AIM: Widely used second-generation antipsychotics are associated with adverse metabolic effects, contributing to increased cardiovascular mortality. To develop strategies to prevent or treat adverse metabolic effects, preclinical models have a clear role in uncovering underlying molecular mechanisms. However, with few exceptions, preclinical studies have been performed in healthy animals, neglecting the contribution of dysmetabolic features inherent to psychotic disorders. METHODS: In this study, methylazoxymethanol acetate (MAM) was prenatally administered to pregnant Sprague-Dawley rats at gestational day 17 to induce a well-validated neurodevelopmental model of schizophrenia mimicking its assumed pathogenesis with persistent phenotype. Against this background, the dysmetabolic effects of acute treatment with olanzapine and haloperidol were examined in female rats. RESULTS: Prenatally MAM-exposed animals exhibited several metabolic features, including lipid disturbances. Half of the MAM rats exposed to olanzapine had pronounced serum lipid profile alteration compared to non-MAM controls, interpreted as a reflection of a delicate MAM-induced metabolic balance disrupted by olanzapine. In accordance with the drugs' clinical metabolic profiles, olanzapine-associated dysmetabolic effects were more pronounced than haloperidol-associated dysmetabolic effects in non-MAM rats and rats exposed to MAM. CONCLUSION: Our results demonstrate metabolic vulnerability in female prenatally MAM-exposed rats, indicating that findings from healthy animals likely provide an underestimated impression of metabolic dysfunction associated with antipsychotics. In the context of metabolic disturbances, neurodevelopmental models possess a relevant background, and the search for adequate animal models should receive more attention within the field of experimental psychopharmacology.

• Keywords
• adipokine, antipsychotic, lipid profile, methylazoxymethanol, schizophrenia,
• MeSH
• Antipsychotic Agents * therapeutic use   MeSH
• Haloperidol * toxicity   MeSH
• Rats MeSH
• Lipids MeSH
• Methylazoxymethanol Acetate toxicity   analogs & derivatives   MeSH
• Disease Models, Animal MeSH
• Olanzapine toxicity   MeSH
• Rats, Sprague-Dawley MeSH
• Pregnancy MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Pregnancy MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Antipsychotic Agents * MeSH
• Haloperidol * MeSH
• Lipids MeSH
• methylazoxymethanol MeSH Browser
• Methylazoxymethanol Acetate MeSH
• Olanzapine MeSH

BACKGROUND: Insulin-degrading enzyme (IDE) is an important gene in studies of the pathophysiology of type 2 diabetes mellitus (T2DM). Recent studies have suggested a possible link between type 2 diabetes mellitus (T2DM) and the pathophysiology of schizophrenia (SZ). At the same time, significant changes in insulin-degrading enzyme (IDE) gene expression have been found in the brains of people with schizophrenia. These findings highlight the need to further investigate the role of IDE in schizophrenia pathogenesis. METHODS: We enrolled 733 participants from the Czech Republic, including 383 patients with schizophrenia and 350 healthy controls. Our study focused on the single nucleotide polymorphism (SNP) rs2421943 in the IDE gene, which has previously been associated with the pathogenesis of Alzheimer's disease. The SNP was analyzed using the PCR-RFLP method. RESULTS: The G allele of the rs2421943 polymorphism was found to significantly increase the risk of developing SZ (p < 0.01) when a gender-based analysis showed that both AG and GG genotypes were associated with a more than 1.55 times increased risk of SZ in females (p < 0.03) but not in males. Besides, we identified a potential binding site at the G allele locus for has-miR-7110-5p, providing a potential mechanism for the observed association. CONCLUSION: Our results confirm the role of the IDE gene in schizophrenia pathogenesis and suggest that future research should investigate the relationship between miRNA and estrogen influence on IDE expression in schizophrenia pathogenesis.

• Keywords
• candidate gene analyses, genetic association study, insulin-degrading enzyme (IDE), miRNA, schizophrenic disorder, single nucleotide polymorphism (SNP),
• MeSH
• Alzheimer Disease * genetics   metabolism   MeSH
• Diabetes Mellitus, Type 2 * epidemiology   genetics   MeSH
• Genotype MeSH
• Insulysin * genetics   metabolism   MeSH
• Polymorphism, Single Nucleotide genetics   MeSH
• Humans MeSH
• Schizophrenia * genetics   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Insulysin * MeSH

BACKGROUND: Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors. METHODS: To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered. FINDINGS: We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration. INTERPRETATION: The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality. FUNDING: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).For the Spanish and French translation of the abstract see Supplementary Materials section.

