Evidence mapPaperPMID 42440199Full record

ArticleDrugs - real world outcomes2026

Pre-existing Comorbidities as Potential Risk Modifiers for New-Onset Myocarditis and Pericarditis following mRNA COVID-19 Vaccination in Males Aged 18-30 in the United States: A Disproportionality Analysis using VAERS Spontaneous Reporting Data.

Mohammed Salah Salem, Yola Moride, Bernard Bégaud

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Article in Drugs - real world outcomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Mohammed Salah SalemRutgers New Jersey Medical School, Newark, New Jersey, USA. ms2376@scarletmail.rutgers.edu.ORCID http://orcid.org/0000-0001-8431-3909
Yola MorideCenter for Pharmacoepidemiology and Treatment Science, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA.ORCID http://orcid.org/0000-0002-8753-2274
Bernard BégaudClinical Pharmacology and Pharmacoepidemiology, Faculty of Medicine, University of Bordeaux, Bordeaux, France.ORCID http://orcid.org/0000-0001-8797-0779

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRare cases of myocarditis and pericarditis following mRNA coronavirus disease-19 (COVID-19) vaccination have been reported, primarily among males under 30 years of age. Although the underlying mechanisms remain unclear, cardiovascular risk factors such as hypertension, diabetes, dyslipidemia, and obesity have been hypothesized as potential contributors. Using the Vaccine Adverse Event Reporting System (VAERS), this study investigated whether these comorbidities were disproportionately reported among young adult males with myocarditis or pericarditis following mRNA COVID-19 vaccination compared with vaccinated and general populations. Additionally, this study represents a novel application of disproportionality principles in spontaneous reporting databases to explore, beyond signal detection, the factors that might be associated with the occurrence of an adverse event.

methodsAn observed-versus-expected analysis was conducted using reports of myocarditis or pericarditis following mRNA COVID-19 vaccination among males aged 18-30 recorded in VAERS between 2021 and 2022. The prevalence of hypertension, diabetes, dyslipidemia, and obesity among reported cases was compared with prevalence estimates derived from epidemiological studies of vaccinated populations and national population statistics. In addition, disproportionality analyses were performed using comparator groups consisting of reports associated with other vaccines and other adverse events following immunization (AEFIs) within VAERS.

resultsA total of 859 eligible reports of myocarditis/pericarditis following mRNA COVID-19 vaccination were identified among males aged 18-30. The prevalence of hypertension, diabetes, dyslipidemia, and obesity was 0.35%, 0.58%, 0.81%, and 1.75%, respectively and was significantly lower than those reported in vaccinated and prepandemic general populations. In disproportionality analyses, myocarditis/pericarditis reports following mRNA vaccination showed higher prevalence of dyslipidemia and obesity, but lower prevalence of hypertension, compared with selected VAERS comparator groups. However, absolute comorbidity prevalence remained low across all groups.

conclusionsNo evidence of a modifying role for hypertension, diabetes, dyslipidemia, or obesity was observed for the increased reporting of myocarditis/pericarditis among young adult males following mRNA COVID-19 vaccination. Despite the limitations inherent to spontaneous reporting systems, this study highlights a novel application of disproportionality-based approaches within VAERS to explore potential risk modifiers of vaccine-related adverse events in a hypothesis-generating manner.

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PMID42440199

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.