Nigerian Journal of Clinical Practice

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 23  |  Issue : 9  |  Page : 1194--1200

Blood glucose fluctuations in patients with coronary heart disease and diabetes mellitus correlates with heart rate variability: A retrospective analysis of 210 cases


Y Chen, T Jia, X Yan, L Dai 
 Department of General Practice, Wenling Hospital of Traditional Chinese Medicine, Wenling, Zhejiang Province, China

Correspondence Address:
Prof. Y Chen
No. 21, Mingyuanbei Road, Taiping District, Wenling Hospital of Traditional Chinese Medicine, Wenling - 317500, Zhejiang Province
China

Abstract

Aim: This retrospective analysis aims to evaluate the correlation between blood glucose fluctuation (BGF) and heart rate variability (HRV) in patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). Subjects and Methods: In total, 210 patients with CHD and T2DM from January 2014 to January 2019 admitted to Wenling Hospital of Traditional Chinese Medicine were enrolled in this study. Based on whether BGF existed, patients were allocated to BG control group and BG fluctuation group. The HRV parameters, frequency of adverse events, and Gensini score between groups were recorded and Pearson analysis was performed. Results: Results displayed that no significant differences in age, gender, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), alcohol consumption history, drinking history, or serum lipid were found between groups (P > 0.05 for all items). However, the BGF parameters were significantly higher while the HRV parameters were significantly lower in BG fluctuation group, compared with BG control group (P < 0.05 for all items). Pearson analysis showed that despite mean blood glucose (MBG) and mean amplitude of glycemic excursions (MAGE) both correlated with a standard deviation of NN intervals (SDNN) level, the correlation coefficient of MAGE-SDNN was much higher (-0.705 vs -0.185). Additionally, the frequencies of adverse events and Gensini scores were also significantly higher in the BG fluctuation group than the BG control group. Conclusions: It suggests that BGF strongly correlated with HRV in patients with CHD and T2DM. It also provides experimental instructions for clinical practice.



How to cite this article:
Chen Y, Jia T, Yan X, Dai L. Blood glucose fluctuations in patients with coronary heart disease and diabetes mellitus correlates with heart rate variability: A retrospective analysis of 210 cases.Niger J Clin Pract 2020;23:1194-1200


How to cite this URL:
Chen Y, Jia T, Yan X, Dai L. Blood glucose fluctuations in patients with coronary heart disease and diabetes mellitus correlates with heart rate variability: A retrospective analysis of 210 cases. Niger J Clin Pract [serial online] 2020 [cited 2020 Sep 27 ];23:1194-1200
Available from: http://www.njcponline.com/text.asp?2020/23/9/1194/294684


Full Text



 Introduction



The incidence of cardiovascular adverse events was significantly increased in patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). The prognosis of patients with CHD and T2DM was worse than that of patients with CHD alone.[1],[2],[3] As an independent risk factor for the progression of CHD, the existence of T2DM in CHD always impairs cardiovascular autonomic regulation and increases the severity of coronary artery stenosis.[4],[5] Previous studies used to consider the sustained high blood glucose (BG) as the key pathological factor of cardiovascular disease in patients with CHD and T2DM.[6],[7] However, more and more studies have shown that the existence of sustained high BG is not the single risk factor, the existence of blood glucose fluctuation (BGF) in patients with T2DM also correlated tightly with the occurrence and development of chronic complications.[8],[9] BGF refers to a chronic intermittent or paroxysmal high glucose state. BGF is an unstable state, in which BG levels oscillate between peak and valley. Vadim et al. reported that interstitial glucose ffluctuations were associated with heart rate variability (HRV) in type 2 diabetic women.[10] It indicates that BGF is associated with impaired function of cardiovascular autonomic regulation and may correlate with HRV in patients with CHD and T2DM. However, clinical reports assessing this hypothesis are still scarce.

Therefore, in this study, to evaluate the correlation between BGF and HRV in patients with CHD and T2DM, we enrolled 210 patients with CHD and T2DM at our hospital. Patients were distributed to the BG control group or BG fluctuation group based on whether BGF existed. HRV parameters between the two groups were compared and Pearson correlation analysis was performed. The results of this study were expected to provide experimental evidences for clinical practice.

