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Association between initial in-hospital heart rate and glycemic control in patients with acute ischemic stroke and diabetes mellitus

BMC Endocrine Disorders volume 23, Article number: 69 (2023) Cite this article

Abstract

Background

A high resting heart rate (HR) has been associated with an increased risk of diabetes mellitus. This study explored the association between initial in-hospital HR and glycemic control in patients with acute ischemic stroke (AIS) and diabetes mellitus.

Methods

We analyzed data from 4,715 patients with AIS and type 2 diabetes mellitus enrolled in the Chang Gung Research Database between January 2010 and September 2018. The study outcome was unfavorable glycemic control, defined as glycated hemoglobin (HbA1c) ≥ 7%. In statistical analyses, the mean initial in-hospital HR was used as both a continuous and categorical variable. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression analysis. The associations between the HR subgroups and HbA1c levels were analyzed using a generalized linear model.

Results

Compared with the reference group (HR < 60 bpm), the adjusted ORs for unfavorable glycemic control were 1.093 (95% CI 0.786–1.519) for an HR of 60–69 bpm, 1.370 (95% CI 0.991–1.892) for an HR of 70–79 bpm, and 1.608 (95% CI 1.145–2.257) for an HR of ≥ 80 bpm. Even after adjusting for possible confounders, the HbA1c levels after admission and discharge among diabetic stroke patients increased significantly in the subgroups with higher HRs (p < 0.001).

Conclusions

High initial in-hospital HR is associated with unfavorable glycemic control in patients with AIS and diabetes mellitus, particularly in those with an HR of ≥ 80 bpm, compared with those with an HR of < 60 bpm.

Peer Review reports

Background

Diabetes mellitus is a chronic disease with long-term complications, and the development of hyperglycemia is common in individuals with diabetes mellitus after an acute stroke [1]. Although the relationship between the control of hyperglycemia and cardiovascular risk remains largely controversial [2], glycemic control remains crucial to managing diabetes mellitus. The American Diabetes Association recommends a target glycated hemoglobin (HbA1c) level of < 7% for most adult patients [3]. In stroke patients with diabetes, medical therapies and the goal of glycemic control should be individualized, but for most patients, an HbA1c level of < 7% is also recommended [4]. However, only approximately 50% of patients with diabetes mellitus reach their HbA1c target [5]. The reasons for poor glycemic control among patients with type 2 diabetes mellitus are complex and multivariable.

A high resting heart rate (HR) is associated with an increased risk of type 2 diabetes mellitus [6,7,8,9] and poor glycemic status [10]. Increased levels of HbA1c and insulin dose are associated with increased diastolic blood pressure and HR in children with type 1 diabetes mellitus [11]. Compared with conventional therapy, the intensive management of diabetes mellitus is associated with a lower resting HR in patients with type 1 diabetes mellitus [12]. However, the association between resting HR and glycemic control in patients with acute ischemic stroke (AIS) and type 2 diabetes mellitus is seldom discussed.

This study evaluated the relationship between mean initial in-hospital HR and glycemic control in patients with AIS and type 2 diabetes mellitus.

Methods

Participants and data collection

We conducted this retrospective cohort study by using data from the Chang Gung Research Database [13], the largest multi-institutional collection of electronic medical records in Taiwan. Patients with AIS were selected per the method of Lee et al. [14]. A total of 21,655 patients with AIS between January 2010 and September 2018 were admitted to one of the seven branch hospitals of the Chang Gung Healthcare System. Patients with type 2 diabetes mellitus were defined as patients who, according to their medical records, received a diagnosis of type 2 diabetes mellitus after discharge, were administered drugs or insulin to treat hyperglycemia after admission, or had laboratory results that indicated the presence of type 2 diabetes mellitus, according to the American Diabetes Association [15]. Among 21,655 patients with AIS, 8,680 patients had type 2 diabetes mellitus, and 4,715 patients were followed up regularly at our outpatient clinics with at least three measurements of HbA1c levels after discharge. Ultimately, the data of 4,715 patients were used for this analysis. The study was conducted in accordance with the Declaration of Helsinki, and local ethical approval was obtained.

