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.

Before and after 1 year of gender-affirming hormone therapy, body composition and oral glucose tolerance tests (OGTTs) were evaluated.

In TM, body weight (2.8 ± 1.0 kg; P < 0.01), fat-free mass (FFM) (3.1 ± 0.9 kg; P < 0.01), and waist-to-hip ratio (–0.03 ± 0.01; P < 0.01) increased. Fasting insulin (–1.4 ± 0.8 mU/L; P = 0.08) and HOMA of insulin resistance (HOMA-IR) (2.2 ± 0.3 vs. 1.8 ± 0.2; P = 0.06) tended to decrease, whereas fasting glucose (–1.6 ± 1.6 mg/dL), glucose-dependent insulinotropic polypeptide (GIP) (–1.8 ± 1.0 pmol/L), and glucagon-like peptide 1 (GLP-1) (–0.2 ± 1.1 pmol/L) were statistically unchanged. Post-OGTT areas under the curve (AUCs) for GIP (2,068 ± 1,134 vs. 2,645 ± 1,248 [pmol/L] x min; P < 0.01) and GLP-1 (2,352 ± 796 vs. 2,712 ± 1,015 [pmol/L] x min; P < 0.01) increased. In TW, body weight tended to increase (1.4 ± 0.8 kg; P = 0.07) with decreasing FFM (–2.3 ± 0.4 kg; P < 0.01) and waist-to-hip ratio (–0.03 ± 0.01; P < 0.01). Insulin (3.4 ± 0.8 mU/L; P < 0.01) and HOMA-IR (1.7 ± 0.1 vs. 2.4 ± 0.2; P < 0.01) rose, fasting GIP (–1.4 ± 0.8 pmol/L; P < 0.01) and AUC GIP dropped (2,524 ± 178 vs. 1,911 ± 162 [pmol/L] x min; P < 0.01), but fasting glucose (–0.3 ± 1.4 mg/dL), GLP-1 (1.3 ± 0.8 pmol/L), and AUC GLP-1 (2,956 ± 180 vs. 2,864 ± 93 [pmol/L] x min) remained unchanged.

In this cohort of transgender persons, insulin sensitivity but also post-OGTT incretin responses tend to increase with masculinization and to decrease with feminization.

Urinary samples of 87 children (44 girls) aged 8.5–17.9 years with obesity (BMI >97th percentile) were quantified for 31 steroid metabolites by GC-MS. Defined as HOMA-IR >95th percentile and fasting glucose-to-insulin ratio >0.3, IR was diagnosed in 20 (of 87 [23%]) of the examined patients. The steroidal fingerprints of subjects with IR were compared with those of obese children without IR (non-IR). The steroidal signature of IR was created from the product of IR – non-IR for each of the 31 steroids.

IR and non-IR groups of children had comparable mean age (13.7 ± 1.9 and 14.6 ± 2.4 years, respectively) and z score BMI (2.7 ± 0.5 and 2.7 ± 0.5, respectively). The steroidal signature of IR was characterized by high adrenal androgens, glucocorticoids, and mineralocorticoid metabolites; higher 5α-reductase (An/Et) (P = 0.007) and 21-hydroxylase [(THE + THF + αTHF)/PT] activity (P = 0.006); and lower 11βHSD1 [(THF + αTHF)/THE] activity (P = 0.012).

The steroidal metabolomic signature of IR in obese children is characterized by enhanced secretion of steroids from all three adrenal pathways. As only the fasciculata and reticularis are stimulated by ACTH, these findings suggest that IR directly affects the adrenals. We suggest a vicious cycle model, whereby glucocorticoids induce IR, which could further stimulate steroidogenesis, even directly. We do not know whether obese children with IR and the new signature may benefit from amelioration of their hyperadrenalism.

