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Background: The prevalence of type 2 diabetes (T2D) is increasing in Hispanic or Latino adults. The American Diabetes Association (ADA) score to identify current risk of T2D was developed with limited Hispanic or Latino representation. Therefore, we sought to validate the ADA score in a cohort of Hispanic or Latino adults. Methods: At enrollment in the Sangre Por Salud Biobank, 3,625 Hispanic or Latino adults from Phoenix, Arizona completed a baseline questionnaire. Current risk of undiagnosed T2D was based on non-laboratory risk factors and ADA score ≥5 (Table). Participants also completed T2D screening (ie, fasting glucose, 2-hour oral glucose tolerance test, and/or hemoglobin A1c). Undiagnosed T2D was based on 'no self-reported T2D but meeting T2D diagnostic’ criteria. Multivariable logistic regression analyzed associations of ADA risk factors and undiagnosed T2D, and model performance was based on sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: At enrollment, 7.3% of participants had undiagnosed T2D (n=265/3,625). In multivariable analysis, most risk factors described for the ADA score were significantly associated with T2D, except for sex, which showed no association. ADA score ≥5 had sensitivity 50%, specificity 79%, PPV 18%, and NPV 95%. However, ADA score ≥4 had sensitivity 72%, specificity 61%, PPV 14%, and NPV 96%. ADA score ≥4 vs ≥5 identified 22% (n=59) additional cases of undiagnosed T2D. Furthermore, using the revised definition for elevated blood pressure (>120/80 mmHg) vs ≥140/90 mmHg (current ADA score) further improved the ADA score ≥4 (sensitivity 81%; specificity 52%). Other scores had lower performance using both hypertension definitions. Conclusion: In Hispanic or Latino adults, a lower ADA score than currently recommended identified additional cases of undiagnosed T2D. The ADA score requires validation and recalibration, including for the updated hypertension guidelines.

Abstract Context The risk of gestational diabetes mellitus (GDM) in twin pregnancies is more than double that of singleton pregnancies. Although twin pregnancies present unique challenges for fetal growth and prenatal management, the approach to GDM diagnosis and treatment is the same regardless of plurality. Data on pregnancy outcomes for individuals with GDM and a twin pregnancy are limited and conflicting. Objective To describe the maternal characteristics associated with GDM in twin pregnancies and to assess the associated pregnancy outcomes compared to twin pregnancies unaffected by GDM. Methods A retrospective cohort study was conducted at Mayo Clinic, Rochester, Minnesota, USA, and included predominantly Causasian women aged 18 to 45 years who received prenatal care for a twin pregnancy from 2017-2022. Maternal characteristics and a broad spectrum of pregnancy outcomes were evaluated. Universal GDM screening involved a 50 g oral glucose challenge test +/− a 100 g oral glucose tolerance test. Results GDM was diagnosed in 23% pregnancies (n = 104/452). Compared to those without, women with GDM had known risk factors including a higher prepregnancy body mass index (31.1vs 26.3 kg/m2; P < .01) and a prior history of GDM (21.7 vs 5.9%; P < .01). There were no differences in maternal pregnancy complications or neonatal outcomes between groups. Attendance at postpartum glucose testing among women with GDM was poor at 27.9% (29/104). Conclusion These data suggest that women with twin pregnancies share a similar GDM risk profile to those with singleton pregnancies and provide reassuring evidence that current management for GDM twin pregnancies produces similar outcomes to twin pregnancies without GDM.

AbstractAimsSodium glucose co‐transporter‐2 (SGLT2) inhibition lowers glucose levels independently of insulin, leading to reduced insulin secretion and increased lipolysis, resulting in elevated circulating free fatty acids (FFAs). While SGLT2 inhibition improves tissue insulin sensitivity, the increase in circulating FFAs could reduce insulin sensitivity in skeletal muscle and the liver. We aimed to investigate the effects of SGLT2 inhibition on substrate utilization in skeletal muscle and the liver and to measure beta‐cell function and glucose tolerance.MethodsThirteen metformin‐treated individuals with type 2 diabetes were randomized to once‐daily empagliflozin 25 mg or placebo for 4 weeks in a crossover design. Skeletal muscle glucose and FFA uptake together with hepatic tissue FFA uptake were measured using [18F]FDG positron emission tomography/computed tomography (PET/CT) and [11C]palmitate PET/CT. Insulin secretion and action were estimated using the oral minimal model.ResultsEmpagliflozin did not affect glucose (0.73 ± 0.30 vs. 1.16 ± 0.64, μmol/g/min p = 0.11) or FFA (0.60 ± 0.30 vs. 0.56 ± 0.3, μmol/g/min p = 0.54) uptake in skeletal muscle. FFA uptake in the liver (21.2 ± 10.1 vs. 19 ± 8.8, μmol/100 ml/min p = 0.32) was unaffected.Empagliflozin increased total beta‐cell responsivity (20 ± 8 vs. 14 ± 9, 10−9 min−1, p < 0.01) and glucose effectiveness (2.6 × 10−2 ± 0.3 × 10−2 vs. 2.4 × 10−2 ± 0.3 × 10−2, dL/kg/min, p = 0.02).ConclusionsDespite improved beta‐cell function and glucose tolerance, empagliflozin does not appear to affect skeletal muscle FFA or glucose uptake.

