Inflammation, Mood Disorders, Schizophrenia, Severe Mental Illness, Viral Infections
Conditions
Brief summary
This observational study aims to identify the underlying neurobiological and environmental mechanisms that influence vulnerability or resilience to mental illness in the context of infection and their contribution to severe infective outcomes in people with pre-existing mental illness. The main questions it aims to answer are: * How do viral infections influence the development of mental illness? * What neurobiological and environmental factors contribute to influence the development of mental illness following infection? * How do these factors relate to the severity of infectious illness in people with pre-existing mental disorders? Researchers will move from large population databases to well-defined, deeply characterised samples to explore the association between infection and subsequent mental health outcomes, and the biological mechanisms behind these changes. Participants's data has already been collected.
Interventions
OTHERThis research doesn't involve any kind of intervention on the study participants
This research doesn't involve any kind of intervention on the study participants
Sponsors
Sara Poletti
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
for the MOOD-MI cohort: * A depressive episode according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria in the course of MDD or BD with: * HDRS score \> 17 * Age 18-65 years; * Signed informed consent, able to understand, speak and write the national language Inclusion Criteria for the COVID-MI cohort: * Having a positive nasopharyngeal swab for COVID-19 in the last 36 months; * Age between 18-25 years; * Signed informed consent, able to understand, speak and write the national language
Exclusion criteria
for the MOOD-MI cohort: * History of schizophrenia, schizoaffective disorder, psychosis not otherwise specified; anorexia or bulimia nervosa; * Taking following medications: antipsychotics, anticonvulsants, mood stabilizers; stimulants * Active infection requiring antibiotics therapy; * Immunosuppressed patient or other chronic diseases * Signs of active infection requiring treatment * Use of anti-inflammatory medication on a regular basis for a chronic inflammatory/autoimmune Disorder. Forbidden treatment: corticosteroids, Non Steroidal Anti-inflammatory Drugs, immunosuppressant IV-Ig based treatment * Ongoing fever, infection treated by antibiotics or uncontrolled diabetes type I or II; * Existing cancer or history of cancer in the last 5 years (except skin epidermoid cancer or in-situ cervix cancer); * Known HIV infection or clinically manifest Acquired Immune Deficiency Syndrome (AIDS), Parkinson's or Alzheimer's disease, or any other serious condition likely to interfere e with the conduct of the trial; * Abuse of drugs or alcohol in the past 6 months
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| number of infections (Pathogen Burden Summary - PBS) in patients with mental illness | 3 years | number of samples positive for antibodies for viruses associated with Mental illness in the literature (IgG, expressed as AU/mL) |
| number of hospital admission for mental illness after infection diagnosis | 3 years | number of onset or relapse of mental illness following infection |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Environmental risk and resilience factors for mental illness and severe outcomes | 3 years | socio-economic socio-demographic variables inflammatory markers levels, timing and type of infection, |
| Neurobiological risk and resilience factors for mental illnesses and severe outcomes | 3 years | differentially expressed gene between patients with high vs low Pathogen Burden Summary (PBS) with or without mental illness |
| Predictive risk models based on an individual biological and environmental signatures | 3 years | stratification of patients in risk and resilence clusters using unsupervised machine learning algorithms based on * history of infection * plasmatic concentrations of immune analytes; * metabolites of the kynurenine pathway; * Polygenic Risk Scores; * gene expression; * measures of brain structure * measures of early and recent stress |
| genes or proteins that might be modified using drugs | 3 years | target genes involved in severe and post-infectious MI. |
Countries
Belgium, Israel, Italy, Norway
Outcome results
None listed