Machine Learning-based Classification of Exercise Status Using miR-21 Expression and Multivariable Participant Profiles
Nana EL Dawy Ahmed Hefny, Fatma Hassan Abd Elbasset Mourgan, Dhamia Ahmed Atiyah, Manaf AlMatarBackground:
Circulating microRNAs (miRNAs) have emerged as promising molecular indicators of exercise-induced physiological adaptation. However, individual miRNAs have shown variable discriminatory performance across exercise contexts. miR-21 is of particular interest because of its proposed involvement in vascular, inflammatory, metabolic, and adaptive responses to exercise.
Methods:
This exploratory observational proof-of-concept study included 20 observations: 10 postexercise (EX) and 10 preexercise/nonexercise (NON). The dataset included age, sex, body mass index, dietary habits, smoking status, alcohol consumption, medical history, exercise type, duration, intensity, frequency, and miR-21 folding values. Univariate analysis of miR-21 folding was performed using Welch’s independent-samples
Results:
miR-21 folding values alone did not clearly separate EX and NON observations, with substantial overlap between the two groups and nonsignificant univariate results. In contrast, the integrated multivariable profile achieved high internal classification performance across the selected models. Mean accuracy was 99.0% for Logistic Regression, 98.5% for SVM-RBF, and 99.5% for Gaussian Naive Bayes. The mean area under the curve (AUC) values ranged from 0.995 to 1.000, indicating strong internal discriminatory performance within the exploratory dataset.
Conclusions:
The findings suggest that miR-21 should not be interpreted as a standalone classifier of exercise status. Instead, miR-21 may contribute to a broader multivariable exercise-response signature when combined with demographic, lifestyle, clinical, and exercise-related variables.