Christine L Chan1, Laura Pyle2,3, Megan M Kelsey1, Lindsey Newnes1, Amy Baumgartner1, Philip S Zeitler1, Kristen J Nadeau1. 1. Department of Pediatrics, Division of Pediatric Endocrinology, Children's Hospital Colorado and University of Colorado Anschutz Medical Campus, Aurora, Colorado. 2. Department of Pediatrics, Administrative Division, University of Colorado Anschutz Medical Campus, Aurora, Colorado. 3. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
Abstract
OBJECTIVE: To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D). STUDY DESIGN: Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c. RESULTS: Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD. CONCLUSIONS: Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.
OBJECTIVE: To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D). STUDY DESIGN: Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c. RESULTS: Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD. CONCLUSIONS: Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.
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