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BackgroundLittle is known regarding the mental health impact of having a significant person (family member and/or close friend) with COVID-19 of different severity.MethodsThe study included five prospective cohorts from four countries (Iceland, Norway, Sweden, and the UK) with self-reported data on COVID-19 and symptoms of depression and anxiety during March 2020-March 2022. We calculated the prevalence ratio (PR) of depression and anxiety in relation to having a significant person with COVID-19 and performed a longitudinal analysis in the Swedish cohort to describe the temporal patterns of the results.Results162,237 and 168,783 individuals were included in the analysis of depression and anxiety, respectively, of whom 24,718 and 27,003 reported a significant person with COVID-19. Overall, the PR was 1.07 (95% CI: 1.05-1.10) for depression and 1.08 (95% CI: 1.03-1.13) for anxiety among significant others of COVID-19 patients. The respective PRs for depression and anxiety were 1.04 (95% CI: 1.01-1.07) and 1.03 (95% CI: 0.98-1.07) if the significant person was never hospitalized, 1.15 (95% CI: 1.08-1.23) and 1.24 (95% CI: 1.14-1.34) if the patient was hospitalized, 1.42 (95% CI: 1.27-1.57) and 1.45 (95% CI: 1.31-1.60) if admitted to the ICU, and 1.34 (95% CI: 1.22-1.46) and 1.36 (95% CI: 1.22-1.51) if the significant person died. Individuals of hospitalized, ICU admitted, or deceased patients showed higher prevalence of depression and anxiety during the entire 12 months after the COVID-19 diagnosis of the significant person.ConclusionsClose friends and family members of critically ill COVID-19 patients show elevated prevalence of depression and anxiety throughout the first year after the diagnosis.

ObjectivesWe investigated whether functional health literacy and cognitive ability were associated with self-reported diabetes.DesignProspective cohort study.SettingData were from waves 2 (2004–2005) to 7 (2014–2015) of the English Longitudinal Study of Ageing (ELSA), a cohort study designed to be representative of adults aged 50 years and older living in England.Participants8669 ELSA participants (mean age=66.7, SD=9.7) who completed a brief functional health literacy test assessing health-related reading comprehension, and 4 cognitive tests assessing declarative memory, processing speed and executive function at wave 2.Primary outcome measureSelf-reported doctor diagnosis of diabetes.ResultsLogistic regression was used to examine cross-sectional (wave 2) associations of functional health literacy and cognitive ability with diabetes status. Adequate (compared with limited) functional health literacy (OR 0.71, 95% CI 0.61 to 0.84) and higher cognitive ability (OR per 1 SD=0.73, 95% CI 0.67 to 0.80) were associated with lower odds of self-reporting diabetes at wave 2. Cox regression was used to test the associations of functional health literacy and cognitive ability measured at wave 2 with self-reporting diabetes over a median of 9.5 years follow-up (n=6961). Adequate functional health literacy (HR 0.64; 95% CI 0.53 to 0.77) and higher cognitive ability (HR 0.77, 95% CI 0.69 to 0.85) at wave 2 were associated with lower risk of self-reporting diabetes during follow-up. When both functional health literacy and cognitive ability were added to the same model, these associations were slightly attenuated. Additionally adjusting for health behaviours and body mass index fully attenuated cross-sectional associations between functional health literacy and cognitive ability with diabetes status, and partly attenuated associations between functional health literacy and cognitive ability with self-reporting diabetes during follow-up.ConclusionsAdequate functional health literacy and better cognitive ability were independently associated with lower likelihood of reporting diabetes.

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Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.Although our genetic code does not change throughout our lives, our genes can be turned on and off as a result of epigenetics. Epigenetics can track how the environment and even certain behaviors add or remove small chemical markers to the DNA that makes up the genome. The type and location of these markers may affect whether genes are active or silent, this is, whether the protein coded for by that gene is being produced or not. One common epigenetic marker is known as DNA methylation. DNA methylation has been linked to the levels of a range of proteins in our cells and the risk people have of developing chronic diseases. Blood samples can be used to determine the epigenetic markers a person has on their genome and to study the abundance of many proteins. Gadd, Hillary, McCartney, Zaghlool et al. studied the relationships between DNA methylation and the abundance of 953 different proteins in blood samples from individuals in the German KORA cohort and the Scottish Lothian Birth Cohort 1936. They then used machine learning to analyze the relationship between epigenetic markers found in people’s blood and the abundance of proteins, obtaining epigenetic scores or ‘EpiScores’ for each protein. They found 109 proteins for which DNA methylation patterns explained between at least 1% and up to 58% of the variation in protein levels. Integrating the ‘EpiScores’ with 14 years of medical records for more than 9000 individuals from the Generation Scotland study revealed 137 connections between EpiScores for proteins and a future diagnosis of common adverse health outcomes. These included diabetes, stroke, depression, Alzheimer’s dementia, various cancers, and inflammatory conditions such as rheumatoid arthritis and inflammatory bowel disease. Age-related chronic diseases are a growing issue worldwide and place pressure on healthcare systems. They also severely reduce quality of life for individuals over many years. This work shows how epigenetic scores based on protein levels in the blood could predict a person’s risk of several of these diseases. In the case of type 2 diabetes, the EpiScore results replicated previous research linking protein levels in the blood to future diagnosis of diabetes. Protein EpiScores could therefore allow researchers to identify people with the highest risk of disease, making it possible to intervene early and prevent these people from developing chronic conditions as they age.

