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Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.

Background Reduced reward learning might contribute to the onset and maintenance of major depressive disorder (MDD). In particular, the inability to utilize rewards to guide behavior is hypothesized to be associated with anhedonia, a core feature and potential trait marker of MDD. Few studies have investigated whether reduced reward learning normalizes with treatment and/or reward learning predicts clinical outcome. Our goal was to test that MDD is characterized by reduced reward learning, especially in the presence of anhedonic symptoms, and to investigate the relationship between reward learning and MDD diagnosis after 8 weeks of treatment. Methods Seventy-nine inpatients and 63 healthy controls performed a probabilistic reward task yielding an objective measure of participants’ ability to modulate behaviour as a function of reward. We compared reward responsiveness between depressed patients and controls, as well as high vs. low anhedonic MDD patients. Further, we evaluated whether reward learning deficits predicted persistence of MDD after 8 weeks of treatment. Results Relative to controls, MDD patients showed reduced reward learning. Moreover, patients with high anhedonia showed diminished reward learning compared to patients with low anhedonia. Reduced reward learning at study entry increased the odds of a persisting diagnosis of MDD after 8 weeks of treatment (OR: 7.84). Conclusions Our findings indicate that depressed patients, especially those with anhedonic features, are characterized by an impaired ability to modulate behaviour as a function of reward. Moreover, reduced reward learning increased the odds for the diagnosis of MDD to persist after 8 weeks of treatment.

The genetic basis of major depressive disorder (MDD) has been investigated extensively, but the identification of MDD genes has been hampered by conflicting results from underpowered studies. We review all MDD case-control genetic association studies published before June 2007 and perform meta-analyses for polymorphisms that had been investigated in at least three studies. The study selection and data extraction were performed in duplicate by two independent investigators. The 183 papers that met our criteria studied 393 polymorphisms in 102 genes. Twenty-two polymorphisms (6%) were investigated in at least three studies. Seven polymorphisms had been evaluated in previous meta-analyses, 5 of these had new data available. Hence, we performed meta-analyses for 20 polymorphisms in 18 genes. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Statistically significant associations were found for the APOE varepsilon2 (OR, 0.51), GNB3 825T (OR, 1.38), MTHFR 677T (OR, 1.20), SLC6A4 44 bp Ins/Del S (OR, 1.11) alleles and the SLC6A3 40 bpVNTR 9/10 genotype (OR, 2.06). To date, there is statistically significant evidence for six MDD susceptibility genes (APOE, DRD4, GNB3, MTHFR, SLC6A3 and SLC6A4).

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Background The coronavirus disease 2019 (COVID-19) may aggravate workplace conditions that impact health-care workers’ mental health. However, it can also place other stresses on workers outside of their work. This study determines the effect of COVID-19 on symptoms of negative and positive mental health and the workforce’s experience with various sources of support. Effect modification by demographic variables was also studied. Methods A cross-sectional survey study, conducted between 2 April and 4 May 2020 (two waves), led to a convenience sample of 4509 health-care workers in Flanders (Belgium), including paramedics (40.6%), nurses (33.4%), doctors (13.4%) and management staff (12.2%). About three in four were employed in university and acute hospitals (29.6%), primary care practices (25.7%), residential care centers (21.3%) or care sites for disabled and mental health care. In each of the two waves, participants were asked how frequently (on a scale of 0–10) they experienced positive and negative mental health symptoms during normal circumstances and during last week, referred to as before and during COVID-19, respectively. These symptoms were stress, hypervigilance, fatigue, difficulty sleeping, unable to relax, fear, irregular lifestyle, flashback, difficulty concentrating, feeling unhappy and dejected, failing to recognize their own emotional response, doubting knowledge and skills and feeling uncomfortable within the team. Associations between COVID-19 and mental health symptoms were estimated by cumulative logit models and reported as odds ratios. The needed support was our secondary outcome and was reported as the degree to which health-care workers relied on sources of support and how they experienced them. Results All symptoms were significantly more pronounced during versus before COVID-19. For hypervigilance, there was a 12-fold odds (odds ratio 12.24, 95% confidence interval 11.11–13.49) during versus before COVID-19. Positive professional symptoms such as the feeling that one can make a difference were less frequently experienced. The association between COVID-19 and mental health was generally strongest for the age group 30–49 years, females, nurses and residential care centers. Health-care workers reported to rely on support from relatives and peers. A considerable proportion, respectively, 18 and 27%, reported the need for professional guidance from psychologists and more support from their leadership. Conclusions The toll of the crisis has been heavy on health-care workers. Those who carry leadership positions at an organizational or system level should take this opportunity to develop targeted strategies to mitigate key stressors of health-care workers’ mental well-being.

Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine.

The factor structure and the convergent validity of the Personality Inventory for DSM-5 (PID-5), a self-report questionnaire designed to measure personality pathology as advocated in the fifth edition, Section III of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), are already demonstrated in general population samples, but need replication in clinical samples. In 240 Flemish inpatients, we examined the factor structure of the PID-5 by means of exploratory structural equation modeling. Additionally, we investigated differences in PID-5 higher order domain scores according to gender, age and educational level, and explored convergent and discriminant validity by relating the PID-5 with the Dimensional Assessment of Personality Pathology-Basic Questionnaire and by comparing PID-5 scores of inpatients with and without a DSM-IV categorical personality disorder diagnosis. Our results confirmed the original five-factor structure of the PID-5. The reliability and the convergent and discriminant validity of the PID-5 proved to be adequate. Implications for future research are discussed.