Dynamic linkage of COVID-19 test results between Public Health England's Second Generation Surveillance System and UK Biobank

Surgical removal of tonsils and risk of COVID-19: a nested case-control study using data from UK Biobank and AMORIS Cohort

BACKGROUND

To investigate the association between surgical removal of tonsils and risk of COVID-19 with different severity.

METHODS

Through a nested case-control study during January 31st to December 31st 2020, including 58,888 participants of the UK Biobank, we investigated the association of tonsillectomy with the future risk of mild and severe COVID-19, using binomial logistic regression. We further examined the associations of such surgery with blood inflammatory, lipid and metabolic biomarkers to understand potential mechanisms. Finally, we replicated the analysis of severe COVID-19 in the Swedish AMORIS Cohort (n = 451,960).

RESULTS

Tonsillectomy was associated with a lower risk of mild (odds ratio [95% confidence interval]: 0.80 [0.75-0.86]) and severe (0.87 [0.77-0.98]) COVID-19 in the UK Biobank. The associations did not differ substantially by sex, age, Townsend deprivation index, or polygenic risk score for critically ill COVID-19. Levels of blood inflammatory, lipid and metabolic biomarkers did, however, not differ greatly by history of surgical removal of tonsils. An inverse association between tonsillectomy and severe COVID-19 was also observed in the AMORIS Cohort, primarily among older individuals (> 70 years) and those with ≤ 12 years of education.

CONCLUSIONS

Surgical removal of tonsils may be associated with a lower risk of COVID-19. This association is unlikely attributed to alterations in common blood inflammatory, lipid and metabolic biomarkers.

Modifiable lifestyle factors and the risk of post-COVID-19 multisystem sequelae, hospitalization, and death

Effective prevention strategies for post-COVID complications are crucial for patients, clinicians, and policy makers to mitigate their cumulative burden. This study evaluated the association of modifiable lifestyle factors (smoking, alcohol intake, BMI, physical activity, sedentary time, sleep duration, and dietary habits) with COVID-19 multisystem sequelae, death, and hospitalization in the UK Biobank cohort (n = 68,896). A favorable lifestyle (6-10 healthy factors; 46.4%) was associated with a 36% lower risk of multisystem sequelae (HR, 0.64; 95% CI, 0.58-0.69; ARR at 210 days, 7.08%; 95% CI, 5.98-8.09) compared to an unfavorable lifestyle (0-4 factors; 12.3%). Risk reductions spanned all 10 organ systems, including cardiovascular, coagulation, metabolic, gastrointestinal, kidney, mental health, musculoskeletal, respiratory disorders, and fatigue. This beneficial effect was largely attributable to direct lifestyle impacts independent of corresponding pre-infection comorbidities (71% for any sequelae). A favorable lifestyle was also related to the risk of post-COVID death (HR 0.59, 0.52-0.66) and hospitalization (HR 0.78, 0.73-0.84). These associations persisted across acute and post-acute infection phases, irrespective of hospitalization status, vaccination, or SARS-CoV-2 variant. These findings underscore the clinical and public health importance of adhering to a healthy lifestyle in mitigating long-term COVID-19 adverse impacts and enhancing future pandemic preparedness.

Associations of proton pump inhibitors with susceptibility to influenza, pneumonia, and COVID-19: Evidence from a large population-based cohort study

Background

Adverse effects of proton pump inhibitors (PPIs) have raised wide concerns. The association of PPIs with influenza is unexplored, while that with pneumonia or COVID-19 remains controversial. Our study aims to evaluate whether PPI use increases the risks of these respiratory infections.

Methods

The current study included 160,923 eligible participants at baseline who completed questionnaires on medication use, which included PPI or histamine-2 receptor antagonist (H2RA), from the UK Biobank. Cox proportional hazards regression and propensity score-matching analyses were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

Comparisons with H2RA users were tested. PPI use was associated with increased risks of developing influenza (HR 1.32, 95% CI 1.12-1.56) and pneumonia (hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.26-1.59). In contrast, the risk of COVID-19 infection was not significant with regular PPI use (HR 1.08, 95% CI 0.99-1.17), while the risks of severe COVID-19 (HR 1.19. 95% CI 1.11-1.27) and mortality (HR 1.37. 95% CI 1.29-1.46) were increased. However, when compared with H2RA users, PPI users were associated with a higher risk of influenza (HR 1.74, 95% CI 1.19-2.54), but the risks with pneumonia or COVID-19-related outcomes were not evident.

Conclusions

PPI users are associated with increased risks of influenza, pneumonia, as well as COVID-19 severity and mortality compared to non-users, while the effects on pneumonia or COVID-19-related outcomes under PPI use were attenuated when compared to the use of H2RAs. Appropriate use of PPIs based on comprehensive evaluation is required.

Funding

This work is supported by the National Natural Science Foundation of China (82171698, 82170561, 81300279, 81741067, 82100238), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), the Guangdong Basic and Applied Basic Research Foundation (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Program of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201923, DFJH201803, KJ012019099, KJ012021143, KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Long-term risk of psychiatric disorder and psychotropic prescription after SARS-CoV-2 infection among UK general population

Despite evidence indicating increased risk of psychiatric issues among COVID-19 survivors, questions persist about long-term mental health outcomes and the protective effect of vaccination. Using UK Biobank data, three cohorts were constructed: SARS-CoV-2 infection (n = 26,101), contemporary control with no evidence of infection (n = 380,337) and historical control predating the pandemic (n = 390,621). Compared with contemporary controls, infected participants had higher subsequent risks of incident mental health at 1 year (hazard ratio (HR): 1.54, 95% CI 1.42-1.67; P = 1.70 × 10-24; difference in incidence rate: 27.36, 95% CI 21.16-34.10 per 1,000 person-years), including psychotic, mood, anxiety, alcohol use and sleep disorders, and prescriptions for antipsychotics, antidepressants, benzodiazepines, mood stabilizers and opioids. Risks were higher for hospitalized individuals (2.17, 1.70-2.78; P = 5.80 × 10-10) than those not hospitalized (1.41, 1.30-1.53; P = 1.46 × 10-16), and were reduced in fully vaccinated people (0.97, 0.80-1.19; P = 0.799) compared with non-vaccinated or partially vaccinated individuals (1.64, 1.49-1.79; P = 4.95 × 10-26). Breakthrough infections showed similar risk of psychiatric diagnosis (0.91, 0.78-1.07; P = 0.278) but increased prescription risk (1.42, 1.00-2.02; P = 0.053) compared with uninfected controls. Early identification and treatment of psychiatric disorders in COVID-19 survivors, especially those severely affected or unvaccinated, should be a priority in the management of long COVID. With the accumulation of breakthrough infections in the post-pandemic era, the findings highlight the need for continued optimization of strategies to foster resilience and prevent escalation of subclinical mental health symptoms to severe disorders.

Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes

The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.

Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias?

BACKGROUND

There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection.

METHODS

Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures.

RESULTS

The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome.

CONCLUSIONS

The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.

Association between ethnic background and COVID-19 morbidity, mortality and vaccination in England: a multistate cohort analysis using the UK Biobank

OBJECTIVES

Despite growing evidence suggesting increased COVID-19 mortality among people from ethnic minorities, little is known about milder forms of SARS-CoV-2 infection. We sought to explore the association between ethnic background and the probability of testing, testing positive, hospitalisation, COVID-19 mortality and vaccination uptake.

DESIGN

A multistate cohort analysis. Participants were followed between 8 April 2020 and 30 September 2021.

SETTING

The UK Biobank, which stores medical data on around half a million people who were recruited between 2006 and 2010.

PARTICIPANTS

405 541 subjects were eligible for analysis, limited to UK Biobank participants living in England. 23 891 (6%) of participants were non-white.