BACKGROUNDThe aim of this multinational study was to assess the development of adverse mental health symptoms among individuals diagnosed with COVID-19 in the general population by acute infection severity up to 16 months after diagnosis.METHODSParticipants consisted of 247 249 individuals from seven cohorts across six countries (Denmark, Estonia, Iceland, Norway, Scotland, and Sweden) recruited from April 2020 through August 2021. We used multivariable Poisson regression to contrast symptom-prevalence of depression, anxiety, COVID-19 related distress, and poor sleep quality among individuals with and without a diagnosis of COVID-19 at entry to respective cohorts by time (0-16 months) from diagnosis. We also applied generalised estimating equations (GEE) analysis to test differences in repeated measures of mental health symptoms before and after COVID-19 diagnosis among individuals ever diagnosed with COVID-19 over time.FINDINGSA total of 9979 individuals (4%) were diagnosed with COVID-19 during the study period and presented overall with a higher symptom burden of depression (prevalence ratio [PR] 1·18, 95% confidence interval [95% CI] 1·03-1·36) and poorer sleep quality (1·13, 1·03-1·24) but not with higher levels of symptoms of anxiety or COVID-19 related distress compared with individuals without a COVID-19 diagnosis. While the prevalence of depression and COVID-19 related distress attenuated with time, the trajectories varied significantly by COVID-19 acute infection severity. Individuals diagnosed with COVID-19 but never bedridden due to their illness were consistently at lower risks of depression and anxiety (PR 0·83, 95% CI 0·75-0·91 and 0·77, 0·63-0·94, respectively), while patients bedridden for more than 7 days were persistently at higher risks of symptoms of depression and anxiety (PR 1·61, 95% CI 1·27-2·05 and 1·43, 1·26-1·63, respectively) throughout the 16-month study period.CONCLUSIONAcute infection severity is a key determinant of long-term mental morbidity among COVID-19 patients.

Type 2 diabetes mellitus (T2D) is one of the most prevalent diseases in the world and presents a major health and economic burden, a notable proportion of which could be alleviated with improved early prediction and intervention. While standard risk factors including age, obesity, and hypertension have shown good predictive performance, we show that the use of CpG DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Whilst previous studies have been largely constrained by linear assumptions and the use of CpGs one-at-the-time, we have adopted a more flexible approach based on a range of linear and tree-ensemble models for classification and time-to-event prediction. Using the Generation Scotland cohort (n=9,537) our best performing model (Area Under the Curve (AUC)=0.880, Precision Recall AUC (PRAUC)=0.539, McFaddens R2=0.316) used a LASSO Cox proportional-hazards predictor and showed notable improvement in onset prediction, above and beyond standard risk factors (AUC=0.860, PRAUC=0.444 R2=0.261). Replication of the main finding was observed in an external test dataset (the German-based KORA study, p=3.7x10-4). Tree-ensemble methods provided comparable performance and future improvements to these models are discussed. Finally, we introduce MethylPipeR, an R package with accompanying user interface, for systematic and reproducible development of complex trait and incident disease predictors. While MethylPipeR was applied to incident T2D prediction with DNA methylation in our experiments, the package is designed for generalised development of predictive models and is applicable to a wide range of omics data and target traits.

