Li AQ, Zhang F. Blood glucose variability impacts heart rate dynamics in older type 2 diabetic and coronary heart disease patients. World J Diabetes 2025; 16(10): 110722 [PMID: 41113498 DOI: 10.4239/wjd.v16.i10.110722]
Corresponding Author of This Article
Feng Zhang, Department of Neurosurgery, Hengyang Central Hospital, No. 12 Yancheng Road, Hengyang 421000, Hunan Province, China. zhangfeng1386@hotmail.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
An-Qi Li, Department of Metabolism and Endocrinology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421000, Hunan Province, China
Feng Zhang, Department of Neurosurgery, Hengyang Central Hospital, Hengyang 421000, Hunan Province, China
Author contributions: Li AQ wrote the paper, designed the research and analyzed the data; Li AQ and Zhang F performed the research; all authors made substantial intellectual contributions to this paper.
Supported by National Natural Science Foundation of China, No. 82270939; The Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China, No. 20214310NHYCG02.
Institutional review board statement: This study was approved by the Ethic Committee of The First Affiliated Hospital, Hengyang Medical School, University of South China.
Informed consent statement: All the study subjects provided informed consent.
Conflict-of-interest statement: All the authors have no conflict of interest related to the manuscript.
Data sharing statement: No additional data are available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Feng Zhang, Department of Neurosurgery, Hengyang Central Hospital, No. 12 Yancheng Road, Hengyang 421000, Hunan Province, China. zhangfeng1386@hotmail.com
Received: June 27, 2025 Revised: August 1, 2025 Accepted: September 16, 2025 Published online: October 15, 2025 Processing time: 110 Days and 1.4 Hours
Abstract
BACKGROUND
Cardiac autonomic neuropathy correlates intimately with cardiovascular complications and unexpected death. It is a typical clinical abnormality seen in coronary artery disease-affected individuals with concurrent type 2 diabetes mellitus (T2DM). Moreover, blood glucose (BG) variability has been clinically shown to induce cardiovascular events and sudden death.
AIM
To investigate how BG variability impacts heart rate (HR) dynamics in older adults with T2DM + coronary heart disease (CHD) and to evaluate the ability of functional myocardial ischemia to predict outcomes in this cohort.
METHODS
We enrolled 143 older T2DM + CHD patients admitted to the First Affiliated Hospital, Hengyang Medical School, University of South China over a 3.5-year period (January 2018 to July 2021). Using a standard deviation of BG cutoff of 1.4 mmol/L, subjects were stratified into abnormal (n = 75) and normal (n = 68) fluctuation groups. All patients underwent 72-hour dynamic BG monitoring to detect BG fluctuation parameters. The time domain index of HR variability was measured by dynamic electrocardiogram. To determine how well glucose fluctuation measures predicted functional myocardial ischemia, the area under the receiver operating characteristic curve (AUC) was calculated.
RESULTS
The abnormal fluctuation group showed greater levels of mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), largest amplitude of glycemic excursions (LAGE), and mean postprandial glucose excursions (MPPGE) relative to the normal group (P < 0.05), along with lower levels of standard deviation of normal-to-normal (NN) interval (SDNN), standard deviation of the average NN interval (SDANN), standard deviation of NN intervals over every 5-minute period (SDNNindex), root mean square of successive differences (rMSSD), and percentage of NN intervals differing by > 50 ms (pNN50; P < 0.05). Pearson correlation analysis showed that MAGE, MODD, LAGE, and MPPGE were negatively correlated with SDNN, SDANN, SDNNindex, rMSSD, and pNN50 in older patients with T2DM complicated by CHD (P < 0.05). The AUC of MAGE combined with MPPGE in predicting the occurrence of functional myocardial ischemia was 0.912, which was significantly higher than 0.694 of SDNN (P < 0.05).
CONCLUSION
A negative correlation was found between BG variability and HR dynamics in older CHD + T2DM patients, and MAGE combined with MPPGE demonstrated better efficacy in predicting functional myocardial ischemia, which deserves clinical attention.
Core Tip: Recent evidence has linked cardio-cerebrovascular events in type 2 diabetes mellitus patients with concurrent coronary artery disease to blood glucose (BG) fluctuation, and the greater the BG fluctuation, the higher the incidence of chronic vascular complications of diabetes mellitus and acute cardiovascular events, and the worse the prognosis. This study analyzes how BG variability impacts heart rate dynamics in such patients and the predictive properties of functional myocardial ischemia.