PRIMARY AND SECONDARY OUTCOME MEASURES

The associations between ethnic background and testing, testing positive, hospitalisation and COVID-19 mortality were studied using multistate survival analyses. The association with single and double-dose vaccination was also modelled. Multistate models adjusted for age, sex and socioeconomic deprivation were fitted to estimate adjusted HRs (aHR) for each of the multistate transitions.

RESULTS

18 172 (4.5%) individuals tested positive, 3285 (0.8%) tested negative and then positive, 1490 (6.9% of those tested positive) were hospitalised, and 129 (0.6%) tested positive at the moment of hospital admission (ie, direct hospitalisation). Finally, 662 (17.4%) died after admission. Compared with white participants, Asian participants had an increased risk of negative to positive transition (aHR 1.24 (95% CI 1.02 to 1.52)), testing positive (95% CI 1.44 (1.33 to 1.55)) and direct hospitalisation (1.61 (95% CI 1.28 to 2.03)). Black participants had an increased risk of hospitalisation following a positive test (1.71 (95% CI 1.29 to 2.27)) and direct hospitalisation (1.90 (95% CI 1.51 to 2.39)). Although not the case for Asians (aHR 1.00 (95% CI 0.98 to 1.02)), black participants had a reduced vaccination probability (0.63 (95% CI 0.62 to 0.65)). In contrast, Chinese participants had a reduced risk of testing negative (aHR 0.64 (95% CI 0.57 to 0.73)), of testing positive (0.40 (95% CI 0.28 to 0.57)) and of vaccination (0.78 (95% CI 0.74 to 0.83)).

CONCLUSIONS

We identified inequities in testing, vaccination and COVID-19 outcomes according to ethnicity in England. Compared with whites, Asian participants had increased risks of infection and admission, and black participants had almost double hospitalisation risk, and a 40% lower vaccine uptake.

Comorbidities associated with the severity of COVID-19, and differences across ethnic groups: a UK Biobank cohort study

BACKGROUND

Disparities in COVID-19 outcomes exist on the basis of ethnicity and comorbidities. Minority ethnic groups in the UK are known to have poorer COVID-19 outcomes, but also an increased prevelance of certain comorbidities associated with severe outcomes. Additionally, despite the prevalence of certain psychiatric disorders there is a lack of research establishing their relationship with COVID-19 outcomes.

METHODS

We used UK Biobank data, involving 472,182 participants, to test for an association between comorbidities and COVID-19 diagnosis (n = 30,901); and to test for an association between comorbidities and severe COVID-19 (n = 3182). This was done by performing univariable and multivariable logistic regression analysis, estimating odds ratios (ORs) and their 95% confidence intervals (95% CIs). The comorbidities studied were coronary heart disease (CHD), hypertension, type II diabetes mellitus (T2DM), obesity, chronic kidney disease (CKD), depression and anxiety. Multivariable models were adjusted for various socioeconomic, demographic and health-related confounders. We then performed sub-group analysis by common UK ethnic groups (White, South Asian, and Black).

RESULTS

Increased prevalence of all studied comorbidities was seen in both outcomes, compared to the rest of the cohort. All studied comorbidities were associated with an increased risk of COVID-19 infection and severity across all models. For example, the adjusted ORs (95% CI) for depression were 1.112 (1.083 - 1.161) for COVID-19 diagnosis and 2.398 (2.163 - 2.658) for severe COVID-19. Sub-group analysis revealed stronger associations of COVID-19 diagnosis and severe COVID-19 for South-Asian participants for CHD (OR 1.585 [95% CI 1.194-2.105] for COVID-19 diagnosis and 3.021 [1.683-5.390] for severe COVID-19), hypertension (1.488 [1.231-1.799]; 3.399 [1.862-6.206]) and T2DM (1.671 [1.346-2.076]; 5.412 [3.130-9.357]) compared to White participants (1.264 [1.195-1.336] and 1.627 [1.441-1.837] for CHD; 1.131 [1.097-1.116] and 2.075 [1.885-2.284] for hypertension; 1.402 [1.331-1.476] and 2.890 [2.596-3.216] for T2DM). Similar results were seen for Black participants with CKD and hypertension.

CONCLUSION

Specific comorbidities are risk factors for poorer COVID-19 outcomes, supporting targeted interventions and policy aimed at individuals with these comorbidities. Although further research is required, there's also a need for targeted policies for ethnic minorities assessing the unique reasons they are at greater risk of poor COVID-19 outcomes.

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.

Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis

BACKGROUND

COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability.

METHODS

We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2).

RESULTS

Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups.

CONCLUSIONS

Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank

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.

Associations of habitual glucosamine use with SARS-CoV-2 infection and hospital admission and death with COVID-19: Evidence from a large population based cohort study

The coronavirus disease 2019 (COVID-19) pandemic has led to a fundamental number of morbidity and mortality worldwide. Glucosamine was indicated to help prevent and control RNA virus infection preclinically, while its potential therapeutic effects on COVID-19-related outcomes are largely unknown. To assess the association of habitual glucosamine use with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hospital admission, and mortality with COVID-19 in a large population based cohort. Participants from UK Biobank were reinvited between June and September 2021 to have SARS-CoV-2 antibody testing. The associations between glucosamine use and the risk of SARS-CoV-2 infection were estimated by logistic regression. Hazard ratios (HRs) and 95% confidence intervals (CIs) for COVID-19-related outcomes were calculated using COX proportional hazards model. Furthermore, we carried out propensity-score matching (PSM) and stratified analyses. At baseline, 42 673 (20.7%) of the 205 704 participants reported as habitual glucosamine users. During median follow-up of 1.67 years, there were 15 299 cases of SARS-CoV-2 infection, 4214 cases of COVID-19 hospital admission, and 1141 cases of COVID-19 mortality. The fully adjusted odds ratio of SARS-CoV-2 infection with glucosamine use was 0.96 (95% CI: 0.92-1.01). The fully adjusted HR were 0.80 (95% CI: 0.74-0.87) for hospital admission, and 0.81 (95% CI: 0.69-0.95) for mortality. The logistic regression and Cox proportional hazard analyses after PSM yielded consistent results. Our study demonstrated that habitual glucosamine use is associated with reduced risks of hospital admission and death with COVID-19, but not the incidence of SARS-CoV-2 infection.

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

BACKGROUND

Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses.

METHODS

We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10-14 years before the COVID-19 time periods.

RESULTS

Here we show, for example, "participant having son and/or daughter in household" was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively).

CONCLUSIONS

Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.

Impact of ultra-processed food intake on the risk of COVID-19: a prospective cohort study

PURPOSE

Nutrition plays a key role in supporting the human immune system and reducing the risk of infections. However, there is limited evidence exploring the relationship between diet and the risk of COVID-19. This study aimed to assess the associations between consumption of ultra-processed foods (UPF) and COVID-19 risk.

METHODS

In total, 41,012 participants from the UK Biobank study with at least 2 of up to 5 times 24-h dietary assessments were included in this study. Dietary intakes were collected using an online 24-h dietary recall questionnaire and food items were categorized according to their degree of processing by the NOVA classification. COVID-19 infection was defined as individuals tested COVID-19 positive or dead of COVID-19. Association between average UPF consumption (% daily gram intake) and COVID-19 infection was assessed by multivariable logistic regression adjusted for potential confounders.

RESULTS

Compared to participants in the lowest quartile of UPF proportion (% daily gram intake) in the diet, participants in the 2nd, 3rd, and highest quartiles were associated with a higher risk of COVID-19 with the odds ratio (OR) value of 1.03 (95% CI: 0.94-1.13), 1.24 (95% CI: 1.13-1.36), and 1.22 (95% CI: 1.12-1.34), respectively (P for trend < 0.001), after adjusting for potential confounders. The results were robust in a series of sensitivity analyses. No interaction effect was identified between the UPF proportions and age groups, education level, body mass index, and comorbidity status. BMI mediated 13.2% of this association.

CONCLUSION

Higher consumption of UPF was associated with an increased risk of COVID-19 infection. Further studies are needed to better understand the underlying mechanisms in such association.

