Popat A, Sathipati S, Sharma P. Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review. World J Cardiol 2026; 18(6): 120747 [DOI: 10.4330/wjc.120747]
Corresponding Author of This Article
Apurva Popat, MD, Department of Cardiology, Sanford Health, Marshfield Clinic, 1000 N Oak Ave, Marshfield, WI 54449, United States. drapurvapopat@gmail.com
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Cardiac & Cardiovascular Systems
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research-article
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Popat A, Sathipati S, Sharma P. Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review. World J Cardiol 2026; 18(6): 120747 [DOI: 10.4330/wjc.120747]
World J Cardiol. Jun 26, 2026; 18(6): 120747 Published online Jun 26, 2026. doi: 10.4330/wjc.120747
Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review
Apurva Popat, Srinivasulu Sathipati, Param Sharma
Apurva Popat, Param Sharma, Department of Cardiology, Sanford Health, Marshfield Clinic, Marshfield, WI 54449, United States
Srinivasulu Sathipati, Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, United States
Author contributions: Popat A, Sathipati S, and Sharma P designed and performed this systematic review.
AI contribution statement: ChatGPT-5.5 and Apple Mac Writing Tools were used solely for grammar, language editing, and readability improvement. NotebookLM was used only as an assistive document-review tool to help locate and compare reported statistical values from source articles; all values were manually verified by the authors against the original publications. No AI tool was used to generate scientific content, perform statistical analyses, interpret results, manipulate data or images, or draw conclusions. No portion of the main text of the response to reviewers was AI-generated. The authors reviewed and approved the final manuscript and take full responsibility for its accuracy, integrity, and originality.
Conflict-of-interest statement: The authors declare no conflicts of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Corresponding author: Apurva Popat, MD, Department of Cardiology, Sanford Health, Marshfield Clinic, 1000 N Oak Ave, Marshfield, WI 54449, United States. drapurvapopat@gmail.com
Received: March 9, 2026 Revised: April 3, 2026 Accepted: May 20, 2026 Published online: June 26, 2026 Processing time: 104 Days and 0.2 Hours
Core Tip
Core Tip: Machine learning integration with microRNA profiles demonstrates promising discriminative performance (area under the curve-receiver operating characteristic often exceeding 0.80) for diagnosing and differentiating subtypes of cardiovascular diseases, including acute myocardial infarction, coronary artery disease, hypertension (HTN) subtypes, post-operative atrial fibrillation after coronary artery bypass grafting, and pulmonary HTN. However, the current evidence is limited by small-to-moderate sample sizes, predominant reliance on internal validation only, and lack of assessment of calibration, incremental value over established biomarkers (e.g., troponin), and standardization of pre-analytical and analytical methods. Large-scale prospective studies with external validation and rigorous standardization are essential before these approaches can be translated into routine clinical practice.