Citation: Li AQ, Zhang F. Blood glucose variability impacts heart rate dynamics in older type 2 diabetic and coronary heart disease patients. World J Diabetes 2025; 16(10): 110722
Coronary heart disease (CHD) is among the most prevalent cardiovascular issues in the older population, and its incidence is increasing, becoming an important factor affecting the life and health of this population[1]. Type 2 diabetes mellitus (T2DM) independently increases CHD risk in older patients, which can further aggravate their condition. For geriatric patients with T2DM-CHD comorbidity, improving condition assessment and prognosis prediction can provide an effective basis for rational diagnosis and treatment. The close connection between blood glucose (BG) variability and health status in older patients with both T2DM and CHD has been well established. It is expected to make significant progress in evaluating patients’ condition[2]. Although heart rate (HR) variability (HRV) is an important indicator for assessing CHD severity and prognosis, it is cumbersome to detect and difficult to widely adopt[3]. At this stage, how BG fluctuations correlate with HRV in older T2DM patients with CHD remains underexplored. Moreover, T2DM-CHD interactions in older patients can directly lead to functional myocardial ischemia. If the risk of this condition can be accurately assessed in advance, timely and targeted therapeutic intervention is crucial for enhancing patient clinical outcomes. However, the predictive effect of BG fluctuation parameters on functional myocardial ischemia remains to be determined. Therefore, our objective was to evaluate whether BG fluctuations influence HRV in senior patients with T2DM and CHD and the predictive performance of functional myocardial ischemia.
MATERIALS AND METHODS
Baseline data
In this retrospective study, the study population included 143 older T2DM + CHD patients visiting The First Affiliated Hospital, Hengyang Medical School, University of South China, over a 3.5-year period (January 2018 to July 2021). Inclusion was restricted to patients who fulfilled these conditions: (1) Aged 60-80 years; (2) Met the diagnostic criteria for type 2 diabetes and diagnosed with CHD by coronary angiography; and (3) Received a stable hypoglycemic treatment plan for nearly 90 days. We applied the following exclusion criteria: (1) Acute complications related to T2DM and CHD; (2) Acute myocardial infarction and unstable angina pectoris occurred within the past 60 days; (3) Severe liver and renal insufficiency and malignant tumors; and (4) Ectopic heart rhythm.
Based on whether the standard deviation of BG was ≥ 1.4 mmol/L[4], participants were categorized into a group with abnormal BG fluctuation (n = 75) and a group with normal BG fluctuation (n = 68). Patient selection flow diagram can be found in Figure 1.
Figure 1 Flow chart of the patients selection.
CHD: Coronary heart disease; T2DM: Type 2 diabetes mellitus; SDBG: Standard deviation of blood glucose.
Detection method
BG level: All patients underwent 72-hour continuous BG monitoring; the Medtronic’s Guardian REAL-Time continuous BG monitoring system was used to monitor and record 288 BG values throughout the day, input finger BG correction probe information no less than four times during the monitoring period, and detect BG fluctuations parameters: Mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), largest amplitude of glycemic excursions (LAGE), mean postprandial glucose excursions (MPPGE). All patients had completed BG monitoring.
When ensuring that the patient had stopped the intake of drugs (such as β-blockers) that may affect the HR and were emotionally stable, the United States Century 3000 24-hour ambulatory electrocardiograph was used to continuously record the 24-hour electrocardiogram signal, and detect the time-domain indicators of HRV, including the standard deviation of normal-to-normal (NN) interval (SDNN), standard deviation of the average NN interval (SDANN) every 5 minutes, standard deviation of NN intervals over every 5-minute period (SDNNindex), root mean square of successive differences (rMSSD), and the percentage of NN intervals differing by > 50 ms (pNN50). In all cases, 80% of the data were collected and recorded.
Observation indicators
Inter-group comparisons were made regarding BG fluctuation parameters (MAGE, MODD, LAGE, and MPPGE) and time-domain HRV metrics (SDNN, SDANN, SDNNindex, rMSSD, and pNN50). Pearson correlation examined how BG fluctuation parameters correlated with HRV time-domain measures. To observe the occurrence of functional myocardial ischemia, in coronary angiography, the blood flow reserve fraction after inducing the maximum congestion of the culprit vessel is ≤ 0.8, which is the basis for judging functional myocardial ischemia[5]. Multivariate logistic regression analyzed key contributors to functional myocardial ischemia, with the predictive power of each BG fluctuation parameter for functional myocardial ischemia assessed by the area under the receiver operating characteristic (ROC) curve (AUC).
Statistical analysis
SPSS version 18.0 processed all the collected data. Measurements demonstrating normality appear as the mean ± SD, with inter-group differences identified by t-tests. Count data were analyzed by the χ2 test. The AUC under the ROC curve of the two groups was compared by the Delong test; Significant differences were noted at the P < 0.05 Level.
