Least squares mean difference (LSMD) in estimated glomerular filtration rate (eGFR) from baseline between the EQW and placebo groups was calculated for 13,844 participants. Cox regression models were used to estimate effects by group on incident macroalbuminuria, retinopathy, and major adverse CV events (MACE). Interval-censored time-to-event models estimated effects on renal composite 1 (40% eGFR decline, renal replacement, or renal death) and renal composite 2 (composite 1 variables plus macroalbuminuria).

EQW did not change eGFR significantly (LSMD 0.21 mL/min/1.73 m² [95% CI –0.27 to 0.70]). Macroalbuminuria occurred in 2.2% of patients in the EQW group and in 2.5% of those in the placebo group (hazard ratio [HR] 0.87 [95% CI 0.70–1.07]). Neither renal composite was reduced with EQW in unadjusted analyses, but renal composite 2 was reduced after adjustment (HR 0.85 [95% CI 0.74–0.98]). Retinopathy rates did not differ by treatment group or in the HbA_1c-lowering or prior retinopathy subgroups. CV outcomes in those with eGFR <60 mL/min/1.73 m² did not differ by group. Those with eGFR ≥60 mL/min/1.73 m² had nominal risk reductions for MACE, all-cause mortality, and CV death, but interactions by renal function group were significant for only stroke (HR 0.74 [95% CI 0.58–0.93]; P for interaction = 0.035) and CV death (HR 1.08 [95% CI 0.85–1.38]; P for interaction = 0.031).

EQW had no impact on unadjusted retinopathy or renal outcomes. CV risk was modestly reduced only in those with eGFR ≥60 mL/min/1.73 m² in analyses unadjusted for multiplicity.

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.

This retrospective cohort study analyzed patients from Tayside and Fife in the Scottish Care Information–Diabetes Collaboration (SCI-DC) who were observable from the diagnosis of diabetes and had at least five HbA_1c measurements before the outcomes were evaluated. We used the previously reported HbA_1c variability score (HVS), calculated as the percentage of the number of changes in HbA_1c >0.5% (5.5 mmol/mol) among all HbA_1c measurements within an individual. The association between HVS and 10 outcomes was assessed using Cox proportional hazards models.

We included 13,111–19,883 patients in the analyses of each outcome. The patients with HVS >60% were associated with elevated risks of all outcomes compared with the lowest quintile (for example, HVS >80 to ≤100 vs. HVS ≥0 to ≤20, hazard ratio 2.38 [95% CI 1.61–3.53] for major adverse cardiovascular events, 2.4 [1.72–3.33] for all-cause mortality, 2.4 [1.13–5.11] for atherosclerotic cardiovascular death, 2.63 [1.81–3.84] for coronary artery disease, 2.04 [1.12–3.73] for ischemic stroke, 3.23 [1.76–5.93] for heart failure, 7.4 [3.84–14.27] for diabetic retinopathy, 3.07 [2.23–4.22] for diabetic peripheral neuropathy, 5.24 [2.61–10.49] for diabetic foot ulcer, and 3.49 [2.47–4.95] for new-onset chronic kidney disease). Four sensitivity analyses, including adjustment for time-weighted average HbA_1c, confirmed the robustness of the results.

Our study shows that higher HbA_1c variability is associated with increased risks of all-cause mortality, cardiovascular events, and microvascular complications of diabetes independently of high HbA_1c.

We performed a cross-sectional study in severely obese individuals before bariatric surgery. Lean control subjects were recruited from a research website. Cognitive deficits were defined by the National Institutes of Health (NIH) Toolbox (<5th percentile for lean control subjects). Cardiovascular autonomic neuropathy (CAN) was defined by an expiration-to-inspiration (E-to-I) ratio of <5th percentile for lean control subjects. Retinopathy was based on retinal photographs and nephropathy on the estimated glomerular filtration rate (<60 mg/dL) and/or the albumin-to-creatinine ratio (ACR) (≥30 mg/g). NIH Toolbox, E-to-I ratio, mean deviation on frequency doubling technology testing, and ACR were used as sensitive measures of these outcomes. We used multivariable linear regression to explore associations between metabolic factors and these outcomes.