Key features • COVIDMENT [www.covidment.is] is a NordForsk-funded research collaboration across six nations, with the overarching aim to significantly advance current knowledge on mental morbidity trajectories associated with the coronavirus disease 2019 (COVID-19) in the general population and in specific risk groups. • From March 2020 through August 2021, 392 817 individuals have been recruited to the seven COVIDMENT cohorts: the Danish Blood Donor Study (N ¼ 71 562), the Estonian Biobank COVID-19 and Mental Health Data Collection cohorts (N ¼ 13 329 and N ¼ 86 116, respectively), the Icelandic COVID-19 National Resilience Cohort (N ¼ 22 849), the Norwegian BRY.DEG2020 (N ¼ 19 343), the Norwegian Mother, Father and Child Cohort Study (N ¼ 132 486), the Scottish Generation Scotland/CovidLife (N ¼ 18 518) and the Swedish Omtanke2020 (N ¼ 28 614). Semi-harmonized questionnaire data have been collected across all COVIDMENT cohorts with longitudinal data available, e.g. through linkage to the national registers. • The average age of participants ranged from 31.8 to 58.5 years across cohorts. The prevalence of depressive symptoms above cut-off point varied considerably across cohorts (4.2–20.8%). The prevalence of depressive symptoms was highest at COVID-19 incidence of 30 cases per week per 100 000 persons, i.e. 14.3% [95% confidence interval (CI): 9.4–21.8%], which was 61.0% (95% CI: 34.0–94.1%) higher than the prevalence at COVID-19 incidence of 0 cases per week per 100 000 persons (P ¼ 1.1 x 10 ^( 6)). •

Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly.

Extended Data for "CovidLife: A resource to understand mental health, well-being and behaviour during the COVID-19 pandemic in the UK" Wellcome Open Research submission.&nbsp

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

Purpose: This study explored predictors of COVID-19-related stress and wellbeing of Scottish adolescents during the COVID-19 lockdown to identify potentially malleable risk and protective factors.Methods: 5,548 participants were surveyed regarding stress, loneliness, wellbeing, schoolwork, support from school, and interaction with friends and family. Multiple linear regressions within a structural equation modelling framework were fit to predict COVID-19-related stress and wellbeing during the UK’s first lockdown.Results: Loneliness, variables related to the ability to continue with schoolwork, and perceived support from school were important predictors of greater COVID-19-related stress and wellbeing during the first lockdown. Female adolescents were also more likely to show higher stress and poorer wellbeing.Conclusions: Facilitating meaningful social interaction and ensuring the ability to continue with schoolwork, and providing social support from school should be priority strategies to help protect the mental health and wellbeing of secondary school students during lockdowns and other disruptions to school attendance.

BackgroundWhereas several predictors of COVID-19 vaccine hesitancy have been examined, the role of cognitive function following the widely publicised development of an inoculation is unknown.ObjectiveTo test the association between scores from an array of cognitive function tests and self-reported vaccine hesitancy after the announcement of the successful testing of the Oxford University/AstraZeneca vaccine.Design, Setting, and ParticipantsWe used individual-level data from a pandemic-focused study (COVID Survey), a prospective cohort study nested within Understanding Society (Main Survey). In the week immediately following the announcement of successful testing of the first efficacious inoculation (November/December 2020), data on vaccine intentionality were collected in 11740 individuals (6702 women) aged 16-95. Pre-pandemic scores on general cognitive function, ascertained from a battery of six tests, were captured in 2011/12 wave of the Main Survey.MeasurementsSelf-reported intention to take up a vaccination for COVID-19. To summarise our results, we computed odds ratios with accompanying 95% confidence intervals for general cognitive function adjusted for selected covariates.ResultsOf the study sample, 17.2% (N=1842) indicated they were hesitant about having the vaccine. After adjustment for age, sex, and ethnicity, study members with a lower baseline cognition score were markedly more likely to be vaccine hesitant (odds ratio per standard deviation lower score in cognition; 95% confidence interval: 1.76; 1.62, 1.90). Adjustment for mental and physical health plus household shielding status had no impact on these results, whereas controlling for educational attainment led to partial attenuation but the probability of hesitancy was still elevated (1.52; 1.37, 1.67). There was a linear association for vaccine hesitancy across the full range of cognition scores (p for trend: pLimitationsOur outcome was based on intention rather than behaviour.ConclusionsErroneous social media reports might have complicated personal decision-making, leading to people with lower cognitive ability test scores being vaccine-hesitant. With people with lower cognition also experiencing higher rates of COVID-19 in studies conducted prior to vaccine distribution, these new findings are suggestive of a potential additional disease burden.

