Copy number variants (CNVs) have been associated with the risk of schizophrenia, autism and intellectual disability. However, little is known about their spectrum of psychopathology in adulthood.

We investigated the psychiatric phenotypes of adult CNV carriers and compared probands, who were ascertained through clinical genetics services, with carriers who were not. One hundred twenty-four adult participants (age 18–76), each bearing one of 15 rare CNVs, were recruited through a variety of sources including clinical genetics services, charities for carriers of genetic variants, and online advertising. A battery of psychiatric assessments was used to determine psychopathology.

The frequencies of psychopathology were consistently higher for the CNV group compared to general population rates. We found particularly high rates of neurodevelopmental disorders (NDDs) (48%), mood disorders (42%), anxiety disorders (47%) and personality disorders (73%) as well as high rates of psychiatric multimorbidity (median number of diagnoses: 2 in non-probands, 3 in probands). NDDs [odds ratio (OR) = 4.67, 95% confidence interval (CI) 1.32–16.51; p = 0.017) and psychotic disorders (OR = 6.8, 95% CI 1.3–36.3; p = 0.025) occurred significantly more frequently in probands (N = 45; NDD: 39[87%]; psychosis: 8[18%]) than non-probands (N = 79; NDD: 20 [25%]; psychosis: 3[4%]). Participants also had somatic diagnoses pertaining to all organ systems, particularly conotruncal cardiac malformations (in individuals with 22q11.2 deletion syndrome specifically), musculoskeletal, immunological, and endocrine diseases.

Adult CNV carriers had a markedly increased rate of anxiety and personality disorders not previously reported and high rates of psychiatric multimorbidity. Our findings support in-depth psychiatric and medical assessments of carriers of CNVs and the establishment of multidisciplinary clinical services.

Risk factors for salivary gland carcinoma are poorly understood. Although links between background radiation, smoking and obesity have been previously suggested, no studies have so far established any significant results. This study aimed to establish correlations between common environmental and lifestyle risk factors and different subtypes of salivary gland carcinoma.

A study of population data in Wales spanning 27 years was conducted; 2 national databases were used to identify 356 cases of primary salivary gland carcinoma over this period. Histological subtype of cancer and geographical location of each case was recorded. Public health data was used to establish radon levels, smoking, obesity and activity levels of populations in each geographical location. A population matched multivariate analysis of variance analysis was performed using histological subtype and risk factor data for each geographical location.

A significantly higher incidence of mucoepidermoid cancer in populations with higher background radon levels (p = 0.006), epithelial-myoepithelial cancer in populations with higher smoking levels (p = 0.029) and adenoid cystic cancer in populations with higher obesity levels (p = 0.028) was found.

To the authors’ knowledge, this is the first study to establish significant links between background radiation, smoking and obesity with different subtypes of salivary gland carcinoma.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

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.

We examined parent- and adolescent-reported executive functioning (EF) behaviors following pediatric traumatic brain injury (TBI) in the context of Online Family Problem-Solving Therapy (OFPST) and moderators of change in EF behaviors.

In total, 274 families were randomized to OFPST or an internet resource comparison group. Parents and adolescents completed the Behavior Rating Inventory of Executive Function at four time points. Mixed models were used to examine EF behaviors, assessing the effects of visit, treatment group, rater, TBI severity, age, socioeconomic status, and family functioning.

Parents rated their adolescents’ EF as poorer (F(3,1156) = 220.15, p < .001; M = 58.11, SE = 0.73) than adolescents rated themselves (M = 51.81, SE = 0.73). Across raters, EF behaviors were poorer for adolescents whose parents had less education (F(3,1156) = 8.60, p = .003; M = 56.76, SE = 0.98) than for those with more education (M = 53.16, SE = 0.88). Age at baseline interacted with visit (F(3,1156) = 5.05, p = .002), such that families of older adolescents reported improvement in EF behaviors over time. Family functioning also interacted with visit (F(3, 1156) = 2.61, p = .049), indicating more improvement in EF behaviors over time in higher functioning families. There were no effects of treatment or TBI severity.

We identified a discrepancy between parent- and adolescent-reported EF, suggesting reduced awareness of deficits in adolescents with TBI. We also found that poorer family functioning and younger age were associated with poorer recovery after TBI, whereas adolescents of parents with less education were reported as having greater EF deficits across time points.

This study aimed to examine the predictors of cognitive performance in patients with pediatric mild traumatic brain injury (pmTBI) and to determine whether group differences in cognitive performance on a computerized test battery could be observed between pmTBI patients and healthy controls (HC) in the sub-acute (SA) and the early chronic (EC) phases of injury.

203 pmTBI patients recruited from emergency settings and 159 age- and sex-matched HC aged 8–18 rated their ongoing post-concussive symptoms (PCS) on the Post-Concussion Symptom Inventory and completed the Cogstate brief battery in the SA (1–11 days) phase of injury. A subset (156 pmTBI patients; 144 HC) completed testing in the EC (~4 months) phase.

