Engagement with mHealth COVID-19 digital biomarker measurements in a longitudinal cohort study: a mixed methods evaluation.

BACKGROUND: The COVID-19 pandemic accelerated the interest in implementing mHealth in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for over 6-months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of UK adults. OBJECTIVE: To investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or as a result of COVID-19 factors. To explore participants' reasons for consenting to the smartphone sub-study and experiences related to initial and continued engagement. METHODS: Participants in the Fenland COVID-19 study were invited to the app sub-study from August-October 2020 until study closure (30 April 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, resting heart rate (RHR)) and possible COVID-19 symptoms in the app three times/week. Participants manually entered measurements, except RHR measured using the smartphone camera. Engagement was categorised from median weekly frequency of completing the three digital biomarker modules (categories; 0, 1-2 and 3 or more/times per week). Socio-demographic and health characteristics of those who did or did not consent to the sub-study, and by engagement category were explored. Semi-structured interviews were conducted with 35 participants and data analysed thematically; 22 who consented to the app sub-study and 13 who did not consent, purposively sampled by sex, age, educational attainment and by engagement category. RESULTS: A total of 2,524 (63%) of Fenland COVID-19 study participants consented to the app sub-study. Of those, 90.2% completed the app onboarding process. Median time in the app sub-study was 34.5 weeks (IQR 34, 37) with no change in engagement from 0-3 months or 3-6 months. Completion rates (1/week) across the study between digital biomarkers were similar (RHR 72.8%, temperature 73.1%, oxygen saturation 73.5%). Older age groups, lower managerial and intermediate occupations were associated with higher engagement whilst working, being a current smoker, overweight or obese and high perceived stress were associated with lower engagement. Reasons for consenting included altruism, previous positive participation experiences and interest in health research, and non-consent was due to confusion regarding study invitation and perceived phone incompatibility. Continued engagement was facilitated by routine and personal motivation, poor engagement was caused by user error and app/equipment malfunctions preventing data input. From these results we developed key recommendations to improve engagement in population-based mHealth studies. CONCLUSIONS: This mixed-method study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over at least 6-month period in UK adults. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement, to inform future applications in population-based health research.

ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19.

Despite two years of intense global research activity, host genetic factors that predispose to a poorer prognosis of COVID-19 infection remain poorly understood. Here, we prioritise eight robust (e.g., ELF5) or suggestive but unreported (e.g., RAB2A) candidate protein mediators of COVID-19 outcomes by integrating results from the COVID-19 Host Genetics Initiative with population-based plasma proteomics using statistical colocalisation. The transcription factor ELF5 (ELF5) shows robust and directionally consistent associations across different outcome definitions, including a >4-fold higher risk (odds ratio: 4.88; 95%-CI: 2.47-9.63; p-value < 5.0 × 10-6) for severe COVID-19 per 1 s.d. higher genetically predicted plasma ELF5. We show that ELF5 is specifically expressed in epithelial cells of the respiratory system, such as secretory and alveolar type 2 cells, using single-cell RNA sequencing and immunohistochemistry. These cells are also likely targets of SARS-CoV-2 by colocalisation with key host factors, including ACE2 and TMPRSS2. In summary, large-scale human genetic studies together with gene expression at single-cell resolution highlight ELF5 as a risk gene for severe COVID-19, supporting a role of epithelial cells of the respiratory system in the adverse host response to SARS-CoV-2.

Associations between reductions in routine care delivery and non-COVID-19-related mortality in people with diabetes in England during the COVID-19 pandemic: a population-based parallel cohort study.

