Childhood-onset type 1 diabetes and subsequent adult psychiatric disorders: a nationwide cohort and genome-wide Mendelian randomization study.

Childhood-onset type 1 diabetes (T1D) is associated with substantial psychiatric morbidity in later life, but it remains unknown whether these associations are due to common underlying biological mechanisms or the impacts of living with the condition and its treatment. Here, using Czech national register data, we identified children with T1D aged ≤14 years between 1994 and 2007 and estimated the risk of psychiatric disorders up to 24 years later. We found that children diagnosed with T1D had an elevated risk of developing substance use, mood, anxiety and personality disorders, and behavioral syndromes. Conversely, we found that children with T1D had a lower risk of developing psychotic disorders. In Mendelian randomization analysis, we found an association with schizophrenia, which, however, did not persist following multiple testing adjustment. The combined observational and Mendelian randomization evidence suggests that T1D diagnosis in childhood predisposes to far-reaching, extensive psychiatric morbidity, which is unlikely to be explicable by common underlying biological mechanisms. The findings of this study highlight that monitoring and addressing the mental health needs of children with T1D is imperative, whereas glucose dysregulation and/or inflammation implicated in schizophrenia pathogenesis warrants future research.

Contribution of severe mental disorders to fatally harmful effects of physical disorders: national cohort study.

BACKGROUND: It remains unknown whether severe mental disorders contribute to fatally harmful effects of physical illness. AIMS: To investigate the risk of all-cause death and loss of life-years following the onset of a wide range of physical health conditions in people with severe mental disorders compared with matched counterparts who had only these physical health conditions, and to assess whether these associations can be fully explained by this patient group having more clinically recorded physical illness. METHOD: Using Czech national in-patient register data, we identified individuals with 28 physical health conditions recorded between 1999 and 2017, separately for each condition. In these people, we identified individuals who had severe mental disorders recorded before the physical health condition and exactly matched them with up to five counterparts who had no recorded prior severe mental disorders. We estimated the risk of all-cause death and lost life-years following each of the physical health conditions in people with pre-existing severe mental disorders compared with matched counterparts without severe mental disorders. RESULTS: People with severe mental disorders had an elevated risk of all-cause death following the onset of 7 out of 9 broadly defined and 14 out of 19 specific physical health conditions. People with severe mental disorders lost additional life-years following the onset of 8 out 9 broadly defined and 13 out of 19 specific physical health conditions. The vast majority of results remained robust after considering the potentially confounding role of somatic multimorbidity and other clinical and sociodemographic factors. CONCLUSIONS: A wide range of physical illnesses are more likely to result in all-cause death in people with pre-existing severe mental disorders. This premature mortality cannot be fully explained by having more clinically recorded physical illness, suggesting that physical disorders are more likely to be fatally harmful in this patient group.

Trajectories of Inflammation in Youth and Risk of Mental and Cardiometabolic Disorders in Adulthood.

Importance: Research suggests that low-grade, nonresolving inflammation may predate adult mental and physical illness. However, evidence to date is largely cross-sectional or focuses on single disorder outcomes. Objectives: To examine trajectories of inflammation as measured by C-reactive protein (CRP) levels in a large sample of children and adolescents, and to explore associations between different identified trajectories and mental and related cardiometabolic health outcomes in early adulthood. Design, Setting, and Participants: In a longitudinal cohort study using data from the large UK-based Avon Longitudinal Study of Parents and Children (ALSPAC), latent class growth analysis (LCGA) was used to explore different trajectories of inflammation, with logistic regression exploring association with mental and physical health outcomes. Participants with measurable CRP data and associated mental and cardiometabolic health outcomes recorded were included in the analysis. Data analysis was performed from May 1, 2023, to March 30, 2024. Exposures: Inflammation was assessed via CRP levels at ages 9, 15, and 17 years. LCGA was used to identify different trajectories of inflammation. Main Outcomes and Measures: Outcomes assessed at age 24 years included psychotic disorders, depressive disorders, anxiety disorders, hypomania, and, as a measure of insulin resistance, Homeostasis Model Assessment (HOMA2) score. Results: A total of 6556 participants (3303 [50.4%] female) were included. Three classes of inflammation were identified: persistently low CRP levels (reference class, n = 6109); persistently raised CRP levels, peaking at age 9 years (early peak, n = 197); and persistently raised CRP levels, peaking at age 17 years (late peak, n = 250). Participants in the early peak group were associated with a higher risk of psychotic disorder (odds ratio [OR], 4.60; 95% CI, 1.81-11.70; P = .008), a higher risk of severe depression (OR, 4.37; 95% CI, 1.64-11.63; P = .02), and higher HOMA2 scores (ß = 0.05; 95% CI, 0.01-0.62, P = .04) compared with participants with persistently low CRP. The late peak group was not associated with any outcomes at age 24 years. Conclusions and Relevance: Low-grade systemic inflammation peaking in midchildhood was associated with specific mental and cardiometabolic disorders in young adulthood. These findings suggest that low-grade persistent inflammation in early life may be an important shared common factor for mental-physical comorbidity and so could be relevant to future efforts of patient stratification and risk profiling.