We recruited 138 severely obese individuals and 46 lean control subjects. The prevalence of cognitive deficits, CAN, retinopathy, and nephropathy were 6.5%, 4.4%, 0%, and 6.5% in lean control subjects; 22.2%, 18.2%, 0%, and 6.1% in obese participants with normoglycemia; 17.7%, 21.4%, 1.9%, and 17.9% in obese participants with prediabetes; and 25.6%, 31.9%, 6.1%, and 16.3% in obese participants with diabetes. Waist circumference was significantly associated with cognitive function (–1.48; 95% CI –2.38, –0.57) and E-to-I ratio (–0.007; 95% CI –0.012, –0.002). Prediabetes was significantly associated with retinal function (–1.78; 95% CI –3.56, –0.002).

Obesity alone is likely sufficient to cause cognitive deficits but not retinopathy or nephropathy. Central obesity is the key metabolic risk factor.

The myocardial metabolic rate of glucose (MRGlu) was measured by using dynamic ¹⁸F-fluorodeoxyglucose positron emission tomography combined with a euglycemic-hyperinsulinemic clamp in 30 volunteers without coronary artery disease. Three groups were studied: 1) those with 1-h postload glucose <155 mg/dL (NGT 1-h low) (n = 10), 2) those with NGT 1-h high (n = 10), 3) and those with IGT (n = 10).

After adjusting for age, sex, and BMI, both subjects with NGT 1-h high (23.7 ± 6.4 mmol/min/100 mg; P = 0.024) and those with IGT (16.4 ± 6.0 mmol/min/100 mg; P < 0.0001) exhibited a significant reduction in global myocardial MRGlu; this value was 32.8 ± 9.7 mmol/min/100 mg in subjects with NGT 1-h low. Univariate correlations showed that MRGlu was positively correlated with insulin-stimulated whole-body glucose disposal (r = 0.441; P = 0.019) and negatively correlated with 1-h (r = –0.422; P = 0.025) and 2-h (r = –0.374; P = 0.05) postload glucose levels, but not with fasting glucose.

This study shows that myocardial insulin resistance is an early defect that is already detectable in individuals with dysglycemic conditions associated with an increased risk of type 2 diabetes, such as IGT and NGT 1-h high.

Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women.

The N-glycan–based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78–0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60–0.72, for men; 0.64, 95% CI 0.55–0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD.

Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

In a post hoc analysis of 14,752 Exenatide Study of Cardiovascular Event Lowering (EXSCEL) participants, we examined time-dependent associations between SHEs and subsequent major adverse cardiac events (CV death, nonfatal myocardial infarction [MI] or stroke), fatal/nonfatal MI, fatal/nonfatal stroke, hospitalization for acute coronary syndrome (hACS), hospitalization for heart failure (hHF), and all-cause mortality (ACM), as well as time-dependent associations between nonfatal CV events and subsequent SHEs.

SHEs were uncommon and not associated with once-weekly exenatide therapy (hazard ratio 1.13 [95% CI 0.94–1.36], P = 0.179). In fully adjusted models, SHEs were associated with an increased risk of subsequent ACM (1.83 [1.38–2.42], P < 0.001), CV death (1.60 [1.11–2.30], P = 0.012), and hHF (2.09 [1.37–3.17], P = 0.001), while nonfatal MI (2.02 [1.35–3.01], P = 0.001), nonfatal stroke (2.30 [1.25–4.23], P = 0.007), hACS (2.00 [1.39–2.90], P < 0.001), and hHF (3.24 [1.98–5.30], P < 0.001) were all associated with a subsequent increased risk of SHEs. The elevated bidirectional time-dependent hazards linking SHEs and a composite of all CV events were approximately constant over time, with those individuals at dual risk showing higher comorbidity scores compared with those without.

These findings, showing greater risk of SHEs after CV events as well as greater risk of CV events after SHEs, validate a bidirectional relationship between CV events and SHEs in patients with high comorbidity scores.

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.

A total of 24 T1D patients (median age 5.1 years) received a single intravenous infusion of autologous UCB cells and underwent metabolic and immunologic assessments.

No infusion-related adverse events were observed. β-Cell function declined after UCB infusion. Area under the curve C-peptide was 24.3% of baseline 1 year postinfusion (P < 0.001) and 2% of baseline 2 years after infusion (P < 0.001). Flow cytometry revealed increased regulatory T cells (Tregs) (P = 0.04) and naive Tregs (P = 0.001) 6 and 9 months after infusion, respectively.

Autologous UCB infusion in children with T1D is safe and induces changes in Treg frequency but fails to preserve C-peptide.