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BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines. METHODS: The cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (nâ¯=â¯205), stable bipolar outpatients (nâ¯=â¯116), hospitalized acute schizophrenia patients (nâ¯=â¯111), stable schizophrenia outpatients (nâ¯=â¯75) and healthy controls (nâ¯=â¯185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-É), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers. RESULTS: The models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV)â¯=â¯86.95; Negative Prediction Value (NPV)â¯=â¯54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-É and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627. CONCLUSIONS: This study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-É, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic.
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A history of Childhood Maltreatment (CM) has been repeatedly associated with an increased risk of developing bipolar disorders (BD) or schizophrenia (SZ). The impact of severe stress induced by CM has been proposed to be mediated by elevated inflammation reflected by dysregulated inflammatory processes. Little is known about the potential impact of CM on lymphocyte subpopulations or the role of pre-existing infections on CM physiological consequences. This study therefore explored the role of CM and past infection exposure impact on lymphocyte subpopulations to give an indication of their relevance as stressors in the pathoetiology of major mood and psychotic disorders. 118 adult patients with SZ, and 152 with BD were included in the analysis. CM history was assessed by the Childhood Trauma Questionnaire (CTQ), with current and past psychiatric symptomatology also evaluated. Circulating immune cell subsets were analyzed using flow cytometry-based analysis. Past exposure to common infectious stigma including toxoplasma, cytomegalovirus (CMV) and Epstein-Barr virus (EBV) were measured by solid phase-enzyme microplate and ELISA immunoassays. The relationship between CM, biological phenotypes (including immune cell subsets distribution and past infectious status) and clinical phenotypes were analyzed using univariate and multivariate analyses. BD patients with, versus without, CM had higher levels of CD3+CD8+ cytotoxic T cells and CMV antibodies along with decreased levels of CD45RA+CCR7+CD8+ naïve CD8+ T cells, and a more severe clinical profile. CMV antibody levels were inversely associated with the CD3 + CD8 + lymphocyte subset level. SZ patients with, versus without, CM, showed lower levels of CD14 + monocytes and no specific clinical characteristics. The accumulation of different types of maltreatment associated with increased body mass index and CMV autoantibodies as well as decreased levels of CD14 + monocytes. In both BD and SZ, further analysis according to the type and the number of CM subtypes showed association with specific changes in lymphocyte cell subsets, clinical profile, and infectious stigma. Adults with BD or SZ exposed to CM exhibit specific immune cell subset profiles, clinical features, and stigma of past infections. In BD, our data indicate an interplay between CM and CMV infections, which may possibly contribute to premature aging and cellular senescence, both of which have previously been shown to associate with mood disorders. Longitudinal studies of CM-exposed patients are required to clarify the interactions of CM and viral infections, including as to the pathophysiological processes driving patient symptomatology.
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Transtorno Bipolar , Maus-Tratos Infantis , Infecções por Citomegalovirus , Infecções por Vírus Epstein-Barr , Esquizofrenia , Adulto , Humanos , Criança , Transtorno Bipolar/complicações , Linfócitos T CD8-Positivos , Infecções por Vírus Epstein-Barr/complicações , Herpesvirus Humano 4 , Infecções por Citomegalovirus/complicações , Citomegalovirus , Maus-Tratos Infantis/psicologiaRESUMO
Schizophrenia (SCZ) and bipolar disorder (BD) are associated with immunological dysfunctions that have been hypothesized to lead to clinical symptomatology in particular through kynurenine pathway abnormalities. The aim of this study was thus to investigate the impact of serum kynurenine metabolite levels on diagnosis, clinical state, symptom severity and clinical course in a large French transdiagnostic cohort of SCZ and BD patients. Four patient groups (total n = 507) were included in a cross-sectional observational study: 1) hospitalized acute bipolar patients (n = 205); 2) stable bipolar outpatients (n = 116); 3) hospitalized acute schizophrenia patients (n = 111) and 4) stable schizophrenia outpatients (n = 75), in addition to healthy controls (HC) (n = 185). The quantitative determination of serum kynurenine metabolites was performed using liquid chromatography-tandem mass spectrometry. Kynurenine levels were lower in all patients combined compared to HC while ANCOVA analyses did not reveal inter-diagnostic difference between SCZ and BD. Interestingly, hospitalized patients of both diagnostic groups combined displayed significantly lower kynurenine levels than stabilized outpatients. Psychotic symptoms were associated with lower quinaldic acid (F = 9.18, p=<.001), which is KAT-driven, whereas a longer duration of illness contributed to abnormalities in tryptophan (F = 5.41, p = .023), kynurenine (F = 16.93, p=<.001), xanthurenic acid (F = 9.34, p = .002), quinolinic acid (F = 9.18, p = .003) and picolinic acid (F = 4.15, p = .043), metabolized through the KMO-branch. These data confirm illness state rather than diagnosis to drive KP alterations in SCZ and BD. Lower levels of KP metabolites can thus be viewed as a transdiagnostic feature of SCZ and BD, independently associated with acute symptomatology and a longer duration of illness. Quinaldic acid has seldomly been investigated by previous studies and appears an important state marker in SCZ and BD. As serum samples are used in this study, it is not possible to extrapolate these findings to the brain.
