ORIGINAL RESEARCH article

Front. Endocrinol., 30 April 2025

Sec. Clinical Diabetes

Volume 16 - 2025 | https://doi.org/10.3389/fendo.2025.1556345

25-hydroxyvitamin D as a predictor of reduced heart rate variability in elderly patients with diabetes mellitus

Tiantian TangTiantian TangXinyu SunXinyu SunXiaoxiang ZhangXiaoxiang ZhangJiaojiao LiJiaojiao LiManjie WangManjie WangCancan HuiCancan HuiYuwei ChengYuwei ChengXiaoming Kong*Xiaoming Kong*Yan Sun*Yan Sun*
  • Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

Objective: To analyze the risk factors for decreased heart rate variability (HRV) in elderly diabetic patients and evaluate the predictive value of 25-hydroxyvitamin D [25-(OH)D].

Methods: A retrospective study was conducted, enrolling 101 elderly diabetic patients admitted to the Department of Geriatric Endocrinology at the First Affiliated Hospital of Anhui Medical University from 2023 to 2024. Patients were divided into two groups based on the standard deviation of all normal RR intervals (SDNN) from 24-hour Holter monitoring: the normal HRV group (180 ms ≥ SDNN ≥ 100 ms) and the decreased HRV group (SDNN < 100 ms). Clinical data collected included age, sex, body mass index (BMI), Diabetic peripheral neuropathy (DPN), Diabetic peripheral vascular disease (DPVD), Diabetic nephropathy (DN), Diabetic retinopathy (DR), as well as laboratory parameters such as fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), creatinine (Cr), glomerular filtration rate (GFR), uric acid (UA), cystatin C (CysC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very-low-density lipoprotein cholesterol (VLDL-C), non-HDL-C, 25-(OH)D, 24-hour urinary total protein (24hUTP), 24-hour urinary uric acid (24hUA), microalbuminuria (MAlb), urinary albumin-to-creatinine ratio (UACR), and calculated ratios including TG/HDL-C, UA/HDL-C, and non-HDL-C/HDL-C. Statistical analyses included χ² test, independent t-test, Mann-Whitney U test, and multivariate logistic regression to identify risk factors for HRV reduction. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive value of these factors.

Results: Compared with the normal HRV group, the decreased HRV group had significantly lower HDL-C and 25-(OH)D levels but higher prevalence of DN, along with elevated 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C (all P < 0.05). These factors were identified as independent risk factors for HRV reduction in elderly diabetic patients.

Conclusion: This study identified key risk factors for HRV reduction in elderly diabetic patients. Furthermore, 25-(OH)D levels may serve as an early predictive marker for decreased HRV in this population.

1 Introduction

With the accelerating global aging population, the prevalence of diabetes in elderly individuals has been increasing annually. This trend not only poses significant threats to the physical and mental health of older patients but also creates a substantial burden on healthcare systems, particularly due to the numerous complications associated with diabetes. Among these, damage to the autonomic nerve fibers innervating the heart and blood vessels, known as diabetic cardiac autonomic neuropathy (DCAN) (1), elevates the risk of adverse cardiovascular events in elderly diabetic patients. However, the early stages of DCAN may be asymptomatic or present with mild symptoms, making it clinically challenging to detect or diagnose during routine medical evaluations. Heart rate variability (HRV) which reflects the combined activity of the sympathetic and parasympathetic nervous systems on cardiac function, is currently recognized as an important non-invasive indicator for assessing cardiac autonomic function (2). HRV is obtained by measuring and analyzing the temporal variations between heartbeats, specifically the R-peak to R-peak intervals (RR intervals) in electrocardiograms (3). The standard deviation of all normal RR intervals (SDNN)serves as an effective parameter for evaluating the status of cardiac autonomic function in diabetic patients (4). Particularly in ambulatory electrocardiographic recordings exceeding 30 seconds, SDNN has been demonstrated to be a reliable metric for assessing DCAN (5).

In recent years, vitamin D has become a research hotspot, and its receptors are widely distributed in various human tissues, including blood vessels, the heart, and the brain (6). Numerous studies have demonstrated that vitamin D deficiency can increase the incidence and mortality of cardiovascular diseases, cerebrovascular diseases, and metabolic disorders (79). However, few studies have explored the impact of vitamin D levels on HRV parameters in elderly diabetic patients. Based on this, the present study aims to investigate the risk factors for reduced HRV in elderly diabetic patients and assess its predictive value, with the goal of providing a basis for early intervention and treatment of DCAN.

