Neuropathy is a common complication of both type 1 and type 2 diabetes and can also develop in individuals with metabolic abnormalities, such as obesity or prediabetes. Based on the area and pattern of nerve involvement, diabetic neuropathy is generally classified into three types: polyneuropathy, mononeuropathy, and autonomic neuropathy [1]. Considerable interest has been paid to the association between neuropathy and complications such as foot ulcers, limb amputation, and associated mortality rates. While research on the relationship with cardiovascular disease (CVD) has primarily focused on cardiovascular autonomic neuropathy, recent studies have revealed a significant association between peripheral polyneuropathy and mortality [2–4]. This review summarizes the reported relationships between autonomic neuropathy, peripheral polyneuropathy, CVD, and mortality.

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The prevalence of autonomic neuropathy among individuals with diabetes varies based on the type of diabetes, the duration of the disease, and the effectiveness of blood glucose control. The ACCORD (Action to Control Cardiovascular Risk in Diabetes) study indicated that the prevalence of cardiovascular autonomic neuropathy (CAN) in those with type 2 diabetes ranges from 1.8% to 7.6% [4]. However, studies of patients who have had diabetes for more than 15 years have reported prevalence rates as high as 60% [4,5]. In a multicenter study conducted by the Neuropathy Study Group of the Korean Diabetes Association across university hospitals, the prevalence of CAN was found to be as high as 88%. The wide range in prevalence rates likely stems from the complexity and lack of standardization in the testing methods for autonomic neuropathy, as well as variations across study populations.

The autonomic nervous system is influenced by both hyperglycemia and hypoglycemia. Factors such as oxidative stress, inflammatory responses, endothelial dysfunction, advanced glycation end products, and blood glucose variability contribute to metabolic and energetic changes in nerve cells. These alterations can lead to impaired perfusion, ultimately damaging the autonomic nervous system [6]. Dysfunction of the autonomic nervous system is associated with nerve length, with longer nerves being more prone to damage. As the longest autonomic nerve and a component of the parasympathetic nervous system, the vagus nerve is often the first to be affected, resulting in early parasympathetic dysfunction. This leads to a relative overactivity of the sympathetic nervous system. Such overactivity may manifest as abnormal tachycardia, even at rest, as well as compromised cardiovascular function during exercise, thereby increasing the heart’s susceptibility to disease [7]. Autonomic neuropathy can result in arrhythmias, including life-threatening conditions such as QT prolongation, atrial fibrillation, and ventricular fibrillation, all of which can lead to cardiovascular abnormalities. CAN is also linked to the occurrence and progression of severe hypoglycemia, which is strongly associated with the development of CVD and progressive kidney disease [8,9]. Thus, autonomic neuropathy is closely connected with the incidence of CVD and cardiovascular mortality [10,11]. Clinical studies have demonstrated an increased risk of mortality in patients with autonomic neuropathy. In the ACCORD study, patients with CAN, identified by measures such as the standard deviation of normal-to-normal intervals and the QT interval, experienced an approximately twofold increase in the risk of all-cause mortality and a threefold increase in the risk of cardiovascular mortality [5]. Other studies have also indicated that autonomic nervous system dysfunction can predict the risk of death following acute coronary syndrome, including myocardial infarction [4].

In addition to the mechanisms previously discussed, autonomic nervous system dysfunction can impact the cardiovascular system by causing the loss or reversal of the normal nocturnal dip in blood pressure. Overactivity of the sympathetic nervous system due to autonomic neuropathy can result in increased sympathetic activity at night. This disrupts the decline in blood pressure that typically occurs during this period. As a result, patients may experience a reduced decrease or, in some instances, an actual rise in blood pressure overnight. Painful sensory neuropathy, another form of nervous system dysfunction, can also contribute to elevated nighttime blood pressure in conjunction with autonomic neuropathy [12]. The ONTARGET/TRANSCEND (Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial/Telmisartan Randomized Assessment Study in ACE-Intolerant Subjects with Cardiovascular Disease) investigation has demonstrated that the loss or reversal of nocturnal blood pressure dipping is significantly associated with an increased risk of major cardiovascular events, cardiovascular mortality, and overall mortality [13].

Sympathetic overactivity augments catecholamine toxicity and insulin resistance, initiating a cascade of events including lipotoxicity, reactive oxygen species production, myocardial energy depletion, calcium abnormalities, and increased oxygen consumption. These factors contribute to fibrosis and apoptosis in cardiomyocytes, ultimately resulting in diabetic cardiomyopathy [14]. Patients with autonomic neuropathy are known to exhibit an increased left ventricular twist and severe torsion of the left ventricle, a condition thought to result from the predominance of radial contraction. This compensatory mechanism is believed to offset the heightened susceptibility of longitudinal myocardial fibers to damage from autonomic neuropathy over time [15]. Furthermore, autonomic dysfunction is associated with increased risks during surgical procedures that require general anesthesia. Extensive evidence indicates that patients with certain spectral analysis abnormalities in total, low-frequency, and high-frequency power—representing markers of CAN—face a higher risk of intraoperative hypotensive events [16].