RESULTS
General information
The abnormal BG fluctuation group included 42 male and 33 female patients aged between 62 years and 78 years (average: 65.63 ± 5.72 years). The body mass index ranged from 20-28 (23.55 ± 1.42) kg/m2. Moreover, 27, 11, and 40 patients had a history of smoking, drinking, and hypertension, respectively.
The normal blood sugar fluctuation group included 40 male and 28 female patients. They were 61-79 years old (average: 64.91 ± 5.68). The body mass index was 20-29 (24.01 ± 1.39) kg/m2. Moreover, 25, 10, and 37, 10, and 37 patients had a history of smoking, drinking, and hypertension, respectively. General data did not differ notably across groups (P > 0.05; Table 1).
Table 1 Comparison of the general information between the two groups.
Patients with BG fluctuation abnormalities exhibited greater MAGE, MODD, LAGE and MPPGE relative to those with normal BG fluctuations (P < 0.05; Table 3).
Table 3 Comparison of blood glucose fluctuation parameters between the two groups (mol/L).
Lower SDNN, SDANN, SDNNindex, rMSSD, and pNN50 were found in patients presenting BG fluctuation abnormalities vs the normal BG fluctuation group (P < 0.05; Table 4).
Table 4 Comparison of heart rate variability time-domain indicators between the two groups.
BG fluctuation parameters and HRV time domain indicators
The Pearson correlation analysis showed that MAGE, MODD, LAGE, and MPPGE were negatively correlated with SDNN, SDANN, SDNNindex, rMSSD, and pNN50 in older patients with T2DM and CHD (P < 0.05; Table 5).
Table 5 Correlation analysis between blood glucose fluctuation parameters and heart rate variability time domain indicators.
Among 143 older patients with T2DM complicated by CHD, functional myocardial ischemia occurred in 26, accounting for 18.18%. Multivariate modeling revealed MAGE, MPPGE, and SDNN as independent predictors of functional myocardial ischemia in older patients with T2DM and CHD (P < 0.05). The data are shown in Table 6.
ROC curve analysis of BG fluctuation parameters predicting the occurrence of functional myocardial ischemia
According to the ROC curve analysis, the AUC of MAGE + MPPGE in predicting functional myocardial ischemia was 0.912, which was significantly greater than that of SDNN, 0.694. The Delong test showed that the difference was significant (Z value was 2.448, P value was 0.019). The ROC curve is shown in Figure 2.
Figure 2 Receiver operating characteristic curve of blood glucose fluctuation parameters predicting the occurrence of functional myocardial ischemia.
MAGE: Mean amplitude of glycemic excursions; MPPGE: Mean postprandial glucose excursions; SDNN: Standard deviation of normal-to-normal interval; ROC: Receiver operating characteristic.
DISCUSSION
Patients with CHD often have impaired BG metabolism, particularly in older patients. Up to 70% of older patients with CHD have T2DM[6]. For older patients with T2DM and CHD, the hyperglycemia-related damage to the heart cannot be ignored, and it may be an important cause of disease progression. For a long time, HbA1c has been recognized as an effective indicator to measure the effect of blood sugar control, and its level is positively correlated with the risk of diabetes-related complications. However, recently, even with the same HbA1c, the occurrence of functional myocardial ischemia in older patients with T2DM and CHD varies[7]. International studies have found that changes in the cardiac structure and function in older patients with T2DM and CHD are closely related to BG fluctuations because both chronic persistent hyperglycemia and BG fluctuations can cause damage to target organs[8]. Zhang et al[9] indicated that fluctuating hyperglycemia has a more significant effect on cardiac structure and function. As shown in Table 1, all time-domain HRV measures were lower in the abnormal BG fluctuation group compared to the normal BG fluctuation group, which is similar to the research results of Shimabukuro et al[10], suggesting that patients with abnormal BG fluctuation have lower HRV, considering that abnormal blood sugar fluctuations can promote cardiac autonomic neuropathy. Li et al’s study[11] have shown that the magnitude of blood sugar fluctuations can better predict the HRV in older patients with T2DM and CHD, which also supports the above point of this study, suggesting that blood sugar fluctuations have greater damage to the myocardium.
The effect of BG fluctuations on HRV in older T2DM patients with concurrent CHD has only received attention in recent years, and related research is still in its infancy. Cardiovascular autonomic neuropathy (CAN), a prevalent but underdiagnosed DM-induced long-term complication, is associated with elevated disease burden and death rates[12]. Although some studies have shown higher glycemic variability in CAN[13], other studies have found no relationship between glycemic variability and CAN[14]. In this study, a 72-hour dynamic BG monitoring system was used, which can accurately reflect BG fluctuations throughout the day[15]. As shown in Table 1, BG fluctuation parameters were higher in patients with BG fluctuation abnormalities compared to normal counterparts, suggesting that the above indicators can effectively reflect changes in BG fluctuations and do not depend on the overall BG level. Studies have shown significantly greater BG fluctuation parameters in older patients with T2DM-CHD comorbidity than those with simple T2DM or CHD, indicating that the former has greater blood sugar fluctuations[16].
