Profiling Vulnerability and Resilience for Mental Illness Following Viral Infections: Translating Epidemiology to Deep-phenotyping.

Abstract:

Infectious diseases are known to significantly increase the risk for later development of a mental disorder. A growing body of research has further demonstrated that individuals with a pre-existing mental disorder are vulnerable to more severe post-infection outcomes. Different mechanisms have been suggested to mediate these associations, while suggesting that neurobiological changes triggered by the infection can serve as a common pathophysiological factor. At the same time, environmental risk factors have also been suggested to contribute to both the increased risk of mental disorder and severe post-infection outcomes. Nonetheless, to date no study has integrated neurobiological, genetic, inflammatory, social, and psychological factors as risk or resilient determinants to provide a comprehensive model of the phenomena. In this state-of-the-art joint transnational study, we aim to investigate the interplay between viral and other clinically relevant infections and mental disorders, starting from epidemiology and proceeding with deep phenotyping of involved variables. Based on extensive empirical and theoretical formulations, we hypothesise that post-infection inflammatory status and its effects in the brain will serve as a shared mechanism for subsequent development of mental illness, as well as for the worse infection outcomes observed in individuals with pre-existing mental disorders. Environmental risk and resilience factors are hypothesised to further moderate these associations. Exploiting wide epidemiological databases (Israel and Norway), deep-phenotyped cohorts (Italy and Belgium), and state of the art neuroimaging techniques and transcriptomics, we will map the immune and neurobiological underpinnings for individual vulnerability or resilience, as well as their interaction with environmental factors in response to viral and other clinically relevant infections. Multimodal neuroimaging, genetic, and immunophenotyping assessments combined with social, economic and cultural factors will be integrated with machine learning analyses to stratify patients according to potential untracked subpopulations and generate individual predictive signatures of vulnerability versus resilience to negative mental and physical outcomes. Finally, state of the art transcriptomic analysis will be performed in proof-of-concept studies to identify druggable targets. This joint collaborative effort can set the stage to consider new personalised therapeutic and preventive strategies, as well as to broaden the scientific exploration of druggable targets to eliminate the adverse consequences of infections.

Protocol summary:

Background:

Severe infections, requiring hospitalization settings, have been shown to significantly increase the risk for later schizophrenia or mood disorders by 60 - 63% (Benros et al., 2011; Benros et al., 2013). More recently we demonstrated that clinically significant depressive psychopathology was present in approximately 30-40% of patients up to 12-month follow-up after SARS-CoV-2 infection, % defined from new psychopathological onset (Mazza et al., 2021), and that severe MI showed an increased COVID-19 morbidity and mortality compared to controls, regardless of sociodemographic and medical factors (Tzur Bitan et al., 2021).