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Background: Metabolic syndrome (MetS) is a highly prevalent and harmful medical disorder often comorbid with psychosis where it can contribute to cardiovascular complications. As immune dysfunction is a key shared component of both MetS and schizophrenia (SZ), this study investigated the relationship between immune alterations and MetS in patients with SZ, whilst controlling the impact of confounding clinical characteristics including psychiatric symptoms and comorbidities, history of childhood maltreatment and psychotropic treatments. Method: A total of 310 patients meeting DSM-IV criteria for SZ or schizoaffective disorders (SZA), with or without MetS, were systematically assessed and included in the FondaMental Advanced Centers of Expertise for Schizophrenia (FACE-SZ) cohort. Detailed clinical characteristics of patients, including psychotic symptomatology, psychiatric comorbidities and history of childhood maltreatment were recorded and the serum levels of 18 cytokines were measured. A penalized regression method was performed to analyze associations between inflammation and MetS, whilst controlling for confounding factors. Results: Of the total sample, 25% of patients had MetS. Eight cytokines were above the lower limit of detection (LLOD) in more than 90% of the samples and retained in downstream analysis. Using a conservative Variable Inclusion Probability (VIP) of 75%, we found that elevated levels of interleukin (IL)-6, IL-7, IL-12/23 p40 and IL-16 and lower levels of tumor necrosis factor (TNF)-α were associated with MetS. As for clinical variables, age, sex, body mass index (BMI), diagnosis of SZ (not SZA), age at the first episode of psychosis (FEP), alcohol abuse, current tobacco smoking, and treatment with antidepressants and anxiolytics were all associated with MetS. Conclusion: We have identified five cytokines associated with MetS in SZ suggesting that patients with psychotic disorders and MetS are characterized by a specific "immuno-metabolic" profile. This may help to design tailored treatments for this subgroup of patients with both psychotic disorders and MetS, taking one more step towards precision medicine in psychiatry.
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Schizophrenia (SZ) and bipolar disorders (BD) are severe mental illnesses that lack reliable biomarkers to guide diagnosis and management. As immune dysregulation is associated with these disorders, we utilized the immunoregulatory functions of the natural killer cell inhibitory HLA-E locus to investigate the relationships between HLA-E genetic and expression diversities with SZ and BD risk and severity. Four hundred and forty-four patients meeting DSM-IV criteria for SZ (N = 161) or BD (N = 283) were compared to 160 heathy controls (HC). Circulating levels of the soluble isoform of HLA-E molecules (sHLA-E) were measured and HLA-E*01:01 and HLA-E*01:03 variants genotyped in the whole sample. sHLA-E circulating levels were significantly higher in both SZ and in BD patients compared to HC (pc < 0.0001 and pc = 0.0007 for SZ and BD, respectively). High sHLA-E levels were also observed in stable SZ patients and in acute BD patients experiencing depressive episodes when comparisons were made between the acute and stable subgroups of each disorder. sHLA-E levels linearly increased along HLA-E genotypes (p = 0.0036). In conclusion, HLA-E variants and level may have utility as diagnostic biomarkers of SZ and BD. The possible roles of HLA diversity in SZ and BD etiology and pathophysiology are discussed.