2 Materials and methods

2.1 Study design

Participants were stratified according to the HRV criteria recommended by the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, using the standard deviation of SDNN threshold. The cohort comprised 101 elderly diabetic patients, divided into:Normal HRV group (180 ms ≥ SDNN ≥ 100 ms): 59 cases;Reduced HRV group (SDNN < 100 ms): 42 cases (10).

2.1.1 Data collection

The study collected the following clinical data: (1) General information including age, gender, body mass index (BMI), duration of diabetes, and presence of complications such as diabetic peripheral neuropathy(DPN), diabetic peripheral vascular disease(DPVD), diabetic nephropathy(DN), and diabetic retinopathy(DR); (2) Laboratory parameters including fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), serum creatinine (Cr), glomerular filtration rate (GFR), uric acid (UA), cystatin C (CysC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), 25-hydroxyvitamin D [25-(OH)D], 24-hour urinary total protein (24hUTP), 24-hour urinary uric acid (24hUA), microalbuminuria (MAlb), urinary albumin-to-creatinine ratio (UACR), and SDNN. Based on these parameters, the study further calculated the ratios of TG/HDL-C, UA/HDL-C, and non-HDL-C/HDL-C.

2.1.2 Statistical analysis

Data were analyzed using SPSS 25.0.Continuous variables: Normally distributed data expressed as mean ± SD (independent t-test); non-normal data as median (P25, P75) (Mann-Whitney U test). Categorical variables: Reported as counts (%) (χ² test). Statistical significance was set at P < 0.05. Multivariate analysis: Logistic regression identified risk factors for HRV reduction. Predictive value: Receiver operating characteristic (ROC) curves evaluated 25-(OH)D’s ability to predict HRV decline.

2.2 Study subjects

A total of 101 elderly patients with diabetes hospitalized in the Geriatric Endocrinology Department of the First Affiliated Hospital of Anhui Medical University from 2023 to 2024 were enrolled as study subjects. All study participants were selected from indoor workers engaged in light physical labor, with potential confounding factors such as physical activity, sunlight exposure, and dietary habits already controlled for. During both the pre-monitoring period and monitoring period, participants were required to: Avoid emotional excitement and vigorous physical activities; Abstain from consuming any caffeine-containing foods or beverages.

2.3 Inclusion and exclusion criteria

The study subjects were elderly diabetic patients hospitalized in the Department of Geriatric Endocrinology at the First Affiliated Hospital of Anhui Medical University from 2023 to 2024. Inclusion criteria: a. Aged 60 years or older; b. Meeting the WHO diagnostic criteria for diabetes and clinically confirmed as diabetic patients; c. Able to cooperate with dynamic electrocardiogram monitoring. Exclusion criteria:a. Patients with secondary diabetes caused by conditions such as Cushing’s syndrome, hyperthyroidism, or pancreatic diseases; b. Those with comorbid cognitive impairment; c. Those with severe cardiovascular diseases, such as acute or chronic heart failure, cardiomyopathy, acute coronary syndrome, rheumatic heart disease, congenital heart disease, or valvular heart disease; d. Those with severe respiratory diseases, severe infections, trauma, stress, hepatic or renal dysfunction, or severe neuropsychiatric disorders; e. Those who received intravenous glucose or glucocorticoids (or other glucose-affecting medications) within one month before admission or during hospitalization; f. Those with diseases affecting the autonomic nervous system, such as acute inflammatory demyelinating polyneuropathy or Parkinson’s disease; g. Those who recently took medications affecting HRV, such as α- or β-blockers or statins; h. Those unable to cooperate with dynamic electrocardiogram monitoring due to physical or mental reasons.

3 Results

3.1 Comparison of baseline data and laboratory indicators between groups

Compared to the HRV-normal group, the HRV-reduced group demonstrated significantly lower levels of HDL-C and 25-(OH)D, along with a higher number of diabetic nephropathy cases, elevated 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C ratios (all P < 0.05; see Table 1).

Table 1
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Table 1. Comparison of baseline characteristics and laboratory parameters between groups.

