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• We used UK Biobank, a large-scale community-based cohort study. • Nine psychosocial factors were associated with an elevated risk of hospitalisation for COVID-19. • The only robust relationship was that for cognitive function, a marker of health literacy. While certain infectious diseases have been linked to socioeconomic disadvantage, mental health problems, and lower cognitive function, relationships with COVID-19 are either uncertain or untested. Our objective was to examine the association of a range of psychosocial factors with hospitalisation for COVID-19. UK Biobank, a prospective cohort study, comprises around half a million people who were aged 40–69 years at study induction between 2006 and 2010 when information on psychosocial factors and covariates were captured. Hospitalisations for COVID-19 were ascertained between 16th March and 26th April 2020. There were 908 hospitalisations for COVID-19 in an analytical sample of 431,051 England-based study members. In age- and sex-adjusted analyses, an elevated risk of COVID-19 was related to disadvantaged levels of education (odds ratio; 95% confidence interval: 2.05; 1.70, 2.47), income (2.00; 1.63, 2,47), area deprivation (2.20; 1.86, 2.59), occupation (1.39; 1.14, 1.69), psychological distress (1.58; 1.32, 1.89), mental health (1.50; 1.25, 1.79), neuroticism (1.19; 1.00, 1.42), and performance on two tests of cognitive function – verbal and numerical reasoning (2.66; 2.06, 3.34) and reaction speed (1.27; 1.08, 1.51). These associations were graded (p-value for trend ≤ 0.038) such that effects were apparent across the full psychosocial continua. After mutual adjustment for these characteristics plus ethnicity, comorbidity, and lifestyle factors, only the relationship between lower cognitive function as measured using the reasoning test and risk of the infection remained (1.98; 1.38, 2.85). A range of psychosocial factors revealed associations with hospitalisation for COVID-19 of which the relation with cognitive function, a marker of health literacy, was most robust. [ABSTRACT FROM AUTHOR]

Focuses on the research on the causes of Alzheimer's disease in Great Britain. Consideration of the disease as the most frequent causes of death; Characteristic of the neuropathological changes in the disease; Relations between neural disorders and Alzheimer's disease.

Background This study investigated differences in cognitive performance between middle-aged adults with and without a lifetime history of mood disorder features, adjusting for a range of potential confounders. Methods Cross-sectional analysis of baseline data from the UK Biobank cohort. Adults aged 40–69 ( n = 143,828) were assessed using measures of reasoning, reaction time and memory. Self-reported data on lifetime features of major depression and bipolar disorder were used to construct groups for comparison against controls. Regression models examined the association between mood disorder classification and cognitive performance, adjusting for sociodemographic, lifestyle and clinical confounders. Results Inverse associations between lifetime history of bipolar or severe recurrent depression features and cognitive performance were attenuated or reversed after adjusting for confounders, including psychotropic medication use and current depressive symptoms. Participants with a lifetime history of single episode or moderate recurrent depression features outperformed controls to a small (but statistically significant) degree, independent of adjustment for confounders. There was a significant interaction between use of psychotropic medication and lifetime mood disorder features, with reduced cognitive performance observed in participants taking psychotropic medication. Conclusions In this general population sample of adults in middle age, lifetime features of recurrent depression or bipolar disorder were only associated with cognitive impairment within unadjusted analyses. These findings underscore the importance of adjusting for potential confounders when investigating mood disorder-related cognitive function. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]