A post hoc analysis was performed of the SWITCH (SWITCH 1: n = 501, type 1 diabetes; SWITCH 2: n = 721, type 2 diabetes) and DEVOTE (n = 7,637, type 2 diabetes) trials utilizing the IHSG low glucose definitions. Patients in all trials were randomized to either insulin degludec or insulin glargine 100 units/mL. In the main analysis, the following definitions were compared: 1) American Diabetes Association (ADA) 2005 (plasma glucose [PG] confirmed ≤3.9 mmol/L with symptoms); and 2) IHSG level 2 (PG confirmed <3.0 mmol/L, independent of symptoms).

In SWITCH 2, the estimated rate ratios of hypoglycemic events indicated increasing differences between treatments with decreasing PG levels until 3.0 mmol/L, following which no additional treatment differences were observed. Similar results were observed for the SWITCH 1 trial. In SWITCH 2, the IHSG level 2 definition produced a rate ratio that was lower than the ADA 2005 definition.

The IHSG level 2 definition was validated in a series of clinical trials, demonstrating its ability to discriminate between basal insulins. This definition is therefore recommended to be uniformly adopted by regulatory bodies and used in future clinical trials.

Fifteen obese adults received intravenous infusion of either saline or GLP-1 (1.2 pmol/kg/min) for 150 min with or without a euglycemic insulin clamp (1 mU/kg/min) superimposed over the last 120 min. Skeletal and cardiac muscle microvascular blood volume (MBV), flow velocity and blood flow, brachial artery diameter and blood flow, and pulse wave velocity (PWV) were determined.

Insulin failed to change MBV or flow in either skeletal or cardiac muscle, confirming the presence of microvascular insulin resistance. GLP-1 infusion alone increased MBV by ~30% and ~40% in skeletal and cardiac muscle, respectively, with no change in flow velocity, leading to a significant increase in microvascular blood flow in both skeletal and cardiac muscle. Superimposition of insulin to GLP-1 infusion did not further increase MBV or flow in either skeletal or cardiac muscle but raised the steady-state glucose infusion rate by ~20%. Insulin, GLP-1, and GLP-1 + insulin infusion did not alter brachial artery diameter and blood flow or PWV. The vasodilatory actions of GLP-1 are preserved in both skeletal and cardiac muscle microvasculature, which may contribute to improving metabolic insulin responses and cardiovascular outcomes.

In obese humans with microvascular insulin resistance, GLP-1’s vasodilatory actions are preserved in both skeletal and cardiac muscle microvasculature, which may contribute to improving metabolic insulin responses and cardiovascular outcomes.

We employed a transethnic approach with a cohort of Asian origin (Singapore) (N = 2,061) and a cohort of European origin (France) (N = 1,438) included in the study. We used survival analyses to study the association of urine haptoglobin with risk of all-cause and cause-specific mortality.

A total of 365 and 525 deaths were registered in the Singapore cohort (median follow-up 7.5 years [interquartile range 3.5–12.8]) and French SURDIAGENE cohort (median follow-up 6.8 years [interquartile range 4.3–10.5], respectively. Singapore participants with urine haptoglobin in quartiles 2 to 4 had higher risk for all-cause mortality compared with quartile 1 (unadjusted hazard ratio [HR] 1.47 [95% CI 1.02–2.11], 2.28 [1.62–3.21], and 4.64 [3.39–6.35], respectively). The association remained significant in quartile 4 after multiple adjustments (1.68 [1.15–2.45]). Similarly, participants in the French cohort with haptoglobin in quartile 4 had significantly higher hazards for all-cause mortality compared with quartile 1 (unadjusted HR 2.67 [2.09–3.42] and adjusted HR 1.49 [1.14–1.96]). In both cohorts, participants in quartile 4 had a higher risk of mortality attributable to cardiovascular disease and infection but not malignant tumor.

Urine haptoglobin predicts risk of mortality independent of traditional risk factors, suggesting that it may potentially be a novel biomarker for risk of mortality in patients with type 2 diabetes.

