Background: Poor glycemic control is associated with increased risk of cardiovascular disease (CVD) in type 1 diabetes mellitus (T1DM); however, little is known about mechanisms specific to T1DM. In T1DM, myocardial injury can induce persistent cardiac autoimmunity. Chronic hyperglycemia causes myocardial injury, raising the possibility that hyperglycemia-induced cardiac autoimmunity could contribute to long-term CVD complications in T1DM. Methods: We measured the prevalence and profiles of cardiac autoantibodies (AAbs) in longitudinal samples from the DCCT (Diabetes Control and Complications Trial) in participants with mean hemoglobin A _1c (HbA _1c ) ≥9.0% (n=83) and ≤7.0% (n=83) during DCCT. We assessed subsequent coronary artery calcification (measured once during years 7–9 in the post-DCCT EDIC [Epidemiology of Diabetes Interventions and Complications] observational study), high-sensitivity C-reactive protein (measured during EDIC years 4–6), and CVD events (defined as nonfatal myocardial infarction, stroke, death resulting from CVD, heart failure, or coronary artery bypass graft) over a 26-year median follow-up. Cardiac AAbs were also measured in matched patients with type 2 diabetes mellitus with HbA _1c ≥9.0% (n=70) and ≤7.0% (n=140) and, as a control for cardiac autoimmunity, patients with Chagas cardiomyopathy (n=51). Results: Apart from HbA _1c levels, the DCCT groups shared similar CVD risk factors at the beginning and end of DCCT. The DCCT HbA _1c ≥9.0% group showed markedly higher cardiac AAb levels than the HbA _1c ≤7.0% group during DCCT, with a progressive increase and decrease in AAb levels over time in the 2 groups, respectively ( P <0.001). In the HbA _1c ≥9.0% group, 46%, 22%, and 11% tested positive for ≥1, ≥2, and ≥3 different cardiac AAb types, respectively, similar to patients with Chagas cardiomyopathy, compared with 2%, 1%, and 0% in the HbA _1c ≤7.0% group. Glycemic control was not associated with AAb prevalence in type 2 diabetes mellitus. Positivity for ≥2 AAbs during DCCT was associated with increased risk of CVD events (4 of 6; hazard ratio, 16.1; 95% CI, 3.0–88.2) and, in multivariable analyses, with detectable coronary artery calcification (13 of 31; odds ratio, 60.1; 95% CI, 8.4–410.0). Patients with ≥2 AAbs subsequently also showed elevated high-sensitivity C-reactive protein levels (6.0 mg/L versus 1.4 mg/L in patients with ≤1 AAbs; P =0.003). Conclusions: Poor glycemic control is associated with cardiac autoimmunity in T1DM. Furthermore, cardiac AAb positivity is associated with an increased risk of CVD decades later, suggesting a role for autoimmune mechanisms in the development of CVD in T1DM, possibly through inflammatory pathways.

BACKGROUND AND OBJECTIVES: Impaired glucose control in very preterm infants is associated with increased morbidity, mortality, and poor neurologic outcome. Strategies based on insulin titration have been unsuccessful in achieving euglycemia in absence of an increase in hypoglycemia and mortality. We sought to assess whether glucose administration guided by continuous glucose monitoring (CGM) is more effective than standard of care blood glucose monitoring in maintaining euglycemia in very preterm infants. METHODS: Fifty newborns ≤32 weeks’ gestation or with birth weight ≤1500 g were randomly assigned (1:1) within 48-hours from birth to receive computer-guided glucose infusion rate (GIR) with or without CGM. In the unblinded CGM group, the GIR adjustments were driven by CGM and rate of glucose change, whereas in the blinded CGM group the GIR was adjusted by using standard of care glucometer on the basis of blood glucose determinations. Primary outcome was percentage of time spent in euglycemic range (72–144 mg/dL). Secondary outcomes were percentage of time spent in mild (47–71 mg/dL) and severe (180 mg/dL) hyperglycemia; and glucose variability. RESULTS: Neonates in the unblinded CGM group had a greater percentage of time spent in euglycemic range (median, 84% vs 68%, P < .001) and decreased time spent in mild (P = .04) and severe (P = .007) hypoglycemia and in severe hyperglycemia (P = .04) compared with the blinded CGM group. Use of CGM also decreased glycemic variability (SD: 21.6 ± 5.4 mg/dL vs 27 ± 7.2 mg/dL, P = .01; coefficient of variation: 22.8% ± 4.2% vs 27.9% ± 5.0%; P < .001). CONCLUSIONS: CGM-guided glucose titration can successfully increase the time spent in euglycemic range, reduce hypoglycemia, and minimize glycemic variability in preterm infants during the first week of life.

