Early life factors and COVID-19 infection in England: A prospective analysis of UK Biobank participants.
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ABSTRACT:
SUBMITTER: Didikoglu A
PROVIDER: S-EPMC7860946 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
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Early life factors and COVID-19 infection in England: A prospective analysis of UK Biobank participants.
Didikoglu Altug A Maharani Asri A Pendleton Neil N Canal Maria Mercè MM Payton Antony A
Early human development 20210204
This study aims to examine whether maternal smoking, birth weight, birth month and breastfeeding are associated with COVID-19 infection and hospitalisation. Maternal smoking was positively associated with COVID-19 infection. Breastfeeding was negatively associated with COVID-19 infection. The odds of being hospitalised due to COVID-19 were higher among those who had lower birthweight and mothers who were smoking during pregnancy. ...[more]
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Project description:BackgroundFrailty and multimorbidity have been suggested as risk factors for severe COVID-19 disease.AimsWe investigated, in the UK Biobank, whether frailty and multimorbidity were associated with risk of hospitalisation with COVID-19.Methods502,640 participants aged 40-69 years at baseline (54-79 years at COVID-19 testing) were recruited across UK during 2006-10. A modified assessment of frailty using Fried's classification was generated from baseline data. COVID-19 test results (England) were available for 16/03/2020-01/06/2020, mostly taken in hospital settings. Logistic regression was used to discern associations between frailty, multimorbidity and COVID-19 diagnoses, after adjusting for sex, age, BMI, ethnicity, education, smoking and number of comorbidity groupings, comparing COVID-19 positive, COVID-19 negative and non-tested groups.Results4510 participants were tested for COVID-19 (positive = 1326, negative = 3184). 497,996 participants were not tested. Compared to the non-tested group, after adjustment, COVID-19 positive participants were more likely to be frail (OR = 1.4 [95%CI = 1.1, 1.8]), report slow walking speed (OR = 1.3 [1.1, 1.6]), report two or more falls in the past year (OR = 1.3 [1.0, 1.5]) and be multimorbid (≥ 4 comorbidity groupings vs 0-1: OR = 1.9 [1.5, 2.3]). However, similar strength of associations were apparent when comparing COVID-19 negative and non-tested groups. However, frailty and multimorbidity were not associated with COVID-19 diagnoses, when comparing COVID-19 positive and COVID-19 negative participants.Discussion and conclusionsFrailty and multimorbidity do not appear to aid risk stratification, in terms of positive versus negative results of COVID-19 testing. Investigation of the prognostic value of these markers for adverse clinical sequelae following COVID-19 disease is urgently needed.
| S-EPMC7377312 | biostudies-literature
Project description:ObjectivesTo investigate severe COVID-19 risk by occupational group.MethodsBaseline UK Biobank data (2006-10) for England were linked to SARS-CoV-2 test results from Public Health England (16 March to 26 July 2020). Included participants were employed or self-employed at baseline, alive and aged <65 years in 2020. Poisson regression models were adjusted sequentially for baseline demographic, socioeconomic, work-related, health, and lifestyle-related risk factors to assess risk ratios (RRs) for testing positive in hospital or death due to COVID-19 by three occupational classification schemes (including Standard Occupation Classification (SOC) 2000).ResultsOf 120 075 participants, 271 had severe COVID-19. Relative to non-essential workers, healthcare workers (RR 7.43, 95% CI 5.52 to 10.00), social and education workers (RR 1.84, 95% CI 1.21 to 2.82) and other essential workers (RR 1.60, 95% CI 1.05 to 2.45) had a higher risk of severe COVID-19. Using more detailed groupings, medical support staff (RR 8.70, 95% CI 4.87 to 15.55), social care (RR 2.46, 95% CI 1.47 to 4.14) and transport workers (RR 2.20, 95% CI 1.21 to 4.00) had the highest risk within the broader groups. Compared with white non-essential workers, non-white non-essential workers had a higher risk (RR 3.27, 95% CI 1.90 to 5.62) and non-white essential workers had the highest risk (RR 8.34, 95% CI 5.17 to 13.47). Using SOC 2000 major groups, associate professional and technical occupations, personal service occupations and plant and machine operatives had a higher risk, compared with managers and senior officials.ConclusionsEssential workers have a higher risk of severe COVID-19. These findings underscore the need for national and organisational policies and practices that protect and support workers with an elevated risk of severe COVID-19.