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

RuralCovidLife is part of Generation Scotland’s CovidLife project, investigating the impact of the COVID-19 pandemic and mitigation measures on people in Scotland. The RuralCovidLife project focuses on Scotland’s rural communities, and how they have been impacted by the pandemic. During survey development, Generation Scotland consulted with people living or working in rural communities, and collaborated with a patient and public involvement and engagement (PPIE) group composed of rural community leaders. Through this consultation work, the RuralCovidLife survey was developed to assess the issues most pertinent to people in rural communities, such as mental health, employment, transport, connectivity, and local communities. Between 14th October and 30th November 2020, 3,365 participants from rural areas in Scotland took part in the survey. Participant ages ranged from 16 to 96 (mean = 58.4, standard deviation [SD] = 13.3), and the majority of the participants were female (70.5%). Over half (51.3%) had taken part in the original CovidLife survey. RuralCovidLife includes a subsample (n = 523) of participants from the Generation Scotland cohort. Pre-pandemic data on health and lifestyle, as well as biological samples, are available for these participants. These participants’ data can also be linked to past and future healthcare records, allowing analysis of retrospective and prospective health outcomes. Like Generation Scotland, RuralCovidLife is designed as a resource for researchers. RuralCovidLife data, as well as the linked Generation Scotland data, is available for use by external researchers following approval from the Generation Scotland Access Committee. RuralCovidLife can be used to investigate mental health, well-being, and behaviour in participants living in rural areas during the COVID-19 pandemic, as well as comparisons with non-rural samples. Moreover, the sub-sample with full Generation Scotland data and linkage can be used to investigate the long-term health consequences of the COVID-19 pandemic in rural communities.

Identifying predictors of cognitive ability and brain structure in later life is an important step towards understanding the mechanisms leading to cognitive decline and dementia. This study used ultra-performance liquid chromatography mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) to measure targeted and untargeted metabolites, mainly lipids and lipoproteins, in ∼600 members of the Lothian Birth Cohort 1936 (LBC1936) at aged ∼73 years. Penalized regression models (LASSO) were then used to identify sets of metabolites that predict variation in general cognitive ability and structural brain variables. UPLC-MS-POS measured lipids, together predicted 19% of the variance in total brain volume and 17% of the variance in both grey matter and normal appearing white matter volumes. Multiple subclasses of lipids were included in the predictor, but the best performing lipid was the sphingomyelin SM(d18:2/14:0) which occurred in 100% of iterations of all three significant models. No metabolite set predicted cognitive ability, or white matter hyperintensities or connectivity. Future studies should concentrate on identifying specific lipids as potential cognitive and brain-structural biomarkers in older individuals.

SummaryBackgroundThe impact of COVID-19 on mental health is unclear. Evidence from longitudinal studies with pre pandemic data are needed to address (1) how mental health has changed from pre-pandemic levels to during the COVID-19 pandemic and (2), whether there are groups at greater risk of poorer mental health during the pandemic?MethodsWe used data from COVID-19 surveys (completed through April/May 2020), nested within two large longitudinal population cohorts with harmonised measures of mental health: two generations of the Avon Longitudinal Study of Parents and Children (ALPSAC): the index generation ALSPAC-G1 (n= 2850, mean age 28) and the parent’s generation ALSPAC-G0 (n= 3720, mean age = 59) and Generation Scotland: Scottish Family Health Study (GS, (n= 4233, mean age = 59), both with validated pre-pandemic measures of mental health and baseline factors. To answer question 1, we used ALSPAC-G1, which has identical mental health measures before and during the pandemic. Question 2 was addressed using both studies, using pre-pandemic and COVID-19 specific factors to explore associations with depression and anxiety in COVID-19.FindingsIn ALSPAC-G1 there was evidence that anxiety and lower wellbeing, but not depression, had increased in COVID-19 from pre-pandemic assessments. The percentage of individuals with probable anxiety disorder was almost double during COVID-19: 24% (95% CI 23%, 26%) compared to pre-pandemic levels (13%, 95% CI 12%, 14%), with clinically relevant effect sizes. In both ALSPAC and GS, depression and anxiety were greater in younger populations, women, those with pre-existing mental and physical health conditions, those living alone and in socio-economic adversity. We did not detect evidence for elevated risk in key workers or health care workers.InterpretationThese results suggest increases in anxiety and lower wellbeing that may be related to the COVID-19 pandemic and/or its management, particularly in young people. This research highlights that specific groups may be disproportionally at risk of elevated levels of depression and anxiety during COVID-19 and supports recent calls for increasing funds for mental health services.FundingThe UK Medical Research Council (MRC), the Wellcome Trust and University of Bristol.

