Cardiometabolic disease risk factors are disproportionately prevalent in bipolar disorder (BD) and are associated with cognitive impairment. It is, however, unknown which health risk factors for cardiometabolic disease are relevant to cognition in BD. This study aimed to identify the cardiometabolic disease risk factors that are the most important correlates of cognitive impairment in BD; and to examine whether the nature of the relationships vary between mid and later life.

Data from the UK Biobank were available for 966 participants with BD, aged between 40 and 69 years. Individual cardiometabolic disease risk factors were initially regressed onto a global cognition score in separate models for the following risk factor domains; (1) health risk behaviors (physical activity, sedentary behavior, smoking, and sleep) and (2) physiological risk factors, stratified into (2a) anthropometric and clinical risk (handgrip strength, body composition, and blood pressure), and (2b) cardiometabolic disease risk biomarkers (CRP, lipid profile, and HbA1c). A final combined multivariate regression model for global cognition was then fitted, including only the predictor variables that were significantly associated with cognition in the previous models.

In the final combined model, lower mentally active and higher passive sedentary behavior, higher levels of physical activity, inadequate sleep duration, higher systolic and lower diastolic blood pressure, and lower handgrip strength were associated with worse global cognition.

Health risk behaviors, as well as blood pressure and muscular strength, are associated with cognitive function in BD, whereas other traditional physiological cardiometabolic disease risk factors are not.

Life engagement represents a holistic concept that encompasses outcomes reflecting life-fulfilment, well-being and participation in valued and meaningful activities, which is recently gaining attention and scientific interest. Despite its conceptual importance and its relevance, life engagement represents a largely unexplored domain in schizophrenia. The aims of the present study were to independently assess correlates and predictors of patient life engagement in a large and well-characterized sample of schizophrenia patients.

To assess the impact of different demographic, clinical, cognitive and functional parameters on life engagement in a large sample of patients with schizophrenia, data from the social cognition psychometric evaluation project were analyzed.

Overall schizophrenia and depressive symptom severity, premorbid IQ, neurocognitive performance, social cognition performance both in the emotion processing and theory of mind domains, functional capacity, social skills performance and real-world functioning in different areas all emerged as correlates of patient life engagement. Greater symptom severity and greater impairment in real-world interpersonal relationships, social skills, functional capacity and work outcomes emerged as individual predictors of greater limitations in life engagement.

Life engagement in people living with schizophrenia represents a holistic and complex construct, with several different clinical, cognitive and functional correlates. These features represent potential treatment targets to improve the clinical condition and also facilitate the process of recovery and the overall well-being of people living with schizophrenia.

Approximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.

Existing questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.

A total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.

This new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.

AbbVie Inc.

The coronavirus disease 2019 (COVID-19) pandemic represents an unprecedented threat to mental health. Herein, we assessed the impact of COVID-19 on subthreshold depressive symptoms and identified potential mitigating factors.

Participants were from Depression Cohort in China (ChiCTR registry number 1900022145). Adults (n = 1722) with subthreshold depressive symptoms were enrolled between March and October 2019 in a 6-month, community-based interventional study that aimed to prevent clinical depression using psychoeducation. A total of 1506 participants completed the study in Shenzhen, China: 726 participants, who completed the study between March 2019 and January 2020 (i.e. before COVID-19), comprised the ‘wave 1’ group; 780 participants, who were enrolled before COVID-19 and completed the 6-month endpoint assessment during COVID-19, comprised ‘wave 2’. Symptoms of depression, anxiety and insomnia were assessed at baseline and endpoint (i.e. 6-month follow-up) using the Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI), respectively. Measures of resilience and regular exercise were assessed at baseline. We compared the mental health outcomes between wave 1 and wave 2 groups. We additionally investigated how mental health outcomes changed across disparate stages of the COVID-19 pandemic in China, i.e. peak (7–13 February), post-peak (14–27 February), remission plateau (28 February−present).

COVID-19 increased the risk for three mental outcomes: (1) depression (odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.04–1.62); (2) anxiety (OR = 1.47, 95% CI: 1.16–1.88) and (3) insomnia (OR = 1.37, 95% CI: 1.07–1.77). The highest proportion of probable depression and anxiety was observed post-peak, with 52.9% and 41.4%, respectively. Greater baseline resilience scores had a protective effect on the three main outcomes (depression: OR = 0.26, 95% CI: 0.19–0.37; anxiety: OR = 1.22, 95% CI: 0.14–0.33 and insomnia: OR = 0.18, 95% CI: 0.11–0.28). Furthermore, regular physical activity mitigated the risk for depression (OR = 0.79, 95% CI: 0.79–0.99).

The COVID-19 pandemic exerted a highly significant and negative impact on symptoms of depression, anxiety and insomnia. Mental health outcomes fluctuated as a function of the duration of the pandemic and were alleviated to some extent with the observed decline in community-based transmission. Augmenting resiliency and regular exercise provide an opportunity to mitigate the risk for mental health symptoms during this severe public health crisis.

Asenapine is indicated in adults for acute treatment of schizophrenia and manic or mixed episodes associated with bipolar I disorder with or without psychotic features. We report the safety, tolerability, and efficacy of asenapine in patients with bipolar I disorder completing up to 52 weeks of treatment.

Patients completing either of two 3-week efficacy trials and a 9-week double-blind extension were eligible for this 40-week double-blind extension. Patients in the 3-week trials were randomized to flexible-dose asenapine (5 or 10 mg BID), placebo, or olanzapine (5-20 mg QD; included for assay sensitivity only). Patients entering the extension continued their preestablished treatment; those originally randomized to placebo received flexible-dose asenapine (placebo/asenapine, 5 or 10 mg BID). Safety and tolerability endpoints included adverse events (AEs), extrapyramidal symptoms, laboratory values, and anthropometric measures. Efficacy was measured as the change in Young Mania Rating Scale (YMRS) total score from 3-week trial baseline to week 52; the placebo/asenapine group was included in the safety analyses.

Incidence of treatment-emergent AEs was 71.9%, 86.1%, and 79.4% with placebo/asenapine, asenapine, and olanzapine, respectively. The most frequent AEs included headache and somnolence (placebo/asenapine); insomnia, sedation, and depression (asenapine); and weight gain, somnolence, and sedation (olanzapine). Mean ± SD changes in YMRS score at week 52 among observed cases in the intent-to-treat population were -28.6±8.1 for asenapine and -28.2±6.8 for olanzapine.

In this 52-week study, asenapine was well tolerated and long-term maintenance of efficacy was supported in patients initially presenting with bipolar mania.

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.