Type 1 diabetes mellitus (T1DM) is characterized by the destruction of insulin-producing beta cells in the pancreas. This leads to insufficient insulin production resulting in elevated blood sugar levels, ketoacidosis, and potentially fatal outcomes [1]. In 2021, there were about 8.4 million individuals worldwide with type 1 diabetes. According to projections, the number of people with the disease will increase to 13.5–17.4 million by 2040 (60–107% higher than in 2021) [2].

Cardiac autonomic neuropathy (CAN) arises from the impairment of sympathetic and parasympathetic nerve fibers that innervate the heart and blood vessels. This results in disturbances in cardiovascular autonomic regulation [3]. CAN poses a significant risk of myocardial dysfunction, arrhythmias, myocardial ischemia, chronic kidney disease, and sudden death. Owing to difficulties in diagnostic testing and the silent nature of early clinical symptoms, CAN is often detected at late stages, with a 5-year mortality rate as high as 50% [4]. CAN is a severe complication of T1DM that results from autonomic neuropathy and cardiovascular neural imbalance caused by diabetes. The prevalence of CAN in patients with T1DM ranges from 2–91% [5], and the wide variation in prevalence may be due to inconsistencies in the criteria used to diagnose CAN as well as significant variations in the study populations, particularly concerning CAN risk factors (such as age, gender, and duration of diabetes). Given the dangers of CAN and the uncertainty of its prevalence, we need to perform a meta-analysis to clarify the situation.

With the increasing prevalence of T1DM, the burden of CAN is expected to rise. Hence, it is imperative to identify factors that contribute to the prevalence of CAN in individuals with T1DM. This knowledge will greatly aid in the prevention and management of CAN. In addition to elevated blood sugar levels, several other important risk factors for CAN include age, duration of diabetes, presence of other microvascular complications, lipid profile, blood pressure, smoking habits, and body mass index (BMI) [6].

To date, comprehensive systematic reviews and meta-analyses examining the prevalence of CAN among individuals with T1DM are lacking. Therefore, this study aimed to address this gap by conducting a thorough literature search and meta-analysis to investigate the prevalence of CAN in T1DM patients. Furthermore, we aimed to identify and analyse risk factors associated with CAN in patients with T1DM.

This systematic meta-analysis reports, for the first time, an overall prevalence of 25.8% (95% CI: 0.208–0.307) for CAN in patients with T1DM. In addition, smoking, dyslipidemia, hypertension, duration of diabetes, higher BMI, increased HbA1c concentrations, diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy have all been associated with CAN in patients with T1DM.

The results of the subgroup analysis indicated that the prevalence of CAN in patients with T1DM varied from 25% to 27% among the five regions studied, and no significant difference was observed. The Nordic region exhibited the highest prevalence of CAN among individuals with T1DM, particularly in Denmark, where the prevalence was approximately 30%. This observation suggests a potential correlation between the incidence of T1DM and the prevalence of CAN in this region [39]. The CARTs, proposed by Ewing and Clarke, are the gold standard for objectively assessing the parasympathetic and sympathetic branches of the autonomic nervous system. According to the subgroup analysis, CARTs showed a higher prevalence than HRV testing, possibly because HRV testing can help identify patients at risk for CAN through electrocardiography [5]. However, due to the limited number of studies on HRV detection, no conclusions could be drawn, and further exploration is needed in larger samples [40]. During subgroup analysis based on diabetes duration, we discovered a noteworthy trend: the prevalence of CAN in patients with T1DM increased significantly as the disease duration increased. This finding strongly suggests that a longer duration of diabetes is a crucial risk factor for the development of CAN. The pathogenesis of CAN is not fully understood, but recent research has found that its development is mainly associated with hyperglycaemic toxicity, which directly increases the production of reactive oxygen species (ROS) and advanced glycation end-products (AGEs). The accumulated AGEs inside and outside cells bind to AGE receptors, stimulate phosphoinositide 3-kinase and protein kinase B, activate nuclear factor-kappa B, and ultimately lead to neuronal damage [41]. As the disease progresses, the ability of patients with diabetes to regulate their blood sugar levels decreases, which consequently increases the severity of hyperglycaemic toxicity, making them more susceptible to autonomic neuropathy. According to CARTs, the prevalence of CAN increases by 4.6–6% per year with the increasing duration of diabetes [16, 41].

According to our meta-analysis, several risk factors were identified for the development of CAN in patients with T1DM. These factors include HbA1c levels, duration of diabetes, hypertension, dyslipidemia, obesity, smoking, and the presence of microvascular complications, such as neuropathy, nephropathy, and retinopathy. These findings are consistent with those of previous studies conducted on this subject [42, 43]. The present study demonstrated a statistically significant correlation between increased HbA1c levels and the incidence of CAN in individuals with T1DM. This finding is consistent with the results of numerous previous studies and pooled data, further supporting the association between elevated HbA1c levels and the development of CAN in this patient population [43, 44]. The primary explanation for this connection lies in the effect of hyperglycemia on the induction of oxidative stress and the formation of toxic AGEs. These biochemical changes can alter mitochondrial function, membrane permeability, and endothelial function, contributing to the development of CAN in individuals with T1DM [45, 46]. These diverse pathways trigger gene expression, transcription factor activation, and the disruption of various cellular functions. Moreover, they induce changes in cell-cell communication and interactions with the surrounding matrix. Collectively, these mechanisms contribute to neuronal dysfunction and eventual cell death [4, 10], thereby increasing the likelihood of developing CAN.