Trends in incident diagnoses and drug prescriptions for anxiety and depression during the COVID-19 pandemic: an 18-month follow-up study based on the UK Biobank

Serious concerns have been raised about the negative effects of the COVID-19 pandemic on population psychological well-being. However, limited data exist on the long-term effects of the pandemic on incident psychiatric morbidities among individuals with varying exposure to the pandemic. Leveraging prospective data from the community-based UK Biobank cohort, we included 308,400 participants free of diagnosis of anxiety or depression, as well as 213,757 participants free of anxiolytics or antidepressants prescriptions, to explore the trends in incident diagnoses and drug prescriptions for anxiety and depression from 16 March 2020 to 31 August 2021, compared to the pre-pandemic period (i.e., 1 January 2017 to 31 December 2019) and across populations with different exposure statuses (i.e., not tested for COVID-19, tested negative and tested positive). The age- and sex-standardized incidence ratios (SIRs) were calculated by month which indicated an increase in incident diagnoses of anxiety or depression among individuals who were tested for COVID-19 (tested negative: SIR 3.05 [95% confidence interval 2.88-3.22]; tested positive: 2.03 [1.76-2.34]), especially during the first six months of the pandemic (i.e., March-September 2020). Similar increases were also observed for incident prescriptions of anxiolytics or antidepressants (tested negative: 1.56 [1.47-1.67]; tested positive: 1.41 [1.22-1.62]). In contrast, individuals not tested for COVID-19 had consistently lower incidence rates of both diagnoses of anxiety or depression (0.70 [0.67-0.72]) and prescriptions of respective psychotropic medications (0.70 [0.68-0.72]) during the pandemic period. These data suggest a distinct rise in health care needs for anxiety and depression among individuals tested for COVID-19, regardless of the test result, in contrast to a reduction in health care consumption for these disorders among individuals not tested for and, presumably, not directly exposed to the disease.

Laboratory diagnosed microbial infection in English UK Biobank participants in comparison to the general population

Understanding the genetic and environmental risk factors for serious bacterial infections in ageing populations remains incomplete. Utilising the UK Biobank (UKB), a prospective cohort study of 500,000 adults aged 40-69 years at recruitment (2006-2010), can help address this. Partial implementation of such a system helped groups around the world make rapid progress understanding risk factors for SARS-CoV-2 infection and COVID-19, with insights appearing as early as May 2020. In principle, such approaches could also to be used for bacterial isolations. Here we report feasibility testing of linking an England-wide dataset of microbial reporting to UKB participants, to enable characterisation of microbial infections within the UKB Cohort. These records pertain mainly to bacterial isolations; SARS-CoV-2 isolations were not included. Microbiological infections occurring in patients in England, as recorded in the Public Health England second generation surveillance system (SGSS), were linked to UKB participants using pseudonymised identifiers. By January 2015, ascertainment of laboratory reports from UKB participants by SGSS was estimated at 98%. 4.5% of English UKB participants had a positive microbiological isolate in 2015. Half of UKB isolates came from 12 laboratories, and 70% from 21 laboratories. Incidence rate ratios for microbial isolation, which is indicative of serious infection, from the UKB cohort relative to the comparably aged general population ranged from 0.6 to 1, compatible with the previously described healthy participant bias in UKB. Data on microbial isolations can be linked to UKB participants from January 2015 onwards. This linked data would offer new opportunities for research into the role of bacterial agents on health and disease in middle to-old age.

High grip strength attenuates risk of severe COVID-19 in males but not females with obesity: A short communication of prospective findings from UK Biobank

We examined the joint associations of BMI category and grip strength tertile with risk of severe COVID-19 (inpatient COVID-19 or COVID-19 mortality) in 327 500 UK Biobank participants. Compared to normal-weight males with high grip strength, the odds ratio (95 % confidence interval) for males with obesity with low grip strength was 2.39 (1.59-3.60), but 1.52 (0.98-2.35) for males with obesity with a high grip strength. A higher grip strength did not appear to be associated with lower risk of severe COVID-19 in females. Muscle mass and strength development should be considered as a means to reduce risk of severe COVID-19 for males with obesity.

Cardiovascular disease and mortality sequelae of COVID-19 in the UK Biobank

OBJECTIVE

To examine association of COVID-19 with incident cardiovascular events in 17 871 UK Biobank cases between March 2020 and 2021.

METHODS

COVID-19 cases were defined using health record linkage. Each case was propensity score-matched to two uninfected controls on age, sex, deprivation, body mass index, ethnicity, diabetes, prevalent ischaemic heart disease (IHD), smoking, hypertension and high cholesterol. We included the following incident outcomes: myocardial infarction, stroke, heart failure, atrial fibrillation, venous thromboembolism (VTE), pericarditis, all-cause death, cardiovascular death, IHD death. Cox proportional hazards regression was used to estimate associations of COVID-19 with each outcome over an average of 141 days (range 32-395) of prospective follow-up.

RESULTS

Non-hospitalised cases (n=14 304) had increased risk of incident VTE (HR 2.74 (95% CI 1.38 to 5.45), p=0.004) and death (HR 10.23 (95% CI 7.63 to 13.70), p<0.0001). Individuals with primary COVID-19 hospitalisation (n=2701) had increased risk of all outcomes considered. The largest effect sizes were with VTE (HR 27.6 (95% CI 14.5 to 52.3); p<0.0001), heart failure (HR 21.6 (95% CI 10.9 to 42.9); p<0.0001) and stroke (HR 17.5 (95% CI 5.26 to 57.9); p<0.0001). Those hospitalised with COVID-19 as a secondary diagnosis (n=866) had similarly increased cardiovascular risk. The associated risks were greatest in the first 30 days after infection but remained higher than controls even after this period.

CONCLUSIONS

Individuals hospitalised with COVID-19 have increased risk of incident cardiovascular events across a range of disease and mortality outcomes. The risk of most events is highest in the early postinfection period. Individuals not requiring hospitalisation have increased risk of VTE, but not of other cardiovascular-specific outcomes.

Accelerated ageing is associated with increased COVID-19 severity and differences across ethnic groups may exist

Background

While increased age is an established risk factor for COVID-19, there is great heterogeneity in outcomes within age groups. This is because chronological age does not reflect health, unlike biological age. We intend to investigate the association between accelerated ageing and COVID-19 outcomes through the lens of three measures, namely phenotypic age acceleration (PhenoAgeAccel), telomere length (Adjusted T/S Ratio) and facial ageing, and to examine whether there are differences across ethnic groups.

Methods

Taking participants from the UK Biobank, we associated accelerated ageing with severe COVID-19 outcomes, defined as COVID-related hospitalisation or death. Separate logistic regressions models were created for age and the three accelerated ageing-related variables, adjusting for a variety of covariates in each model. Multivariable logistic regression models were also created within White, Black, Asian and Other ethnic groups to assess for potential differing associations. Forward likelihood ratio logistic regression models were created to evaluate importance of the variables and to assess for patterns of association across the total population and ethnic groups.

Results

After adjusting for all covariates, the odds ratio (OR) and 95% confidence interval (95% CI) of COVID-19 severe outcomes for age was 1.080 (1.074-1.086). After further adjusting age for the accelerated ageing variables, the ORs were 1.029 (1.020-1.039) for PhenoAgeAccel and 0.847 (0.772-0.929) for Facial Ageing's "Younger Than You Are" while Adjusted T/S ratio and "Older Than You Are" were statistically insignificant. The OR for age remained similar across ethnic groups. Both PhenoAgeAccel and younger facial ages in the White population and PhenoAgeAccel in the Black population had ORs of 1.031 (1.021-1.042), 0.853 (0.774-0.939), and 1.049 (1.001-1.100), respectively. Both Adjusted T/S Ratio and older facial ages showed statistical insignificance in all ethnicities. In forward logistic regression, age and PhenoAgeAccel were the age-related variables selected most frequently in all models.

