Typ-2-Diabetes und HRV: Wann die Herznerven geprüft werden sollten

Type 2 diabetes mellitus is associated with major cardiovascular complications, including cardiac autonomic neuropathy, which contributes to sympathetic-parasympathetic imbalance and increases susceptibility to arrhythmias and sudden cardiac death. Heart rate variability, assessed through R-R intervals on electrocardiography and 24 h Holter monitoring, represents a sensitive, non-invasive marker of autonomic dysfunction and arrhythmogenic risk. In patients with type 2 diabetes mellitus, chronic hyperglycaemia, oxidative stress, and metabolic inflammation lead to early impairment of the autonomic nervous system, manifested by consistent reductions in SDNN, RMSSD, pNN50, total power, and the high-frequency component, indicating diminished parasympathetic tone and sympathetic predominance. Nonlinear HRV indices demonstrate a loss of complexity and fractal organisation, providing additional prognostic value beyond conventional time- and frequency-domain analyses. Reduced HRV correlates with the severity of cardiac autonomic neuropathy, duration of diabetes, and poor glycaemic control, identifying patients with increased arrhythmogenic vulnerability. HRV analysis enables prediction of arrhythmic risk, facilitating the identification of high-risk individuals and guiding personalised interventions. The integration of HRV assessment into routine clinical practice may improve the early detection of subclinical autonomic neuropathy and optimise cardiovascular risk stratification and ma

Heart rate variability (HRV) is a non-invasive marker of autonomic cardiovascular regulation that reflects beat-to-beat fluctuations in cardiac cycle length. Reduced HRV has been associated with adverse cardiovascular outcomes across multiple disease states, yet its clinical use remains limited by substantial interpretive complexity. Unlike static biomarkers, HRV is highly sensitive to measurement modality, recording duration, circadian timing, behavioral factors, environmental exposures, medications, and comorbidities. Therefore, isolated HRV values cannot be interpreted as direct indicators of autonomic health without careful consideration of context. This review summarizes the physiological basis of HRV, key time-domain, frequency-domain, and nonlinear/complexity metrics, and major acquisition methods, including 24 h electrocardiography (ECG), short-term ECG, chest strap monitors, and wrist-worn devices. It also examines how sleep, physical activity, diet, psychological stress, substance use, age and sex, medications, and cardiovascular or metabolic diseases influence HRV interpretation. We propose a context-aware framework for interpreting HRV as a dynamic physiological signal rather than a standalone diagnostic marker. When appropriately contextualized, HRV may serve as a complementary tool for cardiovascular risk assessment and provide insight into residual risk not fully captured by conventional clinical models.

Heart rate variability (HRV) entropy analysis is an emerging tool for assessing autonomic function, particularly in older and diabetic populations. Traditional HRV metrics, limited in capturing signal complexity, have been supplemented by advanced entropy methods such as multi-scale entropy (MSE), permutation entropy (PermEn), and the baroreflex entropy index (BEI). In this review, we introduce novel entropy indices, including percussion entropy (PercEn), and explore their potential to enhance HRV assessment. Additionally, we discuss the integration of novel data sources, such as pulse wave velocity (PWV) and crest time, offering an in-depth evaluation of autonomic dysfunction. Performance metrics such as classification accuracy (up to 92.5% in diabetic autonomic dysfunction prediction), sensitivity (87.3%), and specificity (89.1%) demonstrate the potential clinical utility of entropy-based HRV analysis. The key challenges include the prognostic value of entropy metrics, the impact of confounding factors, and the need for standardised methodologies. Advances in machine learning and wearable technology are also examined for real-time HRV monitoring and personalised healthcare. The findings highlight entropy analysis as a promising avenue for autonomic dysfunction assessment, with future research needed to optimise methodologies and establish clinical validation.