3.2 Multivariate logistic regression analysis

The model showed good goodness-of-fit with Nagelkerke R² of 0.343 and Cox & Snell R² of 0.255. All tolerance values were >0.1 and variance inflation factors (VIF) were <5, indicating no multicollinearity among independent variables. Furthermore, the model met all necessary assumptions: (a) the dependent variable was binary, (b) there was linearity between independent variables and Logit transformation, (c) observations were independent, (d) sample size was adequate, (e) no extreme outliers were present in the data, and (f) no important variables were omitted from the model, thus validating the use of logistic regression analysis.

Using HRV status as the dependent variable (normal=0, abnormal=1) with DN cases, HDL-C, 25-(OH)D, 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C as independent variables, logistic regression analysis showed only 25-(OH)D had P<0.05, indicating statistically significant association. 25-(OH)D was a protective factor against HRV decline (odds ratio [OR]=0.869, 95% confidence interval [95%CI] 0.778-0.97). Other variables (DN, HDL-C, 24hUTP, MAlb, UACR, TG/HDL-C, UA/HDL-C) all showed P>0.05, indicating no statistical significance in this model (see Table 2).

Table 2
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Table 2. Logistic regression analysis of HRV reduction.

3.3 ROC curve analysis

To evaluate the predictive value of DN, HDL-C, 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C for HRV decline in elderly diabetic patients, we performed ROC curve analysis. The results showed that 25-(OH)D had the highest predictive efficacy (area under the curve [AUC]=0.763, P=0.00, 95% CI 0.653-0.874), with a sensitivity of 67.3%, specificity of 76.5%, and maximum Youden index of 43.8%. The optimal predictive cutoff value for serum 25-(OH)D determined by the maximum Youden index was 19.7 ng/mL (reference normal range >20 ng/mL), with positive predictive value [PPV] of 61.9% and negative predictive value [NPV] of 80.5%. Therefore, 25-(OH)D levels have certain predictive value for diagnosing HRV decline in elderly diabetic patients (see Figure 1, Table 3).

Figure 1
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Figure 1. ROC curves of DN status, HDL-C, 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C levels for diagnosing and predicting HRV decline in elderly patients with diabetes mellitus.

Table 3
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Table 3. Area under the curve.

4 Discussion

Currently, DCAN diagnosis primarily utilizes the Ewing test, which consists of five non-invasive assessments: deep breathing heart rate [HR] variation, supine-to-standing HR change, orthostatic blood pressure [BP] difference, Valsalva maneuver, and handgrip test (11). However, this approach has notable limitations including procedural complexity, limited availability, and substantial subjectivity. In comparison, HRV analysis offers a more sensitive and non-invasive evaluation of cardiac autonomic function, providing objective, convenient, and reproducible results through 24-hour Holter monitoring. Multiple studies have confirmed that early HRV monitoring facilitates timely detection and diagnosis of cardiovascular autonomic neuropathy (2, 12). Consequently, proactive intervention targeting HRV decline risk factors, when combined with comprehensive management of diabetic cardiovascular risks, may potentially delay or prevent DCAN progression (13). Our retrospective study identified DN comorbidity, HDL-C, 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C as significant risk factors for HRV decline in elderly diabetic patients, while 25-(OH)D levels demonstrated predictive value for early identification of HRV impairment in this population.

Multiple prospective clinical and epidemiological studies have confirmed a negative correlation between HDL-C and the occurrence and severity of atherosclerosis (14, 15). Additionally, HDL-C plays a role in maintaining normal vascular function (16). Decreased HDL-C may exacerbate arterial stiffness and damaging cardiac microvessels, while the occurrence of reduced HRV in diabetic patients may be related to cardiac microvascular lesions (17). Therefore, higher HDL-C levels can reduce the risk of HRV decline. Studies have shown that a high TG/HDL ratio is closely associated with insulin resistance and atherosclerosis in prediabetic and newly diagnosed diabetic subjects (18, 19). Thus, elevated TG/HDL-C levels may contribute to cardiac microvascular damage and exacerbate diabetes-induced autonomic nerve dysfunction, leading to reduced HRV. UA, the end product of purine metabolism, can influence the progression of cardiac autonomic neuropathy (20). Since UA/HDL-C combines both the damaging and protective mechanisms of cardiac autonomic nerves, UA/HDL-C levels are closely related to HRV decline. Our study found that the HRV decline group had significantly lower HDL-C and significantly higher TG/HDL-C and UA/HDL-C levels, suggesting that lipid and purine metabolism disorders in elderly diabetic patients are correlated with DCAN. Therefore, managing lipid and UA levels should be emphasized throughout diabetes care.

