Longitudinal trajectories of plasma polyunsaturated fatty acids and associations with psychosis-spectrum outcomes in early adulthood.

BACKGROUND: Evidence supports associations between polyunsaturated fatty acids (PUFAs) such as docosahexaenoic acid (DHA) and psychosis. However, PUFA trajectories in the general population have not been characterised and associations with psychosis-spectrum outcomes in early adulthood are unknown. background METHODS: Plasma omega-6:omega-3 ratio and DHA %total fatty acids were measured by nuclear magnetic spectroscopy at 7,15,17 and 24years in the Avon Longitudinal Study of Parents and Children. Curvilinear growth mixture modelling evaluated BMI-adjusted trajectories of both measures. Outcomes were assessed at 24years. Psychotic experiences (PEs), At-Risk-Mental-State status, psychotic disorder and number of PEs were assessed using the Psychosis-Like Symptoms interview PLIKSi (n=3635, 2247 [61.8%]female). Negative symptoms score was measured using the Community Assessment of Psychic Experiences (n=3484, 2161 [62.0%]female). Associations were adjusted for sex, ethnicity, parental social class, cumulative smoking and alcohol use. RESULTS: Relative to stable average, the persistently high omega-6:omega-3 ratio trajectory was associated with increased odds of PEs and psychotic disorder, but attenuated on adjustment for covariates (PEs adjusted odds ratio[aOR] 1.63, 95% confidence interval[CI] 0.92-2.89; psychotic disorder aOR 1.69, 95%CI 0.71-4.07). This was also the case for persistently low DHA (PEs aOR 1.42, 95%CI 0.84-2.37; psychotic disorder aOR 1.14, 95%CI 0.49-2.67). Following adjustment, persistently high omega-6:omega-3 ratio was associated with increased number of PEs (ß0.41, 95%CI 0.05-0.78) and negative symptoms score (ß0.43, 95%CI 0.14-0.72), as was persistently low DHA (number of PEs:ß 0.45, 95%CI 0.14-0.76; negative symptoms:ß 0.35, 95%CI 0.12-0.58). CONCLUSIONS: Optimisation of PUFA status during development warrants further investigation in relation to psychotic symptoms in early adulthood.

Using Electronic Health Records to Facilitate Precision Psychiatry.

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

The relationship of within-individual and between-individual variation in mental health with bodyweight: An exploratory longitudinal study.

BACKGROUND: Poor mental health is associated with obesity, but existing studies are either cross-sectional or have long time periods between measurements of mental health and weight. It is, therefore, unclear how small fluctuations in mental wellbeing within individuals predict bodyweight over short time periods, e.g. within the next month. Studying this could identify modifiable determinants of weight changes and highlight opportunities for early intervention. METHODS: 2,133 UK adults from a population-based cohort completed monthly mental health and weight measurements using a mobile app over a period of 6-9 months. We used random intercept regression models to examine longitudinal associations of depressive symptoms, anxiety symptoms and stress with subsequent weight. In sub-group analyses, we included interaction terms of mental health variables with baseline characteristics. Mental health variables were split into "between-individual" measurements (= the participant's median score across all timepoints) and "within-individual" measurements (at each timepoint, the difference between the participant's current score and their median). RESULTS: Within-individual variation in depressive symptoms predicted subsequent weight (0.045kg per unit of depressive symptom severity, 95% CI 0.021-0.069). We found evidence of a moderation effect of baseline BMI on the association between within-individual fluctuation in depressive symptoms and subsequent weight: The association was only apparent in those with overweight/obesity, and it was stronger in those with obesity than those with overweight (BMI<25kg/m2: 0.011kg per unit of depressive symptom severity [95% CI -0.017 to 0.039]; BMI 25-29.9kg/m2: 0.052kg per unit of depressive symptom severity [95%CI 0.010-0.094kg]; BMI≥30kg/m2: 0.071kg per unit of depressive symptom severity [95%CI 0.013-0.129kg]). We found no evidence for other interactions, associations of stress and anxiety with weight, or for a reverse direction of association. CONCLUSION: In this exploratory study, individuals with overweight or obesity were more vulnerable to weight gain following higher-than-usual (for that individual) depressive symptoms than individuals with a BMI<25kg/m2.

