PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
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
Version 1
: Received: 25 September 2020 / Approved: 25 September 2020 / Online: 25 September 2020 (11:48:43 CEST)
How to cite:
Maes, M.; Brum, J.; Bonifacio, K.; Barbosa, D.; Vargas, H.; Michelin, A.P.; Nunes, S. 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. Preprints2020, 2020090610. https://doi.org/10.20944/preprints202009.0610.v1
Maes, M.; Brum, J.; Bonifacio, K.; Barbosa, D.; Vargas, H.; Michelin, A.P.; Nunes, S. 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. Preprints 2020, 2020090610. https://doi.org/10.20944/preprints202009.0610.v1
Maes, M.; Brum, J.; Bonifacio, K.; Barbosa, D.; Vargas, H.; Michelin, A.P.; Nunes, S. 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. Preprints2020, 2020090610. https://doi.org/10.20944/preprints202009.0610.v1
APA Style
Maes, M., Brum, J., Bonifacio, K., Barbosa, D., Vargas, H., Michelin, A.P., & Nunes, S. (2020). 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. Preprints. https://doi.org/10.20944/preprints202009.0610.v1
Chicago/Turabian Style
Maes, M., Ana Paula Michelin and Sandra Nunes. 2020 "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" Preprints. https://doi.org/10.20944/preprints202009.0610.v1
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
