Kudva, Yogish C.

Objective: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. Methods: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m 2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based “effectiveness index” (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. Results: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. Conclusion: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

Background: Glucose variability (GV) has been increasingly (or more extensively) studied in patients with type 1 diabetes (T1D) in the ambulatory setting; limited data exist on GV in hospitalized patients with T1D. Materials and Methods: Retrospective single center cohort study, we analyzed in-hospital glucose measurements to assess GV in 736 hospitalized patients in different units over a consecutive 5-year period of time. GV was assessed by mean blood glucose (BG), Average daily risk range (ADRR), high BG index, and low BG index. To place our findings in context, we conducted a systematic review using Cochrane collaboration methodology to critically analyze current published literature on GV in hospitalized T1D patients. Results: Overall, glycemic control was suboptimal with mean BG 183 ± 51.5 mg/dL and mean ADRR 35 with only 16% patients being categorized as low risk (ADRR <20) for hypo or hyperglycemia. Patients admitted in medical units had mean BG of 194.4 ± 42.8 mg/dL (95% CI = 101.2–346.6) and ADRR of 39.4 ± 16 (95% CI = 1.3–118.7), which were higher than the patients admitted in the surgical units (mean BG 168.1 ± 35.7 (95% CI = 74.8–301.8) and mean ADRR (28.8 ± 13.1 [95% CI = 0.3–93.1]). For the systematic review, initial search yielded 2336 studies for screening, however, none of them had data specific for T1D. Conclusion: GV is high in hospitalized T1D patients admitted at our tertiary care center. Review of the literature shows paucity of data regarding GV in hospitalized patients with T1D.

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P &lt; 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P &lt; 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P &lt; 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Calcineurin inhibitors (CNIs), including tacrolimus and cyclosporine, are immune‐modulating agents used in autoimmune disorders, glomerulonephritides, and after transplantation. CNIs are implicated as diabetogenic drugs but the mechanism is not clearly elucidated. Calcineurin is a cytosolic‐phosphatase critical for T‐lymphocyte activation. Calcineurin is widely distributed in different tissues responsible for glucose‐regulation including pancreas, liver, skeletal muscle, adipocytes, brain, and gut. We describe the pharmacologic effects of CNIs in different tissues and impact on glucose regulation.

Background: Several clinical trials have been performed to assess safety and efficacy of closed-loop control. Some included physical activity (PA), with the goal of challenging the control algorithms with a rapid change in insulin sensitivity while reducing the risk of hypoglycemia. However, the question remains as to the necessity to inform the control algorithm on the imminent PA. The aim here is to assess in silico (i) if it is necessary to announce upcoming PA and (ii) if this is the case, what is the safest strategy of basal insulin reduction in the context of the closed-loop control. Methods: We modified the University of Virginia/Padova type 1 diabetes simulator to incorporate the effect of PA based on a study in healthy subjects that demonstrated an almost doubling of insulin sensitivity during PA versus rest. Two in silico experiments, including a PA session, have been simulated on the virtual adult population: One in the absence of and one with different degrees of reductions and durations of basal insulin infusion rates. Results: Most in silico subjects experienced hypoglycemia in the absence of basal insulin adjustment. We show that, in the absence of patient-specific information, a safe and effective strategy is to reduce basal insulin by 50% starting 90 min before exercise and by 30% during exercise. Conclusions: Our results suggest that control algorithms could benefit by knowing an upcoming PA. Ideally, the control algorithm should be informed about the patient-specific basal insulin reduction pattern. An alternative strategy that has been proposed here has been deemed safe and effective in in silico experiments.