Interpretation

Accelerated ageing is associated with increased COVID-19 severity. The mechanisms at work here are likely immunosenescence and inflamaging. This association indicates that anti-ageing treatment may improve COVID-19 outcome. The results within ethnic groups and that of telomere length were inconclusive, but point to a need for future, more focused research on the topic.

Television Viewing Time, Overweight, Obesity, and Severe COVID-19: A Brief Report From UK Biobank

BACKGROUND

Overweight and obesity are well-established risk factors for COVID-19 severity; however, less is known about the role of sedentary behaviors such as television (TV) viewing. The purpose of this brief report was to determine whether lower TV viewing time may mitigate the risk of severe COVID-19 in individuals with excess weight.

METHODS

We analyzed 329,751 UK Biobank participants to investigate the independent and combined associations of BMI and self-reported TV viewing time with odds of severe COVID-19 (inpatient COVID-19 or COVID-19 death).

RESULTS

Between March 16 and December 8, 2020, there were 1648 instances of severe COVID-19. Per 1-unit (hours per day) increase in TV viewing time, the odds of severe COVID-19 increased by 5% (adjusted odds ratio = 1.05, 95% confidence interval = 1.02-1.08). Compared with normal-weight individuals with low (≤1 h/d) TV viewing time, the odds ratios for overweight individuals with low and high (≥4 h/d) TV viewing time were 1.17 (0.89-1.55) and 1.66 (1.31-2.11), respectively. For individuals with obesity, the respective ORs for low and high TV viewing time were 2.18 (1.61-2.95) and 2.14 (1.69-2.73).

CONCLUSION

Higher TV viewing time was associated with higher odds of severe COVID-19 independent of BMI and moderate to vigorous physical activity. Additionally, low TV viewing time may partly attenuate the elevated odds associated with overweight, but not obesity.

Biosafety protection and workflow of clinical microbiology laboratory under COVID-19: A review

This paper mainly discusses how to do a good job of daily biosafety protection measures in clinical microbiology laboratories during the epidemic of COVID-19, so as to ensure the safe development of routine clinical microbiology testing items. According to the microbiological and epidemiological characteristics of the novel coronavirus, this paper analyzed the potential risks of the laboratory from the perspective of personal protection before, during, and after testing. Combined with the actual work situation, the improved biosafety protection measures and optimized work flow are introduced to ensure the safety of medical staff and the smooth development of daily work. Danyang People's Hospital of Jiangsu Province, clinical microbiology laboratory of clinical laboratory in strict accordance with the relevant laws and regulations, technical specifications and the expert consensus, combined with their own conditions, the biosafety measures to perfect the working process was optimized, effectively prevent the laboratory exposure, and maintain strict working condition for a long time, continue to improve. We found that the biosafety protection measures of clinical microbiology laboratory have good prevention and control effect on preventing infection of medical staff, which will greatly reduce the risk of infection of medical staff, form good working habits, and provide reference for biosafety protection of microbiology laboratory during the epidemic of COVID-19.

Primary hypertension, anti-hypertensive medications and the risk of severe COVID-19 in UK Biobank

Hypertension appears to be one of the commonest comorbidities in COVID-19 patients, although whether hypertensive individuals have a higher risk of severe COVID-19 compared with non-hypertensives is unclear. It is also unclear whether the absolute level of systolic blood pressure, or the type of anti-hypertensive medication is related to this risk. Analyses were conducted using data from the UK Biobank and linked health records. Logistic regression models were fitted to assess the impact of hypertension, systolic blood pressure (SBP) and medications on the risk of severe COVID-19. 16,134 individuals tested positive for severe acute respiratory syndrome-coronavirus, 22% (n = 3,584) developed severe COVID-19 and 40% (n = 6,517) were hypertensive. Hypertension was associated with 22% higher odds of severe COVID-19 (Odds ratio (OR) 1.22; 95% confidence interval (CI) 1.12, 1.33), compared with normotension after adjusting for confounding variables. In those taking anti-hypertensive medications, elevated SBP showed a dose-response relationship with severe COVID-19 (150-159mmHg versus 120-129mmHg (OR 1.91; 95% CI 1.44, 2.53), >180+mmHg versus 120-129mmHg (OR 1.93; 95% CI 1.06, 3.51)). SBP <120mmHg was associated with greater odds of severe COVID-19 (OR 1.40; 95% CI 1.11, 1.78). Angiotensin-converting enzyme inhibitors or angiotensin-II receptor blockers were not associated with altered risk of severe COVID-19. Hypertension is an important risk factor for COVID-19. A better understanding of the underlying mechanisms is warranted in case of more severe strains or other viruses in the future.

Occupational characteristics associated with SARS-CoV-2 infection in the UK Biobank during August-November 2020: a cohort study

BACKGROUND

Occupational exposures may play a key role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection risk. We used a job-exposure matrix linked to the UK Biobank to measure occupational characteristics and estimate associations with a positive SARS-CoV-2 test.

METHODS

People reporting job titles at their baseline interview in England who were < 65 years of age in 2020 were included. Healthcare workers were excluded because of differential access to testing. Jobs were linked to the US Occupational Information Network (O*NET) job exposure matrix. O*NET-based scores were examined for occupational physical proximity, exposure to diseases/infection, working outdoors exposed to weather, and working outdoors under cover (score range = 1-5). Jobs were classified as remote work using two algorithms. SARS-CoV-2 test results were evaluated between August 5th-November 10th, 2020, when the UK was released from lockdown. Cox regression was used to calculate adjusted hazard ratios (aHRs), accounting for age, sex, race, education, neighborhood deprivation, assessment center, household size, and income.

RESULTS

We included 115,451 people with job titles, of whom 1746 tested positive for SARS-CoV-2. A one-point increase in physical proximity score was associated with 1.14 times higher risk of SARS-CoV-2 (95%CI = 1.05-1.24). A one-point increase in the exposure to diseases/infections score was associated with 1.09 times higher risk of SARS-CoV-2 (95%CI = 1.02-1.16). People reporting jobs that could not be done remotely had higher risk of SARS-CoV-2 regardless of the classification algorithm used (aHRs = 1.17 and 1.20). Outdoors work showed an association with SARS-CoV-2 (exposed to weather aHR = 1.06, 95%CI = 1.01-1.11; under cover aHR = 1.08, 95%CI = 1.00-1.17), but these associations were not significant after accounting for whether work could be done remotely.

CONCLUSION

People in occupations that were not amenable to remote work, required closer physical proximity, and required more general exposure to diseases/infection had higher risk of a positive SARS-CoV-2 test. These findings provide additional evidence that coronavirus disease 2019 (COVID-19) is an occupational disease, even outside of the healthcare setting, and indicate that strategies for mitigating transmission in in-person work settings will remain important.

Clinical and Genetic Risk Factors for Acute Incident Venous Thromboembolism in Ambulatory Patients With COVID-19

IMPORTANCE

The risk of venous thromboembolism (VTE) in ambulatory COVID-19 is controversial. In addition, the association of vaccination with COVID-19-related VTE and relevant clinical and genetic risk factors remain to be elucidated.

OBJECTIVE

To quantify the association between ambulatory COVID-19 and short-term risk of VTE, study the potential protective role of vaccination, and investigate clinical and genetic risk factors for post-COVID-19 VTE.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study of patients with COVID-19 from UK Biobank included participants with SARS-CoV-2 infection that was confirmed by a positive polymerase chain test reaction result between March 1, 2020, and September 3, 2021, who were then propensity score matched to COVID-19-naive people during the same period. Participants with a history of VTE who used antithrombotic drugs (1 year before index dates) or tested positive in hospital were excluded.

EXPOSURES

First infection with SARS-CoV-2, age, sex, ethnicity, socioeconomic status, obesity, vaccination status, and inherited thrombophilia.

MAIN OUTCOMES AND MEASURES

The primary outcome was a composite VTE, including deep vein thrombosis or pulmonary embolism, which occurred 30 days after the infection. Hazard ratios (HRs) with 95% CIs were calculated using cause-specific Cox models.