RuralCovidLife is part of Generation Scotland’s CovidLife project, investigating the impact of the COVID-19 pandemic and mitigation measures on people in Scotland. The RuralCovidLife project focuses on Scotland’s rural communities, and how they have been impacted by the pandemic. During survey development, Generation Scotland consulted with people living or working in rural communities, and collaborated with a patient and public involvement and engagement (PPIE) group composed of rural community leaders. Through this consultation work, the RuralCovidLife survey was developed to assess the issues most pertinent to people in rural communities, such as mental health, employment, transport, connectivity, and local communities. Between 14th October and 30th November 2020, 3,365 participants from rural areas in Scotland took part in the survey. Participant ages ranged from 16 to 96 (mean = 58.4, standard deviation [SD] = 13.3), and the majority of the participants were female (70.5%). Over half (51.3%) had taken part in the original CovidLife survey. RuralCovidLife includes a subsample (n = 523) of participants from the Generation Scotland cohort. Pre-pandemic data on health and lifestyle, as well as biological samples, are available for these participants. These participants’ data can also be linked to past and future healthcare records, allowing analysis of retrospective and prospective health outcomes. Like Generation Scotland, RuralCovidLife is designed as a resource for researchers. RuralCovidLife data, as well as the linked Generation Scotland data, is available for use by external researchers following approval from the Generation Scotland Access Committee. RuralCovidLife can be used to investigate mental health, well-being, and behaviour in participants living in rural areas during the COVID-19 pandemic, as well as comparisons with non-rural samples. Moreover, the sub-sample with full Generation Scotland data and linkage can be used to investigate the long-term health consequences of the COVID-19 pandemic in rural communities.

TeenCovidLife is part of Generation Scotland’s CovidLife projects, a set of longitudinal observational studies designed to assess the psychosocial and health impacts of the COVID-19 pandemic. TeenCovidLife focused on how adolescents in Scotland were coping during the pandemic. As of September 2021, Generation Scotland had conducted three TeenCovidLife surveys. Participants from previous surveys were invited to participate in the next, meaning the age ranges shifted over time. TeenCovidLife Survey 1 consists of data from 5,543 young people age 12 to 17, collected from 22 May to 5 July 2020, during the first school closures period in Scotland. TeenCovidLife Survey 2 consists of data from 2,245 young people aged 12 to 18, collected from 18 August to 14 October 2020, when the initial lockdown measures were beginning to ease, and schools reopened in Scotland. TeenCovidLife Survey 3 consists of data from 597 young people age 12 to 19, collected from 12 May to 27 June 2021, a year after the first survey, after the schools returned following the second lockdown in 2021. A total of 316 participants took part in all three surveys. TeenCovidLife collected data on general health and well-being, as well as topics specific to COVID-19, such as adherence to COVID-19 health guidance, feelings about school closures, and the impact of exam cancellations. Limited work has examined the impact of the COVID-19 pandemic on young people. TeenCovidLife provides relevant and timely data to assess the impact of the pandemic on young people in Scotland. The dataset is available under authorised access from Generation Scotland; see the Generation Scotland website for more information.

CovidLife is a longitudinal observational study designed to investigate the impact of the COVID-19 pandemic on mental health, well-being and behaviour in adults living in the UK. In total, 18,518 participants (mean age = 56.43, SD = 14.35) completed the first CovidLife questionnaire (CovidLife1) between April and June 2020. To date, participants have completed two follow-up assessments. CovidLife2 took place between July and August 2020 (n = 11,319), and CovidLife3 took place in February 2021 (n = 10,386). A range of social and psychological measures were administered at each wave including assessments of anxiety, depression, well-being, loneliness and isolation. Information on sociodemographic, health, and economic circumstances was also collected. Questions also assessed information on COVID-19 infections and symptoms, compliance to COVID-19 restrictions, and opinions on the UK and Scottish Governments’ handling of the pandemic. CovidLife includes a subsample of 4,847 participants from the Generation Scotland cohort (N~24,000, collected 2006-2011); a well-characterised cohort of families in Scotland with pre-pandemic data on mental health, physical health, lifestyle, and socioeconomic factors, along with biochemical and genomic data derived from biological samples. These participants also consented to their study data being linked to Scottish health records. CovidLife and Generation Scotland data can be accessed and used by external researchers following approval from the Generation Scotland Access Committee. CovidLife can be used to investigate mental health, well-being and behaviour during COVID-19; how these vary according to sociodemographic, health and economic circumstances; and how these change over time. The Generation Scotland subsample with pre-pandemic data and linkage to health records can be used to investigate the predictors of health and well-being during COVID-19 and the future health consequences of the COVID-19 pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has been linked to an increase in mental health problems. This study examined their association with socioeconomic position (SEP), as well as potential confounding and mediating factors. Methods: We analysed data from the CovidLife study (N=14,387; 66.4% female; mean [SD] age, 57.4 [13.9] years). Data were collected in an online survey of UK adults (aged 18 years or over) between 17 April and 7 June 2020. SEP measures included area deprivation (the Scottish Index of Multiple Deprivation [SIMD]), education level, household income, and employment status. Mental health was measured using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder-7 (GAD-7) scale. Worry indices were derived using principal component analysis. Logistic regression was used to estimate the associations between SEP and mental health. Results: Low SEP was associated with increased odds of depression and anxiety (odds ratio [OR] range 1.18-2.69). These associations remained significant after adjusting for age, sex, relationship status, and psychiatric history. Multivariable adjustment for medical worry and isolation during the pandemic had the largest attenuating effects (ranges 18-60% and 4-46%, respectively) on mental health outcomes. When adding further adjustment for the remaining SEP markers and all potential confounding and mediating factors, depression was associated with high area deprivation and low education level and income, whilst anxiety was only associated with low education level. No dose-response relationship was observed. Conclusions: SEP was inversely associated with mental health, which was mostly explained by medical worry and isolation during the COVID-19 pandemic. Mental health interventions might target these factors to prevent widening inequalities in mental health. Future studies should use longitudinal data to investigate the association.

