The integration of multi-index diagnostics, such as heart rate variability, continuous glucose monitoring, and machine-learning algorithms, improves the accuracy of early screening and prognosis for diabetic cardiac autonomic neuropathy.
Diabetic cardiac autonomic neuropathy (DCAN) is a common and serious complication of diabetes, and its early diagnosis and treatment are important for preventing cardiovascular events. At present, its diagnosis is mainly based on multiple functional investigations, such as heart rate variability (HRV) and cardiovascular reflex test. However, these methods are cumbersome to perform, time-consuming, and readily affected by patient cooperation and operator technique, resulting in limited clinical application. More importantly, DCAN still lacks standardized early diagnostic criteria and specific biomarkers. In recent years, the integration of multi-index diagnosis such as HRV, electrocardiograms (ECGs), continuous glucose monitoring (CGM) and machine-learning algorithms has improved the accuracy of early screening and prognosis. Here, we systematically review the latest research progress in relation to the pathological mechanism, diagnosis and treatment of DCAN, with a focus on novel biomarkers, therapeutic targets, and the potential for individualized treatment. This review provides new insights into DCAN, as well as the basis for early diagnosis and precise intervention.
Wang et al. (Mon,) conducted a review in Diabetic cardiac autonomic neuropathy. The integration of multi-index diagnostics, such as heart rate variability, continuous glucose monitoring, and machine-learning algorithms, improves the accuracy of early screening and prognosis for diabetic cardiac autonomic neuropathy.