Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk :  A 2-Sample Mendelian Randomization Study

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Key Points

Question

What are the mediators linking cardiometabolic risk factors and severe COVID-19?

Findings

In this genetic association study that used mendelian randomization to analyze 235 biomarkers in samples from 22 101 individuals, the biomarker kidney injury molecule-1 (KIM-1) was associated with reduced COVID-19 hospitalization and KIM-1 circulating levels were upregulated with increased body mass index (BMI).

Meaning

These findings suggest that KIM-1 may attenuate the association of BMI with COVID-19 severity.

Abstract

This genetic association study uses a 2-sample mendelian randomization approach to investigate associations among cardiometabolic risk factors, circulating biomarkers, and COVID-19 hospitalization.

Abstract

Importance

Cardiometabolic parameters are established risk factors for COVID-19 severity. The identification of causal or protective biomarkers for COVID-19 severity may facilitate the development of novel therapies.

Objective

To identify protein biomarkers that promote or reduce COVID-19 severity and that mediate the association of cardiometabolic risk factors with COVID-19 severity.

Design, Setting, and Participants

This genetic association study using 2-sample mendelian randomization (MR) was conducted in 2022 to investigate associations among cardiometabolic risk factors, circulating biomarkers, and COVID-19 hospitalization. Inputs for MR included genetic and proteomic data from 4147 participants with dysglycemia and cardiovascular risk factors collected through the Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Genome-wide association study summary statistics were obtained from (1) 3 additional independent plasma proteome studies, (2) genetic consortia for selected cardiometabolic risk factors (including body mass index [BMI], type 2 diabetes, type 1 diabetes, and systolic blood pressure; all n >10 000), and (3) the COVID-19 Host Genetics Initiative (n = 5773 hospitalized and 15 497 nonhospitalized case participants with COVID-19). Data analysis was performed in July 2022.

Exposures

Genetically determined concentrations of 235 circulating proteins assayed with a multiplex biomarker panel from the ORIGIN trial for the initial analysis.

Main Outcomes and Measures

Hospitalization status of individuals from the COVID-19 Host Genetics Initiative with a positive COVID-19 test result.

Results

Among 235 biomarkers tested in samples totaling 22 101 individuals, MR analysis showed that higher kidney injury molecule-1 (KIM-1) levels reduced the likelihood of COVID-19 hospitalization (odds ratio [OR] per SD increase in KIM-1 levels, 0.86 [95% CI, 0.79-0.93]). A meta-analysis validated the protective association with no observed directional pleiotropy (OR per SD increase in KIM-1 levels, 0.91 [95% CI, 0.88-0.95]). Of the cardiometabolic risk factors studied, only BMI was associated with KIM-1 levels (0.17 SD increase in biomarker level per 1 kg/m ² [95% CI, 0.08-0.26]) and COVID-19 hospitalization (OR per 1-SD biomarker level, 1.33 [95% CI, 1.18-1.50]). Multivariable MR analysis also revealed that KIM-1 partially mitigated the association of BMI with COVID-19 hospitalization, reducing it by 10 percentage points (OR adjusted for KIM-1 level per 1 kg/m ², 1.23 [95% CI, 1.06-1.43]).

Conclusions and Relevance

In this genetic association study, KIM-1 was identified as a potential mitigator of COVID-19 severity, possibly attenuating the increased risk of COVID-19 hospitalization among individuals with high BMI. Further studies are required to better understand the underlying biological mechanisms.

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Most cited references 41

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Is Open Access

Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

Neil M. Davies, Michael V. Holmes, George Davey Smith (2019)

Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence

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Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

Loic Yengo, Julia Sidorenko, Kathryn E Kemper … (2018)

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

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MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

Olena Yavorska, Stephen Burgess (2017)

Abstract MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype—phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3).

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Author and article information

Journal

Journal ID (nlm-ta): JAMA Netw Open

Journal ID (iso-abbrev): JAMA Netw Open

Title: JAMA Network Open

Publisher: American Medical Association

ISSN (Electronic): 2574-3805

Publication date (Electronic): 27 July 2023

Publication date Collection: July 2023

Publication date PMC-release: 27 July 2023

Volume: 6

Issue: 7

Electronic Location Identifier: e2325914

Affiliations

[1 ]Population Health Research Institute, Hamilton, Ontario, Canada

[2 ]Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

[3 ]Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada

[4 ]Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada

[5 ]Deep Genomics Inc, Toronto, Ontario, Canada

[6 ]Department of Medicine, McMaster University, Hamilton, Ontario, Canada

[7 ]Global Medical Diabetes, Sanofi, Frankfurt, Germany

Author notes

Article Information

Accepted for Publication: June 15, 2023.

