"Difference on glucose profile from Continuous Glucose Monitoring in people with prediabetes vs. normoglycemic individuals: a matched-pair analysis"

Abstract

Background and aims: The glycemic profile of prediabetes, a borderline condition with blood glucose levels higher than normal but not enough to be officially diagnosed as diabetes, derived by continuous glucose monitoring (CGM) is currently unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and matched normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

Materials and methods: Participants with prediabetes matched for age, sex and body mass index (BMI) with normoglycemic individuals were selected from an ongoing cohort study in Greece. Participants were ≥18 years, without known history of diabetes, no history of severe cardiovascular, liver, kidney, or pancreas diseases. Individuals with FPG levels between 100-124 mg/dL or HbA1c 5.7-6.4% were classified as prediabetes group, whereas participants with FPG <100 mg/dL and HbA1c <5.7% were categorized as normoglycemic. They were instructed to use professional CGM (Envision™ Pro, Medtronic; access to data retrospectively) for two weeks. In the morning of the 2 nd day, a 75g OGTT was performed during fasting state. The primary outcomes were percentages of glucose readings below range (TBR; <54 or <70 mg/dL), in range (TIR; 70-180 mg/dL) and above range (TAR; >180 or >250 mg/dL). Total and incremental areas under the glucose curve (AUC) were calculated between the beginning of the OGTT until two to four hours later. Glucose variability was depicted by the coefficient of variation (CV), standard deviation (SD) and mean amplitude of glucose excursions (MAGE). Wilcoxon sign-ranked test and McNemar mid p-test were employed to detect any differences between matched pairs. Multiple linear regression models were employed to investigate the differences on all outcomes between pre-diabetic and normal participants.

Results: A total of 36 participants (median age 51 years; median BMI 26.4 kg/m 2 ) formed 18 matched pairs. Statistically significant differences were observed for 24-hour TIR (median 98.5% vs. 99.9%, p = 0.013), TAR>180 mg/dl (0.4% vs. 0%, p = 0.0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, p = 0.0038) between individuals with prediabetes compared to normoglycemic participants. Similarly, there was a statistically significant difference both in daytime and nocturnal percentages of TIR between people with prediabetes and normoglycemic participants (p=0.0273, p=0.0087, respectively). Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, p = 0.0156; median MAGE 44.3 vs. 33.3 mg/dL, p = 0.0043). Only 10 participants (6 in the prediabetes and 4 in the normoglycemic group) had glucose readings below 70 mg/dL and even fewer had below 54 mg/dL (3 overall; 2 individuals with prediabetes, 1 normoglycemic). In contrast, over 60% of participants with prediabetes had glucose readings greater than 180 mg/dL. Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, p = 0.0347 for total and 4666.5 vs. 2792.7, p = 0.0429 for incremental 2-hour post OGTT).

Conclusion: Our study highlights the different glucose profiles of people with prediabetes compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate classification.

FILIS Panagiotis