Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Abstract

Background: The 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.Methods: Twenty-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. Results: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especiallyin 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.Conclusions: Alternate 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.

Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

“…On the basis of this transformation, we have developed our theory of risk analysis of BG data (35), defining a computational risk space that proved to be very suitable for quantifying the extent and frequency of glucose excursions. The utility of the risk analysis has been repeatedly confirmed (9,25,36–38). We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24).…”

“…We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24). Then in 2006, we introduced the ADRR, a measure of GV that is equally sensitive to hypo- and hyperglycemic excursions and is predictive of extreme BG fluctuations (38). Most recently, corrections were introduced that allowed the LBGI and HBGI to be computed from CGM data with results directly comparable to SMBG (39).…”

“…3 come from a previously discussed data set containing SMBG readings for 335 subjects with type 1 ( n = 254) and type 2 ( n = 81) diabetes (38). Figure 3 A presents subjects in good control with most of the data points (70.4%) in the A- and B-zones.…”

“…Thus, the difference in GV depicted by the VGA plots in Fig. 3 and quantified by the ADRR resulted in a several-fold difference in subsequent extreme hypo- and hyperglycemic episodes recorded over 3 months of follow-up observation (38). …”

“…In a large study of 335 patients with type 1 and type 2 diabetes, we compared the LBGI, HBGI, and ADRR to established measures of GV, including SD, MAGE, and others (38). All GV metrics were computed from a month of SMBG data and were then fixed and tested as predictors of low (BG <40 mg/dL and BG <70 mg/dL) and high (BG >180 mg/dL and BG >400 mg/dL) events in the subsequent 3 months.…”