Abstract
Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.
Highlights
Precision medicine refers to the classification of individuals into endophenotype classes based on their susceptibility to a particular disease, the biology of the disease they may develop, the response to a particular treatment, prognosis, and the process of tailoring medical treatments to each patient’s unique features [1–7]
In this conceptual analysis, I will review (1) the aims of precision medicine models and the three steps that characterize precision medicine; (2) the current misconceptions of precision medicine models held by psychiatrists; (3) the core issues in psychiatry which prevent the development of precision psychiatry models of major depressive disorder (MDD)/major depressive episode in bipolar disorder (MDE); and (4) how to construct new precision nomothetic models in mood disorders
We explain that precision medicine can be broken down into three steps: (1) optimizing and enhancing conventional models and generating digital twins; (2) using precision medicine to establish endophenotype classes and pathway phenotypes; and (3) developing a digital self for each patient
Summary
Precision psychiatry is frequently described in psychiatric journals as a novel technique that has the potential to significantly advance psychiatric clinical practice, just as precision cardiology and oncology have been demonstrated to improve the treatment of cardiovascular disease and cancers, respectively [8–13] The aim of this opinion paper is to examine precision psychiatry’s promise in using information about a depressed person’s own genes, pan-omics data, and environmental and lifestyle data to prevent, diagnose, or treat major depressive disorder (MDD) or a major depressive episode in bipolar disorder (MDE), or to tailor preventive measures and medical treatments to endophenotype classes of depressed patients in order to achieve the best clinical outcome for each individual
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