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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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
Abstract
BACKGROUND
MicroRNAs (miRNAs) have emerged as key regulators and promising biomarkers in cardiovascular diseases (CVDs). To date, numerous miRNAs associated with CVDs have been identified. Machine learning (ML) models play a critical role in integrating multiple miRNAs into a unified predictive framework, thereby enhancing diagnostic accuracy in CVDs.
AIM
To synthesize the current evidence on the development and/or validation of ML-based diagnostic models using miRNA expression profiles for the classification and differentiation of CVDs, and to evaluate their diagnostic performance.
METHODS
A literature search was conducted from inception to December 2025 across PubMed, the Cochrane Library, and ScienceDirect databases with specific keywords. Two reviewers independently screened the studies, and disagreements were resolved through discussion with the third reviewer. The reviewers then used a predefined eligibility criterion to determine the eligibility of the studies for inclusion. All the selected studies were qualitatively synthesized.
RESULTS
The initial search identified 390 records, of which nine met the eligibility criteria and were included in this review. The included studies evaluated the diagnostic performance and clinical applicability of ML algorithms incorporating miRNA expression profiles across multiple CVD phenotypes, including acute coronary syndrome, acute myocardial infarction, coronary artery disease, essential hypertension (HTN), atrial fibrillation, pulmonary arterial HTN, and angina. Overall, the included studies consistently demonstrated that ML-based approaches enabled identification of miRNAs with potential diagnostic relevance in CVDs. In addition, models derived from these miRNA profiles showed good to high discriminative performance for classification and differentiation of CVD subtypes.
CONCLUSION
This review found that the integration of ML models and miRNA profiles for CVD diagnosis represents a promising strategy, with the potential to improve diagnostic accuracy through multivariable panels. However, the current evidence base remains limited, and further well-designed studies are needed to validate these models, standardize methodological approaches, and define their role in routine clinical diagnosis of CVDs.
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.
