Prediction of coronary disease incidence by biomarkers of inflammation, oxidation, and metabolism

Subirana Cachinero, Isaac; Fitó Colomer, Montserrat; Díaz Gil, Oscar; Vila, Joan; Francés, Albert; Delpon, Eva; Sanchís, Juan; Elosua Llanos, Roberto; Muñoz-Aguayo, D.; Dégano, Irene R.; Marrugat de la Iglesia, Jaume

doi:http://dx.doi.org/0.1038/s41598-018-21482-y

Prediction of coronary disease incidence by biomarkers of inflammation, oxidation, and metabolism

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Subirana I, Fitó M, Diaz O, Vila J, Francés A, Delpon E. et al. Prediction of coronary disease incidence by biomarkers of inflammation, oxidation, and metabolism. Sci Rep. 2018 Feb 16;8(1):3191. DOI: 10.1038/s41598-018-21482-y

Abstract

The effect of circulating biomarkers in predicting coronary artery disease (CAD) is not fully elucidated. This study aimed to determine the relationship with CAD and the predictive capacity of nine biomarkers of inflammation (TNF-α, IL-10, IL-6, MCP-1, CRP), oxidation (GHS-Px), and metabolism (adiponectin, leptin, and insulin). This was a case-cohort study, within the REGICOR population-cohorts (North-Eastern Spain), of 105 CAD cases and 638 individuals randomly selected from a cohort of 5,404 participants aged 35-74 years (mean follow-up = 6.1 years). Biomarkers' hazard ratio (HR)/standard deviation was estimated with Cox models adjusted for age, sex, and classical risk factors. Discrimination improvement and reclassification were analyzed with the c-index and the Net reclassification index (NRI). GHS-Px (adjusted HRs = 0.77; 95%CI:0.60-0.99), insulin (1.46; 1.08-1.98), leptin (1.40; 1.03-1.90), IL-6 (1.34; 1.03-1.74), and TNF-α (1.80; 1.26-2.57) were significantly associated with CAD incidence. In the model adjusted for all biomarkers, TNF-α (1.87;1.31-2.66) and insulin (1.59;1.16-2.19) were independently associated with CAD. This final model, compared to a model without biomarkers, showed a c-index difference of 1.3% (-0.7, 3.2) and a continuous NRI of 33.7% (2.6, 61.9). TNF-α and insulin are independently associated with CAD incidence and they improve reclassification when added to a model including classical risk factors.