In this study, Pearson correlation analysis was conducted, and the BG fluctuation parameters in older T2DM + CHD patients were negatively correlated with the time domain indicators of HRV (P < 0.05). This finding supports the inverse connection between BG fluctuation degree and HRV reduction in T2DM + CHD patients, indicating that BG fluctuations may affect the degree of HRV reduction in patients. In the broadest sense, variability serves as a crucial element in the body’s major control systems[17]. Insulin action and glucose tolerance are related to circadian rhythms of hormone synthesis[18]. Quantifiable glucose variability (GV) occurs during physiologic glucose tolerance[19]; however, GV shows significant elevation in diabetic and glucose dysregulation conditions[20]. Long-term abnormal BG fluctuations can cause a series of metabolic disorders, trigger oxidative stress, lead to cardiac autonomic neuropathy, mainly vagus nerve damage, and then reduce HRV. Monnier et al[21] conducted a highly cited clinical study involving 21 patients with T2DM, showing a strong positive correlation between 24-hour urinary excretion rates of free 8-iso prostaglandin F2alpha (8-iso PGF2a), an oxidative stress marker, and MAGE. The BG volatility of older patients with T2DM and CHD should be widely concerned. By strengthening BG control and maintaining BG balance, to delay HRV reduction, it is expected to reduce the occurrence of functional myocardial ischemia.
Functional myocardial ischemia is an important complication causing adverse prognosis in older T2DM + CHD cases[22]. Numerous studies have shown that endothelial cell dysfunction and damage is one of the main mechanisms resulting in functional myocardial ischemia onset and progression, and long-term BG fluctuations can seriously damage endothelial cell function[23-25]. Farabi et al’s study[26] have shown that large blood GV can lead to more severe endothelial dysfunction and coronary atherosclerosis, and HRV reduction may be involved in the process. Both impaired cardiac autonomic function and reduced HRV are more likely to contribute to functional myocardial ischemia. As shown in Table 4, MAGE, MPPGE, and SDNN are independent predictors of functional myocardial ischemia in older patients with T2DM-CHD comorbidity (P < 0.05). Consistent with the findings of Buchner[27], they suggested that abnormal BG fluctuations and reduced HRV may increase the risk of functional myocardial ischemia, and the predictive performance of functional myocardial ischemia remains questionable. Moreover, the ROC curve analysis was used in this study. The AUC of MAGE combined with MPPGE in predicting the occurrence of functional myocardial ischemia was 0.912, which was significantly greater than the 0.694 of SDNN, indicating that MAGE combined with MPPGE had better performance in predicting the occurrence of functional myocardial ischemia. The reason is not difficult to explain. Abnormal fluctuation in blood sugar can damage vascular endothelial cells, improve platelet activity, result in lipid metabolism disorder, hypercoagulable state of the blood, and oxygen free radicals increase, which in turn causes microcirculation stasis and aggravates atherosclerosis. It increases the risk of functional myocardial ischemia.
This study still has some limitations. First, the modest cohort size may underlie the absence of statistical significance in some subgroup comparisons. Second, this was a retrospective, and a selection bias might be present. This might limit the ability to establish causal relationships. Third, although we have adjusted for some potential confounding factors, other confounders are at play. Fourth, lacking long-term follow-up data, we were unable to assess the long-term impacts of BG fluctuations on HRV and functional myocardial ischemia. Fifth, how glycemic variability impacts the severity of coronary artery lesions remains to be explored. All in all, a multicenter prospective design with expanded sample sizes and long-term follow-up research to validate correlations and explore causality.
CONCLUSION
In conclusion, BG fluctuations and HRV in older patients with T2DM-CHD comorbidity are negatively correlated, and MAGE combined with MPPGE demonstrated better performance in predicting functional myocardial ischemia, which is worthy of clinical attention. Of course, this study is constrained by its limited case recruitment, lack of extended follow-up results, and absence of analysis of the relationship between BG fluctuations and the degree of coronary artery disease. Expanding the research scale in the future, improving the research design, and deeply analyzing the mechanism of BG fluctuation in the occurrence and development of CHD is necessary.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or Innovation: Grade C, Grade C
Scientific Significance: Grade B, Grade C
P-Reviewer: Allison B, MD, Associate Professor, United Kingdom; Horowitz M, MD, PhD, DSc, FRACP, Professor, Australia; Pratley R, MD, Slovenia S-Editor: Lin C L-Editor: A P-Editor: Wang WB
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