Home The correlation between glucose fluctuation from self-monitored blood glucose and the major adverse cardiac events in diabetic patients with acute coronary syndrome during a 6-month follow-up by WeChat application
Article
Licensed
Unlicensed Requires Authentication

The correlation between glucose fluctuation from self-monitored blood glucose and the major adverse cardiac events in diabetic patients with acute coronary syndrome during a 6-month follow-up by WeChat application

  • Jinggang Xia , Shaodong Hu , Ji Xu , Hengjian Hao , Chunlin Yin EMAIL logo and Dong Xu EMAIL logo
Published/Copyright: July 16, 2018

Abstract

Background

This study aimed to investigate the correlation between glucose fluctuation from self-monitored blood glucose (SMBG) and the major adverse cardiac events (MACE) in diabetic patients with acute coronary syndrome (ACS) during a 6-month follow-up period using the WeChat application.

Methods

From November 2016 to June 2017, 262 patients with ACS were discharged in a stable condition and completed a 6-month follow-up period. SMBG was recorded using the WeChat application. The patients were divided to a high glucose fluctuation group (H group; n=92) and a low glucose fluctuation group (L group; n=170). The 6-month incidence of MACE, lost-to-follow-up rate and satisfaction rate were measured through the WeChat follow-up.

Results

MACE occurred in 17.4% of patients in the H group and in 8.2% of patients in the L group (p=0.04). Multivariable analysis suggested that high glucose fluctuation conferred an 87% risk increment of MACE in the 6-month follow-up period (odds ratio: 2.1, 95% confidence interval 1.95–4.85; p=0.03). The lost-to-follow-up rate was lower and the satisfaction rate was higher in the patients using the WeChat application during follow-up than those of the regular outpatient follow-up during the same period (p<0.05).

Conclusions

The trial demonstrates that higher glucose fluctuation from SMBG after discharge was correlated with a higher incidence of MACE in diabetic patients with ACS. WeChat follow-up might have the potential to promote a good physician-patient relationship.


Corresponding authors: Chunlin Yin, PhD and Dong Xu, PhD, Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China, Phone: +8613621041267, Fax: +86-10-83198252

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was supported by the National Natural Science Foundation of China (No. 81770344) and the China Young and Middle-aged Clinical Research – VG fund (No. 2017-CCA-VG-043).

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Valensi P, Husemoen L, Weatherall J, Monnier L. Association of postprandial and fasting plasma glucose with HbA1c across the spectrum of glycaemic impairment in type 2 diabetes. Int J Clin Pract 2017;71:e13041.10.1111/ijcp.13041Search in Google Scholar PubMed PubMed Central

2. Kuroda M, Shinke T, Sakaguchi K, Otake H, Takaya T, Hirota Y, et al. Effect of daily glucose fluctuation on coronary plaque vulnerability in patients pre-treated with lipid-lowering therapy: a prospective observational study. JACC Cardiovasc Interv 2015;8:800.10.1016/j.jcin.2014.11.025Search in Google Scholar PubMed

3. Kuroda M, Shinke T, Sakaguchi K, Otake H, Takaya T, Hirota Y, et al. Association between daily glucose fluctuation and coronary plaque properties in patients receiving adequate lipid-lowering therapy assessed by continuous glucose monitoring and optical coherence tomography. Cardiovasc Diabetol 2015;14:78.10.1186/s12933-015-0236-xSearch in Google Scholar PubMed PubMed Central

4. Xia J, Xu J, Li B, Liu Z, Hao H, Yin C, et al. Association between glycemic variability and major adverse cardiovascular and cerebrovascular events (MACCE) in patients with acute coronary syndrome during 30-day follow-up. Clin Chim Acta 2017;466:162–6.10.1016/j.cca.2017.01.022Search in Google Scholar PubMed

5. Charleer S, Mathieu C, Nobels F, De Block C, Radermecker RP, Hermans MP, et al. Effect of continuous glucose monitoring on glycemic control, acute admissions and quality of life: a real-world study. J Clin Endocrinol Metab 2018;103:1224–32.10.1210/jc.2017-02498Search in Google Scholar PubMed

