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

Wellbeing and brain structure: A comprehensive phenotypic and genetic study of image-derived phenotypes in the UK Biobank

Wellbeing, an important component of mental health, is influenced by genetic and environmental factors. Previous association studies between brain structure and wellbeing have typically focused on volumetric measures and employed small cohorts. Using the UK Biobank Resource, we explored the relationships between wellbeing and brain morphometrics (volume, thickness and surface area) at both phenotypic and genetic levels. The sample comprised 38,982 participants with neuroimaging and wellbeing phenotype data, of which 19,234 had genotypes from which wellbeing polygenic scores (PGS) were calculated. We examined the association of wellbeing phenotype and PGS with all brain regions (including cortical, subcortical, brainstem and cerebellar regions) using multiple linear models, including (1) basic neuroimaging covariates and (2) additional demographic factors that may synergistically impact wellbeing and its neural correlates. Genetic correlations between genomic variants influencing wellbeing and brain structure were also investigated. Small but significant associations between wellbeing and volumes of several cerebellar structures (β = 0.015–0.029, PFDR = 0.007–3.8 × 10−9), brainstem, nucleus accumbens and caudate were found. Cortical associations with wellbeing included volume of right lateral occipital, thickness of bilateral lateral occipital and cuneus, and surface area of left superior parietal, supramarginal and pre-/post-central regions. Wellbeing-PGS was associated with cerebellar volumes and supramarginal surface area. Small mediation effects of wellbeing phenotype and PGS on right VIIIb cerebellum were evident. No genetic correlation was found between wellbeing and brain morphometric measures. We provide a comprehensive overview of wellbeing-related brain morphometric variation. Notably, small effect sizes reflect the multifaceted nature of this concept

TWIN-10: protocol for a 10-year longitudinal twin study of the neuroscience of mental well-being and resilience

Introduction Mental well-being is a core component of mental health, and resilience is a key process of positive adaptive recovery following adversity. However, we lack an understanding of the neural mechanisms that contribute to individual variation in the trajectories of well-being and resilience relative to risk. Genetic and/or environmental factors may also modulate these mechanisms. The aim of the TWIN-10 Study is to characterise the trajectories of well-being and resilience over 12 years across four timepoints (baseline, 1 year, 10 years, 12 years) in 1669 Australian adult twins of European ancestry (to account for genetic stratification effects). To this end, we integrate data across genetics, environment, psychological self-report, neurocognitive performance and brain function measures of well-being and resilience. Methods and analysis Twins who took part in the baseline TWIN-E Study will be invited back to participate in the TWIN-10 Study, at 10-year and 12-year follow-up timepoints. Participants will complete an online battery of psychological self-reports, computerised behavioural assessments of neurocognitive functions and MRI testing of the brain structure and function during resting and task-evoked scans. These measures will be used as predictors of the risk versus resilience trajectory groups defined by their changing levels of well-being and illness symptoms over time as a function of trauma exposure. Structural equation models will be used to examine the association between the predictors and trajectory groups of resilience and risk over time. Univariate and multivariate twin modelling will be used to determine heritability of the measures, as well as the shared versus unique genetic and environmental contributions. Ethics and dissemination This study involves human participants. This study was approved by the University of New South Wales Human Research Ethics Committee (HC180403) and the Scientific Management Panel of Neuroscience Research Australia Imaging (CX2019-05). Results will be disseminated through publications and presentations to the public and the academic community. Participants gave informed consent to participate in the study before taking part

A Web-Based Well-being Program for Health Care Workers (Thrive): Protocol for a Randomized Controlled Trial

Background: Mental health has come to be understood as not merely the absence of mental illness but also the presence of mental well-being, and recent interventions have sought to increase well-being in various populations. A population that deserves particular attention is that of health care workers, whose occupations entail high levels of stress, especially given the ongoing COVID-19 pandemic. A neuroscience-based web-based well-being program for health care workers-the Thrive program-has been newly developed to promote habits and activities that contribute to brain health and overall mental well-being. Objective: This paper describes the protocol for a randomized controlled trial whose objective is to evaluate the Thrive program in comparison with an active control condition to measure whether the program is effective at increasing well-being and decreasing symptoms of psychological distress in health care workers at a designated Australian hospital. Methods: The trial will comprise two groups (intervention vs active control) and 4 measurement occasions over a 12-week period. A survey will be administered in each of weeks 0, 4, 8, and 12, and the well-being program will be delivered in weeks 1-7 (via web-based video presentations or digital pamphlets). Each of the 4 surveys will comprise a range of questionnaires to measure well-being, psychological distress, and other key variables. The planned analyses will estimate group-by-time interaction effects to test the hypothesis that mental health will increase over time in the intervention condition relative to the active control condition. Results: The Thrive program was delivered to a small number of wards at the hospital between February 2021 and July 2021, and it will be delivered to the remaining wards from October 2021 to December 2021. A power calculation has recommended a sample size of at least 200 participants in total. A linear mixed model will be used to estimate the interaction effects. Conclusions: This trial seeks to evaluate a new web-based well-being program for health care workers at a major public hospital. It will contribute to the growing body of research on mental well-being and ways to promote it

Centeredness Theory: Understanding and measuring well-being across core life domains

Background: Centeredness Theory (CT) is proposed as a new mental health paradigm that focuses on well-being at a systems-level, across the core life domains of the self, the family unit, relationships, community, and work. The current studies aimed to validate the psychometric properties of a new scale that measures CT against existing well-being and mental health measures. Methods: Study 1 included 488 anonymous online respondents (46% females, 28% males, 25% unknown with median age between 31 and 35 years) across 38 countries who completed the CT scale. Study 2 included 49 first-year psychology students (90% females, mean age of 19 years) from Sydney Australia that completed the CT scale and other well-being and mental health questionnaires at baseline and 2-weeks follow-up. Results: Exploratory and confirmatory factor analyses resulted in a refined 60-item CT scale with five domains, each with four sub-domains. The CT scale demonstrated good internal consistency reliability and test-retest reliability, and showed evidence of convergent validity against other well-being measures (e.g., COMPAS-W Wellbeing Scale, SWLS scale, and Ryff's Psychological Well-being scale). Conclusions: The CT scale appears to be a reliable measure of well-being at a systems-level. Future studies need to confirm these findings in larger heterogeneous samples

Corrigendum: Centeredness theory: Understanding and measuring well-being across core life domains [Front. Psychol, 9, 610 (2018)] DOI: 10.3389/fpls.2016.00985

In the original article, there was a mistake in Supplementary Figure 1 as published. The Supplementary Figure that was submitted at the time of publishing was mislabeled. The corrected Supplementary Figure 1 appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way

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

Fracture properties characterization of nuclear fuel using micro-cantilever bending

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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