DN is a common complication of diabetes. Studies indicate that DN markers including urinary albumin excretion and UACR impairment coexist with autonomic neuropathy and reduced HRV (21). The development of DN may be associated with underlying inflammatory effects of microvascular complications and cardiorenal syndrome (22). Given the relatively consistent progression of systemic microvascular damage in diabetic patients, we hypothesize that the presence of DN, elevated UACR and urinary albumin levels may reflect the progression of cardiac microvascular dysfunction. Our results show significantly higher DN incidence, 24hUTP, MAlb and UACR levels in the reduced HRV group, suggesting that DCAN may develop concurrently with DN.

Recent studies have shown that vitamin D plays a significant role in various cardiovascular diseases, including Coronary artery disease (CAD) and heart failure (HF) (23). However, few studies have identified the precise threshold of 25-(OH)D for predicting HRV reduction. Our results indicate that 25-(OH)D is a protective factor against HRV decline (OR=0.869). A serum 25(OH)D level of 19.7 ng/mL demonstrated a PPV of 61.9% and an NPV of 80.5% for predicting HRV decline in elderly diabetic patients. The study by Xuemei Luo et al. suggested that vitamin D deficiency leading to reduced HRV may be associated with the following mechanisms (24):a. Vitamin D can regulate cardiovascular activity by coordinating other molecular mechanisms (e.g., neurotransmitter biosynthesis), as it controls the synthesis of acetylcholine (ACh) and the expression of tyrosine hydroxylase (TH).Vitamin D-deficient rats exhibited decreased TH in cardiac regions and reduced ACh in local cardiac plasma, along with corresponding declines in Nitric oxide (NO) and norepinephrine (NE). ACh slows heart rate, delays atrioventricular node conduction, weakens atrial muscle contraction, and promotes NO release and vasodilation. NE is the primary neurotransmitter of sympathetic nerves, while TH is a rate-limiting enzyme in NE and epinephrine biosynthesis.b. Vitamin D deficiency may reduce potassium channel protein levels by downregulating the expression of inwardly rectifying potassium channel (Kir), human ether-à-go-go-related gene potassium channel (HERG), K+ voltage-gated channel subfamily Q member 1 (KVLQT1), and minimal potassium channel subunit (MinK), thereby disrupting cardiac autonomic homeostasis. Furthermore, decreased 25(OH)D levels may lead to reduced HRV through the following mechanisms:a. As the primary active form of 25(OH)D, reduced calcitriol levels may promote inflammatory factor release in the paraventricular nucleus (PVN) and increase systemic oxidative stress, thereby inducing autonomic dysfunction (25, 26).b. 25(OH)D serves as an inhibitor of renin biosynthesis. Its deficiency can increase production of both renin and angiotensin II (Ang II) (27). Moreover, elevated serum parathyroid hormone (PTH) levels secondary to 25(OH)D deficiency may further stimulate renin secretion (28). The combined effects of 25(OH)D deficiency and increased PTH may activate the renin-angiotensin-aldosterone system (RAAS), resulting in sympathetic and parasympathetic dysfunction that ultimately affects HR. Currently, few studies have demonstrated that autonomic dysfunction can influence 25(OH)D metabolism. However, the report by Nalbant et al. (29) indicated no significant alterations in HRV parameters among vitamin D-deficient individuals within the low cardiovascular risk group. This finding contradicts the results of the present study. The discrepancy may be attributed to the following factors: the mean age of participants in prior studies was younger than that in our investigation, and previous study cohorts exhibited lower cardiovascular risk profiles, whereas our research specifically focused on elderly patients.

Currently, there is no effective reversal strategy for elderly diabetic patients with declined HRV, making early prevention, diagnosis, and intervention crucial. Studies have shown that vitamin D supplementation significantly improves cardiac autonomic dysfunction and glucose tolerance in vitamin D-deficient individuals (30, 31). Given its affordability and convenience, vitamin D supplementation for elderly diabetic patients with low 25-(OH)D levels may be a potential therapeutic approach to improve HRV in this population.

This study has several limitations: (a) relatively small sample size; (b) as a cross-sectional design, longitudinal data on 25-(OH)D levels and HRV decline in elderly diabetics are unavailable. Future studies should incorporate larger cohorts with extended follow-up to monitor 25-(OH)D/HRV dynamics, coupled with vitamin D supplementation trials, to better establish 25-(OH)D’s predictive value for HRV impairment and evaluate its therapeutic potential for early-stage DCAN intervention.

