Continuous Glucose Monitoring for Hypoglycemia Avoidance and Glucose Counterregulation in Long-Standing Type 1 Diabetes.

Context: Patients with long-standing type 1 diabetes (T1D) are at increased risk for severe hypoglycemia because of defects in glucose counterregulation and recognition of hypoglycemia symptoms, in part mediated through exposure to hypoglycemia. Objective: To determine whether implementation of real-time continuous glucose monitoring (CGM) as a strategy for hypoglycemia avoidance could improve glucose counterregulation in patients with long-standing T1D and hypoglycemia unawareness. Design, Setting, Participants, and Intervention: Eleven patients with T1D disease duration of ∼31 years were studied longitudinally in the Clinical & Translational Research Center of the University of Pennsylvania before and 6 and 18 months after initiation of CGM and were compared with 12 nondiabetic control participants. Main Outcome Measure: Endogenous glucose production response derived from paired hyperinsulinemic stepped-hypoglycemic and euglycemic clamps with infusion of 6,6-2H2-glucose. Results: In patients with T1D, hypoglycemia awareness (Clarke score) and severity (HYPO score and severe events) improved (P < 0.01 for all) without change in hemoglobin A1c (baseline, 7.2% ± 0.2%). In response to insulin-induced hypoglycemia, endogenous glucose production did not change from before to 6 months (0.42 ± 0.08 vs 0.54 ± 0.07 mg·kg-1·min-1) but improved after 18 months (0.84 ± 0.15 mg·kg-1·min-1; P < 0.05 vs before CGM), albeit remaining less than in controls (1.39 ± 0.11 mg·kg-1·min-1; P ≤ 0.01 vs all). Conclusions: Real-time CGM can improve awareness and reduce the burden of problematic hypoglycemia in patients with long-standing T1D, but with only modest improvement in the endogenous glucose production response that is required to prevent or correct low blood glucose.

Physiology-Invariant Meal Detection for Type 1 Diabetes.

BACKGROUND: Fully automated artificial pancreas systems require meal detectors to supplement blood glucose level regulation, where false meal detections can cause unnecessary insulin delivery with potentially fatal consequences, and missed detections may cause the patient to experience extreme hyperglycemia. Most existing meal detectors monitor various measures of glucose rate-of-change to detect meals where varying physiology and meal content complicate balancing detector sensitivity versus specificity. METHODS: We developed a novel meal detector based on a minimal glucose-insulin metabolism model and show that the detector is, by design, invariant to patient-specific physiological parameters in the minimal model. Our physiological parameter-invariant (PAIN) detector achieves a near-constant false alarm rate across all individuals and is evaluated against three other major existing meal detectors on a clinical type 1 diabetes data set. RESULTS: In the clinical evaluation, the PAIN-based detector achieves an 86.9% sensitivity for an average false alarm rate of two alarms per day. In addition, for all false alarm rates, the PAIN-based detector performance is significantly better than three other existing meal detectors. In addition, the evaluation results show that the PAIN-based detector uniquely (as compared with the other meal detectors) has low variance in detection and false alarm rates across all patients, without patient-specific personalization. CONCLUSIONS: The PAIN-based meal detector has demonstrated better detection performance than existing meal detectors, and it has the unique strength of achieving a consistent performance across a population with varying physiology without any individual-level parameter tuning or training.