Cited by 114Diabetes Management and ResearchDiabetes Treatment and ManagementDiabetes Management and Education

Beyond HbA1c: comparing glycemic variability and glycemic indices in predicting hypoglycemia in type 1 and type 2 diabetes

Rama Chandran, Suresh, Tay, Wei Lin, Lye, Weng Kit, Lim, Lee Ling, Ratnasingam, Jeyakantha, Tan, Alexander Tong Boon, S.L. Gardner, Daphne·2018·Diabetes Technology & Therapeutics

Abstract

Background: Hypoglycemia is the major impediment to therapy intensification in diabetes. Although higher individualized HbA1c targets are perceived to reduce the risk of hypoglycemia in those at risk of hypoglycemia, HbA1c itself is a poor predictor of hypoglycemia. We assessed the use of glycemic variability (GV) and glycemic indices as independent predictors of hypoglycemia. Methods: A retrospective observational study of 60 type 1 and 100 type 2 diabetes subjects. All underwent professional continuous glucose monitoring (CGM) for 3–6 days and recorded self-monitored blood glucose (SMBG). Indices were calculated from both CGM and SMBG. Statistical analyses included regression and area under receiver operator curve (AUC) analyses. Results: Hypoglycemia frequency (53.3% vs. 24%, P < 0.05) and %CV (40.1% ± 10% vs. 29.4% ± 7.8%, P < 0.001) were significantly higher in type 1 diabetes compared with type 2 diabetes. HbA1c was, at best, a weak predictor of hypoglycemia. %CVCGM, Low Blood Glucose Index (LBGI)CGM, Glycemic Risk Assessment Diabetes Equation (GRADE)HypoglycemiaCGM, and Hypoglycemia IndexCGM predicted hypoglycemia well. %CVCGM and %CVSMBG consistently remained a robust discriminator of hypoglycemia in type 1 diabetes (AUC 0.88). In type 2 diabetes, a combination of HbA1c and %CVSMBG or LBGISMBG could help discriminate hypoglycemia. Conclusion: Assessment of glycemia should go beyond HbA1c and incorporate measures of GV and glycemic indices. %CVSMBG in type 1 diabetes and LBGISMBG or a combination of HbA1c and %CVSMBG in type 2 diabetes discriminated hypoglycemia well. In defining hypoglycemia risk using GV and glycemic indices, diabetes subtypes and data source (CGM vs. SMBG) must be considered.

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Crossref · 10.1089/dia.2017.0388

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Authors
Rama Chandran, S., Tay, W. L., Lye, W. K., Lim, L. L., Ratnasingam, J., Tan, A. T. B., & S.L. Gardner, D.
Affiliations
  • Department of Endocrinology, Singapore General Hospital, Singapore.
  • Singapore General Hospital (SG)
  • National University of Singapore (SG)
  • University of Malaya (MY)
Venue
Diabetes Technology & Therapeutics
Published
2018-05
Volume
20
Issue
5
Pages
353-362
ISSN
  • 1520-9156 (ISSN-L)
  • 1557-8593
OpenAlex
W2802567037
Cited by
114
References
54
Citations by year
2026: 6 · 2025: 5 · 2024: 20 · 2023: 12 · 2022: 12 · 2021: 28 · 2020: 14 · 2019: 14 · 2018: 3
FWCI
11.73
Type
journal article
Language
en
Open access
closed
Locations
  • Diabetes Technology & Therapeutics (publishedVersion)
  • PubMed (publishedVersion)
  • The University of Malaya Research Repository (University of Malaya) (submittedVersion)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords
hypoglycemia, glycemic variability, glycemic index, HbA1c, type 1 diabetes, type 2 diabetes, continuous glucose monitoring, self-monitoring of blood glucose, coefficient of variation, low blood glucose index, hypoglycemia prediction
MeSH terms
Adult, Aged, Blood Glucose, Cross-Sectional Studies, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Glycated Hemoglobin, Humans, Hypoglycemia, Male, Middle Aged, Retrospective Studies, Blood Glucose Self-Monitoring, Risk Assessment, Glycemic Index, Young Adult

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