Browsing by Subject "Hemoglobin A, Glycosylated"

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    CGM-measured glucose values have a strong correlation with C-peptide, HbA1c and IDAAC, but do poorly in predicting C-peptide levels in the two years following onset of diabetes
    (Springer-Verlag, 2015-06) Buckingham, Bruce; Cheng, Peiyao; Beck, Roy W.; Kollman, Craig; Ruedy, Katrina J.; Weinzimer, Stuart A.; Slover, Robert; Bremer, Andrew A.; Fuqua, John; Tamborlane, William; Diabetes Research in Children Network (DirecNet) and Type 1 Diabetes TrialNet Study Groups; Department of Pediatrics, IU School of Medicine
    AIMS/HYPOTHESIS: The aim of this work was to assess the association between continuous glucose monitoring (CGM) data, HbA1c, insulin-dose-adjusted HbA1c (IDAA1c) and C-peptide responses during the first 2 years following diagnosis of type 1 diabetes. METHODS: A secondary analysis was conducted of data collected from a randomised trial assessing the effect of intensive management initiated within 1 week of diagnosis of type 1 diabetes, in which mixed-meal tolerance tests were performed at baseline and at eight additional time points through 24 months. CGM data were collected at each visit. RESULTS: Among 67 study participants (mean age [± SD] 13.3 ± 5.7 years), HbA1c was inversely correlated with C-peptide at each time point (p < 0.001), as were changes in each measure between time points (p < 0.001). However, C-peptide at one visit did not predict the change in HbA1c at the next visit and vice versa. Higher C-peptide levels correlated with increased proportion of CGM glucose values between 3.9 and 7.8 mmol/l and lower CV (p = 0.001 and p = 0.02, respectively) but not with CGM glucose levels <3.9 mmol/l. Virtually all participants with IDAA1c < 9 retained substantial insulin secretion but when evaluated together with CGM, time in the range of 3.9-7.8 mmol/l and CV did not provide additional value in predicting C-peptide levels. CONCLUSIONS/INTERPRETATION: In the first 2 years after diagnosis of type 1 diabetes, higher C-peptide levels are associated with increased sensor glucose levels in the target range and with lower glucose variability but not hypoglycaemia. CGM metrics do not provide added value over the IDAA1c in predicting C-peptide levels.
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    Obstructive sleep apnoea in obese adolescents and cardiometabolic risk markers
    (Wiley Blackwell (Blackwell Publishing), 2014-12) Watson, S. E.; Li, Z.; Tu, W.; Jalou, H.; Brubaker, J. L.; Gupta, S.; Huber, J. N.; Carroll, A.; Hannon, T. S.; Department of Pediatrics, IU School of Medicine
    WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: In paediatric patients, obstructive sleep apnoea is associated with adiposity, especially visceral adiposity. In adults, obstructive sleep apnoea is also associated with a higher prevalence of cardiovascular disease and type 2 diabetes. There are limited and conflicting paediatric studies examining the association between obstructive sleep apnoea and biomarkers of risk for cardiovascular disease and type 2 diabetes in youth. WHAT THIS STUDY ADDS: Obstructive sleep apnoea is linked with greater cardiometabolic risk markers in obese adolescents. Fasting insulin and homeostasis model assessment-insulin resistance may be especially linked with obstructive sleep apnoea among obese male Hispanic adolescents. The relationship between obstructive sleep apnoea and cardiometabolic abnormalities in obese adolescents should be considered when evaluating patients found to have obstructive sleep apnoea. BACKGROUND: Paediatric studies examining the association between obstructive sleep apnoea (OSA) and insulin sensitivity/cardiometabolic risk are limited and conflicting. OBJECTIVE: This study aims to determine if cardiometabolic risk markers are increased among obese youth with obstructive sleep apnoea as compared with their equally obese peers without OSA. METHODS: We performed a retrospective analysis of 96 patients (age 14.2 ± 1.4 years) who underwent polysomnography for suspected OSA. Fasting lipids, glucose, insulin and haemoglobin A1 c (HbA1 c) were performed as part of routine clinical evaluation. Patients were categorized into two groups by degree of OSA as measured by the apnoea-hypopnoea index (AHI): none or mild OSA (AHI < 5) and moderate or severe OSA (AHI ≥ 5). RESULTS: Despite the similar degrees of obesity, patients with moderate or severe OSA had higher fasting insulin (P = 0.037) and homeostasis model assessment-insulin resistance (HOMA-IR [P = 0.0497]) as compared with those with mild or no OSA. After controlling for body mass index, there was a positive association between the AHI and log HOMA-IR (P = 0.005). There was a positive relationship between arousals plus awakenings during the polysomnography and fasting triglycerides. CONCLUSIONS: OSA is linked with greater cardiometabolic risk markers in obese youth.