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Transtorno Bipolar/imunologia , Antígenos de Histocompatibilidade Classe I/sangue , Antígenos de Histocompatibilidade Classe I/genética , Esquizofrenia/imunologia , Regulação para Cima , Biomarcadores/sangue , Transtorno Bipolar/genética , Estudos de Casos e Controles , Variação Genética , Genótipo , Humanos , Masculino , Gravidade do Paciente , Esquizofrenia/genética , Antígenos HLA-ERESUMO
Mitochondria (Mt) are intra-cellular components essential for cellular energy processes whose dysfunction may induce premature cellular senescence and/or inflammation, both observed in bipolar disorders (BD). We investigated mitochondrial DNA copy number (mtDNAcn) levels in patients with BD being in manic, depressive or euthymic phase and in healthy controls (HC) both characterized for the levels of blood-based inflammatory markers and stigma of pathogens. 312 patients with BD were compared to 180 HC. mtDNAcn were measured using a digital droplet PCR. Serum levels of 14 inflammatory molecules and 3 anti-infectious IgG stigma were respectively evaluated by electro-chemiluminescence, ELISA and dedicated immunoassays. The statistical analyses were performed using Spearman's correlation, Wilcoxon signed-rank and Kruskal-Wallis rank sum tests. P-values were adjusted for multiple testing with Benjamini-Hochberg method. We found low levels of mtDNAcn in BD patients as compared to HC (P = 0.008) especially during manic episodes (P = 0.0002). We also observed that low levels of mtDNAcn are negatively correlated with mood and psychotic scales (PANSS, YMRS and CGI) (adjusted P (Adj P) = 0.02, 0.003 and 0.05 respectively) and positively with the GAF severity scale (Adj P = 0.002). They were also correlated with high levels of both intercellular adhesion molecule (ICAM)-1 and vascular cell adhesion molecule (VCAM)-1 (Adj P = 0.003 and 0.001) along with a trend toward increased IL-2, IL-10 and B2M circulating levels (Adj P = 0.05). Here, we report correlations between marker of mitochondria functioning and both clinical scales and inflammatory markers in BD patients experiencing manic episodes. If replicated, these finding might allow to predict transition between disease phases and to design accurate therapeutic options.
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Transtorno Bipolar , Transtorno Bipolar/genética , Variações do Número de Cópias de DNA , DNA Mitocondrial/genética , Humanos , Inflamação , Mania , Mitocôndrias , Índice de Gravidade de DoençaRESUMO
Human endogenous retroviruses (HERVs) are remnants of infections that took place several million years ago and represent around 8% of the human genome. Despite evidence implicating increased expression of HERV type W envelope (HERV-W ENV) in schizophrenia and bipolar disorder, it remains unknown whether such expression is associated with distinct clinical or biological characteristics and symptoms. Accordingly, we performed unsupervised two-step clustering of a multivariate data set that included HERV-W ENV protein antigenemia, serum cytokine levels, childhood trauma scores, and clinical data of cohorts of patients with schizophrenia (n = 29), bipolar disorder (n = 43) and healthy controls (n = 32). We found that subsets of patients with schizophrenia (~41%) and bipolar disorder (~28%) show positive antigenemia for HERV-W ENV protein, whereas the large majority (96%) of controls was found to be negative for ENV protein. Unsupervised cluster analysis identified the presence of two main clusters of patients, which were best predicted by the presence or absence of HERV-W ENV protein. HERV-W expression was associated with increased serum levels of inflammatory cytokines and higher childhood maltreatment scores. Furthermore, patients with schizophrenia who were positive for HERV-W ENV protein showed more manic symptoms and higher daily chlorpromazine (CPZ) equivalents, whereas HERV-W ENV positive patients with bipolar disorder were found to have an earlier disease onset than those who were negative for HERV-W ENV protein. Taken together, our study suggest that HERV-W ENV protein antigenemia and cytokines can be used to stratify patients with major mood and psychotic disorders into subgroups with differing inflammatory and clinical profiles.
