Assessing the use of blood microRNA expression patterns for predictive diagnosis of myxomatous mitral valve disease in dogs

Abstract

Myxomatous mitral valve disease (MMVD) is a common, acquired, and progressive canine heart disease. The presence of heart murmur and current cardiac biomarkers are useful in MMVD cases but are not sufficiently discriminatory for staging an individual patient. Objectives: This study aimed to conduct a preliminary assessment of canine serum and plasma expression profiles of 15 selected miRNA markers for accurate discrimination between MMVD patients and healthy controls. Additionally, we aim to evaluate the effectiveness of this method in differentiating between pre-clinical (stage B1/B2) and clinical (stage C/D) MMVD patients. Animals: Client-owned dogs (n = 123) were recruited for the study. Following sample exclusions (n = 26), healthy controls (n = 50) and MMVD cases (n = 47) were analyzed. Methods: A multicenter, cross-sectional, prospective investigation was conducted. MicroRNA expression profiles were compared among dogs, and these profiles were used as input for predictive modeling. This approach aimed to distinguish between healthy controls and MMVD patients, as well as to achieve a more fine-grained differentiation between pre-clinical and clinical MMVD patients. Results: Performance metrics revealed a compelling ability of the method to differentiate healthy controls from dogs with MMVD (sensitivity 0.85; specificity 0.82; and accuracy 0.83). For the discrimination between the pre-clinical (n = 29) and clinical (n = 18) MMVD cases, the results were promising (sensitivity 0.61; specificity 0.79; and accuracy 0.73). Conclusion and clinical importance: The use of miRNA expression profiles in combination with customized probabilistic predictive modeling shows good scope to devise a reliable diagnostic tool to distinguish healthy controls from MMVD cases (stages B1 to D). Investigation into the ability to discriminate between the pre-clinical and clinical MMVD cases using the same method yielded promising early results, which could be further enhanced with data from an increased study population

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. JPA is grateful for the support of the Department of Research and Universities of the Generalitat de Catalunya [grant number 2021SGR01197] and the Spanish Ministry of Science and Innovation (MCIN/AEI/10:13039/501100011033) and ERDF A way of making Europe [project PID2021-123833OB-I00]