Genetic and environmental factors, including adverse childhood experiences (ACEs), contribute to substance use disorders (SUDs). However, the interactions between these factors are poorly understood.

We examined associations between SUD polygenic scores (PGSs), ACEs, and the initiation of use and severity of alcohol (AUD), opioid use disorder (OUD), and cannabis use disorder (CanUD) in 10,275 individuals (43.5% female, 47.2% African-like ancestry [AFR], and 52.8% European-like ancestry [EUR]). ACEs and SUD severity were modeled as latent factors. We conducted logistic and linear regressions within ancestry groups to examine the associations of ACEs, PGS, and their interaction with substance use initiation and SUD severity.

All three SUD PGS were associated with ACEs in EUR individuals, indicating a gene–environment correlation. Among EUR individuals, only the CanUD PGS was associated with initiating use, whereas ACEs were associated with initiating use of all three substances in both ancestry groups. Additionally, a negative gene-by-environment interaction was identified for opioid initiation in EUR individuals. ACEs were associated with all three SUD severity latent factors in EUR individuals and with AUD and CanUD severity in AFR individuals. PGS were associated with AUD severity in both ancestry groups and with CanUD severity in AFR individuals. Gene-by-environment interactions were identified for AUD and CanUD severity among EUR individuals.

Findings highlight the roles of ACEs and polygenic risk in substance use initiation and SUD severity. Gene-by-environment interactions implicate ACEs as moderators of genetic susceptibility, reinforcing the importance of considering both genetic and environmental influences on SUD risk.

Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.

Contemporary data relating to antipsychotic prescribing in UK primary care for patients diagnosed with severe mental illness (SMI) are lacking.

To describe contemporary patterns of antipsychotic prescribing in UK primary care for patients diagnosed with SMI.

Cohort study of patients with an SMI diagnosis (i.e. schizophrenia, bipolar disorder, other non-organic psychoses) first recorded in primary care between 2000 and 2017 derived from Clinical Practice Research Datalink. Patients were considered exposed to antipsychotics if prescribed at least one antipsychotic in primary care between 2000 and 2019. We compared characteristics of patients prescribed and not prescribed antipsychotics; calculated annual prevalence rates for antipsychotic prescribing; and computed average daily antipsychotic doses stratified by patient characteristics.

Of 309 378 patients first diagnosed with an SMI in primary care between 2000 and 2017, 212,618 (68.7%) were prescribed an antipsychotic between 2000 and 2019. Antipsychotic prescribing prevalence was 426 (95% CI, 420–433) per 1000 patients in the year 2000, reaching a peak of 550 (547–553) in 2016, decreasing to 470 (468–473) in 2019. The proportion prescribed antipsychotics was higher among patients diagnosed with schizophrenia (81.0%) than with bipolar disorder (64.6%) and other non-organic psychoses (65.7%). Olanzapine, quetiapine, risperidone and aripiprazole accounted for 78.8% of all antipsychotic prescriptions. Higher mean olanzapine equivalent total daily doses were prescribed to patients with the following characteristics: schizophrenia diagnosis, ethnic minority status, male gender, younger age and greater relative deprivation.

Antipsychotic prescribing is dominated by olanzapine, quetiapine, risperidone and aripiprazole. We identified potential disparities in both the receipt and prescribed doses of antipsychotics across subgroups. To inform efforts to optimise prescribing and ensure equity of care, further research is needed to understand why certain groups are prescribed higher doses and are more likely to be treated with long-acting injectable antipsychotics compared with others.

The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5–18 years). Potential predictors included demographic, clinical, parental, and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up.

All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission.

These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.

Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.

In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.

Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.

The cause of most CHD is unknown and considered complex, implicating genetic and environmental factors in disease causation. The Kids Heart BioBank was established in 2003 to accelerate genetic investigations into CHD.

Recruitment includes patients undergoing interventions for CHD at The Children’s Hospital at Westmead. Informed consent is obtained from parents/guardians, and blood is collected at the time of cardiac intervention from which DNA is extracted and stored. Associated detailed clinical information and a family history are stored in the purpose-designed database.

To date, the Kids Heart BioBank contains biospecimens and associated clinical information from over 4,900 patients with CHD and their families. Two-thirds (64.1%) of probands have been included in research studies with 28.9% of participants who underwent genomic sequencing receiving a molecular diagnosis with direct clinical utility. The value of this resource to patients and families is highlighted by the high consent rate (94.6%) and the low withdrawal of consent rate (0.4%). The Kids Heart BioBank has supported many large national and international collaborations and contributed significantly to CHD research.

The Kids Heart BioBank is an invaluable resource and, together with other similar resources, the resulting research has paved the way for clinical genetic testing options for CHD patients, previously not possible. With research in the field moving away from diagnosing monogenic disease, the Kids Heart BioBank is ideally placed to support the next chapter of research efforts into complex disease mechanisms, requiring large patient cohorts with detailed phenotypic information.

In older patients with mental and physical multimorbidity (MPM), personality assessment is highly complex. Our aim was to examine personality traits in this population using the Hetero-Anamnestic Personality questionnaire (HAP), and to compare the premorbid perspective of patients’ relatives (HAP) with the present-time perspective of nursing staff (HAP-t).

