Medical resource use data and responses to the EuroQol 5-Dimension (EQ-5D) instrument were collected at baseline and throughout the trial. Medical resources and medications were assigned values by using U.S. Medicare payments and wholesale acquisition costs, respectively. Secondary analyses used English costs.

Patients were followed for an average of 3.3 years, during which time those randomized to EQW experienced 0.41 fewer inpatient days (7.05 vs. 7.46 days; relative rate ratio 0.91; P = 0.05). Rates of outpatient medical visits were similar, as were total inpatient and outpatient costs. Mean costs for nonstudy diabetes medications over the study period were ~$1,600 lower with EQW than with placebo (P = 0.01). Total within-study costs, excluding study medication, were lower in the EQW arm than in the placebo arm ($28,907 vs. $30,914; P ≤ 0.01). When including the estimated cost of EQW, total mean costs were significantly higher in the EQW group than in the placebo group ($42,697 vs. $30,914; P < 0.01). With English costs applied, mean total costs, including exenatide costs, were £1,670 higher in the EQW group than the placebo group (£10,874 vs. £9,204; P < 0.01). There were no significant differences in EQ-5D health utilities between arms over time.

Medical costs were lower in the EQW arm than the placebo arm, but total costs were significantly higher once the cost of branded exenatide was incorporated.

We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ≥25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR).

Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1–8.0) and of undiagnosed diabetes 4.9% (4.6–5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5–8.1), lower-middle-income economies (LMIs) 7.1% (6.6–7.6), and upper-middle-income economies (UMIs) 8.2% (7.5–9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22–1.78], LMIs 1.14 [1.06–1.23], and UMIs 1.28 [1.02–1.61]).

Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.

Data on health care resource utilization from 100,391 people with type 2 diabetes were extracted from the electronic database used at the Catalan Health Institute. Multivariate regression models were carried out to test the impact of glycemic control (HbA_1c) on total health care, hospital admission, and medication costs; model 1 adjusted for a variety of covariates, and model 2 also included micro- and macrovascular complications. Glycemic control was classified as good for HbA_1c <7%, fair for ≥7% to <8%, poor for ≥8% to <10%, and very poor for ≥10%.

Mean per patient annual direct medical costs were 3,039 ± SD 6,581. Worse glycemic control was associated with higher total health care costs: compared with good glycemic control, health care costs increased by 18% (509.82) and 23% (661.35) in patients with very poor and poor glycemic control, respectively, when unadjusted and by 428.3 and 395.1, respectively, in model 2. Medication costs increased by 12% in patients with fair control and by 28% in those with very poor control (model 2). Patients with poor control had a higher probability of hospitalization than those with good control (5% in model 2) and a greater average cost when hospitalization occurred (811).

Poor glycemic control was directly related to higher total health care, hospitalization, and medication costs. Preventive strategies and good glycemic control in people with type 2 diabetes could reduce the economic impact associated with this disease.

Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects).

The strongest associations in the MHC class II region (rs3957146, β [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (β [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, β [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA.

In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

LTL (quantitative PCR) and plasma 8-OHdG concentrations (immunoassay method) were assessed at baseline in the GENEDIAB (Génétique de la Néphropathie Diabétique) type 1 diabetes cohort. Logistic and Cox proportional hazards regression models were fitted to estimate odds ratio (OR) (at baseline) and hazard ratio (HR) (during follow-up), with related 95% CI, by increasing biomarker tertiles (T1, T2, T3).

Among 478 participants (56% male, mean ± SD age 45 ± 12 years and diabetes duration 29 ± 10 years), 84 patients had LEA at baseline. Baseline history of LEA was associated with shorter LTL (OR for T2 vs. T1 0.62 [95% CI 0.32–1.22] and for T3 vs. T1 0.41 [0.20–0.84]) but not with plasma 8-OHdG (1.16 [0.56–2.39] and 1.24 [0.61–2.55], respectively). New cases of LEA occurred in 34 (12.3%) participants during the 10-year follow-up. LTL were shorter (HR T2 vs. T1 0.25 [95% CI 0.08–0.67] and T3 vs. T1 0.29 [0.10–0.77]) and plasma 8-OHdG higher (2.20 [0.76–7.35] and 3.11 [1.07–10.32]) in participants who developed LEA during follow-up compared with others. No significant interaction was observed between biomarkers on their association with LEA.

We report the first independent association between LTL shortening and excess risk of LEA in type 1 diabetes. High plasma 8-OHdG was also associated with incident LEA but partly dependent on cofounding variables.

A Stepped Insulin Secretion Test with Arginine was used to quantify functional β-cell capacity by hyperglycemia and arginine stimulation. Thirty-nine of 57 participants initially achieved remission (HbA_1c <6.5% [<48 mmol/mol] and fasting plasma glucose <7 mmol/L on no antidiabetic drug therapy) with a 16.4 ± 7.7 kg weight loss and were followed up with supportive advice on avoidance of weight regain. At 2 years, 20 participants remained in remission in the study. A nondiabetic control (NDC) group, matched for age, sex, and weight after weight loss with the intervention group, was studied once.

During remission, median (interquartile range) maximal rate of insulin secretion increased from 581 (480–811) pmol/min/m² at baseline to 736 (542–998) pmol/min/m² at 5 months, 942 (565–1,240) pmol/min/m² at 12 months (P = 0.028 from baseline), and 936 (635–1,435) pmol/min/m² at 24 months (P = 0.023 from baseline; n = 20 of 39 of those initially in remission). This was comparable to the NDC group (1,016 [857–1,507] pmol/min/m²) by 12 (P = 0.064) and 24 (P = 0.244) months. Median first-phase insulin response increased from baseline to 5 months (42 [4–67] to 107 [59–163] pmol/min/m²; P < 0.0001) and then remained stable at 12 and 24 months (110 [59–201] and 125 [65–166] pmol/min/m², respectively; P < 0.0001 vs. baseline) but lower than that of the NDC group (250 [226–429] pmol/min/m²; P < 0.0001).

A gradual increase in assessed functional β-cell capacity occurred after weight loss, becoming similar to that of NDC group participants by 12 months. This result was unchanged at 2 years with continuing remission of type 2 diabetes.