Introduction: US rural areas have a disproportionate burden of CVD and hypertension. Physical inactivity is a major risk factor, with 25% prevalence in US adults. However, rural disparities in physical inactivity are incompletely described. The aim was to evaluate differences in prevalence of physical inactivity based on county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined physical inactivity as self-reported lack of leisure time physical activity in the month preceding the survey. Rurality was based on six categories ordered from least rural (large central metro) to most rural (noncore). We used weighted least squares regression to compare prevalence by rurality level, adjusting for 13 county-level sociodemographic factors; results reported as prevalence ratios (95% CI) with partial F -tests and P -values. Results: In age and sex ratio adjusted analysis (model 1), we observed a gradient in the prevalence of physical inactivity across levels of rurality, with significantly higher rates in small metro (1.03 [1.00–1.06]), micropolitan (1.05 [1.02–1.07]), and noncore (1.05 [1.02–1.08]) compared with large central metro counties ( F -statistic=7.5; P <.001). With further adjustment for other sociodemographic factors (model 2), the relative differences were negated in noncore counties (0.99 [0.97-1.02]), attenuated in intermediate counties, and accentuated in large fringe metro counties (1.06 [1.04-1.08]) ( F -statistic=14.3; P <.001). Further adjustment for obesity (model 3) largely attenuated the association of rurality with physical inactivity, resulting in no significant differences relative to large central metro counties ( F -statistic=4.8; P <.001). Conclusions: County-level rurality was associated with prevalence of physical inactivity. These disparities were largely accounted for by obesity, which may be targeted for intervention.

Introduction: US rural, compared with nonrural, counties have a higher burden of CVD. Obesity is a major CVD risk factor with 40% prevalence in adults. However, the rural-nonrural disparities in obesity are incompletely described. We aimed to evaluate differences in prevalence of obesity by county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined obesity (body mass index ≥30 kg/m 2 ) using self-reported weight and height. Rurality was based on six levels from large central metro (most urban) to noncore (most rural). We compared prevalence by rurality level using weighted least squares regression, adjusting for 13 county-level sociodemographic measures, and reported prevalence ratios (95% CI). Results: Counties with increasing rurality reflected older populations, as indicated by higher median age (from 35.6 years to 42.4 years). Also, increasing rurality was associated with differences in income and proportion uninsured. In age and sex ratio adjusted analysis (model 1), obesity prevalence was 1.05- to 1.06-fold higher in medium metro, small metro, and micropolitan counties compared with large central metro counties (global test, P <.001). With further adjustment for sociodemographic measures (model 2; model 1 covariates plus race, state, income, proportion uninsured) these relative differences were preserved but attenuated (each 1.03-fold higher), while a greater difference was revealed for large fringe metro (1.06-fold higher) counties. In both models, differences in obesity prevalence between noncore and large central metro counties were minimal. (Table) Conclusion: Counties with intermediate rurality were associated with higher obesity prevalence. The association was partly accounted for by nonmodifiable and modifiable sociodemographic measures, which may be targeted to reduce rural-nonrural disparities in obesity.

Abstract Context The impact of insulin, particularly exogenous hyperinsulinemia, on insulin secretion in humans is debated. Objective We assessed the effects of exogenous hyperinsulinemia on insulin secretion and whether the response is altered in insulin resistance associated with obesity. Methods Insulin secretion rates (ISRs) during euglycemic hyperinsulinemic clamp studies (52 volunteers) were calculated using a model that employs plasma C-peptide concentrations. One study involved a 2-step insulin clamp and the other study was a single step insulin clamp. For both studies the goal was to achieve plasma glucose concentrations of 95 mg/dL during the clamp irrespective of fasting glucose concentrations. The percent change in ISR from fasting to the end of the insulin clamp interval was the main outcome. Linear regression and analysis of covariance were used to test for the effects of insulin on ISR and to test for group differences. Results ISR was greater in obese volunteers (P &lt; .001) under fasting and hyperinsulinemic clamp conditions. The change in plasma glucose from baseline to the end of the insulin clamp interval was highly correlated with the change in ISR (r = 0.61, P &lt; .001). From baseline to the end of the clamp we observed a 27% (SD 20) suppression of ISR. The participants who underwent a 2-step insulin clamp had greater suppression of ISR during the second step than the first step (P &lt; .001). The proportional suppression of ISR during euglycemic hyperinsulinemia was not different between nonobese and obese groups (P = .19). Conclusion Hyperinsulinemia suppresses endogenous insulin secretion and the relative change in insulin secretion produced by exogenous insulin did not differ between nonobese and obese people.

This experiment studied the effect of changes in overnight concentrations of free fatty acids (FFAs) and glucose on β-cell function and glucose metabolism. In response to elevation of these metabolites, the dynamic component of the β-cell response to glucose was impaired. This suggests that in health overnight hyperglycemia and FFA elevation can deplete preformed insulin granules in the β-cell.