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information.

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

Schizophrenia and cardiometabolic abnormalities: A Mendelian randomization study.

Background: Individuals with a diagnosis of schizophrenia are known to be at high risk of premature mortality due to poor physical health, especially cardiovascular disease, diabetes, and obesity. The reasons for these physical health outcomes within this patient population are complex. Despite well-documented cardiometabolic adverse effects of certain antipsychotic drugs and lifestyle factors, schizophrenia may have an independent effect. Aims: To investigate if there is evidence that schizophrenia is causally related to cardiometabolic traits (blood lipids, anthropometric traits, glycaemic traits, blood pressure) and vice versa using bi-directional two-sample Mendelian randomization (MR) analysis. Methods: We used 185 genetic variants associated with schizophrenia from the latest Psychiatric Genomics Consortium GWAS (n = 130,644) in the forward analysis (schizophrenia to cardiometabolic traits) and genetic variants associated with the cardiometabolic traits from various consortia in the reverse analysis (cardiometabolic traits to schizophrenia), both at genome-wide significance (5 × 10-8). The primary method was inverse-variance weighted MR, supported by supplementary methods such as MR-Egger, as well as median and mode-based methods. Results: In the forward analysis, schizophrenia was associated with slightly higher low-density lipoprotein (LDL) cholesterol levels (0.013 SD change in LDL per log odds increase in schizophrenia risk, 95% CI, 0.001-0.024 SD; p = 0.027) and total cholesterol levels (0.013 SD change in total cholesterol per log odds increase in schizophrenia risk, 95% CI, 0.002-0.025 SD; p = 0.023). However, these associations did not survive multiple testing corrections. There was no evidence of a causal effect of cardiometabolic traits on schizophrenia in the reverse analysis. Discussion: Dyslipidemia and obesity in schizophrenia patients are unlikely to be driven primarily by schizophrenia itself. Therefore, lifestyle, diet, antipsychotic drugs side effects, as well as shared mechanisms for metabolic dysfunction and schizophrenia such as low-grade systemic inflammation could be possible reasons for the apparent increased risk of metabolic disease in people with schizophrenia. Further research is needed to examine the shared immune mechanism hypothesis.

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis.

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples.

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.

Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses.

BACKGROUND: Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology. METHODS: We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology. RESULTS: We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways. CONCLUSIONS: LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.

Prenatal and Childhood Immuno-Metabolic Risk Factors for Adult Depression and Psychosis.

ABSTRACT: Depression and psychosis have a developmental component to their origin. Epidemiologic evidence, which we synthesize in this nonsystematic review, suggests that early-life infection, inflammation, and metabolic alterations could play a role in the etiology of these psychiatric disorders. The risk of depression and psychosis is associated with prenatal maternal and childhood infections, which could be mediated by impaired neurodevelopment. Evidence suggests linear dose-response associations between elevated concentrations of circulating inflammatory markers in childhood, particularly the inflammatory cytokine interleukin 6, and the risk for depression and psychosis subsequently in early adulthood. Childhood inflammatory markers are also associated with persistence of depressive symptoms subsequently in adolescence and early adulthood. Developmental trajectories reflecting persistently high insulin levels during childhood and adolescence are associated with a higher risk of psychosis in adulthood, whereas increased adiposity during and after puberty is associated with the risk of depression. Together, these findings suggest that higher levels of infection, inflammation, and metabolic alterations commonly seen in people with depression and psychosis could be a cause for, rather than simply a consequence of, these disorders. Therefore, early-life immuno-metabolic alterations, as well as factors influencing these alterations such as adversity or maltreatment, could represent targets for prevention of these psychiatric disorders. Inflammation could also be an important treatment target for depression and psychosis. The field requires further research to examine sensitive periods when exposure to such immuno-metabolic alterations is most harmful. Interventional studies are also needed to test the potential usefulness of targeting early-life immuno-metabolic alterations for preventing adult depression and psychosis.