In summary, elderly diabetic patients with reduced HRV showed lower serum 25-(OH)D levels than those with normal HRV, suggesting 25-(OH)D may serve as a potential biomarker for HRV impairment. DN, HDL-C, 25-(OH)D, 24hUTP, MAlb, UACR, TG/HDL-C, and UA/HDL-C were all significantly associated with HRV reduction in this population.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by Clinical Medical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from primarily isolated as part of your previous study for which ethical approval was obtained. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article because Exemption from informed consent has been granted through an ethics application.

Author contributions

TT: Writing – original draft, Writing – review & editing, Investigation, Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization. XS: Investigation, Writing – review & editing, Writing – original draft. XZ: Writing – original draft, Writing – review & editing. JL: Writing – review & editing, Writing – original draft. MW: Writing – original draft, Writing – review & editing. CH: Writing – original draft, Writing – review & editing. YC: Writing – original draft, Writing – review & editing. XK: Writing – review & editing, Writing – original draft. YS: Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article, including funding from the Key Projects of Natural Science Research Program of Higher Education Institutions in Anhui Province (2023AH053309), the National Natural Science Foundation of China (82072134), and the Anhui Medical University Research Fund Project (2021xkj144).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Serhiyenko V, Serhiyenko A, Serhiyenko L, Segin V. Omega-3 polyunsaturated fatty acids: effects on the arterial stiffness and heart rate variability in patients with diabetic cardiac autonomic neuropathy. Atherosclerosis. (2023) 379:S108. doi: 10.1016/j.atherosclerosis.2023.06.384

Crossref Full Text | Google Scholar

2. Cossul S, Andreis FR, Favretto MA, Marques JLB. The use of empirical mode decomposition on heart rate variability signals to assess autonomic nuropathy progression in type 2 diabetes. Appl Sci (Basel). (2023) 13:7824. doi: 10.3390/app13137824

Crossref Full Text | Google Scholar

3. Carricarte Naranjo C, Sanchez-Rodriguez LM, Brown Martínez M, Estévez Báez M, Machado García A. Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus. Comput Biol Med. (2017) 86:90–7. doi: 10.1016/j.compbiomed.2017.05.003

PubMed Abstract | Crossref Full Text | Google Scholar

4. Lai YR, Huang CC, Cheng BC, Tsai NW, Chiu WC, Chang HW, et al. Feasibility of combining heart rate variability and electrochemical skinconductance as screening and severity evaluation of cardiovascular autonomic neuropathy in type 2 diabetes. Diabetes Invest. (2021) 12:1671−1679. doi: 10.1111/jdi.13518

PubMed Abstract | Crossref Full Text | Google Scholar

5. Wehler D, Jelinek HF, Gronau A, Wessel N, Kraemer JF, Krones R, et al. Reliability of heart-rate-variability features derived from ultra-short ECG recordings and their validity in the assessment of cardiac autonomic neuropathy. Biomed Signal Process Control. (2021) 68:102651. doi: 10.1016/J.BSPC.2021.102651

Crossref Full Text | Google Scholar

6. Gonzalez-Parra E, Rojas-Rivera J, Tuñón J, Praga M, Ortiz A, Egido J. Vitamin D receptor activation and cardiovascular disease. Nephrol Dial Transplant. (2012) 27 Suppl 4:iv17–21. doi: 10.1093/ndt/gfs534

PubMed Abstract | Crossref Full Text | Google Scholar

7. Apivatthakakul A, Jaruvongvanich S, Upala S, Jaruvongvanich V. Association between serum vitamin D levels and retinal vascular occlusion: A systematic review and meta-analysis. Cureus. (2024) 16:e64356. doi: 10.7759/cureus.64356

PubMed Abstract | Crossref Full Text | Google Scholar

8. Deo R, Katz R, Shlipak MG, Sotoodehnia N, Psaty BM, Sarnak MJ, et al. Vitamin D, parathyroid hormone, and sudden cardiac death: results from the Cardiovascular Health Study. Hypertension. (2011) 58:1021–8. doi: 10.1161/HYPERTENSIONAHA.111.179135

PubMed Abstract | Crossref Full Text | Google Scholar

9. Argano C, Mirarchi L, Amodeo S, Orlando V, Torres A, Corrao S. The role of vitamin D and its molecular bases in insulin resistance, diabetes, metabolic syndrome, and cardiovascular disease: state of the art. Int J Mol Sci. (2023) 24:15485. doi: 10.3390/ijms242015485