ObjectiveTo examine the association of health literacy and cognitive ability with risk of diabetes.Research Design and Methods: Participants were 8,669 English Longitudinal Study of Ageing participants (mean age 66.7 years, SD 9.7) who completed health literacy and cognitive ability tests at wave 2 (2004-2005), and who answered a self-reported question on whether a doctor had ever diagnosed them with diabetes. Logistic regression was used to examine the cross-sectional associations of health literacy and cognitive ability with diabetes status. In those without diabetes at wave 2, Cox regression was used to test the associations of health literacy and cognitive ability with risk of diabetes over a median of 9.5 years follow-up (n=6,961).ResultsAdequate (compared to limited) health literacy (OR 0.72, 95% CI 0.61-0.84) and higher cognitive ability (OR per 1 SD 0.73, CI 0.67-0.80) were both associated with lower odds of self-reported diabetes. Adequate health literacy (HR 0.64; CI 0.53-0.77) and higher cognitive ability (HR 0.77, CI 0.69-0.85) were also associated with lower risk of self-reporting diabetes during follow-up. When both health literacy and cognitive ability were added to the same model, these associations were slightly attenuated. Additional adjustment for health behaviours, education and social class attenuated associations further, and neither health literacy nor cognitive ability were significantly associated with diabetes.ConclusionsAdequate health literacy and better cognitive ability were associated with reduced risk of diabetes. These associations were independent of each other, but not of other health- and socioeconomic-related variables.

Introduction: Prospective studies reporting associations between cognitive performance and subsequent incident dementia have been subject to attrition bias. Furthermore, the extent to which established risk factors account for such associations requires further elucidation. Methods: We used UK Biobank baseline cognitive data (n ≤ 488,130) and electronically linked hospital inpatient and death records during three‐ to eight‐year follow‐up, to estimate risk of total dementia (n = 1051), Alzheimer's disease (n = 352), and vascular dementia (n = 169) according to four brief cognitive tasks, with/without adjustment for constitutional and modifiable risk factors. Results: We found associations of cognitive task performance with all‐cause and cause‐specific dementia (P < .01); these were not accounted for by established risk factors. Cognitive data added up to 5% to the discriminative accuracy of receiver operating characteristic curve models; areas under the curve ranged from 82% to 86%. Discussion: This study offers robust evidence that brief cognitive testing could be a valuable addition to dementia prediction models.

Higher health literacy is associated with higher cognitive function and better health. Despite its wide use in medical research, no study has investigated the genetic contributions to health literacy. Using 5783 English Longitudinal Study of Ageing (ELSA) participants (mean age = 65.49, SD = 9.55) who had genotyping data and had completed a health literacy test at wave 2 (2004–2005), we carried out a genome-wide association study (GWAS) of health literacy. We estimated the proportion of variance in health literacy explained by all common single nucleotide polymorphisms (SNPs). Polygenic profile scores were calculated using summary statistics from GWAS of 21 cognitive and health measures. Logistic regression was used to test whether polygenic scores for cognitive and health-related traits were associated with having adequate, compared to limited, health literacy. No SNPs achieved genome-wide significance for association with health literacy. The proportion of variance in health literacy accounted for by common SNPs was 8.5% (SE = 7.2%). Greater odds of having adequate health literacy were associated with a 1 standard deviation higher polygenic score for general cognitive ability [OR = 1.34, 95% CI (1.26, 1.42)], verbal-numerical reasoning [OR = 1.30, 95% CI (1.23, 1.39)], and years of schooling [OR = 1.29, 95% CI (1.21, 1.36)]. Reduced odds of having adequate health literacy were associated with higher polygenic profiles for poorer self-rated health [OR = 0.92, 95% CI (0.87, 0.98)] and schizophrenia [OR = 0.91, 95% CI (0.85, 0.96)). The well-documented associations between health literacy, cognitive function and health may partly be due to shared genetic etiology. Larger studies are required to obtain accurate estimates of SNP-based heritability and to discover specific health literacy-associated genetic variants.