Primary demographic and clinical characteristics were collected, including stroke severity as assessed using the claims-based stroke severity index (SSI). The SSI was then converted to the National Institutes of Health Stroke Scale (NIHSS) score by using the following equation: estimated NIHSS (eNIHSS) = 1.1722 × SSI − 0.7533 [16]. Stroke severity was categorized into mild (eNIHSS score < 5), moderate (eNIHSS score 5–14), and severe (eNIHSS score > 14) [17]. The estimated glomerular filtration rate (eGFR) was determined using the Modification of Diet in Renal Disease equation as follows: eGFR = 186 × (serum creatinine level)−1.154 × (age)−0.203 × 0.742 (if female) [18]. Height, body weight, systolic blood pressure, diastolic blood pressure, HR, lipid profiles, and creatinine, alanine aminotransferase, and HbA1c levels after admission were obtained from the records of the enrolled patients. The patient’s history of cancer prior to admission was obtained from the Taiwan Cancer Registry [19]. Mean systolic blood pressure, diastolic blood pressure, and HR were calculated from vital sign measurements taken during the patient’s first 3 days of hospitalization. The mean HbA1c levels after discharge were calculated from the first three recorded HbA1c values after discharge. The intervals between HbA1c measurements accorded with the latest guidelines by the American Diabetes Association and National Institute for Health and Care Excellence [20, 21].

Study outcomes

The study outcome was the status of glycemic control. Favorable glycemic control was defined as mean HbA1c < 7%, and unfavorable glycemic control was defined as mean HbA1c ≥ 7%.

Statistical analysis

Quantitative variables are summarized as mean (standard deviation) or median (interquartile range), depending on the distribution of the data, and categorical variables are presented as number (percentage). Differences in demographic features between the patients with favorable and unfavorable glycemic control were evaluated using the student’s t-test or Wilcoxon’s rank sum test for continuous data and the chi-square test for categorical data.

To further evaluate the effect of initial in-hospital HR on the levels of HbA1c and glycemic control, the patients were classified into four subgroups according to mean HR (< 60, 60–69, 70–79, and ≥ 80 beats per minute [bpm]). Multivariable logistic regression analysis was used to evaluate the relationship between HR and unfavorable glycemic control. In statistical analyses, the mean initial in-hospital heart rate was used as both a continuous and categorical variable. In addition to crude odds ratios (ORs), adjusted ORs and 95% confidence intervals (CIs) were calculated using the HR < 60 bpm subgroup as the reference for the analysis. Model 1 included HR, age, and sex, and model 2 included HR, age, sex, stroke severity, body mass index, hypertension, dyslipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, history of cancer before admission, smoking status, levels of total cholesterol, triglyceride, eGFR, and alanine aminotransferase, systolic blood pressure, diastolic blood pressure, and the use of beta blockers and insulin.

The influence of the initial in-hospital HR on the levels of HbA1c was assessed using the generalized linear model. We examined the effect of initial in-hospital HR on HbA1c level by adjusting for possible confounders, namely age, sex, stroke severity, body mass index, hypertension, dyslipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, history of cancer before admission, smoking status, levels of total cholesterol, triglyceride, eGFR, and alanine aminotransferase, systolic blood pressure, diastolic blood pressure, and the use of beta blockers and insulin. All analyses were performed using SPSS for Windows, version 22 (SPSS Inc., Chicago, IL, USA).

Results

Participant demographics

A total of 4,715 patients with AIS and type 2 diabetes mellitus were included in this study (median age, 66 years, interquartile range: 59–74 years; 61.50% men). The median systolic blood pressure and diastolic blood pressure were 154.00 (interquartile range: 141.42–167.55 mmHg) and 85.13 mmHg (interquartile range: 78.70–92.33 mmHg), respectively, and the median HR was 74.71 bpm (interquartile range: 68.25–80.87 bpm) (Table 1). The intervals between discharge and the first, second, and third measurements of HbA1c levels were 87 (interquartile range: 46–149 days), 208 (interquartile range: 138–328 days), and 341 days (interquartile range: 237–507 days), respectively. Overall, 54.08% of the patients had unfavorable glycemic control after AIS. The values of HbA1c after admission and mean HbA1c after discharge were 7.80% (interquartile range: 6.80–9.60%) and 7.10% (interquartile range: 6.47–8.00%), respectively (Table 2).