Published: July 27, 2023. doi:10.1001/jamanetworkopen.2023.25914

Corresponding Author: Marie Pigeyre, MD, Genetic and Molecular Epidemiology Laboratory, Endocrinology Division, Population Health Research Institute, McMaster University, 237 Barton St E, Hamilton, ON L8L 2X2, Canada ( pigeyrem@ 123456mcmaster.ca ).

Author Contributions: Dr Pigeyre had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sood, Perrot, Paré, Pigeyre.

Acquisition, analysis, or interpretation of data: Sood, Perrot, Chong, Mohammadi-Shemirani, Mushtaha, Leong, Rangarajan, Hess, Yusuf, Gerstein, Paré.

Drafting of the manuscript: Sood, Pigeyre.

Critical review of the manuscript for important intellectual content: Sood, Perrot, Chong, Mohammadi-Shemirani, Mushtaha, Leong, Rangarajan, Hess, Yusuf, Gerstein, Paré.

Statistical analysis: Sood, Perrot, Chong, Mohammadi-Shemirani, Pigeyre.

Obtained funding: Leong, Yusuf, Gerstein, Paré.

Administrative, technical, or material support: Chong, Mushtaha, Rangarajan, Hess, Yusuf.

Supervision: Yusuf, Paré, Pigeyre.

Conflict of Interest Disclosures: Dr Mohammadi-Shemirani reported being employed by Deep Genomics Inc during the conduct of the study. Dr Hess reported being employed by and holding shares in Sanofi during the conduct of the study. Dr Gerstein reported receiving grants from Sanofi for the ORIGIN trial during the conduct of the study. Dr Gerstein also reported receiving grants from Sanofi, Novo Nordisk, and Eli Lilly; personal fees from Sanofi and Novo Nordisk for advisory board service; speaker honoraria from Eli Lilly, AstraZeneca, DKSH, Zuellig, and Jiangsu Hansoh Pharmaceutical; and consulting fees from Kowa, Hanmi, and Boehringer Ingelheim outside the submitted work. Dr Paré reported receiving grants from Bayer during the conduct of the study. Dr Paré also reported receiving personal fees from Bayer, Amgen, Novartis, and Illumina outside the submitted work. No other disclosures were reported.

Funding/Support: The ORIGIN trial and biomarker project were supported by Sanofi and the Canadian Institutes of Health Research (CIHR). The biomarker project was led by ORIGIN investigators at the Population Health Research Institute in Hamilton, Ontario, Canada, with the active collaboration of Sanofi scientists. Sanofi directly compensated Myriad RBM Inc for measurement of the biomarker panel and the Population Health Research Institute for scientific, methodologic, and statistical work. Genetic analysis of ORIGIN participants was supported by award 125794 from the CIHR (Dr Paré). The PURE biomarker project was supported by Bayer and the CIHR. The biomarker project was led by PURE investigators at the Population Health Research Institute in Hamilton, Ontario, Canada, in collaboration with Bayer scientists. Bayer directly compensated the Population Health Research Institute for measurement of the biomarker panels and scientific, methodologic, and statistical work. Genetic analyses were supported by grant G-18–0022359 from the CIHR and application number 399497 from the Heart and Stroke Foundation of Canada (Dr Paré). Mr Sood was supported by an Natural Sciences and Engineering Research Council of Canada scholarship award. Dr Paré is supported by the Canada Research Chair in Genetic and Molecular Epidemiology and the CISCO Professorship in Integrated Health Biosystems. Dr Pigeyre is supported by the E.J. Moran Campbell Internal Career Research Award from McMaster University and an Early Career Award from Hamilton Health Sciences.

Role of the Funder/Sponsor: The funders of the study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication. An employee of one of the funders (Dr Hess) suggested KIM-1 to be measured in the biomarker substudy of the ORIGIN trial, and contributed to the interpretation of the data and the review of the manuscript.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank all of the participants for contributing to this project.

Article

Publisher ID: zoi230746

DOI: 10.1001/jamanetworkopen.2023.25914

PMC ID: 10375306

PubMed ID: 37498601

SO-VID: 08ee958c-ac9f-429c-aa16-a63c3119d2ab

License:

This is an open access article distributed under the terms of the CC-BY License.

Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk : A 2-Sample Mendelian Randomization Study

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Key Points

Question

Findings

Meaning

Abstract

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 41

Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

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