Data for 3,129 U.S. counties, obtained from the Small Area Health Insurance Estimates and Area Health Resources Files, were used to analyze trends in uninsured rates among populations with a household income ≤138% of the federal poverty level. Multivariable analysis adjusted for the percentage of county populations aged 50–64 years, the percentage of women, Distressed Communities Index value, and rurality.

In 2012, 39% of the population in the Diabetes Belt and 34% in non-Belt counties were uninsured (P < 0.001). In 2016 in states where Medicaid was expanded, uninsured rates declined rapidly to 13% in Diabetes Belt counties and to 15% in non-Belt counties. Adjusting for county demographic and economic factors, Medicaid expansion helped reduce uninsured rates by 12.3% in Diabetes Belt counties and by 4.9% in non-Belt counties. In 2016, uninsured rates were 15% higher for both Diabetes Belt and non-Belt counties in the nonexpansion states than in the expansion states.

ACA-driven Medicaid expansion was more significantly associated with reduced uninsured rates in Diabetes Belt than in non-Belt counties. Initial disparities in uninsured rates between Diabetes Belt and non-Belt counties have not existed since 2014 among expansion states. Future studies should examine whether and how Medicaid expansion may have contributed to an increase in the use of health services in order to prevent and treat diabetes in the Diabetes Belt.

Up to September 2018, 1.4 million office BP measurements in 79,849 children and adolescents (aged 5–20 years) with T1DM have been documented in the DPV (Diabetes Prospective Follow-up) registry. BP values of the most recent year were aggregated, and BP values of 74,677 patients without antihypertensive medication were analyzed (median age 16 years and diabetes duration 5.3 years and 52.8% boys). BP values were classified according to AAP 2017 and the references of the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) (2011) and the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents (fourth report) (2004).

Of the patients, 44.1%, 29.5%, and 26.5% were hypertensive according to AAP 2017, KiGGS, and fourth report, respectively. Differences in prevalence of HTN were strongly age dependent: <10 years, AAP 2017 31.4%, KiGGS 30.7%, fourth report 19.6%; 10 to <15 years, AAP 2017 30.9%, KiGGS 31.2%, fourth report 22.4%; and ≥15 years, AAP 2017 53.2%, KiGGS 28.4%, fourth report 30.0%. Among teenagers ≥15 years, 59.1% of boys but only 46.3% of girls were classified as hypertensive by AAP 2017 but only 21.1%/26% of boys and 36.7%/34.4% of girls by KiGGS/fourth report, respectively.

Classification of BP as hypertension depends strongly on the normative data used. Use of AAP 2017 results in a significant increase in HTN in teenagers ≥15 years with T1DM, particularly in boys. AAP 2017 enhances the awareness of elevated BP in children, particularly in patients with increased risk for cardiovascular disease.

We conducted a retrospective, cross-sectional analysis of 2003–2016 National Health and Nutrition Examination Survey (NHANES) data. We included people ≥18 years who had ever been told they had diabetes, had an HbA_1C >6.4%, or had a fasting plasma glucose >125 mg/dL. Pregnant women, and those aged <20 years receiving only insulin were excluded. We assessed trends in use of ADA’s seven preferred classes from 2003–2004 to 2015–2016. We also examined use by hypoglycemia risk (sulfonylureas, insulin, and meglitinides), weight effect (sulfonylureas, thiazolidinediones [TZDs], insulin, and meglitinides), cardiovascular benefit (canagliflozin, empagliflozin, and liraglutide), and cost (brand-name medications and insulin analogs).

The final sample included 6,323 patients. The proportion taking any medication increased from 58% in 2003–2004 to 67% in 2015–2016 (P < 0.001). Use of metformin and insulin analogs increased, while use of sulfonylureas, TZDs, and human insulin decreased. Following the 2012 ADA recommendation, the choice of drug did not vary significantly by older age, weight, or presence of cardiovascular disease. Patients with low HbA_1C, or HbA_1C <6%, and age ≥65 years were less likely to receive hypoglycemia-inducing medications, while older patients with comorbidities were more likely. Insurance, but not income, was associated with the use of higher-cost medications.

Following ADA recommendations, the use of metformin increased, but physicians generally did not individualize treatment according to patients’ characteristics. Substantial opportunities exist to improve pharmacologic management of diabetes.

FPG and urinary biomarkers of oxidative DNA damage (8-hydroxy-deoxyguanosine [8-OHdG]), lipid peroxidation (8-iso-prostaglandin-F2α [8-iso-PGF2α]), and acrylamide exposure (N-acetyl-S-[2-carbamoylethyl]-l-cysteine [AAMA], N-acetyl-S-[2-carbamoyl-2-hydroxyethyl]-l-cysteine [GAMA]) were measured for 3,270 general adults from the Wuhan-Zhuhai cohort. The associations of urinary acrylamide metabolites with 8-OHdG, 8-iso-PGF2α, and FPG were assessed by linear mixed models. The mediating roles of 8-OHdG and 8-iso-PGF2α were evaluated by mediation analysis.