RESULTS

In 18 818 outpatients with COVID-19 (10 580 women [56.2%]; mean [SD] age, 64.3 [8.0] years) and 93 179 matched uninfected participants (52 177 women [56.0%]; mean [SD] age, 64.3 [7.9] years), the infection was associated with an increased risk of VTE in 30 days (incidence rate of 50.99 and 2.37 per 1000 person-years for infected and uninfected people, respectively; HR, 21.42; 95% CI, 12.63-36.31). However, risk was substantially attenuated among the fully vaccinated (HR, 5.95; 95% CI, 1.82-19.5; interaction P = .02). In patients with COVID-19, older age, male sex, and obesity were independently associated with higher risk, with adjusted HRs of 1.87 (95% CI, 1.50-2.33) per 10 years, 1.69 (95% CI, 1.30-2.19), and 1.83 (95% CI, 1.28-2.61), respectively. Further, inherited thrombophilia was associated with an HR of 2.05 (95% CI, 1.15-3.66) for post-COVID-19 VTE.

CONCLUSIONS AND RELEVANCE

In this population-based cohort study of patients with COVID-19, ambulatory COVID-19 was associated with a substantially increased risk of incident VTE, but this risk was greatly reduced in fully vaccinated people with breakthrough infection. Older age, male sex, and obesity were clinical risk factors for post-COVID-19 VTE; factor V Leiden thrombophilia was additionally associated with double the risk, comparable with the risk of 10-year aging. These findings may reinforce the need for vaccination, inform VTE risk stratification, and call for targeted VTE prophylaxis strategies for unvaccinated outpatients with COVID-19.

Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories-a UK Biobank Random Forest classification study

Aging has often been characterized by progressive cognitive decline in memory and especially executive function. Yet some adults, aged 80 years or older, are "super-agers" that exhibit cognitive performance like younger adults. It is unknown if there are adults in mid-life with similar superior cognitive performance ("positive-aging") versus cognitive decline over time and if there are blood biomarkers that can distinguish between these groups. Among 1303 participants in UK Biobank, latent growth curve models classified participants into different cognitive groups based on longitudinal fluid intelligence (FI) scores over 7-9 years. Random Forest (RF) classification was then used to predict cognitive trajectory types using longitudinal predictors including demographic, vascular, bioenergetic, and immune factors. Feature ranking importance and performance metrics of the model were reported. Despite model complexity, we achieved a precision of 77% when determining who would be in the "positive-aging" group (n = 563) vs. cognitive decline group (n = 380). Among the top fifteen features, an equal number were related to either vascular health or cellular bioenergetics but not demographics like age, sex, or socioeconomic status. Sensitivity analyses showed worse model results when combining a cognitive maintainer group (n = 360) with the positive-aging or cognitive decline group. Our results suggest that optimal cognitive aging may not be related to age per se but biological factors that may be amenable to lifestyle or pharmacological changes.

Genome-wide association study of SARS-CoV-2 infection in Chinese population

Coronavirus disease 2019 (COVID-19) is a global public health concern. The purpose of this study was to investigate the association between genetic variants and SARS-CoV-2 infection and the COVID-19 severity in Chinese population. A total of 256 individuals including 87 symptomatic patients (tested positive for SARS-CoV-2), 84 asymptomatic cases, and 85 close contacts of confirmed patients (tested negative for SARS-CoV-2) were recruited from February 2020 to May 2020. We carried out the whole exome genome sequencing between the individuals and conducted a genetic association study for SARS-CoV-2 infection and the COVID-19 severity. In total, we analyzed more than 100,000 single-nucleotide polymorphisms. The genome-wide association study suggested potential correlation between genetic variability in POLR2A, ANKRD27, MAN1A2, and ERAP1 genes and SARS-CoV-2 infection susceptibility. The most significant gene locus associated with SARS-CoV-2 infection was located in POLR2A (p = 5.71 × 10^-6). Furthermore, genetic variants in PCNX2, CD200R1L, ZMAT3, PLCL2, NEIL3, and LINC00700 genes (p < 1 × 10^-5) were closely associated with the COVID-19 severity in Chinese population. Our study confirmed that new genetic variant loci had significant association with SARS-CoV-2 infection and the COVID-19 severity in Chinese population, which provided new clues for the studies on the susceptibility of SARS-CoV-2 infection and the COVID-19 severity. These findings may give a better understanding on the molecular pathogenesis of COVID-19 and genetic basis of heterogeneous susceptibility, with potential impact on new therapeutic options.

Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank

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 (PM_2.5) 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 PM_2.5 and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

Genetic predispositions to psychiatric disorders and the risk of COVID-19

BACKGROUND

Whether a genetic predisposition to psychiatric disorders is associated with coronavirus disease 2019 (COVID-19) is unknown.

METHODS

Our analytic sample consisted of 287,123 white British participants in UK Biobank who were alive on 31 January 2020. We performed a genome-wide association study (GWAS) analysis for each psychiatric disorder (substance misuse, depression, anxiety, psychotic disorder, and stress-related disorders) in a randomly selected half of the study population ("base dataset"). For the other half ("target dataset"), the polygenic risk score (PRS) was calculated as a proxy of individuals' genetic predisposition to a given psychiatric phenotype using discovered genetic variants from the base dataset. Ascertainment of COVID-19 was based on the Public Health England dataset, inpatient hospital data, or death registers in UK Biobank. COVID-19 cases from hospitalization records or death records were considered "severe cases." The association between the PRS for psychiatric disorders and COVID-19 risk was examined using logistic regression. We also repeated PRS analyses based on publicly available GWAS summary statistics.

RESULTS

A total of 143,562 participants (including 10,868 COVID-19 cases) were used for PRS analyses. A higher genetic predisposition to psychiatric disorders was associated with an increased risk of any COVID-19 and severe COVID-19. The adjusted odds ratio (OR) for any COVID-19 was 1.07 (95% confidence interval [CI] 1.02-1.13) and 1.06 (95% CI 1.01-1.11) among individuals with a high genetic risk (above the upper tertile of the PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Slightly higher ORs were noted for severe COVID-19, and similar result patterns were obtained in analyses based on publicly available GWAS summary statistics.

CONCLUSIONS

Our findings suggest a potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19. Our data underscore the need for increased medical surveillance for this vulnerable population during the COVID-19 pandemic.

Indicators of recent COVID-19 infection status: findings from a large occupational cohort of staff and postgraduate research students from a UK university

BACKGROUND

Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators.

METHODS

We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators.

RESULTS

Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%).

CONCLUSIONS

Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

Circulating Polyunsaturated Fatty Acids and COVID-19: A Prospective Cohort Study and Mendelian Randomization Analysis

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 fatty acids, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear. This study aimed to investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity. We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline (blood samples collected from 2007 to 2010) were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, going beyond UK Biobank, we leveraged summary statistics from existing genome-wide association studies to perform bidirectional two-sample Mendelian randomization (MR) analyses to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity. In the observational association analysis of each PUFA measure separately, total, omega-3, and omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. When omega-6, omega-3, and their ratio are jointly analyzed, only omega-3 PUFAs remained significantly and inversely associated with both susceptibility and severity. The forward MR analysis indicated that docosapentaenoic acid (DPA-n3) and arachidonic acid (AA) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.89 (0.81, 0.99) and 0.96 (0.94, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs. Our observational analysis supported that higher circulating omega-3 PUFAs, especially DHA, may lower the susceptibility to and alleviate the severity of COVID-19. Our MR analysis further supported causal associations of DPA-n3 and AA with a lower risk of severe COVID-19.

Associations of sleep and circadian phenotypes with COVID-19 susceptibility and hospitalization: an observational cohort study based on the UK Biobank and a two-sample Mendelian randomization study

Study Objectives

Sleep and circadian phenotypes are associated with several diseases. The present study aimed to investigate whether sleep and circadian phenotypes were causally linked with coronavirus disease 2019 (COVID-19)-related outcomes.