 Subjects and Methods



Patients

A total of 210 patients with CHD and T2DM admitted to the Department of General Practice, Wenling Hospital of Traditional Chinese Medicine from January 2014 to January 2019 were enrolled in this study. Taking into account the sexual differences in cardiac autonomic modulation in patients with T2DM, we exclusively enrolled male patients in this study.[11] T2DM diagnosis was performed according to 1999 World Health Organization (WHO) diagnosis and classification of diabetes.[12] CHD was diagnosed by coronary arteriography. The diagnosis standards meet the American Heart Association (AHA) and the American College of Cardiology (ACC) guidelines for angiography.[13] Exclusion criteria were: (1) if the age of the patient was more than 70 years or less than 45 years, (2) history of drug abuse, alcohol, or opioid abuse, (3) presence of serious basic diseases, such as heart, brain, lung, liver, kidney, or hematopoietic system, (4) if patients refused to sign informed consent, (5) presence of acute complications of diabetes, (6) if CHD combined with serious complications within one month before enrollment, (7) alterations of the treatment plan of T2DM within three months before enrollment, (8) if patients were included in other two or more clinical studies. General characteristics of patients were collected, including age, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking history (at least one cigarette per day, consecutive smoking for more than one year is positive for smoking history), drinking history (drink at least 50 g alcohol a day, consecutive drinking for more than one year is positive for drinking history), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C).

Ethical approval

This retrospective study was approved by the Ethical Committee of Wenling Hospital of Traditional Chinese Medicine (No. 201310). All patients have signed the informed consents and agreed with the publication of this study.

Detection of BGF

BGF refers to an unstable state of BG, in which BG levels oscillate between peak and valley. Therefore, in this study, after patients were diagnosed as CHD combined with T2DM and enrolled in this study, the BG was monitored by Continuous Glucose Monitoring System (CGMS, Medtronic, USA) at the 3rd to 5th day. The BGF was analyzed by 288 independent values automatically recorded by the subcutaneous sensing probe throughout the day. During the monitoring period, the system was corrected at least four times a day by inputting the glucose level, detected from the patient's finger blood. The collected monitoring data were statistically analyzed as follows: mean blood glucose (MBG), standard deviation of blood glucose (SDBG), mean amplitude of glycemic excursion (MAGE), the largest amplitude of glycemic excursion (LAGE), and mean of daily differences (MODD). The standards of BGF were diagnosed according to experts consensus on the management of glycemic variability of diabetes mellitus.[14] SDBG <1.40 mmol/L, MAGE <3.90 mmol/L, LAGE <4.40 mmol/L, and MODD <0.83 mmol/L weres considered as BGF negative. Based on that, the enrolled 210 patients were finally distributed to BG control group (n = 90) and BG fluctuation group (n = 120).

Detection of HRV

The dynamic 24-h electrocardiograph monitoring system (DMS Company, USA) was used to detect the HRV situations. After patients were enrolled, HRV was monitored at the same time with BGF. After data collection, the HRV analysis software was used to calculate the parameters of HRV, including high-frequency (HF) domain (0.16-0.40 Hz); low-frequency (LF) domain (0.05-0.15 Hz); standard deviation of NN (SDNN) intervals; root-mean-square of difference (rMSSD)-value of adjacent NN interval; percent of the number whose difference between adjacent NN interval is more than 50 ms (pNN50).

Gensini score

Coronary angiography was performed using Judkins method.[15] In brief, transfemoral or radial artery puncture was performed for coronary angiography and results were shown as Gensini score. Gensini score results in the lesion partial score multiplied by the stenosis score.[16] The lesion scores were: 5 points for the left main branch lesion; 2.5 points for the lesions of the left anterior descending branch or the circumflex artery in the proximal segment; 1.5 points for the lesions of the left anterior descending artery in the middle segment; 1 point for the lesions of the left anterior descending artery or the circumflex branch in the distal segment; 1 point for the lesions of the right coronary artery; 0.5 point for the small branch lesions. Stenosis evaluation: stenosis <25% counted 1 point; stenosis 26% to 50% counted 2 points; stenosis 51% to 75% counted 4 points; stenosis 76% to 90% counted 8 points; stenosis 91% to 99% counted 16 points; complete occlusion counted 32 points.

Follow-up

Adverse events including angina pectoris, myocardial infarction between two groups were recorded by XJ Yan and LL Dai, who were unaware of the patient's allocation. Adverse events that occurred during in-patient hospitalization or within three months after discharge were collected and analyzed.

Statistical analysis

The statistical analysis was performed by Statistical Package for the Social Sciences (SPSS) 19.0 software. The measurement data were displayed as mean ± standard deviation (X ± SD). The count data were displayed as frequency. Normally distributed measurement data were analyzed by t-test and non-normally distributed measurement data were analyzed by nonparametric analysis. The categorical data were analyzed by the χ2 test. The correlation between MAGE and HF, LF, SDNN, rMSSD, pNN50 was analyzed by Pearson correlation analysis. Two-sided P < 0.05 was considered as statistically significant.

