To examine the association between adherence to plant-based diets and mortality.

Prospective study. We calculated a plant-based diet index (PDI) by assigning positive scores to plant foods and reverse scores to animal foods. We also created a healthful PDI (hPDI) and an unhealthful PDI (uPDI) by further separating the healthy plant foods from less-healthy plant foods.

The VA Million Veteran Program.

315 919 men and women aged 19–104 years who completed a FFQ at the baseline.

We documented 31 136 deaths during the follow-up. A higher PDI was significantly associated with lower total mortality (hazard ratio (HR) comparing extreme deciles = 0·75, 95 % CI: 0·71, 0·79, Ptrend < 0·001]. We observed an inverse association between hPDI and total mortality (HR comparing extreme deciles = 0·64, 95 % CI: 0·61, 0·68, Ptrend < 0·001), whereas uPDI was positively associated with total mortality (HR comparing extreme deciles = 1·41, 95 % CI: 1·33, 1·49, Ptrend < 0·001). Similar significant associations of PDI, hPDI and uPDI were also observed for CVD and cancer mortality. The associations between the PDI and total mortality were consistent among African and European American participants, and participants free from CVD and cancer and those who were diagnosed with major chronic disease at baseline.

A greater adherence to a plant-based diet was associated with substantially lower total mortality in this large population of veterans. These findings support recommending plant-rich dietary patterns for the prevention of major chronic diseases.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.

We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.

We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).

Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.

Little is known about how the Royal College of Emergency Medicine (RCEM) residency programs are selecting their residents. This creates uncertainty regarding alignment between current selection processes and known best practices. We seek to describe the current selection processes of Canadian RCEM programs.

An online survey was distributed to all RCEM program directors and assistant directors. The survey instrument included 22 questions and sought both qualitative and quantitative data from the following six domains: application file, letters of reference, elective selection, interview, rank order, and selection process evaluation.

We received responses from 13 of 14 programs for an aggregate response rate of 92.9%. A candidate's letters of reference were identified as the most important criterion from the paper application (38.5%). Having a high level of familiarity with the applicant was the most important characteristic of a reference letter author (46.2%). In determining rank order, 53.8% of programs weighed the interview more heavily than the paper application. Once final candidate scores are established following the interview stage, all program respondents indicated that further adjustment is made to the final rank order list. Only 1 of 13 program respondents reported ever having completed a formal evaluation of their selection process.

We have identified elements of the selection process that will inform recommendations for programs, students, and referees. We encourage programs to conduct regular reviews of their selection process going forward to be in alignment with best practices.

Brain imaging studies have shown altered amygdala activity during emotion processing in children and adolescents with oppositional defiant disorder (ODD) and conduct disorder (CD) compared to typically developing children and adolescents (TD). Here we aimed to assess whether aggression-related subtypes (reactive and proactive aggression) and callous-unemotional (CU) traits predicted variation in amygdala activity and skin conductance (SC) response during emotion processing.

We included 177 participants (n = 108 cases with disruptive behaviour and/or ODD/CD and n = 69 TD), aged 8–18 years, across nine sites in Europe, as part of the EU Aggressotype and MATRICS projects. All participants performed an emotional face-matching functional magnetic resonance imaging task.

Differences between cases and TD in affective processing, as well as specificity of activation patterns for aggression subtypes and CU traits, were assessed. Simultaneous SC recordings were acquired in a subsample (n = 63). Cases compared to TDs showed higher amygdala activity in response to negative faces (fearful and angry) v. shapes. Subtyping cases according to aggression-related subtypes did not significantly influence on amygdala activity; while stratification based on CU traits was more sensitive and revealed decreased amygdala activity in the high CU group. SC responses were significantly lower in cases and negatively correlated with CU traits, reactive and proactive aggression.

Our results showed differences in amygdala activity and SC responses to emotional faces between cases with ODD/CD and TD, while CU traits moderate both central (amygdala) and peripheral (SC) responses. Our insights regarding subtypes and trait-specific aggression could be used for improved diagnostics and personalized treatment.

To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.

Retrospective cohort study.

Eight tertiary-care referral general hospitals in California.

We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.

For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15–1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, −25%; IQR, −20% to −29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%–105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, −15%; IQR, −14% to −21%) and decreased the SIR at all hospitals (median, −8%; IQR, −4% to −11%).

For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.

Several studies have reported diminished learning from non-social outcomes in depressed individuals. However, it is not clear how depression impacts learning from social feedback. Notably, mood disorders are commonly associated with deficits in social functioning, which raises the possibility that potential impairments in social learning may negatively affect real-life social experiences in depressed subjects.

Ninety-two participants with high (HD; N = 40) and low (LD; N = 52) depression scores were recruited. Subjects performed a learning task, during which they received monetary outcomes or social feedback which they were told came from other people. Additionally, participants answered questions about their everyday social experiences. Computational models were fit to the data and model parameters were related to social experience measures.

HD subjects reported a reduced quality and quantity of social experiences compared to LD controls, including an increase in the amount of time spent in negative social situations. Moreover, HD participants showed lower learning rates than LD subjects in the social condition of the task. Interestingly, across all participants, reduced social learning rates predicted higher amounts of time spent in negative social situations, even when depression scores were controlled for.

These findings indicate that deficits in social learning may affect the quality of everyday social experiences. Specifically, the impaired ability to use social feedback to appropriately update future actions, which was observed in HD subjects, may lead to suboptimal interpersonal behavior in real life. This, in turn, may evoke negative feedback from others, thus bringing about more unpleasant social encounters.

Apolipoprotein E (APOE) E4 is the main genetic risk factor for Alzheimer’s disease (AD). Due to the consistent association, there is interest as to whether E4 influences the risk of other neurodegenerative diseases. Further, there is a constant search for other genetic biomarkers contributing to these phenotypes, such as microtubule-associated protein tau (MAPT) haplotypes. Here, participants from the Ontario Neurodegenerative Disease Research Initiative were genotyped to investigate whether the APOE E4 allele or MAPT H1 haplotype are associated with five neurodegenerative diseases: (1) AD and mild cognitive impairment (MCI), (2) amyotrophic lateral sclerosis, (3) frontotemporal dementia (FTD), (4) Parkinson’s disease, and (5) vascular cognitive impairment.

Genotypes were defined for their respective APOE allele and MAPT haplotype calls for each participant, and logistic regression analyses were performed to identify the associations with the presentations of neurodegenerative diseases.

Our work confirmed the association of the E4 allele with a dose-dependent increased presentation of AD, and an association between the E4 allele alone and MCI; however, the other four diseases were not associated with E4. Further, the APOE E2 allele was associated with decreased presentation of both AD and MCI. No associations were identified between MAPT haplotype and the neurodegenerative disease cohorts; but following subtyping of the FTD cohort, the H1 haplotype was significantly associated with progressive supranuclear palsy.

This is the first study to concurrently analyze the association of APOE isoforms and MAPT haplotypes with five neurodegenerative diseases using consistent enrollment criteria and broad phenotypic analysis.