Table 1 Comparison of clinical characteristics between the groups according to glycemic control
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Table 2 HbA1c after admission and mean HbA1c and SD of HbA1c after discharge, according to mean heart rate subgroups
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Clinical factors associated with glycemic control

Univariate analysis of the data revealed that several clinical factors were significantly associated with glycemic control. These included age, sex, stroke severity, smoking status, HR, use of beta blockers and insulin after discharge, levels of total cholesterol and triglyceride, and history of hypertension, atrial fibrillation, and cancer before admission (Table 1). The levels of HbA1c after admission and mean HbA1c after discharge both increased progressively across the HR subgroups. The standard deviation values of the mean HbA1c levels after discharge also increased progressively across the HR subgroups (Table 2, p for trend < 0.001).

Independent predictors of glycemic control

The results of the multivariable logistic regression analysis for unfavorable glycemic control are presented in Table 3. Age, sex, HR, severe stroke, smoking status, levels of total cholesterol, diastolic blood pressure, and the use of insulin after discharge were independently associated with glycemic control (Table 3).

Table 3 Multivariable logistic regression analysis with heart rate as a continuous variable for unfavorable glycemic control
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Because a significant relationship was detected between the mean initial in-hospital HR and glycemic control, the associations between the mean initial in-hospital HR subgroups and HbA1c levels after admission as well as mean HbA1c levels after discharge were analyzed using a generalized linear model. Even after the possible confounders were adjusted for, the HbA1c levels after admission and discharge among stroke patients with diabetes increased significantly in the subgroups with higher HRs (Table 4, p < 0.001). These results suggested that a lower mean initial in-hospital HR was associated with better glycemic status.

Table 4 Glycemic status among patients with diabetes mellitus who have had a stroke, according to heart rate subgroups
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Association between mean HR subgroup and glycemic control

The crude and adjusted ORs for the HR subgroups are presented in Fig. 1. Relative to the reference group (HR < 60 bpm), the adjusted ORs for unfavorable glycemic control in model 2 were 1.093 (95% CI 0.786–1.519) for an HR of 60–69 bpm, 1.370 (95% CI 0.991–1.892) for an HR of 70–79 bpm, and 1.608 (95% CI 1.145–2.257) for an HR of ≥ 80 bpm (Fig. 1). A higher HR was associated with a lower probability of favorable glycemic control.

Fig. 1
figure 1

Forest plots of crude and adjusted odds ratios (95% CIs) for unfavorable glycemic control by mean initial in-hospital heart rate increments. The analyses were adjusted for age and sex in model 1 and for all of the variables in the fully adjusted model (model 2), including age, sex, stroke severity, body mass index, hypertension, dyslipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, history of cancer before admission, smoking status, levels of total cholesterol, triglyceride, estimated glomerular filtration rate, and alanine aminotransferase, systolic blood pressure, diastolic blood pressure, and the use of beta blockers and insulin. Abbreviations: G, group; OR, odds ratio; CI, confidence interval; bpm, beats per minute

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Discussion

This study illustrated that an increased mean initial in-hospital HR was associated with unfavorable glycemic control in patients with AIS and type 2 diabetes mellitus, particularly in those with a mean initial in-hospital HR of ≥ 80 bpm, compared with those with an HR of < 60 bpm (Table 3; Fig. 1).

A higher resting HR in individuals without diabetes mellitus is associated with future unfavorable changes in insulin levels and insulin sensitivity [22, 23]. In the prospective RISC cohort study, HR predicted beta cell function and impaired glucose regulation after a 3-year follow-up in individuals without diabetes mellitus [24]. Proinsulin, acute insulin secretion, and insulin sensitivity were also associated with HR in individuals without diabetes mellitus [25]. According to National Health and Nutrition Examination Surveys, the mean HR increased with increasing HbA1c levels among individuals with diagnosed diabetes mellitus, independent of other risk factors [26]. These studies have demonstrated that HR is associated with glucose metabolism.

HR is controlled by the autonomic nervous system (sympathetic and parasympathetic nervous systems). In humans at rest, the parasympathetic tone predominates, and therefore, resting HR is lower than the intrinsic rate of the sinoatrial node [27]. An increased HR can be due to imbalances in the autonomic nervous system with increased sympathetic activity or reduced vagal tone. The autonomic nervous system is also involved in glucose homeostasis through the modulation of the release of insulin and glucagon. The brain, particularly the hypothalamus and brain stem, modulates pancreatic insulin and glucagon secretion through parasympathetic and sympathetic efferent nerves that innervate pancreatic alpha and beta cells [28]. The sympathetic nervous system plays a predominant role in stimulating the glucagon secretion that counteracts the actions of insulin by stimulating hepatic glucose production and thereby increasing blood glucose levels [29]. The activation of the parasympathetic nervous system lowers glucose levels by stimulating the secretion of insulin from beta cells and suppressing hepatic glucose production [30]. According to an animal study, parasympathetic dysfunction is associated with insulin resistance in the development of early metabolic and cardiovascular alterations [31]. Analysis of data from the Netherlands Study of Depression and Anxiety revealed that increased sympathetic and decreased parasympathetic nervous system activities are associated with metabolic syndrome [32]. Therefore, an autonomic imbalance may be related to both increased resting HR and unfavorable glycemic control.