We conducted a prospective study in a large, multiethnic cohort of obese adolescents to characterize clinical and genetic features associated with pediatric nonalcoholic fatty liver (NAFL), the most common cause of chronic liver disease in youth. A total of 503 obese adolescents were enrolled, including 191 (38.0%) whites, 134 (26.6%) blacks, and 178 (35.4%) Hispanics. Participants underwent abdominal magnetic resonance imaging (MRI) to quantify hepatic fat fraction (HFF), an oral glucose tolerance test (OGTT) to assess glucose tolerance and insulin sensitivity, and the genotyping of three single‐nucleotide polymorphisms (SNPs) associated with nonalcoholic fatty liver disease (NAFLD) (patatin‐like phospholipase domain‐containing protein 3 [PNPLA3] rs738409, glucokinase regulatory protein [GCKR] rs1260326, and transmembrane 6 superfamily member 2 [TM6SF2] rs58542926). Assessments were repeated in 133 subjects after a 2‐year follow‐up. Prevalence of nonalcoholic fatty liver (NAFL) was 41.6% (209 patients) and ranged widely among ethnicities, being 42.9% in whites, 15.7% in blacks, and 59.6% in Hispanics (P < 0.0001). Among adolescents with NAFL, blacks showed the highest prevalence of altered glucose homeostasis (66%; P = 0.0003). Risk factors for NAFL incidence were white or Hispanic ethnicity (P = 0.021), high fasting C‐peptide levels (P = 0.0006), and weight gain (P = 0.0006), whereas baseline HFF (P = 0.004) and weight loss (P = 0.032) predicted resolution of NAFL at follow‐up. Adding either gene variant to these variables improved significantly the model predictive performance. Conclusion: Black obese adolescents are relatively protected from liver steatosis, but are more susceptible to the deleterious effects of NAFL on glucose metabolism. The combination of ethnicity/race with markers of insulin resistance and genetic factors might help identify obese youth at risk for developing NAFL.

Background: The objective of this study was to assess the safety and performance of the Omnipod ^® personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ≥6 years with type 1 diabetes (T1D) under free-living conditions using an investigational device. Materials and Methods: A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6–65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL. Results: Participants were 11 adults, 10 adolescents, and 15 children aged (mean ± standard deviation) 28.8 ± 7.9, 14.3 ± 1.3, and 9.9 ± 1.0 years, respectively. Percentage time ≥250 mg/dL during HCL was 4.5% ± 4.2%, 3.5% ± 5.0%, and 8.6% ± 8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST ( P = 0.1, P = 0.02, and P = 0.03). Percentage time <70 mg/dL during HCL was 1.9% ± 1.3%, 2.5% ± 2.0%, and 2.2% ± 1.9%, a statistically significant decrease in adults when compared to ST ( P = 0.005, P = 0.3, and P = 0.3). Percentage time 70–180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% ± 7.5% vs. ST 68.0% ± 15.6% for adults ( P = 0.08), HCL 79.0% ± 12.6% vs. ST 60.6% ± 13.4% for adolescents ( P = 0.01), and HCL 69.2% ± 13.5% vs. ST 54.9% ± 12.9% for children ( P = 0.003). Conclusions: The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ≥6 years to adults with T1D under supervised free-living conditions with challenges, including daily physical activity and unrestricted meals.

OBJECTIVE The Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5- to 9-year old children during a camp. RESEARCH DESIGN AND METHODS A total of 30 children, 5- to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensor-augmented pump (SAP). RESULTS Overnight time-in-hypoglycemia was reduced with the AP versus SAP, median (25^th–75^th percentiles): 0.0% (0.0–2.2) vs. 2.2% (0.0–12.3) (P = 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5%) vs. 59.7% (21.2%) (P = 0.430), but with increased mean glucose 173 mg/dL (SD 36) vs. 150 mg/dL (SD 39) (P = 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (SD 13.5) vs. 63.1% (SD 11.0) (P = 0.022) and increased mean glucose 169 mg/dL (SD 23) vs. 147 mg/dL (SD 23) (P < 0.001). CONCLUSIONS This trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.