Adipose tissue dysfunction, inflammation, and insulin resistance: alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study.

Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case-control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = -0.69, 95% CI: -1.28, -0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.

Associations of immunological proteins/traits with schizophrenia, major depression and bipolar disorder: A bi-directional two-sample mendelian randomization study.

BACKGROUND: Schizophrenia, bipolar disorder and depression are associated with inflammation. However, it is unclear whether associations of immunological proteins/traits with these disorders are likely to be causal, or could be explained by reverse causality/residual confounding. METHODS: We used bi-directional two-sample Mendelian randomization (MR) and multi-variable MR (MVMR) analysis to examine evidence of causality, specificity and direction of association of 20 immunological proteins/traits (pro-inflammatory cytokines: interleukin (IL)-6, tumour necrosis factor (TNF)-α, IL-12, IL-16, IL-17, IL-18; anti-inflammatory cytokines: IL-1 receptor antagonist (RA), IL-10, IL-13; chemokines: IL-8, monocyte chemo-attractant protein-1 (MCP-1); lymphoid growth-factors: soluble (s) IL-2Rα, IL-4, IL-7, IL-9; myeloid growth-factor: IL-5; acute phase protein: C-Reactive Protein (CRP); immune cells: neutrophils, lymphocytes; neurotrophic factor: brain derived neurotrophic factor (BDNF)) with schizophrenia, major depression and bipolar disorder. RESULTS: Genetically-predicted IL-6 was associated with increased risk of schizophrenia in univariable MR (OR = 1.24; 95% C.I., 1.05-1.47) and with major depression in MVMR (OR = 1.08; 95% C.I., 1.03-1.12). These results survived Bonferroni-correction. Genetically-predicted sIL-2Rα (OR = 1.07; 95% C.I., 1.01-1.12) and IL-9 (OR = 1.06; 95% C.I., 1.01-1.11) were associated with increased schizophrenia risk. Genetically-predicted BDNF (OR = 0.97; 95% C.I., 0.94-1.00) and MCP-1 (OR = 0.96; 95% C.I., 0.91-0.99) were associated with reduced schizophrenia risk. However, these findings did not survive correction for multiple testing. The CRP-schizophrenia association attenuated completely after taking into account IL-6 and sIL-2Rα in MVMR (OR = 1.02; 95% C.I., 0.81-1.28). No significant associations were observed for bipolar disorder. Evidence from bidirectional MR did not support reverse causality. CONCLUSIONS: We report evidence in support of potential causal associations of several immunological proteins/traits with schizophrenia, and of IL-6 with depression. Some of the findings did not survive correction for multiple testing and so replication in larger samples is required. Experimental studies are also required to further examine causality, mechanisms, and treatment potential for these immunological proteins/pathways for schizophrenia and depression.

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis.

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.

Longitudinal association between CRP levels and risk of psychosis: a meta-analysis of population-based cohort studies.