Methods

Habitual sleep duration, insomnia, excessive daytime sleepiness, daytime napping, and chronotype were selected as exposures. Key outcomes included positivity and hospitalization for COVID-19. In the observation cohort study, multivariable risk ratios (RRs) and their 95% confidence intervals (CIs) were calculated. Two-sample Mendelian randomization (MR) analyses were conducted to estimate the causal effects of the significant findings in the observation analyses. Beta values and the corresponding 95% CIs were calculated and compared using the inverse variance weighting, weighted median, and MR-Egger methods.

Results

In the UK Biobank cohort study, both often excessive daytime sleepiness and sometimes daytime napping were associated with hospitalized COVID-19 (excessive daytime sleepiness [often vs. never]: RR=1.24, 95% CI=1.02-1.5; daytime napping [sometimes vs. never]: RR=1.12, 95% CI=1.02-1.22). In addition, sometimes daytime napping was also associated with an increased risk of COVID-19 susceptibility (sometimes vs. never: RR= 1.04, 95% CI=1.01-1.28). In the MR analyses, excessive daytime sleepiness was found to increase the risk of hospitalized COVID-19 (MR IVW method: OR = 4.53, 95% CI = 1.04-19.82), whereas little evidence supported a causal link between daytime napping and COVID-19 outcomes.

Conclusions

Observational and genetic evidence supports a potential causal link between excessive daytime sleepiness and an increased risk of COVID-19 hospitalization, suggesting that interventions targeting excessive daytime sleepiness symptoms might decrease severe COVID-19 rate.

Higher premorbid serum testosterone predicts COVID-19-related mortality risk in men

Objective

Men are at greater risk from COVID-19 than women. Older, overweight men, and those with type 2 diabetes, have lower testosterone concentrations and poorer COVID-19-related outcomes. We analysed the associations of premorbid serum testosterone concentrations, not confounded by the effects of acute SARS-CoV-2 infection, with COVID-19-related mortality risk in men.

Design

This study is a United Kingdom Biobank prospective cohort study of community-dwelling men aged 40-69 years.

Methods

Serum total testosterone and sex hormone-binding globulin (SHBG) were measured at baseline (2006-2010). Free testosterone values were calculated (cFT). the incidence of SARS-CoV-2 infections and deaths related to COVID-19 were ascertained from 16 March 2020 to 31 January 2021 and modelled using time-stratified Cox regression.

Results

In 159 964 men, there were 5558 SARS-CoV-2 infections and 438 COVID-19 deaths. Younger age, higher BMI, non-White ethnicity, lower educational attainment, and socioeconomic deprivation were associated with incidence of SARS-CoV-2 infections but total testosterone, SHBG, and cFT were not. Adjusting for potential confounders, higher total testosterone was associated with COVID-19-related mortality risk (overall trend P = 0.008; hazard ratios (95% CIs) quintile 1, Q1 vs Q5 (reference), 0.84 (0.65-1.12) Q2:Q5, 0.82 (0.63-1.10); Q3:Q5, 0.80 (0.66-1.00); Q4:Q5, 0.82 (0.75-0.93)). Higher SHBG was also associated with COVID-19 mortality risk (P = 0.008), but cFT was not (P = 0.248).

Conclusions

Middle-aged to older men with the highest premorbid serum total testosterone and SHBG concentrations are at greater risk of COVID-19-related mortality. Men could be advised that having relatively high serum testosterone concentrations does not protect against future COVID-19-related mortality. Further investigation of causality and potential underlying mechanisms is warranted.

Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study

Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization). Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4510 participants with 7539 test cases. We downloaded baseline data from 10 to 14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases in a subset of 80 participants with 124 test cases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean. Model predictions using the full cohort were marginal. The "best-fit" model for predicting COVID-19 risk was found in the subset of participants with antibody titers, which achieved excellent discrimination (AUC 0.969, 95% CI 0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC 0.803, 95% CI 0.663-0.943) and included only serology titers, again in the subset group. Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.

Association between household size and COVID-19: A UK Biobank observational study

OBJECTIVE

To assess the association between household size and risk of non-severe or severe COVID-19.

DESIGN

A longitudinal observational study.

SETTING

This study utilised UK Biobank linked to national SARS-CoV-2 laboratory test data.

PARTICIPANTS

401,910 individuals with available data on household size in UK Biobank.

MAIN OUTCOME MEASURES

Household size was categorised as single occupancy, two-person households and households of three or more. Severe COVID-19 was defined as a positive SARS-CoV-2 test on hospital admission or death with COVID-19 recorded as the underlying cause; and non-severe COVID-19 as a positive test from a community setting. Logistic regression models were fitted to assess associations, adjusting for potential confounders.

RESULTS

Of 401,910 individuals, 3612 (1%) were identified as having suffered from a severe COVID-19 infection and 11,264 (2.8%) from a non-severe infection, between 16 March 2020 and 16 March 2021. Overall, the odds of severe COVID-19 was significantly higher among individuals living alone (adjusted odds ratio: 1.24 [95% confidence interval: 1.14 to 1.36], or living in a household of three or more individuals (adjusted odds ratio: 1.28 [1.17 to 1.39], when compared to individuals living in a household of two. For non-severe COVID-19 infection, individuals living in a single-occupancy household had lower odds compared to those living in a household of two (adjusted odds ratio: 0.88 [0.82 to 0.93].

CONCLUSIONS

Odds of severe or non-severe COVID-19 infection were associated with household size. Increasing understanding of why certain households are more at risk is important for limiting spread of the infection.

Depressive symptoms, but not anxiety, predict subsequent diagnosis of Coronavirus disease 19: a national cohort study

AIMS

Several diseases are linked to increased risk of Coronavirus disease 19 (COVID-19). Our aim was to investigate whether depressive and anxiety symptoms predict subsequent risk of COVID-19, as has been shown for other respiratory infections.

METHODS

We based our analysis on UK Biobank participants providing prospective data to estimate temporal association between depressive and anxiety symptoms and COVID-19. We estimated whether the magnitude of these symptoms predicts subsequent diagnosis of COVID-19 in this sample. Further, we evaluated whether depressive and anxiety symptoms predicted (i) being tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and (ii) COVID-19 in those tested.

RESULTS

Based on data from N = 135 102 participants, depressive symptoms (odds ratio (OR) = 1.052; 95% confidence interval (CI) 1.017-1.086; absolute case risk: (moderately) severe depression: 493 per 100 000 v. minimal depression: 231 per 100 000) but not anxiety (OR = 1.009; 95% CI 0.97-1.047) predicted COVID-19. While depressive symptoms but not anxiety predicted (i) being tested for SARS-CoV-2 (OR = 1.039; 95% CI 1.029-1.05 and OR = 0.99; 95% CI 0.978-1.002), (ii) neither predicted COVID-19 in those tested (OR = 1.015; 95% CI 0.981-1.05 and OR = 1.021; 95% CI 0.981-1.061). Results remained stable after adjusting for sociodemographic characteristics, multimorbidity and behavioural factors.

CONCLUSIONS

Depressive symptoms were associated with a higher risk of COVID-19 diagnosis, irrespective of multimorbidities. Potential underlying mechanisms to be elucidated include risk behaviour, symptom perception, healthcare use, testing likelihood, viral exposure, immune function and disease progress. Our findings highlight the relevance of mental processes in the context of COVID-19.