 Results



Sample characteristics

A total of 210 patients were enrolled in this study with 90 cases in BG control group while 120 cases in BGF group. All cases were male. General characteristics of patients were displayed in [Table 1]. No significant differences in age (P = 0.405), height (P = 0.056), weight (P = 0.075), body mass index (BMI) index (P = 0.969), SBP (P = 0.088), and DBP (P = 0.064) were found between the two groups. The number of patients with smoking history (P = 0.343) or alcohol consumption history (P = 0.550) also displayed no significant differences between the two groups. Further analysis of serum lipid situations was displayed in [Table 2]. Results also displayed that compared with BG control group, the TC (P = 0.158), TG (P = 0.598), high-density lipoprotein cholesterol (HDL-C) (P = 0.119), and LDL-C (P = 0.094) of patients in BG fluctuation group were not significantly different. These results indicate that the biochemical parameters between the two groups were similar.{Table 1}{Table 2}

BGF was associated with HRV in patients with CHD and T2DM

Then, CGMS results were analyzed and depicted in [Table 3] to show the differences of SDBG, MAGE, LAGE, and MODD between BG control and BG fluctuation groups. As shown, there were no significant differences in duration of T2DM between two groups (6.59 ± 2.30 vs 6.92 ± 2.25, P = 0.468). However, the MBG in BG fluctuation group was significantly higher than BG control group (10.13 ± 1.75 vs 8.16 ± 0.79, P < 0.001*). The crucial parameters, SDBG and MAGE, were all less than 1.4 mmol/L and 3.9 mmol/L, respectively in BG control group. Additionally, SDBG (2.50 ± 0.68 vs 1.22 ± 0.13, P < 0.001*), MAGE (5.63 ± 1.45 vs 2.93 ± 0.41, P < 0.001*), LAGE (7.80 ± 2.43 vs 3.66 ± 0.55, P < 0.001*), and MODD (1.77 ± 0.57 vs 0.55 ± 0.18, P < 0.001*) were all significantly higher in BG flutation group, compared with BG control group.{Table 3}

Further, the HRV situations were depicted in [Table 4]. As shown, compared with BG control group, HF (553.12 ± 88.82 vs 720.80 ± 100.38, P < 0.001*), LF (645.33 ± 119.51 vs 934.20 ± 177.85, P < 0.001*), SDNN (97.14 ± 28.78 vs 127.82 ± 26.81, P < 0.001*), rMSSD (25.26 ± 8.46 vs 38.35 ± 9.29, P < 0.001*), and pNN50 (4.91 ± 2.60 vs 14.20 ± 8.15, P < 0.001*) were all significantly lower in BG fluctuation group than that in BG control group. The decrease of HRV in BG fluctuation group meant the impaired function of cardiovascular autonomic regulation. Additionally, Pearson correlation analysis was performed to detect whether BGF was correlated with HRV. As shown in [Figure 1], the key parameter of BGF, MAGE was strongly correlated with the key parameter of HRV, SDNN (r = -0.705, P < 0.001*). MAGE was also significantly correlated with HF (r = -0.396, P < 0.001*), LF (r = -0.450, P < 0.001*), rMSSD (r = -0.615, P < 0.001*), pNN50 (r = -0.686, P < 0.001*). The MBG level was also correlated with SDNN (r = -0.185, P = 0.043), but the correlation coefficient of MBG-SDNN was much lower than that of MAGE-SDNN (-0.185 vs -0.705). It indicates that the existence of BGF is associated with HRV situations in patients with CHD and T2DM.{Table 4}{Figure 1}

BGF affects the prognosis in patients with CHD and T2DM

After in-hospitalization observation and follow-up, the incidence of adverse events also was significantly different between the two groups. The frequency of angina pectoris and myocardial infarction was significantly higher in BG fluctuation group, compared with BG control group. Additionally, the Gensini score was used to define the severity of coronary artery lesion [Table 5]. Results displayed that BG fluctuation group developed significantly higher Gensini scores than BG control group (46.60 ± 20.02 vs 29.49 ± 13.93, P < 0.001*). It indicates that the existence of BGF in patients with CHD and T2DM also deteriorates the prognosis and severely affected the health of the cardiovascular system.{Table 5}

 Discussion



Cardiovascular autonomic neuropathy is one of the common complications of T2DM.[17] Cardiovascular autonomic neuropathy is reported to cause painless myocardial infarction[18] and sudden cardiac death[19] in patients with T2DM, which is one of the important reasons for the increasing diabetes-induced mortality. Maser et al. performed the meta-analysis and supported the strong association between cardiovascular autonomic neuropathy and increased risk of mortality.[20] In clinical practice, HRV is an accurate and sensitivity index for evaluating the severity of cardiovascular autonomic neuropathy.[21] As the non-invasive method, HRV effectively evaluates the activity degree and pathological state of cardiovascular autonomic neuropathy and HRV is currently widely used in clinics. Nolan et al. proved that patients with reduced HRV parameters had a high risk of death and HRV was a better predictor for progressive heart failure.[22] Tsuji et al. demonstrated that HRV was the prognostic factor for evaluating mortality beyond the traditional risk factors in a cohort study.[23] Thus, the application of HRV detection in T2DM helped to predict the prognosis of patients and evaluate the severity of cardiovascular autonomic neuropathy.

