Even before the onset of psychotic symptoms, individuals with schizophrenia display cognitive impairments. Simultaneously, increasing amounts of individuals exhibit dysfunction of the blood–brain barrier (BBB). However, the impact of BBB dysfunction on neurocognitive impairment in people with first-episode psychosis has not yet been investigated.

To advance understanding of said relationship, we considered one of the largest first-episode psychosis cohorts with cerebrospinal fluid parameters available, and investigated whether BBB dysfunction is related to working memory, working speed and attention.

We conducted a retrospective chart review of 121 in-patients diagnosed with a first episode of a schizophrenia spectrum disorder. Patients underwent neurocognitive testing and a lumbar puncture within routine clinical care. To define BBB dysfunction, albumin cerebrospinal fluid/serum quotients, immunoglobulin G ratios and oligoclonal band types were evaluated, and gender-specific differences investigated. Neurocognitive functioning was assessed by the Wechsler Adult Intelligence Scale, Test of Attentional Performance and Repeatable Battery for the Assessment of Neuropsychological Status. We performed simple and multiple linear regression analyses to interpret associations of interest.

Of those tested, 16% showed an alteration in albumin quotients and 12% had an oligoclonal band type indicating BBB dysfunction. Notably, male patients were more likely to have an increased albumin quotient and a higher immunoglobulin G ratio than female patients. We found no significant association between BBB dysfunction and neurocognitive assessments.

The hypothesised relationship between BBB and neurocognitive impairments was not detectable in our retrospective cohort. Further cerebrospinal fluid-based studies with a longitudinal assessment of cognitive functioning and disease trajectory are urgently needed.

The COVID-19 pandemic has affected all our lives, not only through the infection itself but also through the measures taken to control the spread of the virus (e.g. lockdown).

Here, we investigated how the COVID-19 pandemic and unprecedented lockdown affected the mental health of young adults in England and Wales.

We compared the mental health symptoms of up to 4773 twins in their mid-20s in 2018 prior to the COVID-19 pandemic (T1) and during four-wave longitudinal data collection during the pandemic in April, July and October 2020, and in March 2021 (T2–T5) using phenotypic and genetic longitudinal designs.

The average changes in mental health were small to medium and mainly occurred from T1 to T2 (average Cohen d = 0.14). Despite the expectation of catastrophic effects of the pandemic on mental health, we did not observe trends in worsening mental health during the pandemic (T3–T5). Young people with pre-existing mental health problems were disproportionately affected at the beginning of the pandemic, but their increased problems largely subsided as the pandemic persisted. Twin analyses indicated that the aetiology of individual differences in mental health symptoms did not change during the lockdown (average heritability 33%); the average genetic correlation between T1 and T2–T5 was 0.95, indicating that genetic effects before the pandemic were substantially correlated with genetic effects up to a year later.

We conclude that on average the mental health of young adults in England and Wales has been remarkably resilient to the effects of the pandemic and associated lockdown.

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.

Acute and transient psychotic disorders (ATPD) are characterized by an acute onset and a remitting course, and overlap with subgroups of the clinical high-risk state for psychosis. The long-term course and outcomes of ATPD are not completely clear.

Electronic health record-based retrospective cohort study, including all patients who received a first index diagnosis of ATPD (F23, ICD-10) within the South London and Maudsley (SLaM) National Health Service Trust, between 1 st April 2006 and 15th June 2017. The primary outcome was risk of developing persistent psychotic disorders, defined as the development of any ICD-10 diagnoses of non-organic psychotic disorders. Cumulative risk of psychosis onset was estimated through Kaplan-Meier failure functions (non-competing risks) and Greenwood confidence intervals.

