Literature DB >> 29688755

Beyond HbA1c: Comparing Glycemic Variability and Glycemic Indices in Predicting Hypoglycemia in Type 1 and Type 2 Diabetes.

Suresh Rama Chandran1, Wei Lin Tay1, Weng Kit Lye2, Lee Ling Lim3, Jeyakantha Ratnasingam3, Alexander Tong Boon Tan3, Daphne S L Gardner1.   

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.

Entities:  

Keywords:  Coefficient of variation; Continuous glucose monitoring; Glycemic variability; Hypoglycemia; Self-monitored blood glucose

Mesh:

Substances:

Year:  2018        PMID: 29688755     DOI: 10.1089/dia.2017.0388

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  29 in total

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9.  Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

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Review 10.  Hypoglycemia in patient with type 2 diabetes treated with insulin: it can happen.

Authors:  Simon R Heller; Mark Peyrot; Shannon K Oates; April D Taylor
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