Cross-sectional.

Dutch gerontopsychiatric nursing home (GP-NH) units.

Totally, 142 GP-NH residents with MPM (excluding dementia).

NH norm data of the HAP were used to identify clinically relevant premorbid traits. Linear mixed models estimated the differences between HAP and HAP-t trait scores (0–10). Agreement was quantified by intraclass correlation coefficients (ICCs). All HAP-HAP-t analyses were corrected for response tendency (RT) scores (−10–10).

78.4% of the patients had at least one premorbid maladaptive trait, and 62.2% had two or more. Most prevalent were: “disorderly” (30.3%), “unpredictable/impulsive” (29.1%) and “vulnerable” (27.3%) behavior. The RT of relatives appeared significantly more positive than that of nursing staff (+1.8, 95% CI 0.6–2.9, p = 0.002). After RT correction, the traits “vulnerable”, “perfectionist” and “unpredictable/impulsive” behavior scored higher on the HAP than HAP-t (respectively +1.2, 95% CI 0.6–1.7, p < 0.001; +2.1, 95% CI 1.3–2.8, p < 0.001; +0.6, 95% CI 0.1–1.1, p = 0.013), while “rigid” behavior scored lower (−0.7, 95% CI −1.3 to −0.03, p = 0.042). Adjusted ICCs ranged from 0.15 to 0.58.

Our study shows high percentages of premorbid maladaptive personality traits, which calls for attention on personality assessment in MPM NH residents. Results also indicate that the HAP and HAP-t questionnaires should not be used interchangeably for this patient group in clinical practice.

Packed red blood cell transfusions occur frequently after congenital heart surgery to augment haemodynamics, with limited understanding of efficacy. The goal of this study was to analyse the hemodynamic response to packed red blood cell transfusions in a single cohort, as “proof-of-concept” utilising high-frequency data capture of real-time telemetry monitoring.

Retrospective review of patients after the arterial switch operation receiving packed red blood cell transfusions from 15 July 2020 to 15 July 2021. Hemodynamic parameters were collected from a high-frequency data capture system (SickbayTM) continuously recording vital signs from bedside monitors and analysed in 5-minute intervals up to 6 hours before, 4 hours during, and 6 hours after packed red blood cell transfusions—up to 57,600 vital signs per packed red blood cell transfusions. Variables related to oxygen balance included blood gas co-oximetry, lactate levels, near-infrared spectroscopy, and ventilator settings. Analgesic, sedative, and vasoactive infusions were also collected.

Six patients, at 8.5[IQR:5-22] days old and weighing 3.1[IQR:2.8-3.2]kg, received transfusions following the arterial switch operation. There were 10 packed red blood cell transfusions administered with a median dose of 10[IQR:10-15]mL/kg over 169[IQR:110-190]min; at median post-operative hour 36[IQR:10-40]. Significant increases in systolic and mean arterial blood pressures by 5-12.5% at 3 hours after packed red blood cell transfusions were observed, while renal near-infrared spectroscopy increased by 6.2% post-transfusion. No significant changes in ventilation, vasoactive support, or laboratory values related to oxygen balance were observed.

Packed red blood cell transfusions given after the arterial switch operation increased arterial blood pressure by 5-12.5% for 3 hours and renal near-infrared spectroscopy by 6.2%. High-frequency data capture systems can be leveraged to provide novel insights into the hemodynamic response to commonly used therapies such as packed red blood cell transfusions after paediatric cardiac surgery.

The recognition of dementia as a multifactorial disorder encourages the exploration of new pathways to understand its origins. Social health might play a role in cognitive decline and dementia, but conceptual clarity is lacking and this hinders investigation of associations and mechanisms. Social health might provide a new perspective on social connectedness. The objective is to develop a conceptual framework for social health to advance conceptual clarity in future studies and to identify potentially modifiable risk and protective factors in the “Social Health And Reserve in the Dementia patient journey (SHARED)” project.

The methods include the process of building the conceptual framework. We used the following steps: underpinning for concept advancement, concept advancement by the development of a conceptual model, and exploration of its potential feasibility.

Underpinning of the concept drew from a synthesis of theoretical, conceptual and epidemiological work, and resulted in the definition of social health as well-being that relies on capacities both of the individual and the social environment. In the conceptual framework the abstract definition has been elaborated into more precisely defined domains at both the individual and the social environmental levels. This allowed to identify domain related social health characteristics or markers in epidemiological data bases and to investigate associations between these markers and cognitive decline and dementia. The associated social health markers represent potentially modifiable risk and protective factors. Examples are “social engagement” in the participation domain at the individual level, and “frequency of contact” in the structure domain, “exchange of support” in the function domain and “loneliness” in the appraisal domain at the environmental level. The conceptual framework facilitated identification of domain related markers in the SHARED project, thus showing its potential feasibility.

The conceptual framework provides guidance for future research and facilitates identification of potentially modifiable risk and protective factors. These may shape new avenues for preventive interventions. We highlight the paradigm of social health in dementia as a priority for dementia research.