Background:
We used a proteomics array to simultaneously measure 92 proteins that have been suggested to be associated with atherosclerosis and related them to plaque prevalence and echogenicity in carotid arteries in a human population-based study.
Methods:
In the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS; n=931, 50% women, all aged 70 years), the number of carotid arteries with plaques was recorded by ultrasound and plaque echogenicity was determined. Levels of 92 proteins were assessed in plasma by a proximity extension assay (Proseek Multiplex CVD, Olink Bioscience, Uppsala, Sweden) and related to carotid measures in a regression framework.
Results:
Following adjustment for multiple testing, six of the proteins were significantly related to the number of carotid arteries affected by plaques in sex-adjusted models (osteoprotegrin, T-cell immunoglobulin and mucin domain-1, renin, growth hormone, matrix metalloprotease-12 and lecitin-like oxidized LDL receptor 1). When these six proteins were included jointly in a model adjusting for traditional cardiovascular risk factors, renin (odds ratio [OR], 1.30; 95% confidence interval [CI], 1.13-1.50 per standard deviation increase) and growth hormone (OR, 1.26; 95% CI, 1.10-1.45) were related to plaque prevalence independently of each other and known risk factors for atherosclerosis. Thirty-one proteins were related to plaque echogenicity after adjustment for known risk factors. Of these, interleukin-27 subunit alpha (beta, 5.20; P=0.00032), vascular endothelial growth factor-delta (beta, 4.73; P=0.0013) and matrix metalloprotease-1 (beta, 3.93; P=0.0034) were associated with plaque echogenicity independently of each other.
Conclusion:
A novel targeted proteomics approach using the proximity extension technique discovered several new associations of candidate proteins with plaque prevalence and echogenicity in the carotid arteries in a large human sample.
Development of a Pharmaceutical Hepatotoxicity Biomarker Panel Using a Discovery to Targeted Proteomics Approach
