiveness and tolerability of psychopharmacotherapy in multimorbid patients with various somatic diagnoses, considering the complex interaction of the drugs used.

1. Kasyanov ED, Rakitko AS, Rukavishnikov GV, Golimbet VE, Shmukler AB, Iliinsky VV, Neznanov NG, Kibitov AO, Mazo GE. Contemporary GWAS studies of depression: the critical role of phenotyping. Zhurnal nevrologii i psihiatrii im. S.S. Korsakova. 2022;122(1):50-61. (In Russ.). https://doi.org/10.17116/jnevro202212201150.

2. Kibitov AO, Mazo GE, Rakitko AS, Kasyanov ED, Rukavishnikov GV, Ilinsky VV, Golimbet VE, Shmukler AB, Neznanov NG. GWAS-based polygenic risk scores for depression with clinical validation: methods and study design in the Russian population. Zhurnal Nevrologii i Psikhiatrii im. S.S. Korsakova. 2020;120(11):131-140. (In Russ.). https://doi.org/10.17116/jnevro2020120111131

3. Neznanov NG, Kibitov AO, Rukavishnikov 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

4. Neznanov N.G., Mazo G.E., Kibitov A.O. Depressiya i risk razvitiya somaticheskih zabolevanij. Rukovodstvo dlya vrachej. Pod red. N.G. Neznanova, G.E. Mazo, A.O. Kibitova. M.: Special'noe izdatel'stvo medicinskih knig; 2018. (In Russ.).

5. Neznanov NG, Rukavishnikov GV, Kaysanov ED, Zhilyaeva TV, Mazo GE. New approach to mental disorders systematics: starting point or point of view? Obozrenie psihiatrii i medicinskoj psihologii imeni V.M. Bekhtereva. 2020;(3):3-10. (In Russ.). https://doi.org/10.31363/2313-7053-2020-3-3-10

6. Rukavishnikov GV, Kibitov AO, Mazo G, Neznanov NG. Genetic comorbidity of depression and somatic disorders. Zhurnal Nevrologii i Psikhiatrii im. S.S. Korsakova. 2019;119(1):89-96. (In Russ.). https://doi.org/10.17116/jnevro201911901189

7. Rukavishnikov GV, Neznanov NG, Martynov AI, Mazo GE. Treatment of depression in patients with comorbid somatic disease. Terapiya. 2020;6-3(37):76-83. (In Russ.). https://doi.org/10.18565/therapy.2020.3.76-83

8. Amsellem V, Gary-Bobo G, Marcos E, Maitre B, Chaar V, Validire P, Stern JB, Noureddine H, Sapin E, Rideau D, Hue S, Le Corvoisier P, Le Gouvello S, Dubois-Randé JL, Boczkowski J, Adnot S. Telomere dysfunction causes sustained inflammation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2011;184(12):1358-66. https://doi.org/10.1164/rccm.201105-0802OC

9. Bagley SC, Sirota M, Chen R, Butte AJ, Altman RB. Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants. PLoS Comput Biol. 2016;12(4):e1004885. https://doi.org/10.1371/journal.pcbi.1004885

10. Baltramonaityte V, Pingault JB, Cecil CAM, Choudhary P, Järvelin MR, Penninx BWJH, Felix J, Sebert S, Milaneschi Y, Walton E; EarlyCause Consortium. A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity. PLoS Genet. 2023;19(6):e1010508. https://doi.org/10.1371/journal.pgen.1010508

11. Barnes PJ. Mechanisms of development of multimorbidity in the elderly. Eur Respir J. 2015;45(3):790-806. https://doi.org/10.1183/09031936.00229714

12. Birk JL, Kronish IM, Moise N, Falzon L, Yoon S, Davidson KW. Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychol. 2019;38(9):802-811. https://doi.org/10.1037/hea0000737

13. Busija L, Lim K, Szoeke C, Sanders KM, McCabe MP. Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur J Epidemiol. 2019;34(11):1025-1053. https://doi.org/10.1007/s10654-019-00568-5

14. 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.001959

15. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505-522. https://doi.org/10.1038/s41569-018-0064-2

16. Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4-9. https://doi.org/10.1093/gerona/glu057

17. Friedman E, Shorey C. Inflammation in multimorbidity and disability: An integrative review. Health Psychol. 2019;38(9):791-801. https://doi.org/10.1037/hea0000749

18. Ioakeim-Skoufa I, Poblador-Plou B, Carmona-Pírez J, Díez-Manglano J, Navickas R, Gimeno-Feliu LA, González-Rubio F, Jureviciene E, Dambrauskas L, Prados-Torres A, Gimeno-Miguel A. Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study. Int J Environ Res Public Health. 2020;17(12):4242. https://doi.org/10.3390/ijerph17124242

19. Lenti MV, Klersy C, Brera AS, Ballesio A, Croce G, Padovini L, Ciccocioppo R, Bertolino G, Di Sabatino A, Corazza GR. Aging underlies heterogeneity between comorbidity and multimorbidity frameworks. Intern Emerg Med. 2022;17(4):1033-1041. https://doi.org/10.1007/s11739-021-02899-2

20. Masoli JAH, Pilling LC, Frayling TM. Genomics and multimorbidity. Age Ageing. 2022;51(12):afac285. https://doi.org/10.1093/ageing/afac285

