Albuminuria, glycemic variability and effect of flash glucose monitoring based decision making on short term glycemic variability in Indian type 2 diabetes patients: Indi-GlyVar study
Authors: Nathiya Deepak , Singh Mahaveer , Suman Supriya , Bareth Hemant , Pal Nikita , Jain Arjav , Tomar Balvir S. 
Published: November 2022

Aim and scope

Glycemic variability (GV) denotes the fluctuations in the glucose values around the baseline. High glycemic variability is associated with a higher risk of diabetes-associated complications. In this study, we sought to determine the impact of therapeutic interventions based on flash glucose monitoring on rapid, short-term glycemic variability. We also studied the prevalent albuminuria in diabetic kidney disease and its effect on glycemic variability.

Methods

In a 14-day, single-center, prospective intervention study, we measured the GV indices at baseline (days 1–4) and ten days after ambulatory glucose profile-based intervention using flash glucose monitoring (Abbott Libre Pro, Abbott Diabetes Care, Alameda, California, USA) in patients with type 2 diabetes. An EasyGV calculator was used to estimate the flash glucose monitoring (FGM)-derived measures of GV. The primary outcome was to assess the impact of FGMS-based therapeutic interventions on glycemic variability markers: SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV], AUC below 70 mg/dl, low blood glucose index, AUC above 180 mg/dl [AUC &gt;180], high blood glucose index [HBGI], and J index. Time-related matrices (time in range (%), time above range (%), and time below range (%) were also calculated from the ambulatory glucose profile. Renal function parameters (serum creatinine, estimated glomerular filtration rate, urine albumin excretion) were calculated. The GV with regard to albumin excretion rate was compared.

Results

Fifty-eight T2DM patients (63.8%, males) with a mean age of 51.5 ± 11.9 years were studied. When compared with baseline (days 1–4), on day 14, there was a significant improvement in mean sensor glucose (mg/dl) median (IQR) [155 (116–247) vs 131 (103–163) (p ≤0.001)], JINDEX [15,878 (7,706–28,298) vs 8,812 (5,545–14,130) (p ≤0.001)], HBGI [361 (304–492) vs 334 (280–379) (p ≤0.001)], MAGE (mg/dl) [112 (8–146) vs 82 (59–109) (p ≤0.001)], M-value [2,477 (1,883–3,848) vs 2,156 (1,667–2,656) (p ≤ 0.001)], MAG (mg/dl) [111 (88–132) vs 88 (69–102) (p ≤ 0.001)]. Patients with albuminuria at baseline had high mean sensor glucose (mg/dl) median (IQR) [190 (131–200) vs 131 (112–156) (p = 0.001)], CONGA (mg/dl) median (IQR) [155 (101–165) vs 108 (83–120) (p = 0.001)], JINDEX, HBGI, MAGE (mg/dl), and M-value are, median (IQR) [20,715 (10,970–26,217 vs 91,118 (6,504–15,445)) (p ≤ 0.01)], [415 (338–423) vs 328 (292–354) (p = 0.001)], [125 (102–196) vs 103 (74–143) (p ≤ 0.01)], [3,014 (2,233–3,080) vs 2,132 (1,788–2,402) (p ≤0.01)], respectively.

Conclusion

In type 2 diabetes, flash glucose monitoring-guided therapeutic interventions can reduce glycemic variability in a brief span (10 days) of time. Also, albuminuria in type 2 diabetes is associated with high glycemic variability. Reduced diabetes complications may ultimately result from this reduced glycemic variability.