The associations between indices of brain structure and measured intelligence are not clear. In part, this is because the evidence to date comes from mostly small and heterogenous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,363 participants providing both brain MRI and cognitive data, and a minimum N = 7318 providing the MRI data alongside a complete four-test battery. Participants’ age range was 44-81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was extracted from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age-and sex-corrected) total brain volume and a latent factor of general intelligence is r = 0.275, 95% C.I. = [0.252, 0.299]. A model that incorporated multiple global measures of grey and white matter macro-and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.4% and 5.9%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction., Highlights • We used a large sample from UK Biobank (N = 29,004, age range = 44–81 years). • The association between brain volume and intelligence (‘g’) was r = 0.276. • Multiple global tissue measures explained twice the g variance in older than middle age. • The size of the association between g and global brain measures did not vary by sex. • We investigate the regional cortical, subcortical and white matter correlates of g.

Background: Face masks or coverings are effective at reducing airborne infection rates, yet pandemic mitigation measures, including wearing face coverings, have been suggested to contribute to reductions in quality of life and poorer mental health. Complaints of inconvenience, discomfort, and other issues have been repeatedly and loudly voiced by critics, and adherence in many nations is not strong enough to suppress viral spread. We wished to see whether wearing face coverings is associated with mental health and wellbeing. Methods: We analysed survey 1 and 2 of the CovidLife study, a sample of more than 18,000 individuals living in the UK. The study asked a variety of questions about participants’ psychological, economic, and social lives while living under the coronavirus disease 2019 (COVID-19) pandemic in 2020. We measured individuals’ adherence to following guidance on wearing face coverings, as well as several mental health outcomes: depression, anxiety, wellbeing, life satisfaction, and loneliness. Results: We found no association between lower adherence to face covering guidelines and poorer mental health. The opposite appears to be true. Even after controlling for behavioural, social, and psychological confounds, including measures of pre-pandemic mental health, individuals who wore face coverings “most of the time” or “always” had better mental health and wellbeing than those who did not. Individuals who wore masks only “some of the time” or “never” tended to be male, lower income, and already had COVID-19 or COVID-19-like symptoms. Conclusions: These results suggest that wearing face coverings more often does not negatively impact mental health. Wearing a face covering more often is actually linked to better mental health and wellbeing. Implications are discussed and we highlight the potential pathways for addressing a lack of face covering that this study reveals.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32 070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed. National Foundation for Alzheimer's disease and related disorders Institut Pasteur de Lille Centre National de Genotypage Inserm FRC (fondation pour la recherche sur le cerveau) Rotary LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) MEDIALZ Project - ERDF (European Regional Development Fund) 11001003 Conseil Regional Nord Pas de Calais Fondation pour la Recherche Medicale Caisse Nationale Maladie des Travailleurs Salaries Direction Generale de la Sante MGEN Institut de la Longevite Agence Francaise de Securite Sanitaire des Produits de Sante Aquitaine and Bourgogne Regional Councils Fondation de France joint French Ministry of Research/INSERM 'Cohortes et collections de donnees biologiques' program Eisai Wellcome Trust British Heart Foundation Chief Scientist Office of the Scottish Executive NIA N01-AG-12100 National Heart, Lung and Blood Institute Contracts HHSN268201100005C HHSN268201100006C HHSN268201100007C HHSN268201100008C HHSN268201100009C HHSN268201100010C HHSN268201100011C HHSN268201100012C R01HL70825 R01HL087641 R01HL59367 R01HL086694 National Human Genome Research Institute U01HG004402 NIH Roadmap for Medical Research Austrian Science Fond (FWF) P20545-P05 P13180 Intramural Research Program of the NIH, National Institute on Aging NHLBI HHSN268201200036C HHSN268200800007C N01HC55222 N01HC85079 N01HC85080 N01HC85081 N01HC85082 N01HC85083 N01HC850863 U01HL080295 R01HL087652 R01HL105756 R01HL103612 R01HL120393 National Institute of Neurological Disorders and Stroke (NINDS) National Institute on Aging (NIA) R01AG023629 R01AG15928 R01AG20098 R01AG027058 National Center for Advancing Translational Sciences CTSI UL1TR000124 National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) DK063491 Medical Research