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The existence of a diurnal pattern of post prandial glucose tolerance is relevant for management of patients with type 2 diabetes (T2D). We studied 10 healthy (4M) and 9 T2D (6M) subjects matched for age (56±3 vs. 58±3 yrs) and BMI (29±2 vs. 32±2 kg/m2). OGTT was done in healthy subjects to rule out diabetes. Identical mixed meals (8 kcal/kg/meal; 75 gm carb) were ingested during breakfast (B), lunch (L), and dinner (D) at 0700, 1300, and 1900 h in randomized latin square order with one labeled test meal/day on 3 consecutive days. Physical activity was kept same. Postprandial glucose turnover was measured utilizing the triple tracer technique modified with all stable isotopes ([1-13C] Glucose, [2-13C] Glucose and [6, 6-2H2] Glucose). Results shown in Fig 1. Fasting and post-meal glucose were higher while fasting and post-meal insulin and C-peptide concentrations lower in T2D for all three meals compared to healthy. Although meal glucose appearance did not differ between meals in either group, fasting endogenous glucose production (EGP) was higher, percent suppression of EGP lower in T2D and glucose disappearance (corrected for urine glucose loss) lower in T2D than healthy for all meals consumed. This data correlates with lower insulin sensitivity, B-cell responsivity to glucose and disposition index in T2D than healthy throughout the day. Our results suggest an emerging diurnal pattern to glucose tolerance and fluxes in humans, and that the pattern is different in T2D. Disclosure Y.R. Yadav: None. D. Romeres: None. F. Ruchi: None. S. Sawleh: None. R. Carter: None. A. Basu: Consultant; Spouse/Partner; GENFIT. Research Support; Spouse/Partner; AstraZeneca. R. Basu: Consultant; Self; GENFIT. Research Support; Self; AstraZeneca. Funding National Institutes of Health (R01DK029953 to R.B.), (R01DK085516 to A.B.), (DK059637, DK020593)

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 < 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 < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 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.

The role of nocturnal glucagon on regulation of overnight endogenous glucose production (EGP) in T2D is unknown. We studied 13 T2D and 13 age, BMI matched nondiabetic subjects (ND) on two occasions at the Clinical Research Unit at Mayo Clinic. Deuterated water was provided to measure gluconeogenesis (GNG) and glycogenolysis (GGL) using the HMT (hexamethylenetetramine) method. A somatostatin pancreatic clamp was initiated at 10 PM with 0.25 mU/kg/min insulin infusion until 7 AM along with [3-3H] glucose to measure turnover in the presence of glucagon infusions at 0.65 ng/kg/min (basal) or 1.5 ng/kg/min (high) in random order. Blood was sampled periodically for the measurements of plasma glucose, [3-3H] glucose, hormones; EGP rates were measured at 7AM. Results are presented in the Table. Fasting glucose, insulin and c-peptide concentrations were not different in either group during either visit. Glucose infusion rates (GIR) required to maintain euglycemia were lower during high glucagon in both groups and lower in T2D vs. ND regardless of visit indicating insulin resistance. Rates of EGP at 7AM were higher in both groups with the high vs. basal glucagon (matched data shown for calculation). Partial GNG data that are available indicate increased rates of GGL at 7 AM in the presence of high glucagon. In T2D higher glucagon overnight could contribute to higher rates of GGL and EGP. Therapies to reduce GGL may help lower overnight EGP.{F280D615-E887-4E30-8C76-ED88282D5E23}All data are Mean +/- SD. Statistical analyses was not performed on EGP and HMT data as it is preliminary.Diabetes StatusBasal Glucagon 7 AMHigh Glucagon 7 AM7 AM p-value Basal vs HighGlucose (mmol/L)ND T2D5.4 ± 0.6 8.2 ± 4.0 (p < 0.001)5.2 ± 0.6 8.2 ± 4.0 (p < 0.001)0.4 0.9Glucagon (pg/ml)ND T2D82.0 ± 14.0 90.5 ± 12.5 (p = 0.56)138.3 ± 15.4 151.5 µ 17.2 (p = 0.20)p < 0.01 p < 0.01GIR (µmol/kgFFM/min)ND T2D23.0 ± 12.8 6.7 ± 8.4 (p Disclosure A. Basu: Research Support; Spouse/Partner; AstraZeneca. Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Voluntis France. H. Zhai: None. R. Carter: None. R. Basu: Research Support; Self; AstraZeneca.

To provide an understanding of both the preclinical and clinical aspects of closed-loop artificial pancreas systems, we provide a discussion of this topic as part of this two-part Bench to Clinic narrative. Here, the Bench narrative provides an in-depth understanding of insulin-glucose-glucagon physiology in conditions that mimic the free-living situation to the extent possible in type 1 diabetes that will help refine and improve future closed-loop system algorithms. In the Clinic narrative, Doyle and colleagues compare and evaluate technology used in current closed-loop studies to gain further momentum toward outpatient trials and eventual approval for widespread use.

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open- and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3–6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5–6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closed-loop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes.