According to the data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study, a higher HR predicted cardiovascular disease and major adverse cardiovascular events, independent of other risk factors in patients with type 1 diabetes mellitus [33]. A low HR also reduced the risk of patients with type 2 diabetes mellitus developing microalbuminuria, independent of blood pressure [34]. In our previous study, resting HR was also associated with long-term survival in patients with AIS and diabetes mellitus [14]. Rigorous resting HR control in elderly patients with coronary heart disease and diabetes mellitus proved beneficial for both blood glucose control and secondary prevention of coronary heart disease [35]. Further investigations into methods of controlling resting HR as a potential measure to achieve favorable glycemic control and improve cardiovascular outcomes in patients with AIS and diabetes mellitus should be considered.

Because of the propensity of beta blockers to cause insulin resistance and impair glycemic control in patients with diabetes mellitus, the influence of beta blockers in glycemic control remains controversial [36,37,38,39,40]. In the current study, the use of beta blockers in patients with AIS and diabetes mellitus failed to achieve better glycemic control than the nonuse of beta blockers after adjustment for potential confounders (Table 3). In addition to beta blockers, several drugs demonstrated the potential to slow the HR and improve glycemic control. Galantamine is an acetylcholinesterase inhibitor that can prevent the hydrolysis of released acetylcholine and increase the overall amount of acetylcholine. Acetylcholine has the ability to slow the resting HR and stimulate insulin secretion [41]. Low-dose galantamine also alleviates inflammation and insulin resistance in patients with metabolic syndrome [42]. Therefore, it has been deemed a potential antidiabetic agent and an add-on therapy to other oral antidiabetics [43]. Some sodium-glucose cotransporter 2 inhibitors, antidiabetic medications that inhibit the absorption of glucose from the proximal tubule of the kidney, have also been shown to promote parasympathetic nervous activity, thereby decreasing blood pressure and HR [44]. Therefore, in addition to improving glycemic control, these HR slowing agents may also have the potential to improve cardiovascular outcomes for patients with diabetes mellitus and tachycardia.

Many factors are associated with unfavorable glycemic control [45,46,47]. In the current study, female sex, current smoker, and total cholesterol levels generated an increased risk of unfavorable glycemic control (Table 3), which is consistent with previous studies [48,49,50]. The risk of malnutrition is highly prevalent among patients who have had a stroke. Stroke severity and dysphagia are factors related to malnutrition [51,52,53]. Therefore, in this study, because of the tendency of restricted food intake, patients with severe stroke (eNIHSS score > 14) were less likely to have unfavorable glycemic control than those with mild stroke severity (Table 3). Long-term glycemic variability, as determined by variability in HbA1c levels, is associated with an increased risk of cardiovascular disease and microvascular complications [54,55,56]. In this study, increased mean initial in-hospital HR was also associated with higher glycemic variability, as represented by the standard deviation values of HbA1c levels after discharge (Table 2).

Because of the observational nature of the study, this study has several limitations. The association between mean initial in-hospital HR and glycemic control may not imply a cause–effect relationship. Because the vital signs were collected for the first 3 days of hospitalization, patients who were hospitalized for less than 3 days were excluded from the study; therefore, some patients with mild stroke may not have been included. Additionally, because this study was conducted on a population of patients with AIS and diabetes mellitus, our results may not be applicable to patients with other cardiovascular disease with diabetes mellitus.

Conclusions

This study addressed the prognostic implications of the initial in-hospital HR on glycemic control. Treatment for augmenting parasympathetic activity and reducing HR, such as with acetylcholinesterase inhibitors, may potentially improve glycemic control and cardiovascular outcomes in patients with AIS and type 2 diabetes mellitus. Further prospective clinical trials should be conducted to determine whether interventions that aim to decrease HR are beneficial for this population.