Meta-analyses of cross-sectional studies suggest that patients with psychosis have higher circulating levels of C-reactive protein (CRP) compared with healthy controls; however, cause and effect is unclear. We examined the prospective association between CRP levels and subsequent risk of developing a psychotic disorder by conducting a systematic review and meta-analysis of population-based cohort studies. Databases were searched for prospective studies of CRP and psychosis. We obtained unpublished results, including adjustment for age, sex, body mass index, smoking, alcohol use, and socioeconomic status and suspected infection (CRP > 10 mg/L). Based on random effect meta-analysis of 89,792 participants (494 incident cases of psychosis at follow-up), the pooled odds ratio (OR) for psychosis for participants with high (>3 mg/L), as compared to low (≤3 mg/L) CRP levels at baseline was 1.50 (95% confidence interval [CI], 1.09-2.07). Evidence for this association remained after adjusting for potential confounders (adjusted OR [aOR] = 1.31; 95% CI, 1.03-1.66). After excluding participants with suspected infection, the OR for psychosis was 1.36 (95% CI, 1.06-1.74), but the association attenuated after controlling for confounders (aOR = 1.23; 95% CI, 0.95-1.60). Using CRP as a continuous variable, the pooled OR for psychosis per standard deviation increase in log(CRP) was 1.11 (95% CI, 0.93-1.34), and this association further attenuated after controlling for confounders (aOR = 1.07; 95% CI, 0.90-1.27) and excluding participants with suspected infection (aOR = 1.07; 95% CI, 0.92-1.24). There was no association using CRP as a categorical variable (low, medium or high). While we provide some evidence of a longitudinal association between high CRP (>3 mg/L) and psychosis, larger studies are required to enable definitive conclusions.

The potential shared role of inflammation in insulin resistance and schizophrenia: A bidirectional two-sample mendelian randomization study.

BACKGROUND: Insulin resistance predisposes to cardiometabolic disorders, which are commonly comorbid with schizophrenia and are key contributors to the significant excess mortality in schizophrenia. Mechanisms for the comorbidity remain unclear, but observational studies have implicated inflammation in both schizophrenia and cardiometabolic disorders separately. We aimed to examine whether there is genetic evidence that insulin resistance and 7 related cardiometabolic traits may be causally associated with schizophrenia, and whether evidence supports inflammation as a common mechanism for cardiometabolic disorders and schizophrenia. METHODS AND FINDINGS: We used summary data from genome-wide association studies of mostly European adults from large consortia (Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) featuring up to 108,557 participants; Diabetes Genetics Replication And Meta-analysis (DIAGRAM) featuring up to 435,387 participants; Global Lipids Genetics Consortium (GLGC) featuring up to 173,082 participants; Genetic Investigation of Anthropometric Traits (GIANT) featuring up to 339,224 participants; Psychiatric Genomics Consortium (PGC) featuring up to 105,318 participants; and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium featuring up to 204,402 participants). We conducted two-sample uni- and multivariable mendelian randomization (MR) analysis to test whether (i) 10 cardiometabolic traits (fasting insulin, high-density lipoprotein and triglycerides representing an insulin resistance phenotype, and 7 related cardiometabolic traits: low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance, and type 2 diabetes) could be causally associated with schizophrenia; and (ii) inflammation could be a shared mechanism for these phenotypes. We conducted a detailed set of sensitivity analyses to test the assumptions for a valid MR analysis. We did not find statistically significant evidence in support of a causal relationship between cardiometabolic traits and schizophrenia, or vice versa. However, we report that a genetically predicted inflammation-related insulin resistance phenotype (raised fasting insulin (raised fasting insulin (Wald ratio OR = 2.95, 95% C.I, 1.38-6.34, Holm-Bonferroni corrected p-value (p) = 0.035) and lower high-density lipoprotein (Wald ratio OR = 0.55, 95% C.I., 0.36-0.84; p = 0.035)) was associated with schizophrenia. Evidence for these associations attenuated to the null in multivariable MR analyses after adjusting for C-reactive protein, an archetypal inflammatory marker: (fasting insulin Wald ratio OR = 1.02, 95% C.I, 0.37-2.78, p = 0.975), high-density lipoprotein (Wald ratio OR = 1.00, 95% C.I., 0.85-1.16; p = 0.849), suggesting that the associations could be fully explained by inflammation. One potential limitation of the study is that the full range of gene products from the genetic variants we used as proxies for the exposures is unknown, and so we are unable to comment on potential biological mechanisms of association other than inflammation, which may also be relevant. CONCLUSIONS: Our findings support a role for inflammation as a common cause for insulin resistance and schizophrenia, which may at least partly explain why the traits commonly co-occur in clinical practice. Inflammation and immune pathways may represent novel therapeutic targets for the prevention or treatment of schizophrenia and comorbid insulin resistance. Future work is needed to understand how inflammation may contribute to the risk of schizophrenia and insulin resistance.

