ArticleJournal of diabetes science and technology2015
Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?
Article in Journal of diabetes science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Evaluation of Clinical Metrics for Identifying Defective Physiologic Responses to Hypoglycemia in Long-Standing Type 1 Diabetes.Diabetes technology & therapeutics · 2022Trial
- Assessment of Glucose Control Metrics by Discriminant Ratio.Diabetes technology & therapeutics · 2020Trial
- Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses.Journal of diabetes science and technology · 2018Trial
- The Development of New Composite Metrics for the Comprehensive Analytic and Visual Assessment of Hypoglycemia Using the Hypo-Triad.Journal of diabetes science and technology · 2018Trial
- The Evolving Landscape of Continuous Glucose Monitoring Metrics in Type 1 Diabetes: Narrative Literature Review.JMIR diabetes · 2026Review
- Transition from CSII to second-generation basal insulin analogues in hospitalized T2DM patients: a CGM-based study.BMC endocrine disorders · 2026Article
- Observational
- Clinical performance of Hedia Diabetes Assistant bolus calculator for diabetes management: a real-world retrospective cohort study.Frontiers in digital health · 2025Article
- Effects of insulin glargine U300 versus insulin degludec U100 on glycemic variability, hypoglycemia, and diet evaluated by continuous glucose monitoring in type 1 diabetes: a retrospective cross-sectional study.The Kaohsiung journal of medical sciences · 2024Observational
- Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study.Interactive journal of medical research · 2024Article
- Current landscape and comprehensive management of glycemic variability in diabetic retinopathy.Journal of translational medicine · 2024Review
- Association between brain imaging biomarkers and continuous glucose monitoring-derived glycemic control indices in Japanese patients with type 2 diabetes mellitus.BMJ open diabetes research & care · 2024Article
- Potential cost savings in the United States from a reduction in sensor-detected severe hypoglycemia among users of the InPen smart insulin pen system.Journal of managed care & specialty pharmacy · 2023Article
- Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study.JMIR formative research · 2023Article
- Glycemic Variability Patterns Strongly Correlate With Partial Remission Status in Children With Newly Diagnosed Type 1 Diabetes.Diabetes care · 2022Article
- Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review.Communications biology · 2021Review
- Assessing Glycemic Control Using CGM for Women with Diabetes in Pregnancy.Current diabetes reports · 2021Review
- Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the Digital Twin Precision Treatment Program.Scientific reports · 2021Observational
- Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment.Journal of diabetes investigation · 2021Observational
- Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring.Scientific reports · 2021Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.
methodsTwenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.
resultsLBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.
conclusionsAlternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.
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