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The association between weight and depressive symptoms is well established, but the direction of effects remains unclear. Most studies rely on body mass index (BMI) as the sole weight indicator, with few examining the aetiology of the association between weight indicators and depressive symptoms.
We analysed data from the Twins Early Development Study (TEDS) and UK Adult Twin Registry (TwinsUK) (7658 and 2775 twin pairs, respectively). A phenotypic cross-lagged panel model assessed the directionality between BMI and depressive symptoms at ages 12, 16, and 21 years in TEDS. Bivariate correlations tested the phenotypic association between a range of weight indicators and depressive symptoms in TwinsUK. In both samples, structural equation modelling of twin data investigated genetic and environmental influences between weight indicators and depression. Sensitivity analyses included two-wave phenotypic cross-lagged panel models and the exclusion of those with a BMI <18.5.
Within TEDS, the relationship between BMI and depression was bidirectional between ages 12 and 16 with a stronger influence of earlier BMI on later depression. The associations were unidirectional thereafter with depression at 16 influencing BMI at 21. Small genetic correlations were found between BMI and depression at ages 16 and 21, but not at 12. Within TwinsUK, depression was weakly correlated with weight indicators; therefore, it was not possible to generate precise estimates of genetic or environmental correlations.
The directionality of the relationship between BMI and depression appears to be developmentally sensitive. Further research with larger genetically informative samples is needed to estimate the aetiological influence on these associations.
System-level change is crucial for solving society's most pressing problems. However, individual-level interventions may be useful for creating behavioral change before system-level change is in place and for increasing necessary public support for system-level solutions. Participating in individual-level solutions may increase support for system-level solutions – especially if the individual-level solutions are internalized as part of one's social identity.
Waves 1 to 3 (March 2020 to May 2020) of the UK COVID-19 Mental Health and Wellbeing study suggested an improvement in some indicators of mental health across the first 6 weeks of the UK lockdown; however, suicidal ideation increased.
To report the prevalence of mental health and well-being of adults in the UK from March/April 2020 to February 2021.
Quota sampling was employed at wave 1 (March/April 2020), and online surveys were conducted at seven time points. Primary analyses cover waves 4 (May/June 2020), 5 (July/August 2020), 6 (October 2020) and 7 (February 2021), including a period of increased restrictions in the UK. Mental health indicators were suicidal ideation, self-harm, suicide attempt, depression, anxiety, defeat, entrapment, loneliness and well-being.
A total of 2691 (87.5% of wave 1) individuals participated in at least one survey between waves 4 and 7. Depressive symptoms and loneliness increased from October 2020 to February 2021. Defeat and entrapment increased from July/August 2020 to October 2020, and remained elevated in February 2021. Well-being decreased from July/August 2020 to October 2020. Anxiety symptoms and suicidal ideation did not change. Young adults, women, those who were socially disadvantaged and those with a pre-existing mental health condition reported worse mental health.
The mental health and well-being of the UK population deteriorated from July/August 2020 to October 2020 and February 2021, which coincided with the second wave of COVID-19. Suicidal thoughts did not decrease significantly, suggesting a need for continued vigilance as we recover from the pandemic.
The COVID-19 pandemic has disrupted lives and livelihoods, and people already experiencing mental ill health may have been especially vulnerable.
Quantify mental health inequalities in disruptions to healthcare, economic activity and housing.
We examined data from 59 482 participants in 12 UK longitudinal studies with data collected before and during the COVID-19 pandemic. Within each study, we estimated the association between psychological distress assessed pre-pandemic and disruptions since the start of the pandemic to healthcare (medication access, procedures or appointments), economic activity (employment, income or working hours) and housing (change of address or household composition). Estimates were pooled across studies.
Across the analysed data-sets, 28% to 77% of participants experienced at least one disruption, with 2.3–33.2% experiencing disruptions in two or more domains. We found 1 s.d. higher pre-pandemic psychological distress was associated with (a) increased odds of any healthcare disruptions (odds ratio (OR) 1.30, 95% CI 1.20–1.40), with fully adjusted odds ratios ranging from 1.24 (95% CI 1.09–1.41) for disruption to procedures to 1.33 (95% CI 1.20–1.49) for disruptions to prescriptions or medication access; (b) loss of employment (odds ratio 1.13, 95% CI 1.06–1.21) and income (OR 1.12, 95% CI 1.06 –1.19), and reductions in working hours/furlough (odds ratio 1.05, 95% CI 1.00–1.09) and (c) increased likelihood of experiencing a disruption in at least two domains (OR 1.25, 95% CI 1.18–1.32) or in one domain (OR 1.11, 95% CI 1.07–1.16), relative to no disruption. There were no associations with housing disruptions (OR 1.00, 95% CI 0.97–1.03).
People experiencing psychological distress pre-pandemic were more likely to experience healthcare and economic disruptions, and clusters of disruptions across multiple domains during the pandemic. Failing to address these disruptions risks further widening mental health inequalities.