21. Marx P, Antal P, Bolgar B, Bagdy G, Deakin B, Juhasz G. Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression. PLoS Comput Biol. 2017;13(6):e1005487. https://doi.org/10.1371/journal.pcbi.1005487

22. Nicholson K, Almirall J, Fortin M. The measurement of multimorbidity. Health Psychol. 2019;38(9):783-790. https://doi.org/10.1037/hea0000739

23. Ouk M, Wu CY, Colby-Milley J, Fang J, Zhou L, Shah BR, Herrmann N, Lanctôt KL, Linkewich E, Law M, Swartz RH, Kapral MK, Black SE, MacIntosh BJ, Edwards JD, Swardfager W. Depression and Diabetes Mellitus Multimorbidity Is Associated With Loss of Independence and Dementia Poststroke. Stroke. 2020;51(12):3531-3540. https://doi.org/10.1161/STROKEAHA.120.031068

24. Richardson WS, Doster LM. Comorbidity and multimorbidity need to be placed in the context of a framework of risk, responsiveness, and vulnerability. J Clin Epidemiol. 2014;67(3):244-6. https://doi.org/10.1016/j.jclinepi.2013.10.020

25. Roso-Llorach A, Violán C, Foguet-Boreu Q, et al Comparative analysis of methods for identifying multimorbidity patterns: a study of ‘real-world’ data BMJ Open 2018;8:e018986. https://doi.org/10.1136/bmjopen-2017-018986

26. Roth JA, Sakoparnig T, Gerber M, Hug BL. Bayesian networks to identify potential high-risk multimorbidity and intervention clusters in inpatients: an explorative data mining study. Swiss Med Wkly. 2020;150:w20299. https://doi.org/10.4414/smw.2020.20299

27. Schäfer I, Kaduszkiewicz H, Wagner HO, Schön G, Scherer M, van den Bussche H. Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads. BMC Public Health. 2014;14:1285. https://doi.org/10.1186/1471-2458-14-1285

28. Schoepf D, Uppal H, Potluri R, Chandran S, Heun R. Comorbidity and its relevance on general hospital based mortality in major depressive disorder: A naturalistic 12-year follow-up in general hospital admissions. J Psychiatr Res. 2014;52:28–35. https://doi.org/10.1016/j.jpsychires.2014.01.010

29. Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, Boyd CM, Pati S, Mtenga S, Smith SM. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48. https://doi.org/10.1038/s41572-022-00376-4

30. Smith DJ, McLean G, Martin D, Martin JL, Guthrie B, Gunn J, et al. Depression and multimorbidity: a cross-sectional study of 1,751,841 patients in primary care. J Clin Psychiatry. 2014;75(11):1,478– 1208.

31. Sourial N, Wolfson C, Zhu B, Quail J, Fletcher J, Karunananthan S, Bandeen-Roche K, Béland F, Bergman H. Correspondence analysis is a useful tool to uncover the relationships among categorical variables. J Clin Epidemiol. 2010;63(6):638-46. https://doi.org/10.1016/j.jclinepi.2009.08.008

32. Strayer N, Shirey-Rice JK, Shyr Y, Denny JC, Pulley JM, Xu Y. PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS. Bioinformatics. 2021;37(12):1778-1780. https://doi.org/10.1093/bioinformatics/btaa870

33. The Lancet. Making more of multimorbidity: an emerging priority. Lancet. 2018;391(10131):1637. https://doi.org/10.1016/S0140-6736(18)30941-3

34. Thompson DJ, Wells D, Selzam S et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits. medRxiv2022:2022.06.16.22276246

35. Tong L, Pu L, Guo X, Sun M, Guo F, Zhao S, Gao W, Jin L. Multimorbidity study with different levels of depression status. J Affect Disord. 2021;292:30-35. https://doi.org/10.1016/j.jad.2021.05.039.

36. Tugwell P, Knottnerus JA. Multimorbidity and Comorbidity are now separate MESH headings. J Clin Epidemiol. 2019;105:vi-viii. https://doi.org/10.1016/j.jclinepi.2018.11.019

37. Violán, C., Roso-Llorach, A., Foguet-Boreu, Q. et al. Multimorbidity patterns with K-means nonhierarchical cluster analysis. BMC Fam Pract 19, 108 (2018). https://doi.org/10.1186/s12875-018-0790-x

38. WHO, Depressive disorder. [who.int]. Who; 2022 [assessed at 17 Aug 2023]. Available: https://www.who.int/news-room/fact-sheets/detail/depression

39. Wittchen HU, Jacobi F. Size and burden of mental disorders in Europe—a critical review and appraisal of 27 studies. Eur Neuropsychopharmacol. 2005;15(4):357–376. https://doi.org/10.1016/j.euroneuro.2005.04.012

40. Wittchen HU. The burden of mood disorders. Science. 2012;338(6103):15–15. https://doi.org/10.1126/science.1230817

41. Whitty CJM, Watt FM. Map clusters of diseases to tackle multimorbidity. Nature. 2020;579(7800):494-496. https://doi.org/10.1038/d41586-020-00837-4

42. Wu MF, Yang YW, Chen YY. The effect of anxiety and depression on the risk of irritable bowel syndrome in migraine patients. J Clin Neurosci. 2017 Oct;44:342-345. https://doi.org/10.1016/j.jocn.2017.06.009