6. Blasco A, Carmona M, Fernández-Lozano I, Salvador CH, Pascual M, Sagredo PG, et al. Evaluation of a telemedicine service for the secondary prevention of coronary artery disease. J Cardiopulm Rehabil 2012;32:25.10.1097/HCR.0b013e3182343aa7Search in Google Scholar PubMed

7. Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van de Werf F, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. J Am Med Assoc 2004;291:2727–33.10.1001/jama.291.22.2727Search in Google Scholar PubMed

8. Tang X, Li S, Wang Y, Wang M, Yin Q, Mu P, et al. Glycemic variability evaluated by continuous glucose monitoring system is associated with the 10-y cardiovascular risk of diabetic patients with well-controlled HbA1c. Clin Chim Acta 2016;461:146–50.10.1016/j.cca.2016.08.004Search in Google Scholar PubMed

9. Tokue M, Iijima R, Imamura T, Niikura H, Hayashi F, Yazaki Y, et al. Impact of glycemic variability in patients with ST-elevated myocardial infarction. Int J Cardiol 2015;187:660–2.10.1016/j.ijcard.2015.03.365Search in Google Scholar PubMed

10. Tylee TS, Trence DL. Glycemic variability: looking beyond the A1C. Diabetes Spectr 2012;25:149–53.10.2337/diaspect.25.3.149Search in Google Scholar

11. Nalysnyk L, Hernandez-Medina M, Krishnarajah G. Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab 2010;12:288–98.10.1111/j.1463-1326.2009.01160.xSearch in Google Scholar PubMed

12. Shimabukuro M, Tanaka A, Sata M, Dai K, Shibata Y, Inoue Y, et al. α-Glucosidase inhibitor miglitol attenuates glucose fluctuation, heart rate variability and sympathetic activity in patients with type 2 diabetes and acute coronary syndrome: a multicenter randomized controlled (MACS) study. Cardiovasc Diabetol 2017;16:86.10.1186/s12933-017-0571-1Search in Google Scholar PubMed PubMed Central

13. Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, et al. International consensus on use of continuous glucose monitoring. Diabetes Care 2017;40:1631.10.2337/dc17-1600Search in Google Scholar PubMed PubMed Central

14. Klonoff DC, Ahn D, Drincic A. Continuous glucose monitoring: a review of the technology and clinical use. Diabetes Res Clin Pract 2017;133:178.10.1016/j.diabres.2017.08.005Search in Google Scholar PubMed

15. Jung HS. Clinical implications of glucose variability: chronic complications of diabetes. Endocrinol Metab 2015;30:167–74.10.3803/EnM.2015.30.2.167Search in Google Scholar PubMed PubMed Central

16. Service FJ. Glucose variability. Diabetes 2013;62:1398–404.10.2337/db12-1396Search in Google Scholar PubMed PubMed Central

17. Devries JH. Glucose variability: where it is important and how to measure it. Diabetes 2013;62:1405.10.2337/db12-1610Search in Google Scholar PubMed PubMed Central

18. Carlson AL, Mullen DM, Bergenstal RM. Clinical use of continuous glucose monitoring in adults with type 2 diabetes. Diabetes Technol Ther 2017;19 Suppl 2:S4–11.10.1089/dia.2017.0024Search in Google Scholar PubMed PubMed Central

19. Mikolasek M, Berg J, Witt CM, Barth J. Effectiveness of mindfulness- and relaxation-based eHealth interventions for patients with medical conditions: a systematic review and synthesis. Int J Behav Med 2017;25:1–16.10.1007/s12529-017-9679-7Search in Google Scholar PubMed

20. Kebede M, Christianson L, Khan Z, Heise TL, Pischke CR. Effectiveness of behavioral change techniques employed in eHealth interventions designed to improve glycemic control in persons with poorly controlled type 2 diabetes: a systematic review and meta-analysis protocol. Syst Rev 2017;6:211.10.1186/s13643-017-0609-1Search in Google Scholar PubMed PubMed Central