Council (UK) European Commission Framework 6 project EUROSPAN LSHG-CT-2006-018947 Republic of Croatia Ministry of Science, Education and Sports 108-1080315-0302 Netherlands Organization for Scientific Research (NWO) Internationale Stichting Alzheimer Onderzoek (ISAO) Hersenstichting Nederland (HSN) Centre for Medical Systems Biology (CMSB) in the framework of the Netherlands Genomics Initiative (NGI) Russian Foundation for Basic Research (RFBR) National Heart, Lung and Blood Institute's Framingham Heart Study N01-HC-25195 Affymetrix, Inc N02-HL-6-4278 U01 HL096917 R01 HL093029 Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine Boston Medical Center National Institute of Neurological Disorders and Stroke NS17950 National Institute of Aging U01 AG049505 AG033193 AG008122 AG16495 Agence National de la Recherche Leducq Foundation National Heart, Lung and Blood Institute HL054464 HL054457 HL054481 HL071917 HL87660 HL043851 HL080467 National Institute of Neurological Disorders and Stroke of the National Institutes of Health NS041558 Chief Scientist Office of the Scottish Government Health Directorates CZD/16/6 Scottish Funding Council HR03006 Academy of Finland Finnish Diabetes Research Society Folkhalsan Research Foundation Novo Nordisk Foundation Finska Lakaresallskapet Signe and Ane Gyllenberg Foundation University of Helsinki Ministry of Education Ahokas Foundation Emil Aaltonen Foundation Juho Vainio Foundation Wellcome Trust WT089062 University of Newcastle's Strategic Initiative Fund Vincent Fairfax Family Foundation Hunter Medical Research Institute Italian Ministry of Health ICS 110.1RS97.71 US National Institute on Aging N01[AG]916413 N01[AG]821336 263 MD 9164 13 263 MD 821336 Intramural Research Program, National Institute on Aging, National Institutes of Health BBSRC Royal Society Chief Scientist Office of the Scottish Government Research Into Ageing UK Biotechnology and Biological Sciences Research Council (BBSRC) Engineering and Physical Sciences Research Council (EPSRC) Economic and Social Research Council (ESRC) MRC NHMRC 401184 Australian National Health & Medical Research Council 350833 568969 Capacity Building Grant 568940 NHMRC Project 525453 National Institutes of Health CA87969 CA49449 HL34594 U01HG004399 DK058845 CA65725 CA67262 CA50385 5UO1CA098233 EY09611 EY015473 HG004728 HL35464 CA55075 CA134958 DK070756 MRC Human Genetics Unit Arthritis Research UK European Union LSHG-CT-2006-018947 Bristol-Myers Squibb Netherlands Heart Foundation 2001 D 032 European commission 223004 Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging) 050-060-810 Netherlands Organisation of Scientific Research NWO Investments 175.010.2005.011 911-03-012 Research Institute for Diseases in the Elderly 014-93-015 Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project 050-060-810 Erasmus Medical Center Erasmus University, Rotterdam Netherlands Organization for the Health Research and Development (ZonMw) Research Institute for Diseases in the Elderly (RIDE) Ministry of Education, Culture and Science Ministry for Health, Welfare and Sports European Commission 49 (DG XII) Municipality of Rotterdam ZonMW Veni Grant 916.13.054 NIA Grants P30AG10161 R01AG15819 R01AG17917 R01AG30146 K08AG34290 K25AG41906 Federal Ministry of Education and Research 01ZZ9603 01ZZ0103 01ZZ0403 Ministry of Cultural Affairs Social Ministry of the Federal State of Mecklenburg-West Pomerania National Cancer Institute CA047988 Donald W Reynolds Foundation Fondation Leducq Amgen info:eu-repo/grantAgreement/EC/FP7/223004

General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (P−8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function wereSGCZ,ATXN1,MAPT,AUTS2, andP2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt)1; heritability estimated from twin studies ranges from 30 to 50%2, and SNP-based heritability ranges from 6 to 15%3–6. Increased neuroticism is associated with poorer mental and physical health7,8, translating to high economic burden9. Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci3,4. Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P

Neuroticism is a stable personality trait 1; twin studies report heritability between 30% and 50% 2, and SNP-based heritability is about 15% 3. Higher levels of neuroticism are associated with poorer mental and physical health 4,5, and the economic burden of neuroticism for societies is high 6. To date, genome-wide association (GWA) studies of neuroticism have identified up to 11 genetic loci 3,7. Here we report 116 significant independent genetic loci from a GWA of neuroticism in 329,821 UK Biobank participants, with replication available in a GWA meta-analysis of neuroticism in 122,867 individuals. Genetic signals for neuroticism were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = .82, SE=.03), major depressive disorder (rg = .69, SE=.07) and subjective wellbeing (rg = -.68, SE=.03) alongside other mental health traits. These discoveries significantly advance our understanding of neuroticism and its association with major depressive disorder.