Data Availability

The data supporting the findings of the article is available in the Chang Gung Research Databank at Chang Gung Memorial Hospital, Chiayi Branch. These data can be available from the corresponding author (J.D. Lee) after obtaining approval from our local IRB.

Abbreviations

HbA1c:

glycated hemoglobin

HR:

heart rate

AIS:

acute ischemic stroke

SSI:

stroke severity index

NIHSS:

National Institutes of Health Stroke Scale

eNIHSS:

estimated National Institutes of Health Stroke Scale

eGFR:

estimated glomerular filtration rate

bpm:

beats per minute

OR:

odds ratio

CI:

confidence interval

References

  1. Osei E, Fonville S, Zandbergen AA, Koudstaal PJ, Dippel DW, den Hertog HM. Glucose in prediabetic and diabetic range and outcome after stroke. Acta Neurol Scand. 2017;135(2):170–5.

    Article  CAS  PubMed  Google Scholar 

  2. Giorgino F, Leonardini A, Laviola L. Cardiovascular disease and glycemic control in type 2 diabetes: now that the dust is settling from large clinical trials. Ann N Y Acad Sci. 2013;1281:36–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. American Diabetes A. 6. Glycemic targets: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):66–S76.

    Article  Google Scholar 

  4. Kleindorfer DO, Towfighi A, Chaturvedi S, Cockroft KM, Gutierrez J, Lombardi-Hill D, Kamel H, Kernan WN, Kittner SJ, Leira EC, et al. 2021 Guideline for the Prevention of Stroke in patients with stroke and transient ischemic attack: a Guideline from the American Heart Association/American Stroke Association. Stroke. 2021;52(7):e364–e467.

    Article  PubMed  Google Scholar 

  5. del Canizo-Gomez FJ, Moreira-Andres MN. Cardiovascular risk factors in patients with type 2 diabetes. Do we follow the guidelines? Diabetes Res Clin Pract. 2004;65(2):125–33.

    Article  PubMed  Google Scholar 

  6. Zhang X, Shu XO, Xiang YB, Yang G, Li H, Cai H, Gao YT, Zheng W. Resting heart rate and risk of type 2 diabetes in women. Int J Epidemiol. 2010;39(3):900–6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Li YQ, Sun CQ, Li LL, Wang L, Guo YR, You AG, Xi YL, Wang CJ. Resting heart rate as a marker for identifying the risk of undiagnosed type 2 diabetes mellitus: a cross-sectional survey. BMC Public Health. 2014;14:1052.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kim G, Lee YH, Jeon JY, Bang H, Lee BW, Kang ES, Lee IK, Cha BS, Kim CS. Increase in resting heart rate over 2 years predicts incidence of diabetes: a 10-year prospective study. Diabetes Metab. 2017;43(1):25–32.

    Article  CAS  PubMed  Google Scholar 

  9. Xu C, Zhong J, Zhu H, Hu R, Fang L, Wang M, Zhang J, Guo Y, Bian Z, Chen Z, et al. Independent and interactive associations of heart rate and body mass index or blood pressure with type 2 diabetes mellitus incidence: a prospective cohort study. J Diabetes Investig. 2019;10(4):1068–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Valensi P, Extramiana F, Lange C, Cailleau M, Haggui A, Maison Blanche P, Tichet J, Balkau B, Group DS. Influence of blood glucose on heart rate and cardiac autonomic function. The DESIR study. Diabet Med. 2011;28(4):440–9.

    Article  CAS  PubMed  Google Scholar 

  11. Torchinsky MY, Gomez R, Rao J, Vargas A, Mercante DE, Chalew SA. Poor glycemic control is associated with increased diastolic blood pressure and heart rate in children with type 1 diabetes. J Diabetes Complications. 2004;18(4):220–3.

    Article  PubMed  Google Scholar 

  12. Paterson AD, Rutledge BN, Cleary PA, Lachin JM, Crow RS, Diabetes C, Complications Trial/Epidemiology of Diabetes I, Complications Research G. The effect of intensive diabetes treatment on resting heart rate in type 1 diabetes: the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study. Diabetes Care 2007, 30(8):2107–2112.

  13. Tsai MS, Lin MH, Lee CP, Yang YH, Chen WC, Chang GH, Tsai YT, Chen PC, Tsai YH. Chang Gung Research Database: a multi-institutional database consisting of original medical records. Biomed J. 2017;40(5):263–9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lee JD, Kuo YW, Lee CP, Huang YC, Lee M, Lee TH. Initial in-hospital heart rate is associated with long-term survival in patients with acute ischemic stroke.Clin Res Cardiol2021.