Susceptibility to infection such as SARS-CoV-2 may be influenced by host genotype. TwinsUK volunteers (n = 3261) completing the C-19 COVID-19 symptom tracker app allowed classical twin studies of COVID-19 symptoms, including predicted COVID-19, a symptom-based algorithm to predict true infection, derived from app users tested for SARS-CoV-2. We found heritability of 49% (32−64%) for delirium; 34% (20−47%) for diarrhea; 31% (8−52%) for fatigue; 19% (0−38%) for anosmia; 46% (31−60%) for skipped meals and 31% (11−48%) for predicted COVID-19. Heritability estimates were not affected by cohabiting or by social deprivation. The results suggest the importance of host genetics in the risk of clinical manifestations of COVID-19 and provide grounds for planning genome-wide association studies to establish specific genes involved in viral infectivity and the host immune response.
The effects of coronavirus disease 2019 (COVID-19) on the population's mental health and well-being are likely to be profound and long lasting.
To investigate the trajectory of mental health and well-being during the first 6 weeks of lockdown in adults in the UK.
A quota survey design and a sampling frame that permitted recruitment of a national sample was employed. Findings for waves 1 (31 March to 9 April 2020), 2 (10 April to 27 April 2020) and 3 (28 April to 11 May 2020) are reported here. A range of mental health factors was assessed: pre-existing mental health problems, suicide attempts and self-harm, suicidal ideation, depression, anxiety, defeat, entrapment, mental well-being and loneliness.
A total of 3077 adults in the UK completed the survey at wave 1. Suicidal ideation increased over time. Symptoms of anxiety, and levels of defeat and entrapment decreased across waves whereas levels of depressive symptoms did not change significantly. Positive well-being also increased. Levels of loneliness did not change significantly over waves. Subgroup analyses showed that women, young people (18–29 years), those from more socially disadvantaged backgrounds and those with pre-existing mental health problems have worse mental health outcomes during the pandemic across most factors.
The mental health and well-being of the UK adult population appears to have been affected in the initial phase of the COVID-19 pandemic. The increasing rates of suicidal thoughts across waves, especially among young adults, are concerning.
TwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.
Our study examines the contribution of genetic and environmental factors (both shared and unique) to frailty, measured using the Rockwood Frailty Index (FI) in a sample of twins from the St Thomas’ UK Adult Twin Registry. The FI was based on 39 items of potential health deficit. Study participants were 3,375 volunteer adult twins (840 monozygotic and 802 dizygotic twin-pairs) 40.0–84.5 years old. First, we used structural equation modeling to estimate the relative contribution of genetics and of the shared and unique environment to variance in FI adjusted for age. In a second analysis, multiple linear regression was used to examine variance in FI as a function of father's occupational class (a component of shared environment and a measure of childhood socioeconomic status [SES]), adjusting for age, birth weight, marital status, and health behaviors (smoking, alcohol consumption, and physical activity). Statistical analyses were conducted using IBM SPSS® Version 22 software and Mx open source software. Findings showed that 45% (95% confidence intervals [CIs] 30–53%) of the inter-individual variation in FI was heritable and 52% (95% CIs 47–57%) was due to the individual's unique environment. Multiple linear regression also showed a small but statistically significant inverse association between father's occupational class and FI, mediated by one's own educational attainment and birth weight. Our results indicate that frailty is both genetically and environmentally determined. Thus, its prevention and management call for a multifaceted approach that includes addressing deleterious environmental factors, some of which, like childhood SES, may act across the life course.
Little is known about the extent to which aging trajectories of different body systems share common sources of variance. We here present a large twin study investigating the trajectories of change in five systems: cardiovascular, respiratory, skeletal, morphometric, and metabolic. Longitudinal clinical data were collected on 3,508 female twins in the TwinsUK registry (complete pairs:740 monozygotic (MZ), 986 dizygotic (DZ), mean age at entry 48.9 ± 10.4, range 18–75 years; mean follow-up 10.2 ± 2.8 years, range 4–17.8 years). Panel data on multiple age-related variables were used to estimate biological ages for each individual at each time point, in linear mixed effects models. A weighted average approach was used to combine variables within predefined body system groups. Aging trajectories for each system in each individual were then constructed using linear modeling. Multivariate structural equation modeling of these aging trajectories showed low genetic effects (heritability), ranging from 2% in metabolic aging to 22% in cardiovascular aging. However, we found a significant effect of shared environmental factors on the variations in aging trajectories in cardiovascular (54%), skeletal (34%), morphometric (53%), and metabolic systems (53%). The remainder was due to environmental factors unique to each individual plus error. Multivariate Cholesky decomposition showed that among aging trajectories for various body systems there were significant and substantial correlations between the unique environmental latent factors as well as shared environmental factors. However, there was no evidence for a single common factor for aging. This study, the first of its kind in aging, suggests that diverse organ systems share non-genetic sources of variance for aging trajectories. Confirmatory studies are needed using population-based twin cohorts and alternative methods of handling missing data.