Psychosis metabolic risk calculator (PsyMetRiC) in early psychosis: External validation study in Finland.

INTRODUCTION: Accurate detection of cardiometabolic risk in early psychosis is crucial to reducing somatic morbidity and mortality in people with psychotic disorders. We conducted an external validation of the psychosis metabolic risk calculator (PsyMetRiC), a cardiometabolic risk prediction tool developed in the UK and tailored for young people with psychosis. We compared the predictive accuracy and clinical usefulness of PsyMetRiC and a general population-based risk prediction tool for type 2 diabetes, the Finnish Diabetes Risk Score (FINDRISC). METHODS: We included first-episode psychosis and ultra-high-risk for psychosis patients without metabolic syndrome aged 18-35 years from the Helsinki Early Psychosis and Turku Early Psychosis Study cohorts. We tested two versions of PsyMetRiC: the full model including age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations, and the partial-model excluding biochemical predictors, and the simplified FINDRISC including BMI, sex, systolic blood pressure, and fasting glucose. Discrimination, calibration, and decision curve analyses were used to assess the predictive performance and clinical usefulness of both PsyMetRiC and FINDRISC. We performed a site-specific re-calibration of PsyMetRiC (PsyMetRiC-Fi). RESULTS: The study sample consisted of 278 individuals (all White European ethnicity, 58.6% male, mean age 24.8 years, 37.8% smoking, mean BMI 23.5). Discrimination was marginally better in the PsyMetRiC full model (C = 0.72, 95% CI, 0.59-0.82) compared with partial model (C = 0.70, 95% CI 0.59-0.80) or FINDRISC (C = 0.63, 95% CI 0.54-0.71). Calibration plots displayed evidence of minor miscalibration for PsyMetRiC, which corrected following recalibration. Miscalibration was more pronounced for FINDRISC. Decision curve analysis showed that PsyMetRiC offers likely clinical usefulness in improving cardiometabolic risk management in early psychosis compared with giving everyone or no one an intervention. CONCLUSION: PsyMetRiC has utility in predicting cardiometabolic risk in Finnish patients with early psychosis. It has better discriminatory accuracy and offers more accurate risk prediction compared to other available strategies.

Metabolic syndrome risk prediction in an Australian sample with first-episode psychosis using the psychosis metabolic risk calculator: A validation study.

OBJECTIVE: To examine the accuracy and likely clinical usefulness of the Psychosis Metabolic Risk Calculator (PsyMetRiC) in predicting up-to six-year risk of incident metabolic syndrome in an Australian sample of young people with first-episode psychosis. METHOD: We conducted a retrospective study at a secondary care early psychosis treatment service among people aged 16-35 years, extracting relevant data at the time of antipsychotic commencement and between one-to-six-years later. We assessed algorithm accuracy primarily via discrimination (C-statistic), calibration (calibration plots) and clinical usefulness (decision curve analysis). Model updating and recalibration generated a site-specific (Australian) PsyMetRiC version. RESULTS: We included 116 people with baseline and follow-up data: 73% male, mean age 20.1 years, mean follow-up 2.6 years, metabolic syndrome prevalence 13%. C-statistics for both partial- (C = 0.71, 95% CI 0.64-0.75) and full-models (C = 0.72, 95% CI 0.65-0.77) were acceptable; however, calibration plots demonstrated consistent under-prediction of risk. Recalibration and updating led to slightly improved C-statistics, greatly improved agreement between observed and predicted risk, and a narrow window of likely clinical usefulness improved significantly. CONCLUSION: An updated and recalibrated PsyMetRiC model, PsyMetRiC-Australia, shows promise. Validation in a large sample is required to confirm its accuracy and clinical usefulness for the Australian population.

Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study.

Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Modular and divergent synthesis of 2,N3-disubstituted 4-quinazolinones facilitated by regioselective N-alkylation.

The synthesis of a biologically relevant 2-amino-N3-alkylamido 4-quinazolinone has been accomplished in four steps from commercially available materials using design principles from both modular and divergent synthesis. N3-Alkylation of 2-chloro-4(3H)-quinazolinone using methyl bromoacetate, followed by C2-amination produced a suitable scaffold for introducing molecular diversity. Optimization of alkylation conditions afforded full regioselectivity, enabling exclusive access to the N-alkylated isomer. Subsequent C2-amination using piperidine, pyrrolidine, or diethylamine, followed by amide bond formation using variously substituted phenethylamines, generated fifteen unique 4-quinazolinones bearing C2-amino and N3-alkylamido substituents. These efforts highlight the reciprocal influence of C2 and N3 substitution on functionalization at either position, establish an effective synthetic pathway toward 2,N3-disubstituted 4-quinazolinones, and enable preliminary bioactivity studies while providing an experiential learning opportunity for undergraduate student researchers.

Longitudinal Trajectories of Plasma Polyunsaturated Fatty Acids and Associations With Psychosis Spectrum Outcomes in Early Adulthood.

BACKGROUND: Evidence supports associations between polyunsaturated fatty acids such as docosahexaenoic acid (DHA) and psychosis. However, polyunsaturated fatty acid trajectories in the general population have not been characterized, and associations with psychosis spectrum outcomes in early adulthood are unknown. METHODS: Plasma omega-6 to omega-3 ratio and DHA (expressed as percentage of total fatty acids) were measured by nuclear magnetic spectroscopy at 7, 15, 17, and 24 years of age in participants of ALSPAC (Avon Longitudinal Study of Parents and Children). Curvilinear growth mixture modeling evaluated body mass index-adjusted trajectories of both measures. Outcomes were assessed at 24 years. Psychotic experiences (PEs), at-risk mental state status, psychotic disorder, and number of PEs were assessed using the Psychosis-Like Symptoms interview (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, and cumulative smoking and alcohol use. RESULTS: Relative to stable average, the persistently high omega-6 to 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% 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 to omega-3 ratio was associated with increased number of PEs (ß = 0.41, 95% CI = 0.05-0.78) and negative symptoms score (ß = 0.43, 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: Optimization of polyunsaturated fatty acid 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.

Open Source Antibiotics: Simple Diarylimidazoles Are Potent against Methicillin-Resistant Staphylococcus aureus.

Antimicrobial resistance (AMR) is widely acknowledged as one of the most serious public health threats facing the world, yet the private sector finds it challenging to generate much-needed medicines. As an alternative discovery approach, a small array of diarylimidazoles was screened against the ESKAPE pathogens, and the results were made publicly available through the Open Source Antibiotics (OSA) consortium (https://github.com/opensourceantibiotics). Of the 18 compounds tested (at 32 µg/mL), 15 showed >90% growth inhibition activity against methicillin-resistant Staphylococcus aureus (MRSA) alone. In the subsequent hit-to-lead optimization of this chemotype, 147 new heterocyclic compounds containing the diarylimidazole and other core motifs were synthesized and tested against MRSA, and their structure-activity relationships were identified. While potent, these compounds have moderate to high intrinsic clearance and some associated toxicity. The best overall balance of parameters was found with OSA_975, a compound with good potency, good solubility, and reduced intrinsic clearance in rat hepatocytes. We have progressed toward the knowledge of the molecular target of these phenotypically active compounds, with proteomic techniques suggesting TGFBR1 is potentially involved in the mechanism of action. Further development of these compounds toward antimicrobial medicines is available to anyone under the licensing terms of the project.

