Continuous glucose monitoring in patients with post-bariatric hypoglycaemia reduces hypoglycaemia and glycaemic variability

Abstract

Aim: To determine whether continuous glucose monitoring (CGM) can reduce hypoglycaemia in patients with post-bariatric hypoglycaemia (PBH).

Materials and methods: In an open-label, nonrandomized, pre-post design with sequential assignment, CGM data were collected in 22 individuals with PBH in two sequential phases: (i) masked (no access to sensor glucose or alarms); and (ii) unmasked (access to sensor glucose and alarms for low or rapidly declining sensor glucose). Twelve participants wore the Dexcom G4 device for a total of 28 days, while 10 wore the Dexcom G6 device for a total of 20 days.

Results: Participants with PBH spent a lower percentage of time in hypoglycaemia over 24 hours with unmasked versus masked CGM (<3.3 mM/L, or <60 mg/dL: median [median absolute deviation {MAD}] 0.7 [0.8]% vs. 1.4 [1.7]%, P = 0.03; <3.9 mM/L, or <70 mg/dL: median [MAD] 2.9 [2.5]% vs. 4.7 [4.8]%; P = 0.04), with similar trends overnight. Sensor glucose data from the unmasked phase showed a greater percentage of time spent between 3.9 and 10 mM/L (70-180 mg/dL) (median [MAD] 94.8 [3.9]% vs. 90.8 [5.2]%; P = 0.004) and lower glycaemic variability over 24 hours (median [MAD] mean amplitude of glycaemic excursion 4.1 [0.98] vs. 4.4 [0.99] mM/L; P = 0.04). During the day, participants also spent a greater percentage of time in normoglycaemia with unmasked CGM (median [MAD] 94.2 [4.8]% vs. 90.9 [6.2]%; P = 0.005), largely due to a reduction in hyperglycaemia (>10 mM/L, or 180 mg/dL: median [MAD] 1.9 [2.2]% vs. 3.9 [3.6]%; P = 0.02).

Conclusions: Real-time CGM data and alarms are associated with reductions in low sensor glucose, elevated sensor glucose, and glycaemic variability. This suggests CGM allows patients to detect hyperglycaemic peaks and imminent hypoglycaemia, allowing dietary modification and self-treatment to reduce hypoglycaemia. The use of CGM devices may improve safety in PBH, particularly for patients with hypoglycaemia unawareness.

Keywords: bariatric surgery; continuous glucose monitoring; glycaemic control; hypoglycaemia.

FIGURE 1

Effect of unmasking on continuous glucose monitoring-derived sensor glucose patterns and time spent in low glucose ranges. (A) Paired comparison for the entire cohort in masked (red) versus unmasked (green) phase, according to time of day (mean, 95% confidence intervals). (B) Ambulatory glucose profile, comparing median percent distributions between phases over 24 hours. (C) The median percent distributions ± median absolute deviations (MAD) in each phase for the 24-hour data. (D) Line segments representing each participant’s time spent below 3.9 mM/L when masked (M) and unmasked (U) (n = 22). *P < 0.05

FIGURE 2

Effect of unmasking on normalized hypoglycaemic events. Normalized hypoglycaemic events over (A) 24 hours, (B) daytime hours, and (C) nighttime hours. Hypoglycaemic events are defined as having glucose lower than or equal to the threshold for at least 15 minutes, ending when glucose levels exceeded the threshold. Hypoglycaemic event values were normalized by dividing the number of events by the number of days of data

FIGURE 3

Effect of unmasking on percent distribution for daytime and nighttime data. The median percent distributions ± median absolute deviations in each phase for the (A) daytime and (B) nighttime data (n = 22). *P < 0.05. M, masked; U, unmasked