A total of 3074 patients receiving a first index diagnosis of ATPD (F23, ICD-10) within SLaM were included. The mean follow-up was 1495 days. After 8-year, 1883 cases (61.26%) retained the index diagnosis of ATPD; the remaining developed psychosis. The cumulative incidence (Kaplan-Meier failure function) of risk of developing any ICD-10 non-organic psychotic disorder was 16.10% at 1-year (95%CI 14.83–17.47%), 28.41% at 2-year (95%CI 26.80–30.09%), 33.96% at 3-year (95% CI 32.25–35.75%), 36.85% at 4-year (95%CI 35.07–38.69%), 40.99% at 5-year (95% CI 39.12–42.92%), 42.58% at 6-year (95%CI 40.67–44.55%), 44.65% at 7-year (95% CI 42.66–46.69%), and 46.25% at 8-year (95% CI 44.17–48.37%). The cumulative risk of schizophrenia-spectrum disorder at 8-year was 36.14% (95% CI 34.09–38.27%).

Individuals with ATPD have a very high risk of developing persistent psychotic disorders and may benefit from early detection and preventive treatments to improve their outcomes.

Adverse childhood experiences (ACE) exhibit long-lasting consequences on later life and are considered as a major public health problem. ACEs can be divided into household dysfunctions, which affect the child indirectly, and direct maltreatment. As a high correlation between ACEs in general is known, we assessed the risk for child maltreatment associated with the occurrence of household dysfunctions. To provide a better understanding for the mechanisms leading to the deleterious sequelae of ACEs, we furthermore assessed whether the long-term consequences of household dysfunction are mediated by child maltreatment and thereby might be targeted by effective child protection programs.

A representative sample of the German population above the age of 14 (N = 2531) was assessed in a cross-sectional observational population-based survey.

The data reveal that mental illness of a household member was associated with significantly increased risks for all child maltreatment subtypes (ORs 4.95–5.55), just as household substance abuse (ORs 5.32–6.98), violence against the mother (ORs 4.43–10.26), incarceration of a household member (ORs 6.11–14.93) and parental separation (OR 3.37–4.87). Child maltreatment partially mediated the association of household mental illness, substance abuse and parental separation with later depression, anxiety, life satisfaction and subjective general health status and completely mediated the associations of intimate partner violence (IPV) and incarceration of a household member with anxiety, depression and subjective health status in adulthood.

ACEs linked to household dysfunction are associated with an increased risk for all subtypes of child maltreatment. The assessed widespread consequences of household dysfunction are mediated by child maltreatment. This underlines the role of prevention of child maltreatment in families with household dysfunction and implies child protection as a priority in any interventions.

Patients with acute and transient psychotic disorders (ATPDs) are by definition remitting, but have a high risk of developing persistent psychoses, resembling a subgroup of individuals at Clinical High Risk for Psychosis (CHR-P). Their pathways to care, treatment offered and long-term clinical outcomes beyond risk to psychosis are unexplored. We conducted an electronic health record-based retrospective cohort study including patients with ATPDs within the SLaM NHS Trust and followed-up to 8 years.

A total of 2561 ATPDs were included in the study. A minority were detected (8%) and treated (18%) by Early Intervention services (EIS) and none by CHR-P services. Patients were offered a clinical follow-up of 350.40 ± 589.90 days. The cumulative incidence of discharges was 40% at 3 months, 60% at 1 year, 69% at 2 years, 77% at 4 years, and 82% at 8 years. Treatment was heterogeneous: the majority of patients received antipsychotics (up to 52%), only a tiny minority psychotherapy (up to 8%).

Over follow-up, 32.88% and 28.54% of ATPDS received at least one mental health hospitalization or one compulsory hospital admission under the Mental Health Act, respectively. The mean number of days spent in psychiatric hospital was 66.39 ± 239.44 days.

The majority of ATPDs are not detected/treated by EIS or CHR-P services, receive heterogeneous treatments and short-term clinical follow-up. ATPDs have a high risk of developing severe clinical outcomes beyond persistent psychotic disorders and unmet clinical needs that are not targeted by current mental health services.

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation.

Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques.

Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus.

The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.