We found significant linear positive dose-response relationships of urinary acrylamide metabolites with 8-OHdG, 8-iso-PGF2α, and FPG (except GAMA with FPG) and 8-iso-PGF2α with FPG. Each 1-unit increase in log-transformed level of AAMA, AAMA + GAMA (UAAM), or 8-iso-PGF2α was associated with a 0.17, 0.15, or 0.23 mmol/L increase in FPG, respectively (P and/or P trend < 0.05). Each 1% increase in AAMA, GAMA, or UAAM was associated with a 0.19%, 0.27%, or 0.22% increase in 8-OHdG, respectively, and a 0.40%, 0.48%, or 0.44% increase in 8-iso-PGF2α, respectively (P and P trend < 0.05). Increased 8-iso-PGF2α rather than 8-OHdG significantly mediated 64.29% and 76.92% of the AAMA- and UAAM-associated FPG increases, respectively.

Exposure of the general adult population to acrylamide was associated with FPG elevation, oxidative DNA damage, and lipid peroxidation, which in turn partly mediated acrylamide-associated FPG elevation.

Baseline data from the publications of the three trials were obtained and entered into the BRAVO model to predict cardiovascular outcomes. Projected benefits of reducing risk factors of interest (A1C, systolic blood pressure [SBP], LDL, or BMI) on cardiovascular events were evaluated, and simulated outcomes were compared with those observed in each trial.

BRAVO achieved the best prediction accuracy when simulating outcomes of the CANVAS and DECLARE-TIMI 58 trials. For the EMPA-REG OUTCOME trial, a mild bias was observed (~20%) in the prediction of mortality and angina. The effect of risk reduction on outcomes in treatment versus placebo groups predicted by the BRAVO model strongly correlated with the observed effect of risk reduction on the trial outcomes as published. Finally, the BRAVO engine revealed that most of the clinical benefits associated with SGLT2i treatment are through A1C control, although reductions in SBP and BMI explain a proportion of the observed decline in cardiovascular events.

The BRAVO risk engine was effective in predicting the benefits of SGLT2is on cardiovascular health through improvements in commonly measured risk factors, including A1C, SBP, and BMI. Since these benefits are individually small, the use of the complex, dynamic BRAVO model is ideal to explain the cardiovascular outcome trial results.

Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation.

We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia.

Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

We performed exploratory analyses to identify potential mediators of the effect of liraglutide on major adverse CV events (MACE; composite of CV death, nonfatal myocardial infarction, or nonfatal stroke) from the following candidates: glycated hemoglobin (HbA_1c), body weight, urinary albumin-to-creatinine ratio (UACR), confirmed hypoglycemia, sulfonylurea use, insulin use, systolic blood pressure, and LDL cholesterol. These candidates were selected as CV risk factors on which liraglutide had an effect in LEADER such that a reduction in CV risk might result. We used two methods based on a Cox proportional hazards model and the new Vansteelandt method designed to use all available information from the mediator and to control for confounding factors.

Analyses using the Cox methods and Vansteelandt method indicated potential mediation by HbA_1c (up to 41% and 83% mediation, respectively) and UACR (up to 29% and 33% mediation, respectively) on the effect of liraglutide on MACE. Mediation effects were small for other candidates.

These analyses identify HbA_1c and, to a lesser extent, UACR as potential mediators of the CV effects of liraglutide. Whether either is a marker of an unmeasured factor or a true mediator remains a key question that invites further investigation.

This prospective, open-label, randomized clinical trial included 176 patients with poorly controlled T2D (admission blood glucose [BG] 228 ± 82 mg/dL and HbA_1c 9.5 ± 2.2%), treated with oral agents or insulin before admission. Patients were treated with a basal-bolus regimen with glargine U300 (n = 92) or glargine U100 (n = 84) and glulisine before meals. We adjusted insulin daily to a target BG of 70–180 mg/dL. The primary end point was noninferiority in the mean difference in daily BG between groups. The major safety outcome was the occurrence of hypoglycemia.

There were no differences between glargine U300 and U100 in mean daily BG (186 ± 40 vs. 184 ± 46 mg/dL, P = 0.62), percentage of readings within target BG of 70–180 mg/dL (50 ± 27% vs. 55 ± 29%, P = 0.3), length of stay (median [IQR] 6.0 [4.0, 8.0] vs. 4.0 [3.0, 7.0] days, P = 0.06), hospital complications (6.5% vs. 11%, P = 0.42), or insulin total daily dose (0.43 ± 0.21 vs. 0.42 ± 0.20 units/kg/day, P = 0.74). There were no differences in the proportion of patients with BG <70 mg/dL (8.7% vs. 9.5%, P > 0.99), but glargine U300 resulted in significantly lower rates of clinically significant hypoglycemia (<54 mg/dL) compared with glargine U100 (0% vs. 6.0%, P = 0.023).