| S-EPMC7611715 | biostudies-literature
Project description:BackgroundAge and disease prevalence are the 2 biggest risk factors for Coronavirus disease 2019 (COVID-19) symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity.MethodsUsing the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of 2 COVID-19 severity outcomes (inpatient test positivity and COVID-19-related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and preexisting diseases/conditions.ResultsSix hundred and thirteen participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19-related mortality (ORMortality = 1.63 per 5 years, 95% CI: 1.43-1.86, p = 4.7 × 10-13) adjusting for demographics including age at the pandemic. Further adjustment for preexisting diseases/conditions at baseline (ORM = 1.50, 95% CI: 1.30-1.73 per 5 years, p = 3.1 × 10-8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04-1.40 per 5 years, p = .011) decreased the association.ConclusionsPhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.
| S-EPMC7989601 | biostudies-literature
Project description:BackgroundFrailty indices (FIs) measure variation in health between aging individuals. Researching FIs in resources with large-scale genetic and phenotypic data will provide insights into the causes and consequences of frailty. Thus, we aimed to develop an FI using UK Biobank data, a cohort study of 500,000 middle-aged and older adults.MethodsAn FI was calculated using 49 self-reported questionnaire items on traits covering health, presence of diseases and disabilities, and mental well-being, according to standard protocol. We used multiple imputation to derive FI values for the entire eligible sample in the presence of missing item data (N = 500,336). To validate the measure, we assessed associations of the FI with age, sex, and risk of all-cause mortality (follow-up ≤ 9.7 years) using linear and Cox proportional hazards regression models.ResultsMean FI in the cohort was 0.125 (SD = 0.075), and there was a curvilinear trend toward higher values in older participants. FI values were also marginally higher on average in women than in men. In survival models, 10% higher baseline frailty (ie, a 0.1 FI increment) was associated with higher risk of death (hazard ratio = 1.65; 95% confidence interval: 1.62-1.68). Associations were stronger in younger participants than in older participants, and in men than in women (hazard ratios: 1.72 vs. 1.56, respectively).ConclusionsThe FI is a valid measure of frailty in UK Biobank. The cohort's data are open access for researchers to use, and we provide script for deriving this tool to facilitate future studies on frailty.
| S-EPMC6417451 | biostudies-literature
Project description:ObjectivesTo investigate sex differences in risk factors for incident myocardial infarction (MI) and whether they vary with age.DesignProspective population based study.SettingUK Biobank.Participants471?998 participants (56% women; mean age 56.2) with no history of cardiovascular disease.Main outcome measureIncident (fatal and non-fatal) MI.Results5081 participants (1463 (28.8%) of whom were women) had MI over seven years' mean follow-up, resulting in an incidence per 10?000 person years of 7.76 (95% confidence interval 7.37 to 8.16) for women and 24.35 (23.57 to 25.16) for men. Higher blood pressure indices, smoking intensity, body mass index, and the presence of diabetes were associated with an increased risk of MI in men and women, but associations were attenuated with age. In women, systolic blood pressure and hypertension, smoking status and intensity, and diabetes were associated with higher hazard ratios for MI compared with men: ratio of hazard ratios 1.09 (95% confidence interval 1.02 to 1.16) for systolic blood pressure, 1.55 (1.32 to 1.83) for current smoking, 2.91 (1.56 to 5.45) for type 1 diabetes, and 1.47 (1.16 to 1.87) for type 2 diabetes. There was no evidence that any of these ratios of hazard ratios decreased with age (P>0.2). With the exception of type 1 diabetes, the incidence of MI was higher in men than in women for all risk factors.ConclusionsAlthough the incidence of MI was higher in men than in women, several risk factors were more strongly associated with MI in women compared with men. Sex specific associations between risk factors and MI declined with age, but, where it occurred, the higher relative risk in women remained. As the population ages and the prevalence of lifestyle associated risk factors increase, the incidence of MI in women will likely become more similar to that in men.
| S-EPMC6364292 | biostudies-literature
Project description:It is well established that psychological distress (depression and anxiety) is related to an increased risk of mortality. The personality trait of neuroticism, reflecting a relatively stable tendency towards negative emotions, has also been associated with elevated rates of death in some studies. Accordingly, we tested the possibility that it is the neuroticism trait itself, rather than the distress state, that is generating an increased risk of mortality.We used data from the UK Biobank study, a UK-wide prospective cohort study (2006-2010) in which distress was ascertained using the Patient Health Questionnaire and neuroticism using the Eysenck Personality Questionnaire-Revised Short Form.A mean of 6.2 years of follow-up of 308 721 study members gave rise to 4334 deaths. Higher neuroticism was weakly associated with total mortality (age-adjusted and sex-adjusted HR per SD increase; 95% CI 1.05; 1.02 to 1.09), and moderately strongly correlated with distress symptoms (r=0.55, p<0.0001). Distress symptoms were positively related to risk of total mortality (age-adjusted and sex-adjusted HR per SD increase in distress; 95% CI 1.23; 1.20 to 1.26). This gradient was, in fact, slightly strengthened after adding neuroticism to the multivariable model (1.30; 1.26 to 1.34) but markedly attenuated after taking into account other covariates which included health behaviours and somatic disease (1.16; 1.12 to 1.20). Similar results were apparent when cardiovascular disease, cancer and external cause of death were the end points of interest.While there was good a priori reasons to anticipate the neuroticism would at least partially explain the relation between distress symptoms and cause-specific mortality, we found no such evidence in the present study.
