Phenotypic and genetic analysis of a wellbeing factor score in the UK Biobank and the impact of childhood maltreatment and psychiatric illness

Wellbeing is an important aspect of mental health that is moderately heritable. Specific wellbeing-related variants have been identified via GWAS meta-analysis of individual questionnaire items. However, a multi-item within-subject index score has potential to capture greater heritability, enabling improved delineation of genetic and phenotypic relationships across traits and exposures that are not possible on aggregate-data. This research employed data from the UK Biobank resource, and a wellbeing index score was derived from indices of happiness and satisfaction with family/friendship/finances/health, using principal component analysis. GWAS was performed in Caucasian participants (N = 129,237) using the derived wellbeing index, followed by polygenic profiling (independent sample; N = 23,703). The wellbeing index, its subcomponents, and negative indicators of mental health were compared via phenotypic and genetic correlations, and relationships with psychiatric disorders examined. Lastly, the impact of childhood maltreatment on wellbeing was investigated. Five independent genome-wide significant loci for wellbeing were identified. The wellbeing index had SNP-heritability of ~8.6%, and stronger phenotypic and genetic correlations with its subcomponents (0.55–0.77) than mental health phenotypes (−0.21 to −0.39). The wellbeing score was lower in participants reporting various psychiatric disorders compared to the total sample. Childhood maltreatment exposure was also associated with reduced wellbeing, and a moderate genetic correlation (rg = ~−0.56) suggests an overlap in heritability of maltreatment with wellbeing. Thus, wellbeing is negatively associated with both psychiatric disorders and childhood maltreatment. Although notable limitations, biases and assumptions are discussed, this within-cohort study aids the delineation of relationships between a quantitative wellbeing index and indices of mental health and early maltreatment

Portrayal of psychiatric genetics in Australian print news media, 1996-2009

Objective: To investigate how Australian print news media portray psychiatric genetics. Design and setting: Content and framing analysis of a structured sample of print news items about psychiatric genetics published in Australian newspapers between 1996 and 2009. Main outcome measures: Identify dominant discourses about aetiology of mental illness, and perceived clinical outcomes and implications of psychiatric genetics research. Results: We analysed 406 eligible items about the genetics of psychiatric disorders. News coverage of psychiatric genetics has steadily increased since 1996. Items attributing the aetiology of psychiatric disorders to gene-environment interactions (51%) outnumbered items attributing only genetic (30%) or only environmental factors (20%). Of items that referred to heritability of mental illness, frames of genetic determinism (78%) occurred more frequently than probabilistic frames (22%). Of frames related to genetic prophesy, genetic optimism frames (78%) were used more frequently than frames of genetic pessimism (22%). Psychosocial and ethical implications of psychiatric genetics received comparatively relatively little coverage (23%). The analysis identified 22 predictions about psychiatric genetic discoveries and the availability of molecular-based interventions in psychiatry, most of which (20/ 22, 91%) failed to manifest by the predicted year. Conclusions: Excessive optimism about the power of genetic technology in psychiatric health care, perceived clinical benefits, and largely unfulfilled predictions about availability of these benefits could encourage unrealistic expectations about future molecular-based treatment options for mental health

Editorial: Clinical cancer research in vulnerable populations

Vulnerable populations in cancer care include a wide array of possible conditions (see Table 1). Their vulnerabilities, whether medical, sociocultural, age- or socioeconomic-related, cause these cancer populations to be excluded from clinical trials. This introduces bias and a Matheus effect as clinical trial results are often not fully representative of the whole target population. This underrepresentation of the majority of cancer patients in clinical trials is a major drawback. The treatment of cancer patients with surgery, radiotherapy, and systemic anticancer drugs has reached an increasingly high level of effectiveness, sometimes shifting a cancer diagnosis towards the possibility of living a long life with a chronic cancer therapy treatment that takes place on a regular basis over a long period of time. Moreover, research is often so advanced that we may discuss “personalised cancer medicine” for different cancer types. Unfortunately, the progress that cancer research has made in cancer prevention, early detection, and treatment is not equitably accessible and applicable to vulnerable cancer patient populations. The main reason is that the patients included in clinical trials are not always representative of the whole target population. Indeed, the generalizability of trial results to all patients is usually hampered by the strict inclusion criteria of the clinical trials, leading potentially to overinflated reported benefits. In addition, the toxicity may be substantially higher in these vulnerable populations. Therefore, patients from the groups listed in Table 1 may not receive the best treatment option for their condition. Furthermore, the ethical implications of including vulnerable populations in clinical trials are often insurmountable for researchers attempting to gain approval for these studies

Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events

Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of wellbeing to evaluate the sociodemographic, psychosocial, cognitive, and life event predictors of wellbeing using cross-sectional and repeated measures multiple regression models over one year. Sociodemographic (age, sex, education), psychosocial (personality, health behaviours, and lifestyle), emotion and cognitive processing, and life event (recent positive and negative life events) variables were considered. The results showed that while neuroticism, extraversion, conscientiousness, and cognitive reappraisal were the strongest predictors of wellbeing in the cross-sectional model, while extraversion, conscientiousness, exercise, and specific life events (work related and traumatic life events) were the strongest predictors of wellbeing in the repeated measures model. These results were confirmed using tenfold cross-validation procedures. Together, the results indicate that the variables that best explain differences in wellbeing between individuals at baseline can vary from the variables that predict change in wellbeing over time. This suggests that different variables may need to be targeted to improve population-level compared to individual-level wellbeing

