Patients with type 1 diabetes receiving regular care were recruited in this observational cross-sectional study and divided into four groups according to their Lp(a) levels in nmol/L (very low <10, low 10–30, intermediate 30–120, high >120). Prevalence of vascular complications was compared between the groups. In addition, the association between metabolic control, measured as HbA_1c, and Lp(a) was studied.

The patients (n = 1,860) had a median age of 48 years, diabetes duration of 25 years, and HbA_1c of 7.8% (61 mmol/mol). The median Lp(a) was 19 (interquartile range 10–71) nmol/L. No significant differences between men and women were observed, but Lp(a) levels increased with increasing age. Patients in the high Lp(a) group had higher prevalence of complications than patients in the very low Lp(a) group. The age- and smoking-status–adjusted relative risk ratio of having any macrovascular disease was 1.51 (95% CI 1.01–2.28, P = 0.048); coronary heart disease, 1.70 (95% CI 0.97–3.00, P = 0.063); albuminuria, 1.68 (95% CI 1.12–2.50, P = 0.01); and calcified aortic valve disease, 2.03 (95% CI 1.03–4.03; P = 0.042). Patients with good metabolic control, HbA_1c <6.9% (<52 mmol/mol), had significantly lower Lp(a) levels than patients with poorer metabolic control, HbA_1c >6.9% (>52 mmol/mol).

Lp(a) is a significant risk factor for macrovascular disease, albuminuria, and calcified aortic valve disease in patients with type 1 diabetes. Poor metabolic control in patients with type 1 diabetes is associated with increased Lp(a) levels.

A cross-sectional study of 193 patients with type 2 diabetes and 20 age- and sex-matched controls. Cardiovascular magnetic resonance was used in order to evaluate left ventricular structure and function, and MBF at rest and during adenosine-induced stress. MBF was derived as the mean of the flow within all segments of a midventricular slice.

Patients with type 2 diabetes had higher global MBF at rest (0.81 ± 0.19 mL/min/g) and lower global MBF during stress (2.4 ± 0.9 mL/min/g) than did controls (0.61 ± 0.11 at rest, 3.2 ± 0.8 mL/min/g under stress; both P < 0.01). Patients with macroalbuminuria had lower MBF during stress (1.6 ± 0.5 mL/min/g) than did patients with microalbuminuria (2.1 ± 0.7 mL/min/g; P = 0.04), who in turn had lower MBF during stress than did normoalbuminuric patients (2.7 ± 0.9 mL/min/g; P < 0.01). Patients with severe retinopathy had lower MBF during stress (1.8 ± 0.6 mL/min/g) than did patients with simplex retinopathy (2.3 ± 0.7 mL/min/g; P < 0.05) and those who did not have retinopathy (2.6 ± 1.0 mL/min/g; P < 0.05). Albuminuria and retinopathy were associated with reduced MBF during stress in a multiple regression analysis. Stress-related MBF inversely correlated with myocardial extracellular volume (P < 0.001; R² = 0.37), a measure of diffuse myocardial fibrosis. A trend toward lower basal MBF was observed in patients treated with sodium–glucose cotransporter 2 inhibitors (P = 0.07).

Patients with type 2 diabetes have higher global MBF at rest and lower maximal MBF during vasodilator-induced stress than do controls. Reduced MBF during stress is associated with diabetes complications (albuminuria and retinopathy) and is inversely correlated with diffuse myocardial fibrosis.

Standard summary metrics and functional data analysis (FDA) were applied to CGM data from the CONCEPTT trial (RT-CGM, n = 100; SMBG, n = 100) taken at baseline and at 24- and 34-weeks gestation. Multivariable regression analysis determined if temporal differences in 24-h glucose profiles occurred between comparators in each of the three groups.

FDA revealed that women using RT-CGM had significantly lower glucose (0.4–0.8 mmol/L [7–14 mg/dL]) for 7 h/day (0800 h–1200 h and 1600 h–1900 h) compared with those with SMBG. Women using pumps had significantly higher glucose (0.4–0.9 mmol/L [7–16 mg/dL]) for 12 h/day (0300 h to 0600 h, 1300 h to 1800 h, and 2030 h to 0030 h) at 24 weeks with no difference at 34 weeks compared with MDI. Women who had an LGA infant ran a significantly higher glucose by 0.4–0.7 mmol/L (7–13 mg/dL) for 4.5 h/day at baseline; by 0.4–0.9 mmol/L (7–16 mg/dL) for 16 h/day at 24 weeks; and by 0.4–0.7 mmol/L (7–13 mg/dL) for 14 h/day at 34 weeks.

FDA of temporal glucose profiles gives important information about differences in glucose control and its timing, which are undetectable by standard summary metrics. Women using RT-CGM were able to achieve better daytime glucose control, reducing fetal exposure to maternal glucose.

