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Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach

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Abstract

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol – paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

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Acknowledgements

The authors wish to thank the Centre of Approach and Treatment for Smokers, Psychiatric Unit at UEL, Clinical Laboratory of the University Hospital and Laboratory of Research and Graduate College Hospital (LPG), Brazil.

Authorships

All authors contributed to the writing up of the paper. The work was designed by SOVN, MM, DSB, JBM and HOV. Data were collected by SOVN, HOV, AC, and JBM. Laboratory analyses were conducted by KLB, APM and DSB. Statistics were performed by MM. All authors revised and approved the final draft.

Funding

This study was supported by Health Sciences Postgraduate Program at Londrina State University, Parana, Brazil (UEL), and Ministry for Science and Technology of Brazil (CNPq). CNPq number 470344/2013–0 and CNPq number 465928/2014–5. MM is supported by a CNPq - PVE fellowship and the Health Sciences Graduate Program fellowship, State University of Londrina.

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Authors and Affiliations

  1. Department of Psychiatry, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand

    Michael Maes

  2. Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria

    Michael Maes

  3. IMPACT Strategic Research Centre, Deakin University, PO Box 281, Geelong, VIC, 3220, Australia

    Michael Maes

  4. Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Michael Maes

  5. Health Sciences Graduate Program, Health Sciences Center, State University of Londrina, Av. Robert Koch 60, Londrina, PR, 86035-380, Brazil

    Juliana Brum Moraes, Kamila Landucci Bonifacio, Decio Sabbatini Barbosa, Heber Odebrecht Vargas, Ana Paula Michelin & Sandra Odebrecht Vargas Nunes

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  1. Michael Maes
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Correspondence to Michael Maes.

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The authors report no conflict of interest with any commercial or other association in connection with the submitted article.

Research involving human participants

The study was conducted according to International and Brazil ethics and privacy laws. Approval for the study was obtained from the Institutional Review Board of the State University of Londrina, UEL, Londrina, Brazil (protocol number: CAAE 34935814.2.0000.5231).

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All controls and patients gave written informed consent before participation in our study.

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The dataset generated during and/or analyzed during the current study will be available from the corresponding author once the dataset has been fully exploited by the authors.

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Maes, M., Moraes, J.B., Bonifacio, K.L. et al. Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach. Metab Brain Dis 36, 509–521 (2021). https://doi.org/10.1007/s11011-020-00656-6

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