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

July 21, 2026 updated by: Shalvata Mental Health Center

The study protocol was submitted to ERA-NET NEURON for funding on 28/06/2023. Description of the Israeli responsibilities was extracted from the full submitted research protocol. The protocol includes two studies (CHS1 and CHS2). At the time of the study registration CHS1 was partially analyzed whereas CHS2 has not been initiated. See below the full description of the two studies' protocols.

To explore the probability of mental illness (MI) onset or psychiatric relapse following infections, we will utilize two databases from the CHS registries from Israel (n=50,000, n=69,594). Participants with a high load of past infections (cohort 1, n=50,000) will be identified and matched in a 1:1 ratio to controls by age and sex. Probability of MI onset across a broad range of psychiatric disorders, including depression, bipolar disorder, anxiety and psychotic disorders will be explored. The probability of psychiatric relapse among individuals with pre-existing mental disorder following infection will be investigated in a second cohort of 34,797 individuals with schizophrenia matched randomly to age and sex controls with no diagnosis of schizophrenia (n = 34,797) (cohort 2, total n=69,594, 5). Socio and sociodemographic factors which might serve as vulnerability or resilience factors will be assessed across both cohorts, and will include environmental factors such as socioeconomic status, familial status, healthcare utilization information and demographic factors.

In addition, The CHS databases (n=50,000, n=69,594) will be utilized to study outcomes of infections in SMI. From the CHS databases in Israel, outcome of infections will be assessed in the two previously described cohorts. Severe outcomes will be defined as hospital admission ~two weeks after a diagnosis of an infection, among individuals with pre-existing anxiety, depression, bipolar diagnosis (n=50,000), and among patients with schizophrenia (n=69,594), as well as all-cause mortality. The following infections will be considered: Epstein Barr Virus, Cytomegalovirus, Toxoplasma Gondii, COVID-19, and Herpes viruses. Environmental protective and risk factors and their moderating role in the association between infection and outcome will include marital status, number of siblings, and sociodemographic factors. Vulnerability factors such as smoking, obesity, and comorbid physical illness will also be examined.

The presence of pre-existing viral infections will be assessed as a potential vulnerability factor.

Study Overview

Status

Active, not recruiting

Conditions

Detailed Description

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).

Hypothesis:

History of viral infections will be associated with increased risk for later development of a MI, as well as with psychiatric relapse among individuals with pre-existing MI.

Methods:

The Israel cohort consists of two databases from the Clalit Health Services (CHS) registries from Israel (CHS1 n=50,000, CHS2 n=69,594). The CHS is the largest of four operating healthcare organisations to provide healthcare to all citizens of Israel, and covers more than 50% of Israel's population. The CHS databases undergo periodic updating processes and have been validated by the registry algorithm as well as by many scientific organisations utilising the database. The diagnoses of chronic diseases are based on real-time input from healthcare providers, pharmacies, medical care facilities, and administrative computerised operating systems. Psychiatric Diagnoses are based on the ICD-9 and ICD-10 classifications. To evaluate probability of MI following infection and post-infection outcomes (CHS1), individuals with a high load of past infections will be identified and matched in a 1:1 ratio to controls by age and sex. Probability of MI onset across a broad range of psychiatric disorders will be explored among the high load and control groups, including depression, BD, anxiety and psychotic disorders. Inclusion criteria: individuals insured by the CHS since birth and with at least 10 years of follow up; exclusion criteria include termination of insurance and lack of successive medical follow up. To evaluate the probability of psychiatric relapse among individuals with pre-existing mental disorder following infection (CHS2), a cohort of 34,797 individuals with schizophrenia matched randomly to age and sex HC with no diagnosis of schizophrenia will be exploited. Inclusion criteria for this sample is an active diagnosis of schizophrenia in CHS registries during stages of analyses, and place of residence is at the CHS hospital catchment areas (to ensure psychiatric hospitalisation is fully registered); exclusion criteria include lack of active diagnosis and place of residency outside of CHS catchment area. Across both cohorts, socioeconomic status, familial status, marital status, number of siblings, smoking, obesity, diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease and ischemic heart disease, as well as healthcare utilisation and other demographic variables have been collected. The following infections will be considered across both cohorts: Epstein Barr Virus, Cytomegalovirus, Toxoplasma Gondii, COVID-19, and Herpes viruses. Measures of inflammation include neutrophil/lymphocyte ratio and systemic inflammatory index.

Power and sample size considerations:

To maximise accuracy, restrictive estimates of the least prevalent MIs were utilised for sample size estimation. Based on O'Donoghue et al. findings (O'Donoghue et al., 2022), first episode psychosis before COVID-19 epidemic was 104.5 per 100,000 with an incidence rate ratio after the pandemic of 1.94. Assuming that at least some of the variance in psychosis incidence is associated with post-viral infection, we estimate sample size needed to detect exposure (infection) impact with. 80% power and 95% Ci, the χ2 test with continuity correction indicated that the most conservative estimated sample size needed to detect such difference was 27,168. Thus, the sample size of both the Israel and Norway cohort far exceeds the minimum sample size needed to produce the most conservative estimates.