PubMed Abstract | Crossref Full Text | Google Scholar

10. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task force of the european society of cardiology and the north american society of pacing and electrophysiology. Circulation. (1996) 93:1043–65. doi: 10.1186/s43044-021-00168-3

PubMed Abstract | Crossref Full Text | Google Scholar

11. Ahmad O. Cardiovascular autonomic neuropathy in people with type 2 diabetes mellitus; investigation of its association with classical cardiovascular risk factors using cardiovascular autonomic reflex tests: a cross-sectional study. Egyptian Heart journal: (EHJ): Off Bull Egyptian Soc Cardiol. (2021) 73:44–4.

PubMed Abstract | Google Scholar

12. Eleftheriadou A, Spallone V, Tahrani AA, Alam U. Cardiovascular autonomic neuropathy in diabetes: an update with a focus on management. Diabetologia. (2024) 67:1–15. doi: 10.1007/s00125-024-06242-0

PubMed Abstract | Crossref Full Text | Google Scholar

13. Tesfaye S, Boulton AJM, Dyck PJ, Freeman R, Horowitz M, Kempler P, et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care. (2010) 33:2285−2293. doi: 10.2337/dc10-1303

PubMed Abstract | Crossref Full Text | Google Scholar

14. Yamada HM. 444-P: associations of LDL-C with new-onset coronary artery disease (CAD) according to HDL-C levels and glucose status. Diabetes. (2022) 71:444-P. doi: 10.2337/db22-444-P

Crossref Full Text | Google Scholar

15. Xu JJ, Chen J, Liu YX, Song Y, Jiang L, Yan SD, et al. The impact of LDL-C/HDL-C ratio on severity of coronary artery disease and 2-year outcome in patients with premature coronary heart disease:results of a prospective,multicenter,observational cohort study. Zhonghua xin xue guan bing za zhi. (2023) 51:702–8. doi: 10.3760/cma.j.cn112148-20230128-00043

PubMed Abstract | Crossref Full Text | Google Scholar

16. März W, Kleber ME, Scharnagl H, Speer T, Zewinger S, Ritsch A, et al. Klinische bedeutung des HDL-cholesterins. Herz. (2017) 42:58–66. doi: 10.1007/s00059-016-4499-0

PubMed Abstract | Crossref Full Text | Google Scholar

17. Mohanad A, Mamunur R, Mukit MA, Ahmed KI, Mostafa R, Parveen S, et al. Screening cardiovascular autonomic neuropathy in diabetic patients with microvascular complications using machine learning:A 24-hour heart rate variability study. IEEE Access. (2021) 9:119171–87. doi: 10.1109/ACCESS.2021.3107687

Crossref Full Text | Google Scholar

18. Scicali R, Giral P, D'Erasmo L, Cluzel P, Redheuil A, Di Pino A, et al. High TG to HDL ratio plays a significant role on atherosclerosis extension in prediabetes and newly diagnosed type 2 diabetes subjects. Atherosclerosis. (2021) 331:e237–7. doi: 10.1016/j.atherosclerosis.2021.06.731

PubMed Abstract | Crossref Full Text | Google Scholar

19. Kosmas CE, Rodriguez Polanco S, Bousvarou MD, Papakonstantinou EJ, Peña Genao E, Guzman E, et al. The triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio as a risk marker for metabolic syndrome and cardiovascular disease. Diagnostics (Basel). (2023) 13:929. doi: 10.3390/diagnostics13050929

PubMed Abstract | Crossref Full Text | Google Scholar

20. Liao XP, Zhu HW, Zeng F, Tang ZH. The association and interaction analysis of hypertension and uric acid on cardiovascular autonomic neuropathy. J Endocrinol Invest. (2015) 38:1075–82. doi: 10.1007/s40618-015-0291-x

PubMed Abstract | Crossref Full Text | Google Scholar

21. Karavanaki K, Baum JD. Coexistence of impaired indices of autonomic neuropathy and diabetic nephropathy in a cohort of children with type 1 diabetes mellitus. J Pediatr Endocrinol Metab. (2003) 16:79–90. doi: 10.1515/JPEM.2003.16.1.79

