ApoE e4e4 Genotype and Mortality With COVID-19 in UK Biobank.
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ABSTRACT:
SUBMITTER: Kuo CL
PROVIDER: S-EPMC7337688 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
ApoE e4e4 Genotype and Mortality With COVID-19 in UK Biobank.
Kuo Chia-Ling CL Pilling Luke C LC Atkins Janice L JL Masoli Jane A H JAH Delgado João J Kuchel George A GA Melzer David D
The journals of gerontology. Series A, Biological sciences and medical sciences 20200901 9
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Project description:Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18-3.49] per S.D. [8.1 years], p = 2.6 × 10-17), male sex (OR = 1.47 [1.26-1.73], p = 1.3 × 10-6) and Black versus White ethnicity (OR = 1.21 [1.12-1.29], p = 3.0 × 10-7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin-angiotensin-aldosterone system inhibitors may be explained by the aforementioned factors.
| S-EPMC7882869 | biostudies-literature
Project description:AbstractThe risk of COVID-19 in those with chronic pain is unknown. We investigated whether self-reported chronic pain was associated with COVID-19 hospitalisation or mortality. UK Biobank recruited 502,624 participants aged 37 to 73 years between 2006 and 2010. Baseline exposure data, including chronic pain (>3 months, in at least 1 of 7 prespecified body sites) and chronic widespread pain (>3 months, all over body), were linked to COVID-19 hospitalisations or mortality. Univariable or multivariable Poisson regression analyses were performed on the association between chronic pain and COVID-19 hospitalisation and Cox regression analyses of the associations with COVID-19 mortality. Multivariable analyses adjusted incrementally for sociodemographic confounders, then lifestyle risk factors, and finally long-term condition count. Of 441,403 UK Biobank participants with complete data, 3180 (0.7%) were hospitalised for COVID-19 and 1040 (0.2%) died from COVID-19. Chronic pain was associated with hospital admission for COVID-19 even after adjustment for all covariates (incidence rate ratio 1.16; 95% confidence interval [CI] 1.08-1.24; P < 0.001), as was chronic widespread pain (incidence rate ratio 1.33; 95% CI 1.06-1.66; P = 0.012). There was clear evidence of a dose-response relationship with number of pain sites (fully adjusted global P-value < 0.001). After adjustment for all covariates, there was no association between chronic pain (HR 1.01; 95% CI 0.89-1.15; P = 0.834) but attenuated association with chronic widespread pain (HR 1.50, 95% CI 1.04-2.16, P-value = 0.032) and COVID-19 mortality. Chronic pain is associated with higher risk of hospitalisation for COVID-19, but the association with mortality is unclear. Future research is required to investigate these findings further and determine whether pain is associated with long COVID.
| S-EPMC9756431 | biostudies-literature
Project description:BackgroundHospitalized COVID-19 patients tend to be older and frequently have hypertension, diabetes, or coronary heart disease, but whether these comorbidities are true risk factors (ie, more common than in the general older population) is unclear. We estimated associations between preexisting diagnoses and hospitalized COVID-19 alone or with mortality, in a large community cohort.MethodsUK Biobank (England) participants with baseline assessment 2006-2010, followed in hospital discharge records to 2017 and death records to 2020. Demographic and preexisting common diagnoses association tested with hospitalized laboratory-confirmed COVID-19 (March 16 to April 26, 2020), alone or with mortality, in logistic models.ResultsOf 269 070 participants aged older than 65, 507 (0.2%) became COVID-19 hospital inpatients, of which 141 (27.8%) died. Common comorbidities in hospitalized inpatients were hypertension (59.6%), history of fall or fragility fractures (29.4%), coronary heart disease (21.5%), type 2 diabetes (type 2, 19. 9%), and asthma (17.6%). However, in models adjusted for comorbidities, age group, sex, ethnicity, and education, preexisting diagnoses of dementia, type 2 diabetes, chronic obstructive pulmonary disease, pneumonia, depression, atrial fibrillation, and hypertension emerged as independent risk factors for COVID-19 hospitalization, the first 5 remaining statistically significant for related mortality. Chronic kidney disease and asthma were risk factors for COVID-19 hospitalization in women but not men.ConclusionsThere are specific high-risk preexisting comorbidities for COVID-19 hospitalization and related deaths in community-based older men and women. These results do not support simple age-based targeting of the older population to prevent severe COVID-19 infections.
