Abstract
Heart rate variability (HRV) has emerged as a valuable non-invasive marker for assessing autonomic nervous system function in diabetes mellitus. This narrative review examines the pathophysiological mechanisms linking HRV alterations to diabetic complications, the clinical utility of HRV as a prognostic and diagnostic tool, and the innovative application of machine learning techniques in HRV analysis. Reduced HRV is consistently observed in individuals with diabetes and serves as an early indicator of cardiovascular autonomic neuropathy, a common yet underdiagnosed complication. Traditional time-domain, frequency-domain, and nonlinear HRV metrics have demonstrated significant associations with glycemic control, microvascular complications, and cardiovascular events. Recent advances in machine learning and artificial intelligence have revolutionized HRV interpretation, enabling more sophisticated pattern recognition, risk stratification, and personalized medicine approaches. This review synthesizes current evidence on HRV pathophysiology in diabetes, evaluates established and emerging HRV assessment methods, and explores the transformative potential of computational approaches in optimizing diabetes management and preventing complications.

Key words: Autonomic neuropathy; Diabetes; Heart rate variability; Machine learning; Non linear analysis; Spectral analysis.