BACKGROUND: The Office for Health Improvement and Disparities, part of the UK Government Department of Health and Social Care, highlighted an emerging signal of increased non-COVID-19-related deaths in England between July and October, 2021, with a potentially disproportionate higher increase in people with diabetes. We aimed to substantiate and quantify this apparent excess mortality, and to investigate the association between diabetes routine care delivery and non-COVID-19-related-mortality in people with diabetes before and after the onset of the pandemic. METHODS: In this population-based parallel cohort study, we used the National Diabetes Audit (NDA) to identify people with diabetes in England. The primary outcome was non-COVID-19-related deaths between July 3, 2021, and Oct 15, 2021, in participants in the 2021 COVID-19 cohort (registered in the NDA in the periods Jan 1, 2019, to March 31, 2020, and Jan 1, 2020, to March 31, 2021) compared with deaths between June 29, 2019, and Oct 11, 2019 (the equivalent 15-week period in 2019) in the 2019 pre-COVID-19 comparator cohort (people registered in the NDA in the periods Jan 1, 2017, to March 31, 2018, and Jan 1, 2018 to March 31, 2019). In each cohort, multivariable logistic regression examined whether completion of eight diabetes care processes in each of the two years before the index mortality year was associated with non-COVID-19-related death, adjusting for diabetes type, age, sex, ethnicity, and socioeconomic deprivation. FINDINGS: There were 3 218 570 people in the 2021 cohort and 2 973 645 people in the 2019 comparator cohort. In the 2021 cohort, there were 30 118 non-COVID-19-related deaths in people with diabetes, compared with 27 132 in the comparator cohort, representing an 11% increase (95% CI 9-13). The unadjusted incidence rate ratio (IRR) for mortality in the 2021 cohort compared to the 2019 cohort was 1·026 (1·009-1·043; p=0·003), which was unchanged after adjustment for age, sex, ethnicity, socioeconomic deprivation, and diabetes type (IRR 1·023 (1·006-1·040); p=0·007). In the 2021 cohort, 853 660 (26·5%) people received all eight care processes in 2020-21 compared with 1 547 240 (48·1%) people in 2019-20; a 44·8% (95% CI 44·7-45·0) relative reduction. In the pre-COVID-19 comparator cohort, 1 370 315 (46·1%) people with diabetes received all eight care processes in 2018-19 compared with 1 437 740 (48·3%) in 2017-18; a 4·7% (95% CI 4·5-4·9) relative decrease. Non-COVID-19-related mortality in the 2021 cohort was highest in people who did not receive all eight care processes in either of the two previous years (OR 2·67 [95% CI 2·56-2·77]; p<0·001) compared with those who received all eight care processes in both previous years. Mortality was also significantly higher in those who received all eight care processes in 2019-20 but not in 2020-21 (OR 1·66 [95% CI 1·59-1·73]; p<0·001) or not in 2019-20 but in 2020-21 (OR 1·27 [1·20-1·35]; p<0·001). This pattern of association was similar in the 2019 pre-COVID-19 cohort. INTERPRETATION: Our results show an increased risk of mortality in those who did not receive all eight care processes in one or both of the previous two years. Our results provide evidence that the increased rate of non-COVID-19-related mortality in people with diabetes in England observed between July 3, and Oct 15 of 2021 is associated with a reduction in completion of routine diabetes care processes following the pandemic onset in 2020. FUNDING: None.

The development, validation and application of remote blood sample collection in telehealth programmes.

INTRODUCTION: The ability to collect blood samples remotely without the involvement of healthcare professionals is a key element of future telehealth applications. We developed and validated the application of the Drawbridge OneDraw device for use at home for blood sample collection. The device was then applied in a large population-based remote monitoring study to assess changes in SARS-CoV-2 IgG antibody levels. METHODS: We tested: (1) feasibility of participants using the device at home without a healthcare professional on the upper arm and thigh sites (2) stability of the dried blood sample collected remotely (3) participant acceptability of the device compared with finger-prick and venous blood samples and the validity of SARS-CoV-2 virus antibody measurement versus venous blood sample (4) application to the Fenland COVID-19 study in which 4023 participants at 3 timepoints across 6 months. RESULTS: Participant acceptability was high, with a significantly lower median perceived pain score and 76% of participants preferring the OneDraw device over the other blood collection methods. There was high level of agreement in SARS-CoV-2 virus antibody results with venous blood samples in 120 participants (Cohen's kappa 0.68 (95% CI 0.56, 0.83). In the Fenland COVID-19 study, 92% of participants returned a sample at baseline (3702/4023), 89% at 3 months (3492/3918) and 93% at 6 months (3453/3731), with almost all samples received successfully processed (99.9%). DISCUSSION: The OneDraw device enables a standardised blood sample collection at home by participants themselves. Due to its ease-of-use and acceptability the OneDraw device is particularly useful in telehealth approaches where multiple samples need to be collected.

Prescription of glucose-lowering therapies and risk of COVID-19 mortality in people with type 2 diabetes: a nationwide observational study in England.