Plasmid-encoded lactose metabolism and mobilized colistin resistance (mcr-9) genes in Salmonella enterica serovars isolated from dairy facilities in the 1980s.

Horizontal gene transfer by plasmids can confer metabolic capabilities that expand a host cell's niche. Yet, it is less understood whether the coalescence of specialized catabolic functions, antibiotic resistances and metal resistances on plasmids provides synergistic benefits. In this study, we report whole-genome assembly and phenotypic analysis of five Salmonella enterica strains isolated in the 1980s from milk powder in Munich, Germany. All strains exhibited the unusual phenotype of lactose-fermentation and encoded either of two variants of the lac operon. Surprisingly, all strains encoded the mobilized colistin resistance gene 9 (mcr-9), long before the first report of this gene in the literature. In two cases, the mcr-9 gene and the lac locus were linked within a large gene island that formed an IncHI2A-type plasmid in one strain but was chromosomally integrated in the other strain. In two other strains, the mcr-9 gene was found on a large IncHI1B/IncP-type plasmid, whereas the lac locus was encoded on a separate chromosomally integrated plasmidic island. The mcr-9 sequences were identical and genomic contexts could not explain the wide range of colistin resistances exhibited by the Salmonella strains. Nucleotide variants did explain phenotypic differences in motility and exopolysaccharide production. The observed linkage of mcr-9 to lactose metabolism, an array of heavy-metal detoxification systems, and other antibiotic resistance genes may reflect a coalescence of specialized phenotypes that improve the spread of colistin resistance in dairy facilities, much earlier than previously suspected.

Structure-Property Optimization of a Series of Imidazopyridines for Visceral Leishmaniasis.

Leishmaniasis is a collection of diseases caused by more than 20 Leishmania parasite species that manifest as either visceral, cutaneous, or mucocutaneous leishmaniasis. Despite the significant mortality and morbidity associated with leishmaniasis, it remains a neglected tropical disease. Existing treatments have variable efficacy, significant toxicity, rising resistance, and limited oral bioavailability, which necessitates the development of novel and affordable therapeutics. Here, we report on the continued optimization of a series of imidazopyridines for visceral leishmaniasis and a scaffold hop to a series of substituted 2-(pyridin-2-yl)-6,7-dihydro-5H-pyrrolo[1,2-a]imidazoles with improved absorption, distribution, metabolism, and elimination properties.

Large-scale interrogation of retinal cell functions by 1-photon light-sheet microscopy.

Visual processing in the retina depends on the collective activity of large ensembles of neurons organized in different layers. Current techniques for measuring activity of layer-specific neural ensembles rely on expensive pulsed infrared lasers to drive 2-photon activation of calcium-dependent fluorescent reporters. We present a 1-photon light-sheet imaging system that can measure the activity in hundreds of neurons in the ex vivo retina over a large field of view while presenting visual stimuli. This allows for a reliable functional classification of different retinal cell types. We also demonstrate that the system has sufficient resolution to image calcium entry at individual synaptic release sites across the axon terminals of dozens of simultaneously imaged bipolar cells. The simple design, large field of view, and fast image acquisition make this a powerful system for high-throughput and high-resolution measurements of retinal processing at a fraction of the cost of alternative approaches.

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.

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.

Collaborative Virtual Screening Identifies a 2-Aryl-4-aminoquinazoline Series with Efficacy in an In Vivo Model of Trypanosoma cruzi Infection.

Probing multiple proprietary pharmaceutical libraries in parallel via virtual screening allowed rapid expansion of the structure-activity relationship (SAR) around hit compounds with moderate efficacy against Trypanosoma cruzi, the causative agent of Chagas Disease. A potency-improving scaffold hop, followed by elaboration of the SAR via design guided by the output of the phenotypic virtual screening efforts, identified two promising hit compounds 54 and 85, which were profiled further in pharmacokinetic studies and in an in vivo model of T. cruzi infection. Compound 85 demonstrated clear reduction of parasitemia in the in vivo setting, confirming the interest in this series of 2-(pyridin-2-yl)quinazolines as potential anti-trypanosome treatments.

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.