ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study

A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach

In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.

Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes

BACKGROUND

Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes.

METHODS

Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets.

RESULTS

In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases.

CONCLUSIONS

Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

Unravelling the Mechanistic Role of ACE2 and TMPRSS2 in Hypertension: A Risk Factor for COVID-19

BACKGROUND

This review explores the mechanistic action of angiotensin-converting enzyme- 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) in the renin-angiotensinaldosterone system (RAAS) that predisposes hypertensive patients to the adverse outcome of severe COVID-19.

METHODS AND RESULTS

Entry of SARS-CoV-2 into the host cell via ACE2 disrupts the RAAS system, creating an imbalance between ACE and ACE2, with an increased inflammatory response, leading to hypertension (HTN), pulmonary vasoconstriction and acute respiratory distress. SARSCoV- 2 may also predispose infected individuals with existing HTN to a greater risk of severe COVID-19 complications. In the duality of COVID-19 and HTN, the imbalance of ACE and ACE2 results in an elevation of AngII and a decrease in Ang (1-7), a hyperinflammatory response and endothelial dysfunction. Endothelial dysfunction is the main factor predisposing hypertensive patients to severe COVID-19 and vice-versa.

CONCLUSION

Despite the increase in ACE2 expression in hypertensive SARS-CoV-2 infected patients, ARBs/ACE inhibitors do not influence their severity and clinical outcomes, implicating continued usage. Future large-scale clinical trials are warranted to further elucidate the association between HTN and SARS-CoV-2 infection and the use of ARBs/ACEIs in SARS-CoV-2 hypertensive patients.

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.

Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis

BACKGROUND

Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension.

METHODS

In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296.

FINDINGS

Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons.

INTERPRETATION

No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19.

FUNDING

Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

Plasma ACE2 and risk of death or cardiometabolic diseases: a case-cohort analysis

BACKGROUND

Angiotensin-converting enzyme 2 (ACE2) is an endogenous counter-regulator of the renin-angiotensin hormonal cascade. We assessed whether plasma ACE2 concentrations were associated with greater risk of death or cardiovascular disease events.

METHODS

We used data from the Prospective Urban Rural Epidemiology (PURE) prospective study to conduct a case-cohort analysis within a subset of PURE participants (from 14 countries across five continents: Africa, Asia, Europe, North America, and South America). We measured plasma concentrations of ACE2 and assessed potential determinants of plasma ACE2 levels as well as the association of ACE2 with cardiovascular events.

FINDINGS

We included 10 753 PURE participants in our study. Increased concentration of plasma ACE2 was associated with increased risk of total deaths (hazard ratio [HR] 1·35 per 1 SD increase [95% CI 1·29-1·43]) with similar increases in cardiovascular and non-cardiovascular deaths. Plasma ACE2 concentration was also associated with higher risk of incident heart failure (HR 1·27 per 1 SD increase [1·10-1·46]), myocardial infarction (HR 1·23 per 1 SD increase [1·13-1·33]), stroke (HR 1·21 per 1 SD increase [1·10-1·32]) and diabetes (HR 1·44 per 1 SD increase [1·36-1·52]). These findings were independent of age, sex, ancestry, and traditional cardiac risk factors. With the exception of incident heart failure events, the independent relationship of ACE2 with the clinical endpoints, including death, remained robust after adjustment for BNP. The highest-ranked determinants of ACE2 concentrations were sex, geographic ancestry, and body-mass index (BMI). When compared with clinical risk factors (smoking, diabetes, blood pressure, lipids, and BMI), ACE2 was the highest ranked predictor of death, and superseded several risk factors as a predictor of heart failure, stroke, and myocardial infarction.

INTERPRETATION

Increased plasma ACE2 concentration was associated with increased risk of major cardiovascular events in a global study.

FUNDING

Canadian Institute of Health Research, Heart & Stroke Foundation of Canada, and Bayer.