As previously noted, several approaches are available for detecting autonomic neuropathy beyond the traditional Ewing method. Commonly used measures include time- and frequency-domain analyses of heart rate variability, with ongoing research exploring nonlinear analysis techniques. Nonlinear approaches assess not only the magnitude of variability but also the characteristics of heart rate variability signals and their correlational properties. For instance, fractal analysis is used to assess long-range correlations and short-term complexity, a Poincaré plot may be employed to examine standard deviation indicators within the plot, and entropy or chaotic behavior can be examined. Recent proposals have suggested incorporating deep learning into these analyses [17]. Traditionally, fractal analysis using detrended fluctuation analysis α1 has been understood to significantly predict outcomes in cases of acute coronary syndrome. Similarly, studies that have utilized standard deviation from Poincaré plots have demonstrated meaningful correlations. However, the advantages of these methods over conventional approaches have not been clearly established. While further research and analysis continue, nonlinear analysis has not yet been widely adopted in clinical practice.

Deep learning primarily characterizes the risk of CVD by recognizing patterns in electrocardiograms (ECGs), rather than by calculating specific biomarkers. Although deep learning is often described as a “black box” due to the opacity of its decision-making process, recent methodologies have enabled the interpretation of the importance of input variables. Previous studies that employed deep learning to analyze key ECG features associated with CVD identified the QT interval as particularly significant [18]. The strength of deep learning in pattern analysis lies in its capacity to detect subtle variations as more standardized data are fed into the system. This suggests that it can identify a range of changes and assess risks by integrating established time- and frequency-domain indicators. Prior research aimed to classify various types of CAN using deep learning with diverse input factors. However, these studies failed to recognize that the ultimate goal of assessing the risk of CAN is to facilitate the early detection of CVD risk. Given the significance of the quality of input variables and the methodological approach, focusing on variables and methods that evaluate early cardiovascular risk may be more beneficial [19]. With the rapid progress in deep learning techniques and the associated improvements in data quality, it is anticipated that models capable of predicting CVD risk by incorporating factors related to autonomic neuropathy will soon be developed.

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Like autonomic neuropathy, the prevalence of peripheral polyneuropathy varies widely, with reports indicating a range from 32% to 55%. Neuropathy can develop not only during the prediabetes stage, which is an early phase of diabetes, but also in individuals with normal glucose levels. This phenomenon is thought to arise from the shared pathophysiological mechanisms of peripheral neuropathy and various metabolic conditions, including obesity, in addition to hyperglycemia [20]. Peripheral neuropathy is primarily associated with diabetic complications, such as diabetic foot disease and peripheral artery disease. However, its association with CVD has not been thoroughly investigated.

In 2021, a study examined two Danish diabetes cohorts using the Michigan Neuropathy Screening Instrument (MNSI) to define diabetic peripheral neuropathy and explore its relationship with CVD. The findings revealed that individuals with MNSI questionnaire scores of 4 or higher faced a significantly elevated risk of developing CVD [2]. Additionally, previous research has established a connection between three specific MNSI questionnaire items—numbness in the lower extremities, open sores on the feet, and pain in the lower extremities during movement—and a higher risk of CVD [21]. These results indicate that the MNSI questionnaire, which detects early signs of diabetic peripheral neuropathy, may also represent a useful early indicator of CVD risk. Moreover, the findings suggest that peripheral neuropathy and CVD could share common disease pathways or risk factors.

Another research team analyzed data from the 2021 National Health and Nutrition Examination Survey in the United States, tracking 7,116 individuals over an average period of 13 years [3]. This study aimed to explore the relationship between peripheral neuropathy and mortality risk. Peripheral neuropathy was identified using a 10-g monofilament to assess sensory deficits, regardless of diabetes status. The findings indicated that 15% of participants without diabetes exhibited peripheral neuropathy, and this group faced a 30% higher mortality risk than those without the condition. Among participants with diabetes, even after adjusting for various factors, 50% higher mortality was observed. While well-established links connect peripheral neuropathy and outcomes such as diabetic foot disease, diabetes-related amputation, and CVD, the mechanisms relating peripheral neuropathy to CVD remain poorly understood. Peripheral neuropathy is closely associated with peripheral vascular disease and CAN, both of which are closely linked to CVD and share common risk factors [22,23]. It is still uncertain whether adopting a healthy lifestyle alone can significantly lower the risk of CVD in patients with peripheral neuropathy, or whether more aggressive pharmacological interventions are necessary for prevention.

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CVD is the leading cause of death among patients with diabetes, underscoring the importance of early screening, stratification of high-risk individuals, and timely interventions to prevent and manage the disease’s onset and progression. Autonomic neuropathy, which is closely associated with CVD, occurs more frequently than commonly recognized. However, the complexity of testing and the difficulty in interpreting results often lead to its under-recognition in clinical settings. Adjusting medications may be necessary to manage abnormal nocturnal hypertension and palpitations, which are symptoms of an overactive sympathetic nervous system. Moreover, closer monitoring and adjustments to medication are recommended during and after surgical procedures. Patients with autonomic neuropathy may require more intensive preventive care for both CVD and kidney disease. Peripheral neuropathy, which can develop in the early stages of diabetes or even during prediabetes, has been associated with CVD. Recent studies have indicated that early screening tools for neuropathy, such as the MNSI questionnaire and the monofilament test, can also independently detect the risk of CVD. Consequently, it may be necessary to promptly evaluate patients exhibiting signs of neuropathy and to employ screening tests and early management strategies for CVD prevention in these individuals, who might face a comparatively high risk. Further research is required to determine the appropriate level of intervention for preventive treatment.

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