Grey matter covariation and the role of emotion reappraisal in mental wellbeing and resilience after early life stress exposure

Resilience is a process of adaptive recovery crucial in maintaining mental wellbeing after stress exposure. A psychological factor known to buffer stress and promote positive wellbeing outcomes is the ability to regulate emotions. However, the neural networks underlying resilience, and the possible mediating role of emotion regulation, remain largely unknown. Here, we examined the association between resilience and grey matter covariation (GMC) in healthy adults with and without early life stress (ELS) exposure, and whether emotion regulation mediated this brain-resilience association. Source-based morphometry was used to identify spatial patterns of common GMC in 242 healthy participants. Wellbeing was measured using the COMPAS-W Wellbeing Scale. Linear mixed models were run to establish associations between GMC and wellbeing scores. Moderated mediation models were used to examine a conditional mediating effect of emotion regulation on the brain-wellbeing relationship, moderated by ELS exposure. Distinct ELS-related morphometric patterns were found in association with resilience. In participants without ELS exposure, decreased GMC in the temporo-parietal regions was associated with wellbeing. In participants with ELS exposure, we observed increased patterns of covariation in regions related to the salience and executive control networks, and decreased GMC in temporo-parietal areas, which were associated with resilience. Cognitive reappraisal mediated the brain-wellbeing relationship in ELS-exposed participants only. Patterns of stronger GMC in regions associated with emotional and cognitive functioning in ELS-exposed participants with high levels of wellbeing may indicate possible neural signatures of resilience. This may be further heightened by utilising an adaptive form of emotion regulation

Diverse phenotypic measurements of wellbeing: Heritability, temporal stability and the variance explained by polygenic scores

Wellbeing, a key aspect of mental health, is moderately heritable with varying estimates reported from independent studies employing a variety of instruments. Recent genome-wide association studies (GWAS) have enabled the construction of polygenic scores (PGS) for wellbeing, providing the opportunity for direct comparisons of the variance explained by PGS for different instruments commonly employed in the field. Nine wellbeing measurements (multi-item and single-item), two personality domains (NEO-FFI neuroticism and extraversion), plus the depression domain of the DASS-42 were drawn from a larger self-report battery applied to the TWIN-E study—an Australian longitudinal twin cohort (N = 1660). Heritability was estimated using univariate twin modeling and 12-month test–retest reliability was estimated using intra-class correlation. PGS were constructed using wellbeing GWAS summary-statistics from Baselmans et al. (Nat Genet. 2019), and the variance explained estimated using linear models. Last, a GWAS was performed using COMPAS-W, a quantitative composite wellbeing measure, to explore its utility in genomic studies. Heritability estimates ranged from 23% to 47% across instruments, and multi-item measures showed higher heritability and test–retest reliability than single-item measures. The variance explained by PGS was ~0.5% to 1.5%, with considerable variation between measures, and within each measure over 12 months. Five loci with suggestive association (p < 1 × 10−5) were identified from this initial COMPAS-W wellbeing GWAS. This work highlights the variability across measures currently employed in wellbeing research, with multi-item and composite measures favored over single-item measures. While wellbeing PGS are useful in a research setting, they explain little of the phenotypic variance, highlighting gaps for improved gene discovery

Psychosocial Care after Cancer Diagnosis: Recent Advances and Challenges

Psychosocial oncology is coming of age [...]</jats:p

Dementia incidence, APOE genotype, and risk factors for cognitive decline in Aboriginal Australians

Background and Objectives Aboriginal Australians are disproportionately affected by dementia, with incidence in remote populations approximately double that of non-Indigenous populations. This study aimed to identify dementia incidence and risk factors in Aboriginal Australians residing in urban areas, which are currently unknown. Methods A population-based cohort of Aboriginal Australians ≥60 years of age was assessed at baseline and 6-year follow-up. Life-course risk factors (baseline) were examined for incident dementia or mild cognitive impairment (MCI) through logistic regression analyses; adjustments were made for age. APOE genotyping was available for 86 people. Results Data were included from 155 participants 60 to 86 years of age (mean 65.70 years, SD 5.65 years; 59 male). There were 16 incident dementia cases (age-standardized rate 35.97/1,000 person-years, 95% confidence interval [CI] 18.34–53.60) and 36 combined incident MCI and dementia cases. Older age (odds ratio [OR] 2.29, 95% CI 1.42–3.70), male sex (OR 4.14, 95% CI 1.60–10.77), unskilled work history (OR 5.09, 95% CI 1.95–13.26), polypharmacy (OR 3.11, 95% CI 1.17–8.28), and past smoking (OR 0.24, 95% CI 0.08–0.75) were associated with incident MCI/dementia in the final model. APOE e4 allele frequency was 24%; heterozygous or homozygous e4 was associated with incident MCI/dementia (bivariate OR 3.96, 95% CI 1.25–12.50). Discussion These findings provide evidence for higher dementia incidence in Aboriginal Australians from urban areas, where the majority of Aboriginal people reside. This study also sheds light on sociodemographic, health, and genetic factors associated with incident MCI/dementia at older ages in this population, which is critical for targeted prevention strategies