Michelle J Ormseth1,2, Joseph F Solus3, Quanhu Sheng3, Sheau-Chiann Chen3, Fei Ye3, Qiong Wu3, Annette M Oeser3, Ryan Allen3, Paolo Raggi4, Kasey C Vickers3, C Michael Stein3. 1. Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA. michelle.ormseth@vumc.org. 2. Vanderbilt University Medical Center, 1161 21st Avenue South, T-3113 MCN, Nashville, TN, 37232-2681, USA. michelle.ormseth@vumc.org. 3. Vanderbilt University Medical Center, 1161 21st Avenue South, T-3113 MCN, Nashville, TN, 37232-2681, USA. 4. University of Alberta, Edmonton, Canada.
Abstract
OBJECTIVE: MicroRNAs (miRNAs) regulate gene expression and are disease biomarkers. Rheumatoid arthritis (RA) patients have accelerated atherosclerosis leading to excess cardiovascular morbidity and mortality, but traditional risk factors for cardiovascular risk stratification are inadequate. In the general population, miRNAs improve cardiovascular risk estimation beyond traditional risk factors. Our objective was to develop a miRNA panel that predicts coronary atherosclerosis in RA patients. METHODS: Plasma small RNA next-generation sequencing (NGS) was performed on 161 RA patients whose Agatston scores for coronary artery calcium were previously measured. Random forest analysis of plasma NGS miRNA expression was used to determine which miRNAs best differentiated between those patients with and without coronary artery calcium. Top predictive miRNAs were assayed by quantitative PCR (qPCR). Elastic net regression was used to develop the most parsimonious models with qPCR-measured miRNA concentrations and clinical variables (age, sex, ACC/AHA 10-year risk score, DAS28 score, and diabetes) separately to predict the presence of coronary artery calcium and high coronary artery calcium. C-statistics were used to assess performance model performance. RESULTS: The top miRNAs which differentiated those with and without coronary atherosclerosis based on random forest analysis included let-7c-5p, miR-30e-5p, miR-30c-5p, miR-4446-3p, miR-126-5p, miR-3168, miR-425-5p, miR-126-3p, miR-30a-5p, and miR-125a-5p. For coronary artery calcium prediction, addition of all miRNAs except miR-126-3p to clinical factors improved the c-statistic modestly from 0.86 to 0.87. For high coronary artery calcium prediction, addition of all miRNAs except miR-30c-5p to clinical factors improved the c-statistic from 0.75 to 0.80. CONCLUSION: A plasma miRNA panel improved the prediction of high coronary artery calcium beyond traditional risk factors and RA disease activity. Further evaluation of the miRNA panel for prediction of coronary events in RA is necessary. Key Point • A plasma microRNA panel including let-7c-5p, miR-30a-5p, miR-30e-5p, miR-125a-5p, miR-126-3p, miR-126-5p, miR-425-5p, miR-3168, and miR-4446-3p improved the prediction of high coronary artery calcium beyond clinical factors in patients with rheumatoid arthritis.
OBJECTIVE: MicroRNAs (miRNAs) regulate gene expression and are disease biomarkers. Rheumatoid arthritis (RA) patients have accelerated atherosclerosis leading to excess cardiovascular morbidity and mortality, but traditional risk factors for cardiovascular risk stratification are inadequate. In the general population, miRNAs improve cardiovascular risk estimation beyond traditional risk factors. Our objective was to develop a miRNA panel that predicts coronary atherosclerosis in RA patients. METHODS: Plasma small RNA next-generation sequencing (NGS) was performed on 161 RA patients whose Agatston scores for coronary artery calcium were previously measured. Random forest analysis of plasma NGS miRNA expression was used to determine which miRNAs best differentiated between those patients with and without coronary artery calcium. Top predictive miRNAs were assayed by quantitative PCR (qPCR). Elastic net regression was used to develop the most parsimonious models with qPCR-measured miRNA concentrations and clinical variables (age, sex, ACC/AHA 10-year risk score, DAS28 score, and diabetes) separately to predict the presence of coronary artery calcium and high coronary artery calcium. C-statistics were used to assess performance model performance. RESULTS: The top miRNAs which differentiated those with and without coronary atherosclerosis based on random forest analysis included let-7c-5p, miR-30e-5p, miR-30c-5p, miR-4446-3p, miR-126-5p, miR-3168, miR-425-5p, miR-126-3p, miR-30a-5p, and miR-125a-5p. For coronary artery calcium prediction, addition of all miRNAs except miR-126-3p to clinical factors improved the c-statistic modestly from 0.86 to 0.87. For high coronary artery calcium prediction, addition of all miRNAs except miR-30c-5p to clinical factors improved the c-statistic from 0.75 to 0.80. CONCLUSION: A plasma miRNA panel improved the prediction of high coronary artery calcium beyond traditional risk factors and RA disease activity. Further evaluation of the miRNA panel for prediction of coronary events in RA is necessary. Key Point • A plasma microRNA panel including let-7c-5p, miR-30a-5p, miR-30e-5p, miR-125a-5p, miR-126-3p, miR-126-5p, miR-425-5p, miR-3168, and miR-4446-3p improved the prediction of high coronary artery calcium beyond clinical factors in patients with rheumatoid arthritis.