The Dapagliflozin Effect on Cardiovascular Events (DECLARE)–TIMI 58 assessed cardiac and renal outcomes of dapagliflozin versus placebo in patients with type 2 diabetes. Efficacy and safety outcomes were studied within age subgroups for treatment effect and age-based treatment interaction.

Of the 17,160 patients, 9,253 were <65 years of age, 6,811 ≥65 to <75 years, and 1,096 ≥75 years. Dapagliflozin reduced the composite of cardiovascular death or hospitalization for heart failure consistently, with a hazard ratio (HR) of 0.88 (95% CI 0.72, 1.07), 0.77 (0.63, 0.94), and 0.94 (0.65, 1.36) in age-groups <65, ≥65 to <75, and ≥75 years, respectively (interaction P value 0.5277). Overall, dapagliflozin did not significantly decrease the rates of major adverse cardiovascular events, with HR 0.93 (95% CI 0.81, 1.08), 0.97 (0.83, 1.13), and 0.84 (0.61, 1.15) in age-groups <65, ≥65 to <75, and ≥75 years, respectively (interaction P value 0.7352). The relative risk reduction for the secondary prespecified cardiorenal composite outcome ranged from 18% to 28% in the different age-groups with no heterogeneity. Major hypoglycemia was less frequent with dapagliflozin versus placebo, with HR 0.97 (95% CI 0.58, 1.64), 0.50 (0.29, 0.84), and 0.68 (0.29, 1.57) in age-groups <65, ≥65 to <75, and ≥75 years, respectively (interaction P value 0.2107). Safety outcomes, including fractures, volume depletion, cancer, urinary tract infections, and amputations were balanced with dapagliflozin versus placebo, and acute kidney injury was reduced, all regardless of age. Genital infections that were serious or led to discontinuation of the study drug and diabetic ketoacidosis were uncommon, yet more frequent with dapagliflozin versus placebo, without heterogeneity (interaction P values 0.1058 and 0.8433, respectively).

The overall efficacy and safety of dapagliflozin are consistent regardless of age.

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.

We identified 8,528 patients with diabetes (self-report, medication use, or electronic medical record diagnosis) from the Henry Ford Exercise Testing Project (FIT Project). Patients with a BMI <18.5 kg/m² or cancer were excluded. Fitness was measured as the METs achieved during a physician-referred treadmill stress test and categorized as low (<6), moderate (6–9.9), or high (≥10). Adjusted hazard ratios for mortality were calculated using standard BMI (kilograms per meter squared) cutoffs of normal (18.5–24.9), overweight (25–29.9), and obese (≥30). Adjusted splines centered at 22.5 kg/m² were used to examine BMI as a continuous variable.

Patients had a mean age of 58 ± 11 years (49% women) with 1,319 deaths over a mean follow-up of 10.0 ± 4.1 years. Overall, obese patients had a 30% lower mortality hazard (P < 0.001) compared with normal-weight patients. In adjusted spline modeling, higher BMI as a continuous variable was predominantly associated with a lower mortality risk in the lowest fitness group and among patients with moderate fitness and BMI ≥30 kg/m². Compared with the lowest fitness group, patients with higher fitness had an ~50% (6–9.9 METs) and 70% (≥10 METs) lower mortality hazard regardless of BMI (P < 0.001).

Among patients with diabetes, the obesity paradox was less pronounced for patients with the highest fitness level, and these patients also had the lowest risk of mortality.

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.

EarlyBird is a nonintervention prospective cohort study that recruited 307 healthy U.K. children at 5 years of age and followed them throughout childhood. We genotyped 121 single nucleotide polymorphisms (SNPs) previously associated with diabetes risk, identified in the adult population. Association of SNPs with fasting insulin and glucose and HOMA indices of insulin resistance and β-cell function, available from 5 to 16 years of age, were tested. Association analysis with hormones was performed on selected SNPs.

Several candidate loci influenced the course of glycemic and insulin traits, including rs780094 (GCKR), rs4457053 (ZBED3), rs11257655 (CDC123), rs12779790 (CDC123 and CAMK1D), rs1111875 (HHEX), rs7178572 (HMG20A), rs9787485 (NRG3), and rs1535500 (KCNK16). Some of these SNPs interacted with age, the growth hormone–IGF-1 axis, and adrenal and sex steroid activity.

The findings that genetic markers influence both elevated and average courses of glycemic traits and β-cell function in children during puberty independently of BMI are a significant step toward early identification of children at risk for diabetes. These findings build on our previous observations that pancreatic β-cell defects predate insulin resistance in the onset of prediabetes. Understanding the mechanisms of interactions among genetic factors, puberty, and weight gain would allow the development of new and earlier disease-management strategies in children.

