Citations

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.

Abstract Context: Early postprandial hyperglycemia and delayed hypoglycemia remain major problems in current management of type 1 diabetes (T1D). Objective: Our objective was to investigate the effects of pramlintide, known to suppress glucagon and delay gastric emptying, on postprandial glucose fluxes in T1D. Design: This was a single-center, inpatient, randomized, crossover study. Patients: Twelve patients with T1D who completed the study were analyzed. Interventions: Subjects were studied on two occasions with or without pramlintide. Triple tracer mixed-meal method and oral minimal model were used to estimate postprandial glucose turnover and insulin sensitivity (SI). Integrated liver insulin sensitivity was calculated based on glucose turnover. Plasma glucagon and insulin were measured. Main Outcome Measure: Glucose turnover and SI were the main outcome measures. Results: With pramlintide, 2-hour postprandial glucose, insulin, glucagon, glucose turnover, and SI indices showed: plasma glucose excursions were reduced (difference in incremental area under the curve [iAUC], 444.0 mMmin, P = .0003); plasma insulin concentrations were lower (difference in iAUC, 7642.0 pMmin; P = .0099); plasma glucagon excursions were lower (difference in iAUC, 1730.6 pg/mlmin; P = .0147); meal rate of glucose appearance was lower (difference in iAUC: 1196.2 μM/kg fat free mass [FFM]; P = .0316), endogenous glucose production was not different (difference in iAUC: −105.5 μM/kg FFM; P = .5842), rate of glucose disappearance was lower (difference in iAUC: 1494.2 μM/kg FFM; P = .0083). SI and liver insulin sensitivity were not different between study visits (P &gt; .05). Conclusions: Inhibition of glucagon and gastric emptying delaying reduced 2-hour prandial glucose excursions in T1D by delaying meal rate of glucose appearance.

Physical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second half of 2015. Studies (1) involving patients with other disorders that have lasted 12 weeks or longer and tracked physical activity and (2) including both aerobic and resistance activity may offer insights about the user experience and device optimization even as single input CLC heads into real-world clinical trials over the next few years and nonglucose input is introduced as the next advance.