  15. American Diabetes A. 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):15–S33.

    Article  Google Scholar 

  16. Sung SF, Hsieh CY, Lin HJ, Chen YW, Chen CH, Kao Yang YH, Hu YH. Validity of a stroke severity index for administrative claims data research: a retrospective cohort study. BMC Health Serv Res. 2016;16(1):509.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, Spilker J, Holleran R, Eberle R, Hertzberg V, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20(7):864–70.

    Article  CAS  PubMed  Google Scholar 

  18. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130(6):461–70.

    Article  CAS  PubMed  Google Scholar 

  19. Chiang CJ, You SL, Chen CJ, Yang YW, Lo WC, Lai MS. Quality assessment and improvement of nationwide cancer registration system in Taiwan: a review. Jpn J Clin Oncol. 2015;45(3):291–6.

    Article  PubMed  Google Scholar 

  20. Standards of Medical Care in Diabetes-2017. Summary of revisions. Diabetes Care. 2017;40(Suppl 1):4–S5.

    Google Scholar 

  21. National Institute of Health and Care Excellence. Type 2 diabetes in adults: management, Guideline NICE. (NG28). https://www.nice.org.uk/guidance/ng28

  22. Hansen CS, Faerch K, Jorgensen ME, Malik M, Witte DR, Brunner EJ, Tabak AG, Kivimaki M, Vistisen D. Heart rate, autonomic function, and future changes in glucose metabolism in individuals without diabetes: the Whitehall II Cohort Study. Diabetes Care. 2019;42(5):867–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yang Z, Zhang W, Zhu L, Lin N, Niu Y, Li X, Lu S, Zhang H, Wang X, Wen J, et al. Resting heart rate and impaired glucose regulation in middle-aged and elderly chinese people: a cross-sectional analysis. BMC Cardiovasc Disord. 2017;17(1):246.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bonnet F, Empana JP, Natali A, Monti L, Golay A, Lalic K, Dekker J, Mari A, Balkau B, Group RS. Elevated heart rate predicts beta cell function in non-diabetic individuals: the RISC cohort. Eur J Endocrinol. 2015;173(3):409–15.

    Article  CAS  PubMed  Google Scholar 

  25. Festa A, D’Agostino R Jr, Hales CN, Mykkanen L, Haffner SM. Heart rate in relation to insulin sensitivity and insulin secretion in nondiabetic subjects. Diabetes Care. 2000;23(5):624–8.

    Article  CAS  PubMed  Google Scholar 

  26. Casagrande SS, Cowie CC, Sosenko JM, Mizokami-Stout K, Boulton AJM, Pop-Busui R. The Association Between Heart Rate and Glycemic Status in the National Health and Nutrition Examination Surveys.J Clin Endocrinol Metab2020, 105(3).

  27. Kenney WL. Parasympathetic control of resting heart rate: relationship to aerobic power. Med Sci Sports Exerc. 1985;17(4):451–5.

    Article  CAS  PubMed  Google Scholar 

  28. Thorens B. Brain glucose sensing and neural regulation of insulin and glucagon secretion. Diabetes Obes Metab. 2011;13(Suppl 1):82–8.

    Article  CAS  PubMed  Google Scholar 

  29. Jiang G, Zhang BB. Glucagon and regulation of glucose metabolism. Am J Physiol Endocrinol Metab. 2003;284(4):E671–678.

    Article  CAS  PubMed  Google Scholar 

  30. Roh E, Song DK, Kim MS. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp Mol Med. 2016;48:e216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brito JO, Ponciano K, Figueroa D, Bernardes N, Sanches IC, Irigoyen MC, De Angelis K. Parasympathetic dysfunction is associated with insulin resistance in fructose-fed female rats. Braz J Med Biol Res. 2008;41(9):804–8.

    Article  CAS  PubMed  Google Scholar 

  32. Licht CM, Vreeburg SA, van Reedt Dortland AK, Giltay EJ, Hoogendijk WJ, DeRijk RH, Vogelzangs N, Zitman FG, de Geus EJ, Penninx BW. Increased sympathetic and decreased parasympathetic activity rather than changes in hypothalamic-pituitary-adrenal axis activity is associated with metabolic abnormalities. J Clin Endocrinol Metab. 2010;95(5):2458–66.