Several studies have consistently demonstrated a significant involvement of cardiovascular risk factors in the development of CAN [46, 47]. Risk factors include hypertension, BMI, dyslipidemia, and smoking. Our findings align closely with those of previous studies, further emphasizing the important roles of these factors in the occurrence and progression of CAN [46, 47]. Notably, these factors have at least two common points: they may be modifiable and are associated with insulin resistance, suggesting that CAN may occur concurrently with metabolic syndrome. The altered autonomic nervous function is often seen in cases of obesity [16, 17]; however, the mechanisms by which being overweight leads to autonomic dysfunction remain unclear. Several interrelated functional changes, such as inflammation [29], endothelial dysfunction [22], leptin dysfunction [30], and gastric hunger hormone regulation [31], may lead to increased sympathetic neural activity, decreased cardiac stress reflex sensitivity, and promote CAN development [32]. Diabetic nephropathy is also closely related to the development of CAN, as it may mediate changes in renal glomerular hemodynamics, blood pressure, and the diurnal rhythm of proteinuria [48].

Furthermore, distinct autonomic dysfunction states have been described in patients with CAN, including erythropoietin deficiency, anaemia, and early dysregulation of erythropoietin production. These findings highlight the additional factors that contribute to the development of autonomic dysfunction in individuals with CAN [33]. Anaemia is a predictive factor for the progression of kidney disease, whereas erythropoietin has a direct renal protective effect. Therefore, both anaemia and erythropoietin deficiency may contribute to diabetic kidney damage. Based on the data presented in Table 3, our findings align with those of the multicentre EURODIAB (EUROpe and DIABetes) Insulin-dependent Diabetes Mellitus (IDDM) Complications Study [49], indicating that there was no significant difference in the prevalence of CAN between male (35%) and female (37%) patients with T1DM. This suggests that sex is not a risk factor for the development of CAN, further indicating that our results are in line with those of previous research. In previous studies, increased age has been considered a risk factor for CAN [43, 50, 51]. However, our meta-analysis of risk factors of CAN showed that, contrary to the reports of previous studies, age was not a significant risk factor. Considering that these studies were based solely on four individual studies that evaluated the impact of age on CAN, further analysis is necessary as new data become available to update our understanding.

In addition, Pan et al. [25] found that in the baseline data of the two groups of T1DM patients diagnosed with CAN and non-CAN, the proportion of coronary heart disease complications was 4.4% versus 0%, p $>$ 0.05, and the proportion of cerebral infarction complications was 2.2% versus 0%, p $>$ 0.05. Stella et al. [21] demonstrated that macrovascular disease is a risk factor for the development of CAN in the T1DM population [risk ratio = 1.57, 95% CI: 1.01–2.44] (specific p value not extracted). In the study by Witte et al. [28], we learned that a history of CVD disease was a risk factor for the development of CAN [OR = 1.88, 95% CI: 1.02–3.47, p 0.05].

This study has several limitations that should be acknowledged. First, all the studies included in this meta-analysis were observational, which may introduce potential biases and limit the generalizability of the findings. Second, this systematic review did not include non-English articles, which may have introduced language bias. The included studies predominantly came from Europe and may not be representative of regions such as the Eastern or Western Mediterranean, thus limiting the generalizability of the results to these areas. Third, certain unpublished articles and studies included in this analysis did not employ reliable and objective methods for diagnosing T1DM, nor did they provide clear diagnostic criteria. This may have introduced publication bias and affected the overall accuracy and reliability of the results. Fourth, relatively few articles on certain influencing factors were included in this study, making it difficult to determine the relationship between these factors and the occurrence of CAN in patients with T1DM. Fifth, unfortunately, we did not include data on macrovascular disease associated with CAN in our study and only described these data in the text. Due to data limitations, we need to interpret this information with caution, and more studies should be conducted subsequently to expand this data. Moreover, methodological variations among the included studies inevitably led to high levels of heterogeneity in the overall prevalence rates. Additionally, several studies had relatively small sample sizes. To address these limitations, future research should prioritize large-scale, multicentre epidemiological studies. Such studies would enable a deeper understanding of the risk factors associated with CAN in patients with T1DM.

The findings of this study suggest that CAN is prevalent worldwide, and efforts should be made to enhance early screening and intervention for CAN as well as to implement strategies aimed at improving or controlling the risk factors of CAN in the early stages. The results of this study should be updated in a timely and regular manner with the availability of new epidemiological investigations.

T1DM, type 1 diabetes mellitus; CAN, cardiac autonomic neuropathy; CARTs, cardiac autonomic reflex test; HRV, heart rate variability; CI, confidence interval; I${}^{\mathrm{2}}$, I-squared; NOS, Newcastle-Ottawa Scale; BMI, body mass index; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

The data used to support the findings of this study are included within the article.

XH had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: XH, LT. Acquisition, analysis, or interpretation of data: XH, JW. Drafting of the manuscript: XH. Statistical analysis: XH, YB. Obtained funding: LT. Administrative, technical, or material support: LT, YB. Supervision: LT, YB. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Not applicable.

We would like to express our gratitude to all those who helped us during the writing of this manuscript.

This work was supported by grant from the Gansu Province Major Science and Technology Project (No.22ZD6FA033).

The authors declare no conflict of interest.