Evaluation of the existence of a diurnal pattern of glucose tolerance after mixed meals is important to inform a closed-loop system of treatment for insulin requiring diabetes. We studied 20 healthy volunteers with normal fasting glucose (4.8 ± 0.1 mmol/L) and HbA1c (5.2 ± 0.0%) to determine such a pattern in nondiabetic individuals. Identical mixed meals were ingested during breakfast, lunch, or dinner at 0700, 1300, and 1900 h in randomized Latin square order on 3 consecutive days. Physical activity was the same on all days. Postprandial glucose turnover was measured using the triple tracer technique. Postprandial glucose excursion was significantly lower (P < 0.01) at breakfast than lunch and dinner. β-Cell responsivity to glucose and disposition index was higher (P < 0.01) at breakfast than lunch and dinner. Hepatic insulin extraction was lower (P < 0.01) at breakfast than dinner. Although meal glucose appearance did not differ between meals, suppression of endogenous glucose production tended to be lower (P < 0.01) and insulin sensitivity tended to be higher (P < 0.01) at breakfast than at lunch or dinner. Our results suggest a diurnal pattern to glucose tolerance in healthy humans, and if present in type 1 diabetes, it will need to be incorporated into artificial pancreas systems.

OBJECTIVE To test whether diabetic polyneuropathies (DPNs), retinopathy, or nephropathy is more prevalent in subjects with impaired glycemia (IG) (abnormality of impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or impaired HbA1c [IA1C]) than in healthy subjects (non-IG). RESEARCH DESIGN AND METHODS Matched IG and non-IG volunteers were randomly identified from population-based diagnostic and laboratory registries, restudied, and reclassified as non-IG (n = 150), IG (n = 174), or new diabetes (n = 218). RESULTS Frequency (%) of DPN in non-IG, IG, and new diabetes was 3 (2.0%), 3 (1.7%), and 17 (7.8%) narrowly defined (no other cause for polyneuropathy) and 19 (12.7%), 22 (12.6%), and 38 (17.4%) broadly defined. Mean and frequency distribution of composite scores of nerve conduction and quantitative sensation tests were not significantly different between IG and non-IG but were worse in new diabetes. Frequency of retinopathy and nephropathy was significantly increased only in new diabetes. In secondary analysis, small but significant increases in retinopathy and nephropathy were found in IGT, IFG, and IGT combined groups. CONCLUSIONS In population studies of Olmsted County, Minnesota, inhabitants, prevalence of typical DPN, retinopathy, and nephropathy was significantly increased only in subjects with new diabetes—not in subjects with IG as defined by American Diabetes Association (ADA) criteria of abnormality of IFG, IGT, or IA1C. For atypical DPN, such an increase was not observed even in subjects with new diabetes. In medical practice, explanations other than IG should be sought for patients with atypical DPN (chronic idiopathic axonal polyneuropathy) who have IG.

OBJECTIVE—Because endothelial cell dysfunction and inflammation are key contributors to the development of complications in type 1 diabetes, we studied risk factors related to endothelial dysfunction and inflammation (C-reactive protein and fibrinogen, soluble vascular cell adhesion molecule-1, intracellular adhesion molecule-1, and E-selectin, and fibrinolytic markers) in a subgroup of patients from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Intervention and Complications (EDIC) study cohort. RESEARCH DESIGN AND METHODS—We determined which of these risk factors or clusters thereof are associated with the presence of and subsequent development of nephropathy and macrovascular complications (reflected by carotid intima-media thickness [IMT]). RESULTS—After adjustment for conventional risk factors (age, sex, DCCT treatment group, diabetes duration, A1C, systolic blood pressure, waist-to-hip ratio, total and HDL cholesterol, and smoking status), fibrinogen remained strongly associated with progression of internal and common carotid IMT (P < 0.01) and soluble E-selectin had a strong association with nephropathy (P < 0.01). CONCLUSIONS—The best predictor for IMT progression in the DCCT/EDIC cohort was plasma fibrinogen, and the levels of soluble E-selectin discriminate patients with albuminuria better than conventional risk factors.

Peripheral arterial disease (PAD) is a significant contributor to the morbidity and mortality of >2 million Americans (1). In addition to its own specific disease risks, PAD has been shown to be a predictor of cardiovascular mortality and coronary artery disease, as well as a general marker for the atherosclerotic disease process (2,3). PAD complications, especially in the lower limb, are significantly greater in individuals with diabetes compared with those without diabetes, and the risk imparted by diabetes is similar or greater in magnitude to that seen for ischemic heart disease and stroke (4,5). The aortofemoral arteries are prime sites for PAD; hence, determination of claudication and measurement of blood flow in the lower extremities are the most common assessments (6). A relatively low ankle systolic blood pressure (ankle-to-brachial ratio index [ABI]) has been found to be an indicator of atherosclerosis/occlusion in this region (5). Conversely, in diabetes, a high-pressure ABI due to medial wall arterial calcification and noncompressible vessels may also be associated with adverse outcomes, including diabetic kidney disease (7). Sex differences have been reported in ABI measurements in type 1 diabetes, with women having a greater frequency of low ABI and men having a higher frequency of high ABI (7). Here, we extend these analyses by examining the effect of previous intensive diabetes management on the development of abnormally high and low ABIs in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) cohort with the hypothesis that …

A recent Perspective in Nature issued a call for more transparency in the reporting of preclinical research (1). Although this article focused primarily on experimental design, it emphasized the need for improved reporting in the scientific literature. Within the context of preclinical studies, there have been discussions regarding the appropriate reporting of standard error (SE) and standard deviation (SD) (2–5); however, despite the recommendations, opportunities remain to improve upon the reporting of these statistics in the literature. To first set the stage for the distinction between SD and SE, we start with the similarities. Both SD and SE measure variability or, informally, “spread.” As such, both statistics give a numerical summary of variability. Given this, how does one distinguish SE from SD? The distinction is that one summarizes the variability of data and the other describes the variability of …