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Transtorno Bipolar , Retrovirus Endógenos , Esquizofrenia , Análise por Conglomerados , Produtos do Gene env/genética , Humanos , Esquizofrenia/genéticaRESUMO
BACKGROUND: Mental disorders might be a risk factor for severe COVID-19. We aimed to assess the specific risks of COVID-19-related mortality, hospitalisation, and intensive care unit (ICU) admission associated with any pre-existing mental disorder, and specific diagnostic categories of mental disorders, and exposure to psychopharmacological drug classes. METHODS: In this systematic review and meta-analysis, we searched Web of Science, Cochrane, PubMed, and PsycINFO databases between Jan 1, 2020, and March 5, 2021, for original studies reporting data on COVID-19 outcomes in patients with psychiatric disorders compared with controls. We excluded studies with overlapping samples, studies that were not peer-reviewed, and studies written in languages other than English, Danish, Dutch, French, German, Italian, and Portuguese. We modelled random-effects meta-analyses to estimate crude odds ratios (OR) for mortality after SARS-CoV-2 infection as the primary outcome, and hospitalisation and ICU admission as secondary outcomes. We calculated adjusted ORs for available data. Heterogeneity was assessed using the I2 statistic, and publication bias was tested with Egger regression and visual inspection of funnel plots. We used the GRADE approach to assess the overall strength of the evidence and the Newcastle Ottawa Scale to assess study quality. We also did subgroup analyses and meta-regressions to assess the effects of baseline COVID-19 treatment setting, patient age, country, pandemic phase, quality assessment score, sample sizes, and adjustment for confounders. This study is registered with PROSPERO, CRD42021233984. FINDINGS: 841 studies were identified by the systematic search, of which 33 studies were included in the systematic review and 23 studies in the meta-analysis, comprising 1 469 731 patients with COVID-19, of whom 43 938 had mental disorders. The sample included 130 807 females (8·9% of the whole sample) and 130 373 males (8·8%). Nine studies provided data on patient race and ethnicity, and 22 studies were rated as high quality. The presence of any mental disorder was associated with an increased risk of COVID-19 mortality (OR 2·00 [95% CI 1·58-2·54]; I2=92·66%). This association was also observed for psychotic disorders (2·05 [1·37-3·06]; I2=80·81%), mood disorders (1·99 [1·46-2·71]; I2=68·32%), substance use disorders (1·76 [1·27-2·44]; I2=47·90%), and intellectual disabilities and developmental disorders (1·73 [1·29-2·31]; I2=90·15%) but not for anxiety disorders (1·07 [0·73-1·56]; I2=11·05%). COVID-19 mortality was associated with exposure to antipsychotics (3·71 [1·74-7·91]; I2=90·31%), anxiolytics (2·58 [1·22-5·44]; I2=96·42%), and antidepressants (2·23 [1·06-4·71]; I2=95·45%). For psychotic disorders, mood disorders, antipsychotics, and anxiolytics, the association remained significant after adjustment for age, sex, and other confounders. Mental disorders were associated with increased risk of hospitalisation (2·24 [1·70-2·94]; I2=88·80%). No significant associations with mortality were identified for ICU admission. Subgroup analyses and meta-regressions showed significant associations of baseline COVID-19 treatment setting (p=0·013) and country (p<0·0001) with mortality. No significant associations with mortality were identified for other covariates. No evidence of publication bias was found. GRADE assessment indicated high certainty for crude mortality and hospitalisation, and moderate certainty for crude ICU admission. INTERPRETATION: Pre-existing mental disorders, in particular psychotic and mood disorders, and exposure to antipsychotics and anxiolytics were associated with COVID-19 mortality in both crude and adjusted models. Although further research is required to determine the underlying mechanisms, our findings highlight the need for targeted approaches to manage and prevent COVID-19 in at-risk patient groups identified in this study. FUNDING: None. TRANSLATIONS: For the Italian, French and Portuguese translations of the abstract see Supplementary Materials section.