• Keywords
• Early Intervention, International Validation, Metabolic Syndrome, PAFIP, PsyMetab, Psychosis, Risk Prediction Algorithm,
• Publication type
• Journal Article MeSH

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust.

• MeSH
• Algorithms * MeSH
• Adult MeSH
• Cardiometabolic Risk Factors * MeSH
• Humans MeSH
• Metabolic Syndrome diagnosis   MeSH
• Adolescent MeSH
• Young Adult MeSH
• Psychotic Disorders * diagnosis   MeSH
• Reproducibility of Results MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Validation Study MeSH

IMPORTANCE: Cardiometabolic disorders often occur concomitantly with psychosis and depression, contribute to high mortality rates, and are detectable from the onset of the psychiatric disorders. However, it is unclear whether longitudinal trends in cardiometabolic traits from childhood are associated with risks for adult psychosis and depression. OBJECTIVE: To examine whether specific developmental trajectories of fasting insulin (FI) levels and body mass index (BMI) from early childhood were longitudinally associated with psychosis and depression in young adults. DESIGN, SETTING, AND PARTICIPANTS: A cohort study from the Avon Longitudinal Study of Parents and Children, a prospective study including a population-representative British cohort of 14 975 individuals, was conducted using data from participants aged 1 to 24 years. Body mass index and FI level data were used for growth mixture modeling to delineate developmental trajectories, and associations with psychosis and depression were assessed. The study was conducted between July 15, 2019, and March 24, 2020. EXPOSURES: Fasting insulin levels were measured at 9, 15, 18, and 24 years, and BMI was measured at 1, 2, 3, 4, 7, 9, 10, 11, 12, 15, 18, and 24 years. Data on sex, race/ethnicity, paternal social class, childhood emotional and behavioral problems, and cumulative scores of sleep problems, average calorie intake, physical activity, smoking, and alcohol and substance use in childhood and adolescence were examined as potential confounders. MAIN OUTCOMES AND MEASURES: Psychosis risk (definite psychotic experiences, psychotic disorder, at-risk mental state status, and negative symptom score) depression risk (measured using the computerized Clinical Interview Schedule-Revised) were assessed at 24 years. RESULTS: From data available on 5790 participants (3132 [54.1%] female) for FI levels and data available on 10 463 participants (5336 [51.0%] female) for BMI, 3 distinct trajectories for FI levels and 5 distinct trajectories for BMI were noted, all of which were differentiated by mid-childhood. The persistently high FI level trajectory was associated with a psychosis at-risk mental state (adjusted odds ratio [aOR], 5.01; 95% CI, 1.76-13.19) and psychotic disorder (aOR, 3.22; 95% CI, 1.29-8.02) but not depression (aOR, 1.38; 95% CI, 0.75-2.54). A puberty-onset major increase in BMI was associated with depression (aOR, 4.46; 95% CI, 2.38-9.87) but not psychosis (aOR, 1.98; 95% CI, 0.56-7.79). CONCLUSIONS AND RELEVANCE: The cardiometabolic comorbidity of psychosis and depression may have distinct, disorder-specific early-life origins. Disrupted insulin sensitivity could be a shared risk factor for comorbid cardiometabolic disorders and psychosis. A puberty-onset major increase in BMI could be a risk factor or risk indicator for adult depression. These markers may represent targets for prevention and treatment of cardiometabolic disorders in individuals with psychosis and depression.

• MeSH
• Depressive Disorder epidemiology   MeSH
• Child MeSH
• Adult MeSH
• Risk Assessment MeSH
• Body Mass Index * MeSH
• Insulin blood   MeSH
• Cardiometabolic Risk Factors * MeSH
• Infant MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Adolescent MeSH
• Young Adult MeSH
• Child, Preschool MeSH
• Psychotic Disorders epidemiology   MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• United Kingdom epidemiology   MeSH
• Names of Substances
• Insulin MeSH