Assessment of blood microRNA expression patterns by predictive classification algorithms can diagnose myxomatous mitral valve disease in dogs

Abstract

Background: Myxomatous Mitral Valve Disease (MMVD) is a commonly presenting and progressive cardiac pathology in dogs, and early medical intervention can delay progression. Current cardiac biomarkers can be useful in advanced clinical MMVD cases, but are unreliable in pre-clinical disease. Objectives: Assessment of canine serum and plasma expression profiles of 15 miRNA markers as a method to accurately discriminate MMVD patients from healthy controls. Additionally, an assessment of the same method to discriminate pre-clinical (stage B1/B2) from clinical (stage C/D) MMVD patients. Animals: Client-owned dogs (n = 123) were recruited. Following sample exclusions (n=26), healthy controls (n=50) and MMVD cases (n=47) were analyzed Methods: Multicenter, cross-sectional, retrospective investigation. MicroRNA expression profiles were compared between dogs, and the performance of predictive modelling to distinguish healthy controls from MMVD patients, and pre-clinical from clinical MMVD patients was evaluated. Results: Analysis of miRNA expression patterns by predictive classification algorithms could differentiate healthy controls from dogs with MMVD (sensitivity 0.85; specificity 0.82; accuracy; 0.83). Discrimination of pre-clinical (n=29) from clinical (n=18) MMVD cases resulted in promising results (sensitivity 0.61; specificity 0.79; accuracy 0.73). The method also compared advantageously to current biomarkers in a limited population. Conclusions and clinical importance: The analysis of miRNA expression profiles by probabilistic predictive classification algorithms provides a useful diagnostic tool to distinguish healthy controls from MMVD cases (stage B1 to D). Discrimination between pre-clinical and clinical MMVD cases by the same method yielded promising results, which could be further enhanced with an increased study population.