This study applied observational and one-sample Mendelian randomization (MR) analyses to individual-level data from 117,193 Danish individuals, and validation by two-sample MR analyses on summary-level data from 133,010 individuals from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), 117,165 from the CKDGen Consortium, and 452,264 from the UK Biobank.

Observationally, glucose levels in the normoglycemic range and higher were associated with high risks of retinopathy, neuropathy, diabetic nephropathy, PAD, and MI (all P for trend <0.001). In genetic causal analyses, the risk ratio for a 1 mmol/L higher glucose level was 2.01 (95% CI 1.18–3.41) for retinopathy, 2.15 (1.38–3.35) for neuropathy, 1.58 (1.04–2.40) for diabetic nephropathy, 0.97 (0.84–1.12) for estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m², 1.19 (0.90–1.58) for PAD, and 1.49 (1.02–2.17) for MI. Summary-level data from the MAGIC, the CKDGen Consortium, and the UK Biobank gave a genetic risk ratio of 4.55 (95% CI 2.26–9.15) for retinopathy, 1.48 (0.83–2.66) for peripheral neuropathy, 0.98 (0.94–1.01) for eGFR <60 mL/min/1.73 m², and 1.23 (0.57–2.67) for PAD per 1 mmol/L higher glucose level.

Glucose levels in the normoglycemic range and higher were prospectively associated with a high risk of retinopathy, neuropathy, diabetic nephropathy, eGFR <60 mL/min/1.73 m², PAD, and MI. These associations were confirmed in genetic causal analyses for retinopathy, neuropathy, diabetic nephropathy, and MI, but they could not be confirmed for PAD and seemed to be refuted for eGFR <60 mL/min/1.73 m².

Five cohort studies’ participants (n = 8,270) were classified as statin users when they were on statin therapy at the time of DNAm assessment with Illumina 450K or EPIC array or noncurrent users otherwise. Associations of DNAm with various outcomes like incident type 2 diabetes, plasma glucose, insulin, and insulin resistance (HOMA of insulin resistance [HOMA-IR]) as well as with gene expression were investigated.

Discovery (n = 6,820) and replication (n = 1,450) phases associated five DNAm sites with statin use: cg17901584 (1.12 x 10^–25 [DHCR24]), cg10177197 (3.94 x 10^–08 [DHCR24]), cg06500161 (2.67 x 10^–23 [ABCG1]), cg27243685 (6.01 x 10^–09 [ABCG1]), and cg05119988 (7.26 x 10^–12 [SC4MOL]). Two sites were associated with at least one glycemic trait or type 2 diabetes. Higher cg06500161 methylation was associated with higher fasting glucose, insulin, HOMA-IR, and type 2 diabetes (odds ratio 1.34 [95% CI 1.22, 1.47]). Mediation analyses suggested that ABCG1 methylation partially mediates the effect of statins on high insulin and HOMA-IR. Gene expression analyses showed that statin exposure and ABCG1 methylation were associated with ABCG1 downregulation, suggesting epigenetic regulation of ABCG1 expression. Further, outcomes insulin and HOMA-IR were significantly associated with ABCG1 expression.

This study sheds light on potential mechanisms linking statins with type 2 diabetes risk, providing evidence on DNAm partially mediating statins’ effects on insulin traits. Further efforts shall disentangle the molecular mechanisms through which statins may induce DNAm changes, potentially leading to ABCG1 epigenetic regulation.

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.

In this double-blind study, 587 patients taking stable-dose insulin glargine (±metformin ≥1,500 mg/day) were randomized (1:1:1:1:1) to MK-0941 10, 20, 30, or 40 mg or matching placebo t.i.d. before meals (a.c.). This study included an initial 14-week, dose-ranging phase followed by a 40-week treatment phase during which patients were to be uptitrated as tolerated to 40 mg (or placebo) t.i.d. a.c. The primary efficacy end point was change from baseline in A1C at Week 14.

At Week 14, A1C and 2-h postmeal glucose (PMG) improved significantly versus placebo with all MK-0941 doses. Maximal placebo-adjusted least squares mean changes from baseline in A1C (baseline A1C 9.0%) and 2-h PMG were –0.8% and –37 mg/dL (–2 mmol/L), respectively. No significant effects on fasting plasma glucose were observed at any dose versus placebo. By 30 weeks, the initial glycemic responses noted at 14 weeks were not sustained. MK-0941 at one or more doses was associated with significant increases in the incidence of hypoglycemia, triglycerides, systolic blood pressure, and proportion of patients meeting criteria for predefined limits of change for increased diastolic blood pressure.

In patients receiving stable-dose insulin glargine, the GKA MK-0941 led to improvements in glycemic control that were not sustained. MK-0941 was associated with an increased incidence of hypoglycemia and elevations in triglycerides and blood pressure.