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Prognostic impact of visit-to-visit glycemic variability on the risks of major adverse cardiovascular outcomes and hypoglycemia in patients with different glycemic control and type 2 diabetes

  • Bao Sun
  • Fazhong He
  • Yongchao Gao
  • Jiecan Zhou
  • Lei Sun
  • Rong Liu
  • Heng Xu
  • Xiaoping Chen
  • Honghao Zhou
  • Zhaoqian Liu
  • Wei ZhangEmail author
Original Article



The prognostic impact of visit-to-visit glycemic variability on clinical outcomes in patients with different glycemic control and type 2 diabetes remains obscure. We investigated glucose variability and clinical outcomes for patients in the groups of Good glycemic control (GC), Insufficient glycemic control (IC), and Poor glycemic control (PC) in a prospective cohort study.


By using data from Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE), 930 patients were enrolled from 61 centers in China and grouped into GC, IC, and PC according to their glycated hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG). Visit-to-visit glycemic variability was defined using the coefficient of variation (CV) of five measurements of HbA1c and FPG taken 3–24 months after treatment. Multivariable Cox proportional hazards models were employed to estimate adjusted hazard ratio (aHR).


Among 930 patients in the intensive glucose control, 82, 538, and 310 patients were assigned to GC, IC, and PC, respectively. During the median of 4.8 years of follow-up, 322 patients were observed hypoglycemia and 244 patients experienced major adverse cardiovascular events (MACE). The CV of HbA1c and FPG was significantly lower for GC (6.0 ± 3.8, 11.2 ± 6.2) than IC (8.3 ± 5.6, 17.9 ± 10.6) and PC (9.5 ± 6.3, 19.3 ± 10.8). High glycemic variability was associated with a greater risk of MACE (aHR: 2.21; 95% confidence interval (CI): 1.61–3.03; p < 0.001) and hypoglycemia (aHR: 1.36; 95% CI: 1.04–1.79; p = 0.025) than low glycemic variability in total patients. The consistent trend was also found in subgroups of GC, IC, and PC.


This prospective cohort study showed that glycemic variability was significantly lower for GC than IC and PC. Furthermore, glycemic variability was associated with the risk of MACE and hypoglycemia in total patients and subgroups of different glycemic control.


Visit-to-visit glycemic variability Different glycemic control Type 2 diabetes Major adverse cardiovascular events Hypoglycemia 



Good glycemic control


Insufficient glycemic control


Poor glycemic control


Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation


Glycated hemoglobin A1c


Fasting plasma glucose


Coefficient of variation


Adjusted hazard ratio


Major adverse cardiovascular events


Standard deviations


Body mass index



We acknowledge the contributions of ADVANCE group at 61 centers in China. We also thank all patients and participants who have contributed to the register.


This research was funded by grants from National Key Research and Development Program (No. 2016YFC0905000), National Natural Science Foundation of China (Nos. 81522048, 81573511, and 81874329) and the Innovation Driven Project of Central South University (No. 2016CX024).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the local ethics committee and was in accordance with the 1964 Declaration of Helsinki and its later amendmentsmed consent.

Informed consent

All patients provide written informed consent.

Supplementary material

12020_2019_1893_MOESM1_ESM.docx (16 kb)
Supplementary table (We found some mistakes in the Supplementary Table 1. So we validated the original data and replaced it by a new Supplementary Table 1 in the attachments.)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Clinical Pharmacology, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical PharmacologyCentral South UniversityChangshaPeople’s Republic of China
  3. 3.Data Analysis Technology Lab, School of Mathematics and StatisticsHenan UniversityKaifengPeople’s Republic of China
  4. 4.Department of Laboratory Medicine, National Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China HospitalSichuan UniversityChengduPeople’s Republic of China

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