OBJECTIVESustained hyperglycemia is associated with low cellular levels of the antioxidant glutathione (GSH), which leads to tissue damage attributed to oxidative stress. We tested the hypothesis that diminished GSH in adult patients with uncontrolled type 2 diabetes is attributed to decreased synthesis and measured the effect of dietary supplementation with its precursors cysteine and glycine on GSH synthesis rate and oxidative stress.RESEARCH DESIGN AND METHODSWe infused 12 diabetic patients and 12 nondiabetic control subjects with [2H2]-glycine to measure GSH synthesis. We also measured intracellular GSH concentrations, reactive oxygen metabolites, and lipid peroxides. Diabetic patients were restudied after 2 weeks of dietary supplementation with the GSH precursors cysteine and glycine.RESULTSCompared with control subjects, diabetic subjects had significantly higher fasting glucose (5.0 ± 0.1 vs. 10.7 ± 0.5 mmol/l; P < 0.001), lower erythrocyte concentrations of glycine (514.7 ± 33.1 vs. 403.2 ± 18.2 μmol/l; P < 0.01), and cysteine (25.2 ± 1.5 vs. 17.8 ± 1.5 μmol/l; P < 0.01); lower concentrations of GSH (6.75 ± 0.47 vs. 1.65 ± 0.16 μmol/g Hb; P < 0.001); diminished fractional (79.21 ± 5.75 vs. 44.86 ± 2.87%/day; P < 0.001) and absolute (5.26 ± 0.61 vs. 0.74 ± 0.10 μmol/g Hb/day; P < 0.001) GSH synthesis rates; and higher reactive oxygen metabolites (286 ± 10 vs. 403 ± 11 Carratelli units [UCarr]; P < 0.001) and lipid peroxides (2.6 ± 0.4 vs. 10.8 ± 1.2 pg/ml; P < 0.001). Following dietary supplementation in diabetic subjects, GSH synthesis and concentrations increased significantly and plasma oxidative stress and lipid peroxides decreased significantly.CONCLUSIONSPatients with uncontrolled type 2 diabetes have severely deficient synthesis of glutathione attributed to limited precursor availability. Dietary supplementation with GSH precursor amino acids can restore GSH synthesis and lower oxidative stress and oxidant damage in the face of persistent hyperglycemia.

Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified. Conclusions: Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

To identify aspects of family behavior associated with glycemic control in youth with type 1 diabetes mellitus (T1DM) during the transition to adolescence, we studied 121 9-to 14-year-olds (M = 12.1 yrs) and their parents, who completed the Diabetes Family Conflict Scale (DFCS) and the Diabetes Family Responsibility Questionnaire (DFRQ). From the DFRQ, we derived 2 dyadic variables, frequency of agreement (parent and child concurred exactly on who was responsible for a task) and frequency of discordance (parent and child had completely opposite reports of who was responsible). To examine the relationship between these variables and age, we divided the cohort into Younger (n = 57, M = 10.6 yrs) and Older (n = 64, M = 13.5 yrs) age groups. Family conflict was significantly related to glycemic control in the entire cohort and in both the Younger and Older age groups. However, only in the Younger (pre-teen) group was Agreement about responsibility for diabetes tasks related to glycemic control, with higher Agreement associated with better glycemic control. Findings suggest that Agreement about sharing of diabetes management responsibilities may be an important target for family-based interventions with pre-teen youth to optimize glycemic control during their transition to adolescence.