Within the SA phase, a group difference was only observed for the visual learning task (One-Card Learning), with pmTBI patients being less accurate relative to HC. Follow-up analyses indicated higher ongoing PCS and higher 5P clinical risk scores were significant predictors of lower One-Card Learning accuracy within SA phase, while premorbid variables (estimates of intellectual functioning, parental education, and presence of learning disabilities or attention-deficit/hyperactivity disorder) were not.

The absence of group differences at EC phase is supportive of cognitive recovery by 4 months post-injury. While the severity of ongoing PCS and the 5P score were better overall predictors of cognitive performance on the Cogstate at SA relative to premorbid variables, the full regression model explained only 4.1% of the variance, highlighting the need for future work on predictors of cognitive outcomes.

Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.

To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.

We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.

Individuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.

This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.

It is not clear to what extent associations between schizophrenia, cannabis use and cigarette use are due to a shared genetic etiology. We, therefore, examined whether schizophrenia genetic risk associates with longitudinal patterns of cigarette and cannabis use in adolescence and mediating pathways for any association to inform potential reduction strategies.

Associations between schizophrenia polygenic scores and longitudinal latent classes of cigarette and cannabis use from ages 14 to 19 years were investigated in up to 3925 individuals in the Avon Longitudinal Study of Parents and Children. Mediation models were estimated to assess the potential mediating effects of a range of cognitive, emotional, and behavioral phenotypes.

The schizophrenia polygenic score, based on single nucleotide polymorphisms meeting a training-set p threshold of 0.05, was associated with late-onset cannabis use (OR = 1.23; 95% CI = 1.08,1.41), but not with cigarette or early-onset cannabis use classes. This association was not mediated through lower IQ, victimization, emotional difficulties, antisocial behavior, impulsivity, or poorer social relationships during childhood. Sensitivity analyses adjusting for genetic liability to cannabis or cigarette use, using polygenic scores excluding the CHRNA5-A3-B4 gene cluster, or basing scores on a 0.5 training-set p threshold, provided results consistent with our main analyses.

Our study provides evidence that genetic risk for schizophrenia is associated with patterns of cannabis use during adolescence. Investigation of pathways other than the cognitive, emotional, and behavioral phenotypes examined here is required to identify modifiable targets to reduce the public health burden of cannabis use in the population.

Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes.

We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as ‘Responders’ (⩾50% improvement in depression severity ratings) or ‘Non-responders’ (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups.

Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively).

These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Registry-based trials have emerged as a potentially cost-saving study methodology. Early estimates of cost savings, however, conflated the benefits associated with registry utilisation and those associated with other aspects of pragmatic trial designs, which might not all be as broadly applicable. In this study, we sought to build a practical tool that investigators could use across disciplines to estimate the ranges of potential cost differences associated with implementing registry-based trials versus standard clinical trials.

We built simulation Markov models to compare unique costs associated with data acquisition, cleaning, and linkage under a registry-based trial design versus a standard clinical trial. We conducted one-way, two-way, and probabilistic sensitivity analyses, varying study characteristics over broad ranges, to determine thresholds at which investigators might optimally select each trial design.

Registry-based trials were more cost effective than standard clinical trials 98.6% of the time. Data-related cost savings ranged from $4300 to $600,000 with variation in study characteristics. Cost differences were most reactive to the number of patients in a study, the number of data elements per patient available in a registry, and the speed with which research coordinators could manually abstract data. Registry incorporation resulted in cost savings when as few as 3768 independent data elements were available and when manual data abstraction took as little as 3.4 seconds per data field.

Registries offer important resources for investigators. When available, their broad incorporation may help the scientific community reduce the costs of clinical investigation. We offer here a practical tool for investigators to assess potential costs savings.

To evaluate the long-term tolerability and effectiveness of aripiprazole adjunctive to lithium or valproate in bipolar mania.

Completers of a 6-week double-blind comparison of adjunctive aripiprazole versus placebo in bipolar mania partially responsive to monotherapy were followed up over 46-weeks on open-label aripiprazole plus lithium (ARI+LI) or valproate (ARI+VAL).

283 (ARI+LI n=108; ARI+VAL n=175) patients entered and 146 (ARI+LI n=55; ARI+VAL n=91) completed the 46-week extension. Safety results for both combinations were consistent with the known tolerability profile of aripiprazole, lithium and valproate. No clinically significant changes in lipids or glucose were observed with either ARI+LI or ARI+VAL. Mean (SE) weight change from double-blind endpoint to Week 46 (LOCF) was 2.3 (0.6) kg with ARI+LI and 2.0 (0.4) kg with ARI+VAL. Temporal analysis of the time of first onset of adverse events showed that akathisia and insomnia tended to occur early in treatment, with few new cases in patients previously treated with aripiprazole during the 6-week study.

Significant improvements from baseline in YMRS total score and MADRS total score were sustained over the 52 weeks with both ARI+LI and ARI+VAL treatment.

Long-term aripiprazole adjunctive to lithium/valproate in bipolar mania was safe and well-tolerated. Improvements in manic and depressive symptoms observed during the first 6 weeks of treatment were maintained.