Studies used to consider the pathophysiological alterations in cardiac neurons or nerve fibers induced by hyperglycemia were the key pathologic mechanism of cardiovascular autonomic neuropathy. High blood concentration of glucose[7] or HbA1c[24] level was the main pathogenic risk factor. However, recent studies have pointed out that diabetes-induced oxidative stress is the key factor in the initiation of diabetic complications, including diabetes-induced cardiovascular autonomic neuropathy.[25] Diabetes always induces oxidative stress and free radicals in cells, which were more than the ability of cells to resist oxidative stress. Studies also found that the accumulation of free radicals will oxidize proteins, fats, and nucleic acids, which lead to the dysregulations of cellular metabolism, signal transduction, and gene transcription. Finally, the severe accumulation of oxidative stress in cells leads to cell necrosis or apoptosis. Piconi et al. reported that compared with stable hyperglycemia, fluctuating hyperglycemia induced more severe oxidative stress and increased the expression of adhesion molecules (ICAM-1).[26] Due to the transient huge elevation and large fluctuation of BG, the fluctuation of BG directly induced toxic effects through oxidative stress, resulting in the necrosis or apoptosis of the cardiac neuronal cell. Based on that, fluctuation of BG induced the imbalance of sympathetic-parasympathetic nerves and accelerate the progress of cardiovascular autonomic neuropathy.

It is reported that compared with CHD alone, the existence of T2DM in CHD affects the prognosis of CHD and increases mortality.[3] However, whether BGF was the key influential factor that is affecting the progression of cardiovascular diseases in patients with CHD and T2DM remains unclear. This study enrolled 210 patients, diagnosed as CHD and T2DM. Patients were distributed to BG control group or BG fluctuation group mainly based on BGF parameters. The enrolled patients had good homogeneity and the risk factors were similar between groups. Then, CGMS result displayed that patients in BG control group had no BGF: SDBG <1.40 mmol/L, MAGE <3.90 mmol/L, LAGE <4.40 mmol/L and MODD <0.83 mmol/L. SDBG, MAGE, LAGE, MODD were significantly increased in patients of BG fluctuation group than that of BG control group. HRV results displayed that compared with BG control group, LF, HF, SDNN, rMSSD, and pNN50 were all significantly decreased in BG fluctuation group. It indicates that BG fluctuations were associated with HRV. Further, Pearson correlation analysis revealed that the most influential factor of HRV was MAGE and not MBG. MAGE also significantly correlated with LF, HF, SDNN, rMSSD, and pNN50 parameters. These results were also consistent with the previous study. Vadim et al. reported that the interstitial glucose ffluctuation was associated with HRV in type 2 diabetic women.[10] However, it is more appropriate to recruit only male patients for the study of HRV, as HRV is also affected by the level of estrogen. Combining with our result, it indicates that BGF was the main cause of reduced HRV in T2DM patients. Additionally, BG fluctuation group developed a higher Gensini score and more adverse events than BG control group. The existence of BGF also affects the prognosis and increases the severity of coronary artery stenosis, which was consistent with Naito's standpoint. He suggests that the existence of T2DM is the major risk of coronary artery diseases. Early control of abnormal glucose metabolism may be beneficial in preventing the progression of T2DM and coronary artery diseases.[27]

A few limitations also existed in this study, for instance: this study only enrolled male patients, follow-up period in this study was short, etc., Additionally, this study failed to prove whether BGF-induced oxidative stress was correlated with HRV in patients and whether oxidative stress could exert as the therapeutic target in preventing the progress of HRV. These limitations and remaining questions could be better addressed in a further large-sample, multicenter cohort study.

 Conclusion



This study displayed that HRV parameters were significantly decreased in BG fluctuation group, compared with BG control group. BGF parameter: MAGE also correlated strongly with HRV parameters: LF, HF, SDNN, rMSSD, and pNN50. Patients in BG fluctuation group suffered from more frequencies of adverse events and higher Gensini score. It suggests that BGF strongly correlates with HRV in patients with CHD and T2DM. The existence of BGF also deteriorates the prognosis and the severity of coronary stenosis.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

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