| Title: | Plasma MERTK is causally associated with infection mortality |
| Journal: | Journal of Infection |
| Published: | 4 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39241967/ |
| DOI: | https://doi.org/10.1016/j.jinf.2024.106262 |
Publication 13042
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Abstract
BACKGROUND: Infectious diseases are a major cause of mortality in spite of existing public health, anti-microbial and vaccine interventions. We aimed to define plasma proteomic associates of infection mortality and then apply Mendelian randomisation (MR) to yield biomarkers that may be causally associated.</p>
METHODS: We used UK Biobank plasma proteomic data to associate 2923 plasma proteins with infection mortality before 31st December 2019 (240 events in 52,520 participants). Since many plasma proteins also predict non-infection mortality, we focussed on those associated with >1.5-fold risk of infection mortality in an analysis excluding survivors. Protein quantitative trait scores (pQTS) were then used to identify whether genetically predicted protein levels also associated with infection mortality. To conduct Two Sample MR, we performed a genome-wide association study (GWAS) of infection mortality using UK Biobank participants without plasma proteomic data (n = 363,953 including 984 infection deaths).</p>
FINDINGS: After adjusting for clinical risk factors, 1142 plasma proteins were associated with risk of infection mortality (false discovery rate <0.05). 259 proteins were associated with >1.5-fold increased risk of infection versus non-infection mortality. Of these, we identified genetically predicted increasing MERTK concentration was associated with increased risk of infection mortality. MR supported a causal association between increasing plasma MERTK protein and infection mortality (odds ratio 1.46 per unit; 95% CI 1.15- 1.85; p = 0.002).</p>
CONCLUSION: Plasma MERTK is causally associated with infection mortality and warrants exploration as a potential therapeutic target.</p>
16 Keywords
- Adult
- Aged
- Biomarkers
- Blood Proteins
- Communicable Diseases
- Female
- Genome-Wide Association Study
- Humans
- Infections
- Male
- Mendelian Randomization Analysis
- Middle Aged
- Proteomics
- Risk Factors
- United Kingdom
- c-Mer Tyrosine Kinase
12 Authors
- Michael Drozd
- Fergus Hamilton
- Chew W Cheng
- Patrick J Lillie
- Oliver I Brown
- Natalie Chaddock
- Sinisa Savic
- Khalid Naseem
- Mark M Iles
- Ann W Morgan
- Mark T Kearney
- Richard M Cubbon
1 Application
| Application ID | Title |
|---|---|
| 98520 | Understanding the interaction between non-communicable disease, multi-morbidity and infection. |
Enabling scientific discoveries that improve human health