Received: 2018-03-01
Accepted: 2018-06-15
Published Online: 2018-07-16
Published in Print: 2018-11-27

©2018 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Observing an analyzer’s operational life cycle: a useful management tool for clinical laboratories
  4. Reviews
  5. Personalized laboratory medicine: a patient-centered future approach
  6. Circular RNAs: a new class of biomarkers as a rising interest in laboratory medicine
  7. Mini Review
  8. Impact of interactions between drugs and laboratory test results on diagnostic test interpretation – a systematic review
  9. Opinion Paper
  10. Uncertainty in measurement and total error: different roads to the same quality destination?
  11. Guidelines and Recommendations
  12. Joint EFLM-COLABIOCLI Recommendation for venous blood sampling
  13. General Clinical Chemistry and Laboratory Medicine
  14. Evidence for the positive impact of ISO 9001 and ISO 15189 quality systems on laboratory performance – evaluation of immunohaematology external quality assessment results during 19 years in Austria
  15. Effects of high-dose, intravenous lipid emulsion on laboratory tests in humans: a randomized, placebo-controlled, double-blind, clinical crossover trial
  16. Commutability of the certified reference materials for the standardization of β-amyloid 1-42 assay in human cerebrospinal fluid: lessons for tau and β-amyloid 1-40 measurements
  17. Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?
  18. Evaluation of serum alkaline phosphatase measurement through the 4-year trueness verification program in China
  19. Increased serum concentrations of soluble ST2 predict mortality after burn injury
  20. The clinical significance of borderline results of the Elia CTD Screen assay
  21. Reference Values and Biological Variations
  22. Reference intervals for 33 biochemical analytes in healthy Indian population: C-RIDL IFCC initiative
  23. Cancer Diagnostics
  24. BCL2L12 improves risk stratification and prediction of BFM-chemotherapy response in childhood acute lymphoblastic leukemia
  25. Cardiovascular Diseases
  26. The correlation between glucose fluctuation from self-monitored blood glucose and the major adverse cardiac events in diabetic patients with acute coronary syndrome during a 6-month follow-up by WeChat application
  27. Diabetes
  28. Impact of blood cell counts and volumes on glucose concentration in uncentrifuged serum and lithium-heparin blood tubes
  29. Letters to the Editor
  30. Standard process-oriented workflow introduces pre-analytical error when used in large study sample batches
  31. Comparison of three staining methods in the automated digital cell imaging analyzer Sysmex DI-60
  32. Detection of Plasmodium falciparum using automated digital cell morphology analyzer Sysmex DI-60
  33. Serum ischemia-modified albumin concentration may reflect long-term hypoxia in chronic respiratory disease: a pilot study
  34. Wet absorptive microsampling at home for HbA1c monitoring in diabetic children
  35. Serum endocan levels in patients with chronic obstructive pulmonary disease: a potential role in the evaluation of susceptibility to exacerbation
  36. Analytical and clinical validation of the new Roche Elecsys Vitamin D Total II assay
  37. Analytical validation of two second generation thyroglobulin immunoassays (Roche and Thermo Fisher)
  38. Omission of preservatives during 24-h of urine collection for the analysis of fractionated metanephrines enhance patient convenience
  39. Transient monoclonal gammopathy in a 2-year-old child with combined viral and bacterial infection
  40. Nephelometric assay of urine free light chains: an alternative and early clinical test for Bence-Jones protein quantification
  41. Congress Abstracts
  42. Congress of Laboratory Medicine and Clinical Chemistry 7th Annual Meeting of the Austrian Society for Laboratory Medicine and Clinical Chemistry (ÖGLMKC)
  43. 50th National Congress of the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC – Laboratory Medicine)
  44. Revolution drives Evolution – from measuring to understanding: Annual meeting of Swiss Society of Clinical Chemistry (SSCC) in Bern, November 15-16 2018
Downloaded on 20.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2018-0220/html?lang=en
Scroll to top button