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Project description:Pharmacogenetics studies how genetic variation leads to variability in drug response. Guidelines for selecting the right drug and right dose for patients based on their genetics are clinically effective, but are widely unused. For some drugs, the normal clinical decision making process may lead to the optimal dose of a drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and drug maintenance dose as well as differential drug response phenotypes. We find support for several known drug-gene associations as well as novel pharmacogenetic interactions.
| S-EPMC7951365 | biostudies-literature
Project description:COVID-19 (Coronavirus disease-19) may present with neurological signs, but whether people already affected by neurological conditions are at a higher risk of contracting COVID-19 is still not known. We, therefore, aimed to investigate the association of previously diagnosed neurological conditions with COVID-19. 502,536 community-dwelling UK Biobank participants (54.4% male, mean age 56.6 ± 10.3 years) were included. Among these, 57,463 participants had a diagnosis of neurological conditions (11.43%) and a total of 1326 COVID-19-positive cases were identified (0.26%). Neurological conditions were identified through medical history and linkage to data on hospital admissions (ICD-10 code G00-G99). COVID-19 presence was diagnosed using the data provided by Public Health England. The association of previous diagnosis of neurological conditions with COVID-19 was evaluated through logistic regressions, adjusted for potential confounders, reported as odds ratios (ORs) with their 95% confidence intervals (CIs). Nerve, nerve root and plexus disorders (G50-G59) were the most common conditions identified. The presence of COVID-19 was almost doubled in neurological conditions compared to the general population (0.45 vs. 0.24%, p < 0.0001). Previously diagnosed neurological conditions were associated with 60% higher odds of COVID-19 positive in the multivariable-adjusted model (OR = 1.6, 95% CI 1.4-1.8). Other degenerative diseases of the nervous system, extrapyramidal and movement disorders, polyneuropathies and other disorders of the peripheral nervous system, cerebral palsy and other paralytic syndromes were significantly associated with a higher odds of COVID-19. The presence of neurological conditions was associated with a significantly higher likelihood of COVID-19 compared to the general population.
| S-EPMC8098789 | biostudies-literature
Project description:BACKGROUND AND AIMS:COVID-19 and low levels of vitamin D appear to disproportionately affect black and minority ethnic individuals. We aimed to establish whether blood 25-hydroxyvitamin D (25(OH)D) concentration was associated with COVID-19 risk, and whether it explained the higher incidence of COVID-19 in black and South Asian people. METHODS:UK Biobank recruited 502,624 participants aged 37-73 years between 2006 and 2010. Baseline exposure data, including 25(OH)D concentration and ethnicity, were linked to COVID-19 test results. Univariable and multivariable logistic regression analyses were performed for the association between 25(OH)D and confirmed COVID-19, and the association between ethnicity and both 25(OH)D and COVID-19. RESULTS:Complete data were available for 348,598 UK Biobank participants. Of these, 449 had confirmed COVID-19 infection. Vitamin D was associated with COVID-19 infection univariably (OR = 0.99; 95% CI 0.99-0.999; p = 0.013), but not after adjustment for confounders (OR = 1.00; 95% CI = 0.998-1.01; p = 0.208). Ethnicity was associated with COVID-19 infection univariably (blacks versus whites OR = 5.32, 95% CI = 3.68-7.70, p-value<0.001; South Asians versus whites OR = 2.65, 95% CI = 1.65-4.25, p-value<0.001). Adjustment for 25(OH)D concentration made little difference to the magnitude of the association. CONCLUSIONS:Our findings do not support a potential link between vitamin D concentrations and risk of COVID-19 infection, nor that vitamin D concentration may explain ethnic differences in COVID-19 infection.
| S-EPMC7204679 | biostudies-literature
Project description:BackgroundIdentifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions.Methods and findingsUsing data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain).ConclusionsOur AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the "information gain" achieved by considering more risk factors in the predictive model was significantly higher than the "modeling gain" achieved by adopting complex predictive models.
| S-EPMC6519796 | biostudies-literature