| S-EPMC7454409 | biostudies-literature
Project description:Alzheimer's disease (AD) risk is increased in carriers of the apolipoprotein E (APOE) ε4 allele and decreased in ε2 allele carriers compared with the ε3ε3 genotype. The aim of this study was to determine whether: the APOE genotype affects brain grey (GM) or white matter (WM) structure; and if differences exist, the age when they become apparent and whether there are differential effects by sex. We used cross-sectional magnetic resonance imaging data from ~43,000 (28,494 after pre-processing) white British cognitively healthy participants (7,446 APOE ε4 carriers) aged 45-80 years from the UK Biobank cohort and investigated image-derived phenotypes (IDPs). We observed no statistically significant effects of APOE genotype on GM structure volumes or median T2* in subcortical structures, a measure related to iron content. The volume of white matter hyperintensities differed significantly between APOE genotype groups with higher volumes in APOE ε4ε4 (effect size 0.14 standard deviations [SD]) and ε3ε4 carriers (effect size 0.04 SD) but no differences in ε2 carriers compared with ε3ε3 carriers. WM integrity measures in the dorsal (mean diffusivity [MD]) and ventral cingulum (MD and intracellular volume fraction), posterior thalamic radiation (MD and isotropic volume fraction) and sagittal stratum (MD) indicated lower integrity in APOE ε4ε4 carriers (effect sizes around 0.2-0.3 SD) and ε3ε4 (effect sizes around 0.05 SD) carriers but no differences in ε2 carriers compared with the APOE ε3ε3 genotype. Effects did not differ between men and women. APOE ε4 homozygotes had lower WM integrity specifically at older ages with a steeper decline of WM integrity from the age of 60 that corresponds to around 5 years greater "brain age". APOE genotype affects various white matters measures, which might be indicative of preclinical AD processes. This hypothesis can be assessed in future when clinical outcomes become available.
| S-EPMC10933274 | biostudies-literature
Project description:The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.
| S-EPMC8376891 | biostudies-literature
Project description:Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM2.5 and NO2 was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.
| S-EPMC9243647 | biostudies-literature
Project description:BackgroundThe severity of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly heterogenous. Studies have reported that males and some ethnic groups are at increased risk of death from COVID-19, which implies that individual risk of death might be influenced by host genetic factors.MethodsIn this project, we consider the mortality as the trait of interest and perform a genome-wide association study (GWAS) of data for 1,778 infected cases (445 deaths, 25.03%) distributed by the UK Biobank. Traditional GWAS failed to identify any genome-wide significant genetic variants from this dataset. To enhance the power of GWAS and account for possible multi-loci interactions, we adopt the concept of super-variant for the detection of genetic factors. A discovery-validation procedure is used for verifying the potential associations.ResultsWe find 8 super-variants that are consistently identified across multiple replications as susceptibility loci for COVID-19 mortality. The identified risk factors on Chromosomes 2, 6, 7, 8, 10, 16, and 17 contain genetic variants and genes related to cilia dysfunctions (DNAH7 and CLUAP1), cardiovascular diseases (DES and SPEG), thromboembolic disease (STXBP5), mitochondrial dysfunctions (TOMM7), and innate immune system (WSB1). It is noteworthy that DNAH7 has been reported recently as the most downregulated gene after infecting human bronchial epithelial cells with SARS-CoV2.ConclusionsEight genetic variants are identified to significantly increase risk of COVID-19 mortality among the patients with white British ancestry. These findings may provide timely evidence and clues for better understanding the molecular pathogenesis of COVID-19 and genetic basis of heterogeneous susceptibility, with potential impact on new therapeutic options.
| S-EPMC7668757 | biostudies-literature
Project description:BackgroundThe severity of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly heterogeneous. Studies have reported that males and some ethnic groups are at increased risk of death from COVID-19, which implies that individual risk of death might be influenced by host genetic factors.MethodsIn this project, we consider the mortality as the trait of interest and perform a genome-wide association study (GWAS) of data for 1778 infected cases (445 deaths, 25.03%) distributed by the UK Biobank. Traditional GWAS fails to identify any genome-wide significant genetic variants from this dataset. To enhance the power of GWAS and account for possible multi-loci interactions, we adopt the concept of super variant for the detection of genetic factors. A discovery-validation procedure is used for verifying the potential associations.ResultsWe find 8 super variants that are consistently identified across multiple replications as susceptibility loci for COVID-19 mortality. The identified risk factors on chromosomes 2, 6, 7, 8, 10, 16, and 17 contain genetic variants and genes related to cilia dysfunctions (DNAH7 and CLUAP1), cardiovascular diseases (DES and SPEG), thromboembolic disease (STXBP5), mitochondrial dysfunctions (TOMM7), and innate immune system (WSB1). It is noteworthy that DNAH7 has been reported recently as the most downregulated gene after infecting human bronchial epithelial cells with SARS-CoV-2.ConclusionsEight genetic variants are identified to significantly increase the risk of COVID-19 mortality among the patients with white British ancestry. These findings may provide timely clues and potential directions for better understanding the molecular pathogenesis of COVID-19 and the genetic basis of heterogeneous susceptibility, with potential impact on new therapeutic options.
| S-EPMC7856608 | biostudies-literature
Project description:Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.
| S-EPMC10006374 | biostudies-literature