Comparative effectiveness of the BNT162b2 and ChAdOx1 vaccines against Covid-19 in people over 50

Although pivotal trials with varying populations and study methods suggest higher efficacy for mRNA than adenoviral Covid-19 vaccines, not many studies have directly compared vaccine effectiveness in the population. Here, we conduct a head-to-head comparison of BNT162b2 versus ChAdOx1 against Covid-19. We analyse 235,181 UK Biobank participants aged 50 years or older and vaccinated with one or two doses of BNT162b2 or ChAdOx1. People are followed from the vaccination date until 18/10/2021. Inverse probability weighting is used to minimise confounding and the Cox models to derive hazard ratio. We find that, compared with one dose of ChAdOx1, vaccination with BNT162b2 is associated with a 28% (95% CI, 12-42) decreased risk of SARS-CoV-2 infection. Also, two doses of BNT162b2 vs ChAdOx1 confers 30% (95% CI, 25-35) and 29% (95% CI, 10-45) lower risks of both infection and hospitalisation during the study period when the Delta variant is dominant. Furthermore, the comparative protection against the infection persists for at least six months among the fully vaccinated, suggesting no differential waning between the two vaccines. These findings can inform evidence-based Covid-19 vaccination campaigns and booster strategies.

Circulating polyunsaturated fatty acids and COVID-19: a prospective cohort study and Mendelian randomization analysis

BACKGROUND

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 ones, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear.

OBJECTIVE

To investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity.

DESIGN

We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, bidirectional two-sample Mendelian randomization (MR) analyses were performed to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity using summary statistics from existing genome-wide association studies.

RESULTS

In the observational association analysis, total PUFAs, omega-3 PUFAs, omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. The forward MR analysis indicated that arachidonic acid (AA) and docosapentaenoic acid (DPA-n3) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.96 (0.94, 0.99) and 0.89 (0.81, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs.

CONCLUSIONS

Our observational analysis supported that higher circulating PUFAs, either omega-3 or omega-6, are protective against severe COVID-19, while omega-3 PUFAs, especially DHA, were also associated with reducing COVID-19 susceptibility. Our MR analysis further supported causal associations of AA and DPA-n3 with a lower risk of severe COVID-19.

Sarcopenic obesity and the risk of hospitalization or death from coronavirus disease 2019: findings from UK Biobank

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2. The role of skeletal muscle mass in modulating immune response is well documented. Whilst obesity is well established as a key factor in COVID-19 and outcome, no study has examined the influence of both sarcopenia (low muscle mass) and obesity, termed 'sarcopenic obesity' on the risk of severe COVID-19.

METHODS

This study uses data from UK Biobank. Probable sarcopenia was defined as low handgrip strength. Sarcopenic obesity was mutually exclusively defined as the presence of obesity and low muscle mass [based on two established criteria: appendicular lean mass (ALM) adjusted for either (i) height or (ii) body mass index]. Severe COVID-19 was defined by a positive severe acute respiratory syndrome coronavirus 2 test result in a hospital setting and/or death with a primary cause reported as COVID-19. Fully adjusted logistic regression models were used to analyse the associations between sarcopenic status and severe COVID-19. This work was conducted under UK Biobank Application Number 52553.

RESULTS

We analysed data from 490 301 UK Biobank participants (median age 70.0 years, 46% male); 2203 (0.4%) had severe COVID-19. Individuals with probable sarcopenia were 64% more likely to have had severe COVID-19 (odds ratio 1.638; P < 0.001). Obesity increased the likelihood of severe COVID-19 by 76% (P < 0.001). Using either ALM index or ALM/body mass index to define low muscle mass, those with sarcopenic obesity were 2.6 times more likely to have severe COVID-19 (odds ratio 2.619; P < 0.001). Sarcopenia alone did not increase the risk of COVID-19.

CONCLUSIONS

Sarcopenic obesity may increase the risk of severe COVID-19, over that of obesity alone. The mechanisms for this are complex but could be a result of a reduction in respiratory functioning, immune response, and ability to respond to metabolic stress.

The association between vitamin D status and COVID-19 in England: A cohort study using UK Biobank

BACKGROUND

Recent studies indicate that vitamin D supplementation may decrease respiratory tract infections, but the association between vitamin D and COVID-19 is still unclear.

OBJECTIVE

To explore the association between vitamin D status and infections, hospitalisation, and mortality due to COVID-19.

METHODS

We used UK Biobank, a nationwide cohort of 500,000 individuals aged between 40 and 69 years at recruitment between 2006 and 2010. We included people with at least one serum vitamin D test, living in England with linked primary care and inpatient records. The primary exposure was serum vitamin D status measured at recruitment, defined as deficiency at <25 nmol/L, insufficiency at 25-49 nmol/L and sufficiency at ≥ 50 nmol/L. Secondary exposures were self-reported or prescribed vitamin D supplements. The primary outcome was laboratory-confirmed or clinically diagnosed SARS-CoV-2 infections. The secondary outcomes included hospitalisation and mortality due to COVID-19. We used multivariable Cox regression models stratified by summertime months and non-summertime months, adjusting for demographic factors and underlying comorbidities.

RESULTS

We included 307,512 participants (54.9% female, 55.9% over 70 years old) in our analysis. During summertime months, weak evidence existed that the vitamin D deficiency group had a lower hazard of being diagnosed with COVID-19 (hazard ratio [HR] = 0.86, 95% confidence interval [CI] = 0.77-0.95). During non-summertime, the vitamin D deficiency group had a higher hazard of COVID-19 compared with the vitamin D sufficient group (HR = 1.14, 95% CI = 1.01-1.30). No evidence was found that vitamin D deficiency or insufficiency was associated with either hospitalisation or mortality due to COVID-19 in any time strata.

CONCLUSION

We found no evidence of an association between historical vitamin D status and hospitalisation or mortality due to COVID-19, along with inconsistent results for any association between vitamin D and diagnosis of COVID-19. However, studies using more recent vitamin D measurements and systematic COVID-19 testing are needed.

Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort

BACKGROUND

Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.

METHODS

We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).

RESULTS

There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).

INTERPRETATION

Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

Association Between Accelerometer-Assessed Physical Activity and Severity of COVID-19 in UK Biobank

OBJECTIVE

To quantify the association between accelerometer-assessed physical activity and coronavirus disease 2019 (COVID-19) outcomes.

METHODS

Data from 82,253 UK Biobank participants with accelerometer data (measured 2013-2015), complete covariate data, and linked COVID-19 data from March 16, 2020, to March 16, 2021, were included. Two outcomes were investigated: severe COVID-19 (positive test result from in-hospital setting or COVID-19 as primary cause of death) and nonsevere COVID-19 (positive test result from community setting). Logistic regressions were used to assess associations with moderate to vigorous physical activity (MVPA), total activity, and intensity gradient. A higher intensity gradient indicates a higher proportion of vigorous activity.

RESULTS

Average MVPA was 48.1 (32.7) min/d. Physical activity was associated with lower odds of severe COVID-19 (adjusted odds ratio per standard deviation increase: MVPA, 0.75 [95% CI, 0.67 to 0.85]; total, 0.83 [0.74 to 0.92]; intensity, 0.77 [0.70 to 0.86]), with stronger associations in women (MVPA, 0.63 [0.52 to 0.77]; total, 0.76 [0.64 to 0.90]; intensity, 0.63 [0.53 to 0.74]) than in men (MVPA, 0.84 [0.73 to 0.97]; total, 0.88 [0.77 to 1.01]; intensity, 0.88 [0.77 to 1.00]). In contrast, when mutually adjusted, total activity was associated with higher odds of a nonsevere infection (1.10 [1.04 to 1.16]), whereas the intensity gradient was associated with lower odds (0.91 [0.86 to 0.97]).

CONCLUSION

Odds of severe COVID-19 were approximately 25% lower per standard deviation (∼30 min/d) MVPA. A greater proportion of vigorous activity was associated with lower odds of severe and nonsevere infections. The association between total activity and higher odds of a nonsevere infection may be through greater community engagement and thus more exposure to the virus. Results support calls for public health messaging highlighting the potential of MVPA for reducing the odds of severe COVID-19.

COVID-19 and risk of subsequent life-threatening secondary infections: a matched cohort study in UK Biobank

BACKGROUND

With the increasing number of people infected with and recovered from coronavirus disease 2019 (COVID-19), the extent of major health consequences of COVID-19 is unclear, including risks of severe secondary infections.