PubMed Abstract | Crossref Full Text | Google Scholar

22. Lynn J, Park M, Ogunwale C, Acquaah-Mensah GK. A tale of two diseases: exploring mechanisms linking diabetes mellitus with Alzheimer's disease. J Alzheimers Dis. (2022) 85:485−501. doi: 10.3233/JAD-210612

PubMed Abstract | Crossref Full Text | Google Scholar

23. Annuzzi G, Della Pepa G, Vetrani C. Vitamin D and cardiovascular disease: is there evidence to support the bandwagon? Curr Atheroscler Rep. (2012) 14:525–34. doi: 10.1007/s11883-012-0281-9

PubMed Abstract | Crossref Full Text | Google Scholar

24. Luo X, Xiong J, Cai H, Zou R, Li F, Wang Y, et al. Effects of vitamin D deficiency on the function of the cardiac autonomic nervous system in rats. Cardiovasc Ther. (2022) 2022:4366948. doi: 10.1155/2022/4366948

PubMed Abstract | Crossref Full Text | Google Scholar

25. Xu ML, Yu XJ, Zhao JQ, Du Y, Xia WJ, Su Q, et al. Calcitriol ameliorated autonomic dysfunction and hypertension by down-regulating inflammation and oxidative stress in the paraventricular nucleus of SHR. Toxicol Appl Pharmacol. (2020) 394:114950. doi: 10.1016/j.taap.2020.114950

PubMed Abstract | Crossref Full Text | Google Scholar

26. Xie JY, Liu WX, Ji L, Chen Z, Gao JM, Chen W, et al. Relationship between inflammatory factors and arrhythmia and heart rate variability in OSAS patients. Eur Rev Med Pharmacol Sci. (2020) 24:2037–53. doi: 10.26355/eurrev_202002_20382

PubMed Abstract | Crossref Full Text | Google Scholar

27. Li YC, Qiao G, Uskokovic M, Xiang W, Zheng W, Kong J. Vitamin D A negative endocrine regulator of the renin-angiotensin system and blood pressure. J Steroid Biochem Mol Biol. (2004) 89-90:387–92. doi: 10.1016/j.jsbmb.2004.03.004

PubMed Abstract | Crossref Full Text | Google Scholar

28. Ullah MI, Uwaifo GI, Nicholas WC, Koch CA. Does vitamin d deficiency cause hypertensionκ Current evidence from clinical studies and potential mechanisms. Int J Endocrinol. (2010) 2010:579–640. doi: 10.1155/2010/579640

PubMed Abstract | Crossref Full Text | Google Scholar

29. Nalbant A, Vatan MB, Varim P, Varım C, Kaya T, Tamer A. Does vitamin D deficiency effect heart rate variability in low cardiovascular risk population? Open Access Maced J Med Sci. (2017) 5:197–200. doi: 10.3889/oamjms

PubMed Abstract | Crossref Full Text | Google Scholar

30. Dogdus M, Burhan S, Bozgun Z, Cinier G, Koyuncu I, Yucel Karabay C, et al. Cardiac autonomic dysfunctions are recovered with vitamin D replacement in apparently healthy individuals with vitamin D deficiency. Ann Noninvasive Electrocardiology. (2019) 24:e12677. doi: 10.1111/anec.12677

PubMed Abstract | Crossref Full Text | Google Scholar

31. Dimova R, Tankova T, Chakarova N. Vitamin D in the spectrum of prediabetes and cardiovascular autonomic dysfunction. J Nutr. (2017) 147:1607–15. doi: 10.3945/jn.117.250209

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: elderly diabetes, heart rate variability, cardiac autonomic neuropathy, 25-(OH)D, diabetic nephropathy

Citation: Tang T, Sun X, Zhang X, Li J, Wang M, Hui C, Cheng Y, Kong X and Sun Y (2025) 25-hydroxyvitamin D as a predictor of reduced heart rate variability in elderly patients with diabetes mellitus. Front. Endocrinol. 16:1556345. doi: 10.3389/fendo.2025.1556345

Received: 06 January 2025; Accepted: 11 April 2025;
Published: 30 April 2025.

Edited by:

Óscar Barquero-Pérez, Rey Juan Carlos University, Spain

Reviewed by:

Barbara Ciastek, University of Opole, Poland
Saima Zaki, Sharda University, India

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*Correspondence: Xiaoming Kong, a3htMTg2QDE2My5jb20=; Yan Sun, c3lhbjk1NkAxNjMuY29t

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