BACKGROUND: In patients with type 2 diabetes, hyperglycaemia is an independent risk factor for COVID-19-related mortality. Associations between pre-infection prescription for glucose-lowering drugs and COVID-19-related mortality in people with type 2 diabetes have been postulated but only investigated in small studies and limited to a few agents. We investigated whether there are associations between prescription of different classes of glucose-lowering drugs and risk of COVID-19-related mortality in people with type 2 diabetes. METHODS: This was a nationwide observational cohort study done with data from the National Diabetes Audit for people with type 2 diabetes and registered with a general practice in England since 2003. Cox regression was used to estimate the hazard ratio (HR) of COVID-19-related mortality in people prescribed each class of glucose-lowering drug, with covariate adjustment with a propensity score to address confounding by demographic, socioeconomic, and clinical factors. FINDINGS: Among the 2 851 465 people with type 2 diabetes included in our analyses, 13 479 (0·5%) COVID-19-related deaths occurred during the study period (Feb 16 to Aug 31, 2020), corresponding to a rate of 8·9 per 1000 person-years (95% CI 8·7-9·0). The adjusted HR associated with recorded versus no recorded prescription was 0·77 (95% CI 0·73-0·81) for metformin and 1·42 (1·35-1·49) for insulin. Adjusted HRs for prescription of other individual classes of glucose-lowering treatment were as follows: 0·75 (0·48-1·17) for meglitinides, 0·82 (0·74-0·91) for SGLT2 inhibitors, 0·94 (0·82-1·07) for thiazolidinediones, 0·94 (0·89-0·99) for sulfonylureas, 0·94 (0·83-1·07) for GLP-1 receptor agonists, 1·07 (1·01-1·13) for DPP-4 inhibitors, and 1·26 (0·76-2·09) for α-glucosidase inhibitors. INTERPRETATION: Our results provide evidence of associations between prescription of some glucose-lowering drugs and COVID-19-related mortality, although the differences in risk are small and these findings are likely to be due to confounding by indication, in view of the use of different drug classes at different stages of type 2 diabetes disease progression. In the context of the COVID-19 pandemic, there is no clear indication to change prescribing of glucose-lowering drugs in people with type 2 diabetes. FUNDING: None.

Mapping the proteo-genomic convergence of human diseases.

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.

Genetic architecture of host proteins interacting with SARS-CoV-2

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Genetic architecture of host proteins involved in SARS-CoV-2 infection.

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Implications of COVID-19 control measures for diet and physical activity, and lessons for addressing other pandemics facing rapidly urbanising countries.

At the time of writing, it is unclear how the COVID-19 pandemic will play out in rapidly urbanising regions of the world. In these regions, the realities of large overcrowded informal settlements, a high burden of infectious and non-communicable diseases, as well as malnutrition and precarity of livelihoods, have raised added concerns about the potential impact of the COVID-19 pandemic in these contexts. COVID-19 infection control measures have been shown to have some effects in slowing down the progress of the pandemic, effectively buying time to prepare the healthcare system. However, there has been less of a focus on the indirect impacts of these measures on health behaviours and the consequent health risks, particularly in the most vulnerable. In this current debate piece, focusing on two of the four risk factors that contribute to >80% of the NCD burden, we consider the possible ways that the restrictions put in place to control the pandemic, have the potential to impact on dietary and physical activity behaviours and their determinants. By considering mitigation responses implemented by governments in several LMIC cities, we identify key lessons that highlight the potential of economic, political, food and built environment sectors, mobilised during the pandemic, to retain health as a priority beyond the context of pandemic response. Such whole-of society approaches are feasible and necessary to support equitable healthy eating and active living required to address other epidemics and to lower the baseline need for healthcare in the long term.

Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study.