A two-sample MR analysis included 17 independent genetic variants associated with ACE serum concentration in 4,147 participants from the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (clinical trial reg. no. NCT00069784) trial, and their effects on type 2 diabetes risk were estimated from 18 studies of the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. A genetic risk score (GRS) underpinning lower ACE concentration was then tested for association with type 2 diabetes prevalence in 341,872 participants, including 16,320 with type 2 diabetes, from the UK Biobank. MR estimates were compared after standardization for blood pressure change, with the estimate obtained from a randomized controlled trial (RCT) meta-analysis of ACE inhibitors versus placebo (n = 31,200).

Genetically lower ACE concentrations were associated with a lower risk of type 2 diabetes (odds ratio [OR] per SD 0.92 [95% CI 0.89–0.95]; P = 1.79 x 10^–7). This result was replicated in the UK Biobank (OR per SD 0.97 [0.96–0.99]; P = 8.73 x 10^–4). After standardization, the ACE GRS was associated with a larger decrease in type 2 diabetes risk per 2.4-mmHg lower mean arterial pressure (MAP) compared with that obtained from an RCT meta-analysis (OR per 2.4-mmHg lower MAP 0.19 [0.07–0.51] vs. 0.76 [0.60–0.97], respectively; P = 0.007 for difference).

These results support the causal protective effect of ACE inhibitors on type 2 diabetes risk and may guide therapeutic decision making in clinical practice.

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.

People with type 1 diabetes (n = 100) investigated at the University Hospital of Montpellier (France) underwent continuous glucose monitoring (CGM) on two consecutive days, providing a total of 200 24-h glycemic profiles. The following parameters were computed: MDG concentration, within-day glycemic variability (coefficient of variation for glucose [%CV]), and risk of hypoglycemia (presented as the percentage of time spent below three glycemic thresholds: 3.9, 3.45, and 3.0 mmol/L).

MDG was significantly higher, and %CV significantly lower (both P < 0.001), when comparing the 24-h glycemic profiles according to whether no time or a certain duration of time was spent below the thresholds. Univariate regression analyses showed that MDG and %CV were the two explanatory variables that entered the model with the outcome variable (time spent below the thresholds). The classification and regression tree procedure indicated that the predominant predictor for hypoglycemia was %CV when the threshold was 3.0 mmol/L. In people with mean glucose ≤7.8 mmol/L, the time spent below 3.0 mmol/L was shortest (P < 0.001) when %CV was below 34%.

In type 1 diabetes, short-term glycemic variability relative to mean glucose (i.e., %CV) explains more hypoglycemia than does mean glucose alone when the glucose threshold is 3.0 mmol/L. Minimizing the risk of hypoglycemia requires a %CV below 34%.

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.

DPN was assessed with the Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ) in adults with ≥5 years of type 1 diabetes duration. A score of ≥4 defined DPN. Associations of demographic, clinical, and laboratory factors with DPN were assessed.

Among 5,936 T1D Exchange participants (mean ± SD age 39 ± 18 years, median type 1 diabetes duration 18 years [interquartile range 11, 31], 55% female, 88% non-Hispanic white, mean glycated hemoglobin [HbA_1c] 8.1 ± 1.6% [65.3 ± 17.5 mmol/mol]), DPN prevalence was 11%. Compared with those without DPN, DPN participants were older, had higher HbA_1c, had longer duration of diabetes, were more likely to be female, and were less likely to have a college education and private insurance (all P < 0.001). DPN participants also were more likely to have cardiovascular disease (CVD) (P < 0.001), worse CVD risk factors of smoking (P = 0.008), hypertriglyceridemia (P = 0.002), higher BMI (P = 0.009), retinopathy (P = 0.004), reduced estimated glomerular filtration rate (P = 0.02), and Charcot neuroarthropathy (P = 0.002). There were no differences in insulin pump or continuous glucose monitor use, although DPN participants were more likely to have had severe hypoglycemia (P = 0.04) and/or diabetic ketoacidosis (P < 0.001) in the past 3 months.

The prevalence of DPN in this national cohort with type 1 diabetes is lower than in prior published reports but is reflective of current clinical care practices. These data also highlight that nonglycemic risk factors, such as CVD risk factors, severe hypoglycemia, diabetic ketoacidosis, and lower socioeconomic status, may also play a role in DPN development.