Statistical Analysis:

To demonstrate an association between infections, with a particular focus on viral ones, severe MI and post-infection severe outcomes (Objective 1), we will compute hazard ratios (HRs) to assess the risk of SMI development or psychiatric relapse following infection using Cox proportional hazard regression models in all cohorts (CHS1, CHS2). Incidence rates and crude and adjusted models controlling for demographic and clinical factors will be reported. The proportional hazard assumption will be tested as the correlation between the Schoenfeld residuals and survival time, with significance level of p<0.05 indicating non-proportionality. Estimated projections of the cumulative probability of severe outcome among individuals with pre-existing SMI will be obtained by Kaplan-Meier analysis. Confounding, moderation and mediation patterns of environmental (socio and sociodemographic) and biological (inflammatory, polygenic risk score (PRS) for MI and immune-related conditions) mechanisms will be assessed using the PROCESS macro, a simulation-based strategy based on re-sampling (bootstrapping) techniques. Direct and indirect effects, standard errors and confidence intervals will be estimated based on the bootstrap distribution found with 10,000 bias-corrected resamples. PRS will be computed following the method described by Purcell et al. (Tzur Bitan et al., 2021) Analyses will be performed based on the directed acyclic graph (DAG) causal framework, ensuring transparent model assumptions and minimising bias. All three databases provide nation-wide representative data and have proven their efficiency in characterising MI cohorts and associations with environmental factors, as shown by high impact publication (Greenberg et al., 2020; Vai et al., 2020).

Study Type

Observational

Enrollment (Estimated)

100000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Israel
      • Haifa, Israel
        • University of Haifa

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

The Israel cohort consists of two databases from the Clalit Health Services (CHS) registries from Israel. The CHS is the largest of four operating healthcare organisations to provide healthcare to all citizens of Israel, and covers more than 50% of Israel's population. The CHS databases undergo periodic updating processes and have been validated by the registry algorithm as well as by many scientific organisations utilising the database. The diagnoses of chronic diseases are based on real-time input from healthcare providers, pharmacies, medical care facilities, and administrative computerised operating systems.

Description

In CHS 1:

Inclusion criteria:

- Individuals insured by the CHS since birth and with at least 10 years of follow up.

Exclusion criteria:

- Termination of insurance and lack of successive medical follow up.

In CHS 2:

Inclusion criteria:

  • Active diagnosis of schizophrenia in CHS registries during stages of analyses
  • Place of residence is at the CHS hospital catchment areas (to ensure psychiatric hospitalisation is fully registered).

Exclusion criteria:

- Lack of active diagnosis and place of residency outside of CHS catchment area.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
CHS 1
Participants exposed to infections and unexposed to infections.
CHS 2
Schizophrenia and controls.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Incidence of new-onset mental illness
Time Frame: Up to 30 years.
In CHS1, incidence of new psychiatric disorder diagnoses following viral infection, including anxiety disorders, depressive disorders, schizophrenia, and bipolar disorder, identified using ICD-10 diagnostic codes extracted from electronic health records, reported as the percentage of participants with a new diagnosis during follow-up. Participants are followed from the date of first infection until their first following mental disorder diagnosis (or end of followup). The outcome will be expressed as the proportion of participants with an incident diagnosis and as the hazard ratio estimated by Cox proportional hazards regression.
Up to 30 years.
Incidence of post-infection psychiatric relapse
Time Frame: 3 months post infection
CHS2 will assess incidence of post-infection psychiatric relapse, defined as psychiatric-related hospital admissions or emergency room visits, following viral infection. These will be identified using electronic health records. The outcome will be expressed as occurrence psychiatric relapse as described above, through hazard ratio estimated by Cox proportional hazards regression.
3 months post infection

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shalvata Mental Health Center

Collaborators

Oslo University Hospital
University Hospital, Antwerp
San Raffaele University Hospital, Italy

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 17, 2024

Primary Completion (Estimated)

March 1, 2027

Study Completion (Estimated)

March 1, 2027

Study Registration Dates

First Submitted

March 1, 2026

First Submitted That Met QC Criteria

March 1, 2026

First Posted (Actual)

March 6, 2026

Study Record Updates

Last Update Posted (Actual)

July 23, 2026

Last Update Submitted That Met QC Criteria

July 21, 2026

Last Verified

July 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • VIRAL-MI
  • NEURON_RV-086 (Other Grant/Funding Number: NEURON, the 'Network of European Funding for Neuroscience Research' established under the ERA-NET)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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