Exploring Drugs and Vaccines Associated with Altered Risks and Severity of COVID-19: A UK Biobank Cohort Study of All ATC Level-4 Drug Categories Reveals Repositioning Opportunities
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Abstract
Effective therapies for COVID-19 are still lacking, and drug repositioning is a promising approach to address this problem. Here, we adopted a medical informatics approach to repositioning. We leveraged a large prospective cohort, the UK-Biobank (UKBB, N ~ 397,000), and studied associations of prior use of all level-4 ATC drug categories (N = 819, including vaccines) with COVID-19 diagnosis and severity. Effects of drugs on the risk of infection, disease severity, and mortality were investigated separately. Logistic regression was conducted, controlling for main confounders. We observed strong and highly consistent protective associations with statins. Many top-listed protective drugs were also cardiovascular medications, such as angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), calcium channel blocker (CCB), and beta-blockers. Some other drugs showing protective associations included biguanides (metformin), estrogens, thyroid hormones, proton pump inhibitors, and testosterone-5-alpha reductase inhibitors, among others. We also observed protective associations by influenza, pneumococcal, and several other vaccines. Subgroup and interaction analyses were also conducted, which revealed differences in protective effects in various subgroups. For example, protective effects of flu/pneumococcal vaccines were weaker in obese individuals, while protection by statins was stronger in cardiovascular patients. To conclude, our analysis revealed many drug repositioning candidates, for example several cardiovascular medications. Further studies are required for validation.
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SciScore for 10.1101/2020.12.05.20244426: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We have performed analysis on infected subjects (models A and B), the whole population (models C, D, E) and the tested population (models F, G, H) to obtain a more comprehensive picture of drug effects under different settings, and to avoid limitations (e.g. selection/collider bias) of some designs. We note that sometimes the different …
SciScore for 10.1101/2020.12.05.20244426: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We have performed analysis on infected subjects (models A and B), the whole population (models C, D, E) and the tested population (models F, G, H) to obtain a more comprehensive picture of drug effects under different settings, and to avoid limitations (e.g. selection/collider bias) of some designs. We note that sometimes the different models may yield different results. One main observation is that analysis on the tested population appears to result in more findings of drugs with protective effects. We also observed that some drugs in model F (infected vs tested negative) may show different effects under model E (infected vs general population). Several reasons may explain this finding. First and foremost, confounding by indication is inevitable and may play a more important role when analyzing general population samples. It is possible that apparent harmful effects of drugs are due to the diseases/conditions that the prescription is related to, or to poorer health in general. Based on an machine learning model to predict testing probability (see Figure S1), we observed that people who are older, having more comorbidities and taking more medications, suffering from cardiovascular conditions etc. were more likely to be tested. Compared to the general population, the tested group may represent a more ‘homogeneous’ population, enriched for people with poorer health and more comorbidities in general. Therefore a proportion of confounders, which overlap with factors associated wi...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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