BACKGROUND: Diabetes has been associated with increased COVID-19-related mortality, but the association between modifiable risk factors, including hyperglycaemia and obesity, and COVID-19-related mortality among people with diabetes is unclear. We assessed associations between risk factors and COVID-19-related mortality in people with type 1 and type 2 diabetes. METHODS: We did a population-based cohort study of people with diagnosed diabetes who were registered with a general practice in England. National population data on people with type 1 and type 2 diabetes collated by the National Diabetes Audit were linked to mortality records collated by the Office for National Statistics from Jan 2, 2017, to May 11, 2020. We identified the weekly number of deaths in people with type 1 and type 2 diabetes during the first 19 weeks of 2020 and calculated the percentage change from the mean number of deaths for the corresponding weeks in 2017, 2018, and 2019. The associations between risk factors (including sex, age, ethnicity, socioeconomic deprivation, HbA1c, renal impairment [from estimated glomerular filtration rate (eGFR)], BMI, tobacco smoking status, and cardiovascular comorbidities) and COVID-19-related mortality (defined as International Classification of Diseases, version 10, code U07.1 or U07.2 as a primary or secondary cause of death) between Feb 16 and May 11, 2020, were investigated by use of Cox proportional hazards models. FINDINGS: Weekly death registrations in the first 19 weeks of 2020 exceeded the corresponding 3-year weekly averages for 2017-19 by 672 (50·9%) in people with type 1 diabetes and 16 071 (64·3%) in people with type 2 diabetes. Between Feb 16 and May 11, 2020, among 264 390 people with type 1 diabetes and 2 874 020 people with type 2 diabetes, 1604 people with type 1 diabetes and 36 291 people with type 2 diabetes died from all causes. Of these total deaths, 464 in people with type 1 diabetes and 10 525 in people with type 2 diabetes were defined as COVID-19 related, of which 289 (62·3%) and 5833 (55·4%), respectively, occurred in people with a history of cardiovascular disease or with renal impairment (eGFR <60 mL/min per 1·73 m2). Male sex, older age, renal impairment, non-white ethnicity, socioeconomic deprivation, and previous stroke and heart failure were associated with increased COVID-19-related mortality in both type 1 and type 2 diabetes. Compared with people with an HbA1c of 48-53 mmol/mol (6·5-7·0%), people with an HbA1c of 86 mmol/mol (10·0%) or higher had increased COVID-19-related mortality (hazard ratio [HR] 2·23 [95% CI 1·50-3·30, p<0·0001] in type 1 diabetes and 1·61 [1·47-1·77, p<0·0001] in type 2 diabetes). In addition, in people with type 2 diabetes, COVID-19-related mortality was significantly higher in those with an HbA1c of 59 mmol/mol (7·6%) or higher than in those with an HbA1c of 48-53 mmol/mol (HR 1·22 [95% CI 1·15-1·30, p<0·0001] for 59-74 mmol/mol [7·6-8·9%] and 1·36 [1·24-1·50, p<0·0001] for 75-85 mmol/mol [9·0-9·9%]). The association between BMI and COVID-19-related mortality was U-shaped: in type 1 diabetes, compared with a BMI of 25·0-29·9 kg/m2, a BMI of less than 20·0 kg/m2 had an HR of 2·45 (95% CI 1·60-3·75, p<0·0001) and a BMI of 40·0 kg/m2 or higher had an HR of 2·33 (1·53-3·56, p<0·0001); the corresponding HRs for type 2 diabetes were 2·33 (2·11-2·56, p<0·0001) and 1·60 (1·47-1·75, p<0·0001). INTERPRETATION: Deaths in people with type 1 and type 2 diabetes rose sharply during the initial COVID-19 pandemic in England. Increased COVID-19-related mortality was associated not only with cardiovascular and renal complications of diabetes but, independently, also with glycaemic control and BMI. FUNDING: None.

Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study.

BACKGROUND: Although diabetes has been associated with COVID-19-related mortality, the absolute and relative risks for type 1 and type 2 diabetes are unknown. We assessed the independent effects of diabetes status, by type, on in-hospital death in England in patients with COVID-19 during the period from March 1 to May 11, 2020. METHODS: We did a whole-population study assessing risks of in-hospital death with COVID-19 between March 1 and May 11, 2020. We included all individuals registered with a general practice in England who were alive on Feb 16, 2020. We used multivariable logistic regression to examine the effect of diabetes status, by type, on in-hospital death with COVID-19, adjusting for demographic factors and cardiovascular comorbidities. Because of the absence of data on total numbers of people infected with COVID-19 during the observation period, we calculated mortality rates for the population as a whole, rather than the population who were infected. FINDINGS: Of the 61 414 470 individuals who were alive and registered with a general practice on Feb 16, 2020, 263 830 (0·4%) had a recorded diagnosis of type 1 diabetes, 2 864 670 (4·7%) had a diagnosis of type 2 diabetes, 41 750 (0·1%) had other types of diabetes, and 58 244 220 (94·8%) had no diabetes. 23 698 in-hospital COVID-19-related deaths occurred during the study period. A third occurred in people with diabetes: 7434 (31·4%) in people with type 2 diabetes, 364 (1·5%) in those with type 1 diabetes, and 69 (0·3%) in people with other types of diabetes. Unadjusted mortality rates per 100 000 people over the 72-day period were 27 (95% CI 27-28) for those without diabetes, 138 (124-153) for those with type 1 diabetes, and 260 (254-265) for those with type 2 diabetes. Adjusted for age, sex, deprivation, ethnicity, and geographical region, compared with people without diabetes, the odds ratios (ORs) for in-hospital COVID-19-related death were 3·51 (95% CI 3·16-3·90) in people with type 1 diabetes and 2·03 (1·97-2·09) in people with type 2 diabetes. These effects were attenuated to ORs of 2·86 (2·58-3·18) for type 1 diabetes and 1·80 (1·75-1·86) for type 2 diabetes when also adjusted for previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure. INTERPRETATION: The results of this nationwide analysis in England show that type 1 and type 2 diabetes were both independently associated with a significant increased odds of in-hospital death with COVID-19. FUNDING: None.

Genetic architecture of host proteins interacting with SARS-CoV-2.

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).