    Article  CAS  PubMed  Google Scholar 

  33. Keshavarzi S, Braffett BH, Pop-Busui R, Orchard TJ, Soliman EZ, Lorenzi GM, Barnie A, Karger AB, Gubitosi-Klug RA, Dagogo-Jack S, et al. Risk factors for longitudinal resting heart rate and its Associations with Cardiovascular Outcomes in the DCCT/EDIC study. Diabetes Care. 2021;44(5):1125–32.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schmieder RE, Bramlage P, Haller H, Ruilope LM, Bohm M, Investigators R. The effect of resting heart rate on the New Onset of Microalbuminuria in patients with type 2 diabetes: a subanalysis of the ROADMAP Study. Med (Baltim). 2016;95(15):e3122.

    Article  CAS  Google Scholar 

  35. Liang DL, Li XY, Wang L, Xu H, Tuo XP, Jian ZJ, Wang XN, Yun JL, Zhang X, Wang SY. [Correlation between resting heart rate and blood glucose level in elderly patients with coronary heart disease and diabetes mellitus]. Nan Fang Yi Ke Da Xue Xue Bao. 2016;36(5):609–16.

    CAS  PubMed  Google Scholar 

  36. Wai B, Kearney LG, Hare DL, Ord M, Burrell LM, Srivastava PM. Beta blocker use in subjects with type 2 diabetes mellitus and systolic heart failure does not worsen glycaemic control. Cardiovasc Diabetol. 2012;11:14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lithell HO. Effect of antihypertensive drugs on insulin, glucose, and lipid metabolism. Diabetes Care. 1991;14(3):203–9.

    Article  CAS  PubMed  Google Scholar 

  38. Fonseca VA. Effects of beta-blockers on glucose and lipid metabolism. Curr Med Res Opin. 2010;26(3):615–29.

    Article  CAS  PubMed  Google Scholar 

  39. Mills GA, Horn JR. Beta-blockers and glucose control. Drug Intell Clin Pharm. 1985;19(4):246–51.

    CAS  PubMed  Google Scholar 

  40. Hirst JA, Farmer AJ, Feakins BG, Aronson JK, Stevens RJ. Quantifying the effects of diuretics and beta-adrenoceptor blockers on glycaemic control in diabetes mellitus - a systematic review and meta-analysis. Br J Clin Pharmacol. 2015;79(5):733–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Del Rio G, Procopio M, Bondi M, Marrama P, Menozzi R, Oleandri SE, Grottoli S, Maccario M, Velardo A, Ghigo E. Cholinergic enhancement by pyridostigmine increases the insulin response to glucose load in obese patients but not in normal subjects. Int J Obes Relat Metab Disord. 1997;21(12):1111–4.

    Article  PubMed  Google Scholar 

  42. Consolim-Colombo FM, Sangaleti CT, Costa FO, Morais TL, Lopes HF, Motta JM, Irigoyen MC, Bortoloto LA, Rochitte CE, Harris YT et al. Galantamine alleviates inflammation and insulin resistance in patients with metabolic syndrome in a randomized trial.JCI Insight2017, 2(14).

  43. Ali MA, El-Abhar HS, Kamel MA, Attia AS. Antidiabetic effect of Galantamine: Novel Effect for a known centrally acting drug. PLoS ONE. 2015;10(8):e0134648.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Nguyen T, Wen S, Gong M, Yuan X, Xu D, Wang C, Jin J, Zhou L. Dapagliflozin activates neurons in the Central Nervous System and regulates Cardiovascular activity by inhibiting SGLT-2 in mice. Diabetes Metab Syndr Obes. 2020;13:2781–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kassahun T, Eshetie T, Gesesew H. Factors associated with glycemic control among adult patients with type 2 diabetes mellitus: a cross-sectional survey in Ethiopia. BMC Res Notes. 2016;9:78.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhu HT, Yu M, Hu H, He QF, Pan J, Hu RY. Factors associated with glycemic control in community-dwelling elderly individuals with type 2 diabetes mellitus in Zhejiang, China: a cross-sectional study. BMC Endocr Disord. 2019;19(1):57.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cheng LJ, Wang W, Lim ST, Wu VX. Factors associated with glycaemic control in patients with diabetes mellitus: a systematic literature review. J Clin Nurs. 2019;28(9–10):1433–50.

