Search

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.

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.

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.

UK Biobank includes 502,649 middle- and older-aged adults from the general population who have undergone detailed phenotypic assessment. The majority of participants completed tests of cognitive functioning, and on average four years later a sub-group of N = 20,346 participants repeated most of the assessment. These measures will be used in a range of future studies of health outcomes in this cohort. The format and content of the cognitive tasks were partly novel. The aim of the present study was to validate and characterize the cognitive data: to describe the inter-correlational structure of the cognitive variables at baseline assessment, and the degree of stability in scores across longitudinal assessment. Baseline cognitive data were used to examine the inter-correlational/factor-structure, using principal components analysis (PCA). We also assessed the degree of stability in cognitive scores in the subsample of participants with repeat data. The different tests of cognitive ability showed significant raw inter-correlations in the expected directions. PCA suggested a one-factor solution (eigenvalue = 1.60), which accounted for around 40% of the variance. Scores showed varying levels of stability across time-points (intraclass correlation range = 0.16 to 0.65). UK Biobank cognitive data has the potential to be a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population.

Causes of the well-documented association between low levels of cognitive functioning and many adverse neuropsychiatric outcomes, poorer physical health and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric and cognitive traits in the participants of UK Biobank, a very large population-based sample (N=112?151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics and to the method of linkage disequilibrium score regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, body mass index, intracranial volume, infant head circumference and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants, we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.Molecular Psychiatry advance online publication, 26 January 2016; doi:10.1038/mp.2015.225.

The causes of the known associations between poorer cognitive function and many adverse neuropsychiatric outcomes, poorer physical health, and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric, and cognitive traits in the participants of UK Biobank, a very large population-based sample (N = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics applied to the method of linkage disequilibrium regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, BMI, intracranial volume, infant head circumference, and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.