Hospital treatment with glargine U300 resulted in similar glycemic control compared with glargine U100 and may be associated with a lower incidence of clinically significant hypoglycemia.

UK Biobank participants without baseline CVD or known diabetes (n = 357,833) were included. Associations of HbA1c with CVD was assessed using Cox models adjusting for classical risk factors. Predictive utility was determined by the C-index and net reclassification index (NRI). A separate analysis was conducted in 16,596 participants with known baseline diabetes.

Incident fatal or nonfatal CVD, as defined in the QRISK3 prediction model, occurred in 12,877 participants over 8.9 years. Of participants, 3.3% (n = 11,665) had prediabetes (42.0–47.9 mmol/mol [6.0–6.4%]) and 0.7% (n = 2,573) had undiagnosed diabetes (≥48.0 mmol/mol [≥6.5%]). In unadjusted models, compared with the reference group (<42.0 mmol/mol [<6.0%]), those with prediabetes and undiagnosed diabetes were at higher CVD risk: hazard ratio (HR) 1.83 (95% CI 1.69–1.97) and 2.26 (95% CI 1.96–2.60), respectively. After adjustment for classical risk factors, these attenuated to HR 1.11 (95% CI 1.03–1.20) and 1.20 (1.04–1.38), respectively. Adding HbA_1c to the QRISK3 CVD risk prediction model (C-index 0.7392) yielded a small improvement in discrimination (C-index increase of 0.0004 [95% CI 0.0001–0.0007]). The NRI showed no improvement. Results were similar for models based on the ACC/AHA and SCORE risk models.

The near twofold higher unadjusted risk for CVD in people with prediabetes is driven mainly by abnormal levels of conventional CVD risk factors. While HbA_1c adds minimally to cardiovascular risk prediction, those with prediabetes should have their conventional cardiovascular risk factors appropriately measured and managed.

CVD events in the 1,441 DCCT/EDIC participants were analyzed separately by type (CVD death, acute myocardial infarction [MI], stroke, silent MI, angina, percutaneous transluminal coronary angioplasty/coronary artery bypass graft [PTCA/CABG], and congestive heart failure [CHF]) or as composite outcomes (CVD or major adverse cardiovascular events [MACE]). Proportional rate models and conditional models assessed associations between risk factors and CVD outcomes.

Over a median follow-up of 29 years, 239 participants had 421 CVD events, and 120 individuals had 149 MACE. Age was the strongest risk factor for acute MI, silent MI, stroke, and PTCA/CABG, while glycemia was the strongest risk factor for CVD death, CHF, and angina, second strongest for acute MI and PTCA/CABG, third strongest for stroke, and not associated with silent MI. HbA_1c was the strongest modifiable risk factor for a first CVD event (CVD: HR 1.38 [95% CI 1.21, 1.56] per 1% higher HbA_1c; MACE: HR 1.54 [1.30, 1.82]) and also for subsequent CVD events (CVD: incidence ratio [IR] 1.28 [95% CI 1.09, 1.51]; MACE: IR 1.89 [1.36, 2.61]).

Intensive glycemic management is recommended to lower the risk of initial CVD events in T1D. After a first event, optimal glycemic control may reduce the risk of recurrent CVD events and should be maintained.

The Swedish Obese Subjects study is a prospective matched study performed at 25 surgical departments and 480 primary health care centers. Participants aged 37–60 years with BMI ≥34 kg/m² (men) or ≥38 kg/m² (women) were recruited between 1987 and 2001; 2,007 participants received bariatric surgery and 2,040 usual care. Self-reported body weight at 20 years of age was used to stratify patients into subgroups with normal BMI (<25 kg/m²), overweight (BMI 25–29.9 kg/m²), or obesity (BMI ≥30 kg/m²). Body weight, energy intake, and type 2 diabetes status were examined over 10 years, and incidence of cardiovascular and microvascular disease was determined over up to 26 years using data from health registers.

There were small but statistically significant differences in reduction of body weight among the subgroups after bariatric surgery (interaction P = 0.032), with the largest reductions among those with obesity aged 20 years. Bariatric surgery increased type 2 diabetes remission (odds ratios 4.51, 4.90, and 5.58 in subgroups with normal BMI, overweight, or obesity at 20 years of age, respectively; interaction P = 0.951), reduced type 2 diabetes incidence (odds ratios 0.15, 0.13, and 0.15, respectively; interaction P = 0.972), and reduced microvascular complications independent of obesity status at 20 years of age (interaction P = 0.650). The association between bariatric surgery and cardiovascular disease was similar in the subgroups (interaction P = 0.674). Surgical complications were similar in the subgroups.

The treatment benefits of bariatric surgery in adults are similar regardless of obesity status at 20 years of age.

A total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m² and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use.

The prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient’s data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery.

The IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.