Childhood inflammatory markers and risks for psychosis and depression at age 24: Examination of temporality and specificity of association in a population-based prospective birth cohort.

BACKGROUND: Cross-sectional studies have reported elevated concentrations of inflammatory markers in psychosis and depression. However, questions regarding temporality and specificity of association, crucial for understanding the potential role of inflammation, remain. METHODS: Based on 2224 ALSPAC birth cohort participants, we used regression analyses to test associations of interleukin-6 (IL-6) and C-reactive protein (CRP) levels at age 9 with risks for psychosis (psychotic experiences; negative symptoms; psychotic disorder), and depression (depressive episode; symptom score) at age 24. Regression models were adjusted for sex, ethnicity, social class and body mass index. We tested for linearity (using quadratic terms) and specificity (using bi-variate probit regression) of association, and used multiple imputation to explore the impact of missing data. RESULTS: After adjustments, higher IL-6 levels at age 9 were associated with increased risk of psychotic disorder (OR = 1.56; 95% C.I., 1.09-2.21 per SD increase in IL-6; OR=2.60; 95% C.I., 1.04-6.53 for the top compared with bottom third of IL-6) and depressive episode (OR = 1.14; 95% C.I., 0.99-1.32 per SD increase in IL-6; OR = 1.49; 95% C.I., 1.02-2.18 for the top compared with bottom third of IL-6). IL-6 was associated with negative symptoms after adjusting for depression (ß = 0.09; 95% C.I., 0.01-0.22). There was no evidence for outcome-specific associations of IL-6. Childhood CRP was not associated with adult psychosis or depression. CONCLUSIONS: Evidence for similar, longitudinal, dose-response associations suggest that elevated childhood IL-6 could be a shared risk factor for adult psychosis and depression. The IL-6 pathway may represent a novel target for treatment and prevention of these disorders.

Longitudinal Trends in Childhood Insulin Levels and Body Mass Index and Associations With Risks of Psychosis and Depression in Young Adults.

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.

Inflammatory and cardiometabolic markers at presentation with first episode psychosis and long-term clinical outcomes: A longitudinal study using electronic health records.

Approximately one third of patients presenting with a first episode of psychosis need long-term support, but there is a limited understanding of the sociodemographic or biological factors that predict this outcome. We used electronic health records from a naturalistic cohort of consecutive patients referred to an early intervention in psychosis service to address this question. We extracted data on demographic (age, sex, ethnicity and marital status), immune (differential cell count measures and C-reactive protein (CRP)) and metabolic (cholesterol, triglycerides, glucose, glycated haemoglobin, blood pressure, body mass index (BMI)) factors at baseline, and subsequent need for long-term secondary (specialist) psychiatric care. Of 749 patients with outcome data available, 447 (60%) had a good outcome and were discharged to primary care, while 302 (40%) required follow-up by secondary mental health services indicating a worse outcome. The need for ongoing secondary mental healthcare was associated with high triglyceride levels (adjusted odds ratio/OR = 7.32, 95% CI 2.26-28.06), a low basophil:lymphocyte ratio (adjusted OR = 0.14, 95% CI 0.02-0.58), and a high monocyte count (adjusted OR = 2.78, 95% CI 1.02-8.06) at baseline. The associations for baseline basophil (unadjusted OR = 0.27 per SD, 95% CI 0.10-0.62) and platelet counts (unadjusted OR = 2.88, 95% CI 1.29-6.63) attenuated following adjustment for BMI. Baseline CRP levels or BMI were not associated with long-term psychiatric outcomes. In conclusion, we provide evidence that triglyceride levels and several blood cell counts measured at presentation may be clinically useful markers of long-term prognosis for first episode psychosis in clinical settings. These findings will require replication.