    Article  PubMed  Google Scholar 

  48. da Silva Moreira FGD, Almeida S, de Souza Teles M, Andrade CA, Reingold CS, Moreira AL Jr. Sex differences and correlates of poor glycaemic control in type 2 diabetes: a cross-sectional study in Brazil and Venezuela. BMJ Open. 2019;9(3):e023401.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wang S, Ji X, Zhang Z, Xue F. Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China.Int J Environ Res Public Health2020, 17(15).

  50. Peng K, Chen G, Liu C, Mu Y, Ye Z, Shi L, Zhao J, Chen L, Li Q, Yang T, et al. Association between smoking and glycemic control in diabetic patients: results from the risk evaluation of cAncers in chinese diabeTic individuals: a lONgitudinal (REACTION) study. J Diabetes. 2018;10(5):408–18.

    Article  CAS  PubMed  Google Scholar 

  51. Hsieh DY, Hung JW, Chang KC, Huang YC, Lee TH, Chen HM. Malnutrition in Acute Stroke Patients stratified by Stroke Severity- A Hospital based study. Acta Neurol Taiwan. 2017;26(3):120–7.

    Google Scholar 

  52. Sabbouh T, Torbey MT. Malnutrition in stroke patients: risk factors, Assessment, and management. Neurocrit Care. 2018;29(3):374–84.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Foley NC, Martin RE, Salter KL, Teasell RW. A review of the relationship between dysphagia and malnutrition following stroke. J Rehabil Med. 2009;41(9):707–13.

    Article  PubMed  Google Scholar 

  54. Brownlee M, Hirsch IB. Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications. JAMA. 2006;295(14):1707–8.

    Article  CAS  PubMed  Google Scholar 

  55. Jung HS. Clinical implications of glucose variability: chronic complications of diabetes. Endocrinol Metab (Seoul). 2015;30(2):167–74.

    Article  CAS  PubMed  Google Scholar 

  56. Shen Y, Zhou J, Shi L, Nauman E, Katzmarzyk PT, Price-Haywood EG, Horswell R, Bazzano AN, Nigam S, Hu G. Association between visit-to-visit HbA1c variability and the risk of cardiovascular disease in patients with type 2 diabetes. Diabetes Obes Metab. 2021;23(1):125–35.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Health Information and Epidemiology Laboratory for the comments and assistance in data analysis.

Funding

This work was supported by grants from Chang Gung Memorial Hospital (CGRPG6L0041). The sponsors played no role in the study design, data collection and analysis, or decision to submit the article for publication.

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Authors and Affiliations

  1. Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan

    Ya-Wen Kuo

  2. Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan

    Ya-Wen Kuo

  3. Department of Neurology, Chiayi Chang Gung Memorial Hospital, No.6, W. Sec., Jiapu Rd., Puzi City, Chiayi County 613, Chiayi, Taoyuan, Taiwan (R.O.C.)

    Jiann-Der Lee, Yen-Chu Huang & Meng Lee

  4. College of Medicine, Chang Gung University, Taoyuan, Taiwan

    Jiann-Der Lee, Yen-Chu Huang & Meng Lee

  5. Health Information and Epidemiology Laboratory, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan

    Chuan-Pin Lee

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  1. Ya-Wen Kuo
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  2. Jiann-Der Lee
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  3. Chuan-Pin Lee
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  5. Meng Lee
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Contributions

Y.K. and J.L. designed the research, collected data, analyzed and interpreted data and wrote the manuscript; C.L. performed statistical analysis and contributed to subsequent manuscript discussion; Y.H and M.L. performed the research and contributed to subsequent manuscript discussion.

Corresponding author

Correspondence to Jiann-Der Lee.

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Ethical approval and consent to participate

The study was approved by the local Institutional Review Board of Chang Gung Memorial Hospital, Chiayi Branch, Taiwan (202001990B0). All methods were carried out in accordance with relevant guidelines and regulations. Before being released for analysis, the clinical data are anonymized and de-identified to ensure confidentiality. The informed consent was waived by Institutional Review Board of Chang Gung Memorial Hospital due to retrospective nature of study.

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The authors declare that they do not have any conflicts of interest.

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Kuo, YW., Lee, JD., Lee, CP. et al. Association between initial in-hospital heart rate and glycemic control in patients with acute ischemic stroke and diabetes mellitus. BMC Endocr Disord 23, 69 (2023). https://doi.org/10.1186/s12902-023-01325-2

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Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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