METHODS

Based on 445,845 UK Biobank participants registered in England, we conducted a matched cohort study where 5151 individuals with a positive test result or hospitalized with a diagnosis of COVID-19 were included in the exposed group. We then randomly selected up to 10 matched individuals without COVID-19 diagnosis for each exposed individual (n = 51,402). The life-threatening secondary infections were defined as diagnoses of severe secondary infections with high mortality rates (i.e., sepsis, endocarditis, and central nervous system infections) from the UK Biobank inpatient hospital data, or deaths from these infections from mortality data. The follow-up period was limited to 3 months after the initial COVID-19 diagnosis. Using a similar study design, we additionally constructed a matched cohort where exposed individuals were diagnosed with seasonal influenza from either inpatient hospital or primary care data between 2010 and 2019 (6169 exposed and 61,555 unexposed individuals). After controlling for multiple confounders, Cox models were used to estimate hazard ratios (HRs) of life-threatening secondary infections after COVID-19 or seasonal influenza.

RESULTS

In the matched cohort for COVID-19, 50.22% of participants were male, and the median age at the index date was 66 years. During a median follow-up of 12.71 weeks, the incidence rate of life-threatening secondary infections was 2.23 (123/55.15) and 0.25 (151/600.55) per 1000 person-weeks for all patients with COVID-19 and their matched individuals, respectively, which corresponded to a fully adjusted HR of 8.19 (95% confidence interval [CI] 6.33-10.59). The corresponding HR of life-threatening secondary infections among all patients with seasonal influenza diagnosis was 4.50, 95% CI 3.34-6.08 (p for difference < 0.01). Also, elevated HRs were observed among hospitalized individuals for life-threatening secondary infections following hospital discharge, both in the COVID-19 (HR = 6.28 [95% CI 4.05-9.75]) and seasonal influenza (6.01 [95% CI 3.53-10.26], p for difference = 0.902) cohorts.

CONCLUSION

COVID-19 patients have increased subsequent risks of life-threatening secondary infections, to an equal extent or beyond risk elevations observed for patients with seasonal influenza.

Associations of obesity, physical activity level, inflammation and cardiometabolic health with COVID-19 mortality: a prospective analysis of the UK Biobank cohort

OBJECTIVES

To investigate the associations of physical activity level with COVID-19 mortality risk across body mass index (BMI) categories, and to determine whether any protective association of a higher physical activity level in individuals with obesity may be explained by favourable levels of cardiometabolic and inflammatory biomarkers.

DESIGN

Prospective cohort study (baseline data collected between 2006 and 2010). Physical activity level was assessed using the International Physical Activity Questionnaire (high: ≥3000 Metabolic Equivalent of Task (MET)-min/week, moderate: ≥600 MET-min/week, low: not meeting either criteria), and biochemical assays were conducted on blood samples to provide biomarker data.

SETTING

UK Biobank.

MAIN OUTCOME MEASURES

Logistic regressions adjusted for potential confounders were performed to determine the associations of exposure variables with COVID-19 mortality risk. Mortality from COVID-19 was ascertained by death certificates through linkage with National Health Service (NHS) Digital.

RESULTS

Within the 259 397 included participants, 397 COVID-19 deaths occurred between 16 March 2020 and 27 February 2021. Compared with highly active individuals with a normal BMI (reference group), the ORs (95% CIs) for COVID-19 mortality were 1.61 (0.98 to 2.64) for highly active individuals with obesity, 2.85 (1.78 to 4.57) for lowly active individuals with obesity and 1.94 (1.04 to 3.61) for lowly active individuals with a normal BMI. Of the included biomarkers, neutrophil count and monocyte count were significantly positively associated with COVID-19 mortality risk. In a subanalysis restricted to individuals with obesity, adjusting for these biomarkers attenuated the higher COVID-19 mortality risk in lowly versus highly active individuals with obesity by 10%.

CONCLUSIONS

This study provides novel evidence suggesting that a high physical activity level may attenuate the COVID-19 mortality risk associated with obesity. Although the protective association may be partly explained by lower neutrophil and monocyte counts, it still remains largely unexplained by the biomarkers included in this analysis.

Remote history of VTE is associated with severe COVID-19 in middle and older age: UK Biobank cohort study

BACKGROUND

Venous thromboembolism (VTE) is a common, life-threatening complication of COVID-19 infection. COVID-19 risk-prediction models include a history of VTE. However, it is unclear whether remote history (>9 years previously) of VTE also confers increased risk of COVID-19.

OBJECTIVES

To investigate possible association between VTE and COVID-19 severity, independent of other risk factors.

METHODS

Cohort study of UK Biobank participants recruited between 2006 and 2010. Baseline data, including history of VTE, were linked to COVID-19 test results, COVID-19-related hospital admissions, and COVID-19 deaths. The risk of COVID-19 hospitalization or death was compared for participants with a remote history VTE versus without. Poisson regression models were run univariately then adjusted stepwise for sociodemographic, lifestyle, and comorbid covariates.

RESULTS

After adjustment for sociodemographic and lifestyle confounders and comorbid conditions, remote history of VTE was associated with nonfatal community (RR 1.61, 95% CI 1.02-2.54, p = .039), nonfatal hospitalized (RR 1.52, 95% CI 1.06-2.17, p = .024) and severe (hospitalized or fatal) (RR 1.40, 95% CI 1.04-1.89, p = .025) COVID-19. Associations with remote history of VTE were stronger among men (severe COVID-19: RR 1.68, 95% CI 1.14-2.42, p = .009) than for women (severe COVID-19: RR 1.07, 95% CI 0.66-1.74, p = .786).

CONCLUSION

Our findings support inclusion of remote history of VTE in COVID-19 risk-prediction scores, and consideration of sex-specific risk scores.

Neurological diseases and COVID-19: prospective analyses using the UK Biobank

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.

Shift work is associated with increased risk of COVID-19: Findings from the UK Biobank cohort

Despite the strong evidence on circadian rhythm disruption in shift workers and consequent increased vulnerability for infection, longitudinal association between shift work and COVID-19 infection is unexplored. In this study, data from UK Biobank participants who were tested for COVID-19 infection (16 March to 7 September 2020) were used to explore the link between shift work and COVID-19 infection. Using the baseline occupational information, participants were categorised as non-shift workers, day shift workers, mixed shift workers and night shift workers. Multivariable regression models were used to assess the association between shift work and COVID-19 infection. Among the 18,221 participants (9.4% positive cases), 11.2% were health workers, and 16.4% were involved in shift-work-based jobs. Ethnic minorities (18%) and people in night-shift-based jobs (18.1%) had a significantly higher prevalence of COVID-19 infection than others. Adjusted logistics regression model suggest that, compared with their counterparts, people employed in a night-shift-based job were 1.85-fold (95% CI: 1.42-2.41) more likely to have COVID-19 infection. Sensitivity analysis focusing on people working in a non-healthcare setting suggests that people in shift-work-based jobs had 1.81-fold (95% CI: 1.04%-3.18%) higher odds of COVID-19 infection than their counterparts. Shift workers, particularly night shift workers, irrespective of their occupational group, seem to be at high risk of COVID-19 infection. If similar results are obtained from other studies, then it would mandate to revisit the criteria for defining high-risk groups for COVID-19 and implementing appropriate interventions to protect people in shift-based jobs.

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K

BACKGROUND

Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection.

METHODS

We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25-hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein.

RESULTS

Among 360,283 participants, the median age was 68 [range 48-85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]).

CONCLUSION

The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

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.

Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

Background

Age 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.

Methods

Using 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.

Results

Six 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 (OR_Mortality = 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 (OR_M = 1.50, 95% CI: 1.30–1.73 per 5 years, p = 3.1 × 10⁻⁸) and at the early pandemic (OR_M = 1.21, 95% CI: 1.04–1.40 per 5 years, p = .011) decreased the association.

Conclusions

PhenoAge 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.