Forty-five patients with poorly controlled (HbA_1c 7–11%) type 2 diabetes mellitus (T2DM) on metformin with or without sulfonylurea received a 9-h measurement of EGP with [3-³H]glucose infusion, after which they were randomized to receive 1) liraglutide 1.2 mg/day (LIRA); 2) canagliflozin 100 mg/day (CANA); or 3) liraglutide 1.2 mg plus canagliflozin 100 mg (CANA/LIRA) for 16 weeks. At 16 weeks, the EGP measurement was repeated.

The mean decrease from baseline to 16 weeks in HbA_1c was –1.67 ± 0.29% (P = 0.0001), –0.89 ± 0.24% (P = 0.002), and –1.44 ± 0.39% (P = 0.004) in patients receiving CANA/LIRA, CANA, and LIRA, respectively. The decrease in body weight was –6.0 ± 0.8 kg (P < 0.0001), –3.5 ± 0.5 kg (P < 0.0001), and –1.9 ± 0.8 kg (P = 0.03), respectively. CANA monotherapy caused a 9% increase in basal rate of EGP (P < 0.05), which was accompanied by a 50% increase (P < 0.05) in plasma glucagon-to-insulin ratio. LIRA monotherapy reduced plasma glucagon concentration and inhibited EGP. In CANA/LIRA-treated patients, EGP increased by 15% (P < 0.05), even though the plasma insulin response was maintained at baseline and the CANA-induced rise in plasma glucagon concentration was blocked.

These results demonstrate that liraglutide failed to block the increase in EGP caused by canagliflozin despite blocking the rise in plasma glucagon and preventing the decrease in plasma insulin concentration caused by canagliflozin. The failure of liraglutide to prevent the increase in EGP caused by canagliflozin explains the lack of additive effect of these two agents on HbA_1c.

We studied the risk of all-cause mortality in relation to long-term visit-to-visit HbA_1c variability, expressed as coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV), from the 8th month to the transition. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (HR) and 95% CI.

Compared with the standard therapy group (n = 4,728), the intensive therapy group (n = 4,755) had significantly lower mean HbA_1c (6.6% [49 mmol/mol] vs. 7.7% [61 mmol/mol], P < 0.0001) and lower CV, VIM, and ARV (P < 0.0001). In multivariate adjusted analysis, all three HbA_1c variability indices were significantly associated with total mortality in all patients as well as in the standard- and intensive-therapy groups analyzed separately. The hazard ratios for a 1-SD increase in HbA_1c variability indices for the all-cause mortality were 1.19 and 1.23 in intensive and standard therapy, respectively. Cross-tabulation analysis showed the third tertile of HbA_1c mean and VIM had significantly higher all-cause mortality (HR 2.05; 95% CI, 1.17–3.61; P < 0.01) only in the intensive-therapy group.

Long-term visit-to-visit HbA_1c variability was a strong predictor of all-cause mortality. HbA_1c VIM combined with HbA_1c mean conferred an increased risk for all-cause mortality in the intensive-therapy group.

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.

Cox regression models were constructed to analyze 20 years of data from the Danish National Patient Registry with respect to effect of the antihyperglycemic therapies on the three end points.

A total of 66,807 people with type 2 diabetes were treated with metformin (MET) including a combination of second- and third-line therapies. People on MET plus sulfonylurea (SU) had the highest risk of all end points, except for severe hypoglycemia, for which people on MET plus basal insulin (BASAL) had a higher risk. The lowest risk of major adverse cardiovascular events was seen for people on a regimen including a glucagon-like peptide 1 (GLP-1) receptor agonist. People treated with MET, GLP-1, and BASAL had a lower risk of all three end points than people treated with MET and BASAL, especially for severe hypoglycemia. The lowest risk of all three end points was, in general, seen for people treated with MET, sodium–glucose cotransporter 2 inhibitor, and GLP-1.

Findings from this study do not support SU as the second-line treatment choice for patients with type 2 diabetes. Moreover, the results indicate that adding a GLP-1 for people treated with MET and BASAL could be considered, especially if those people suffer from severe hypoglycemia.

We randomized 56 patients with T2DM and HF with LV systolic dysfunction to dapagliflozin 10 mg daily or placebo for 1 year, on top of usual therapy. The primary end point was difference in LV end-systolic volume (LVESV) using cardiac MRI. Key secondary end points included other measures of LV remodeling and clinical and biochemical parameters.

In our cohort, dapagliflozin had no effect on LVESV or any other parameter of LV remodeling. However, it reduced diastolic blood pressure and loop diuretic requirements while increasing hemoglobin, hematocrit, and ketone bodies. There was a trend toward lower weight.

We were unable to determine with certainty whether dapagliflozin in patients with T2DM and HF had any effect on LV remodeling. Whether the benefits of dapagliflozin in HF are due to remodeling or other mechanisms remains unknown.

This was a cohort study using an active-comparator, new-user design and nationwide register data from Sweden, Denmark, and Norway during 2010–2016. The cohort included 38,731 new users of GLP-1 receptor agonists (liraglutide 92.5%, exenatide 6.2%, lixisenatide 0.7%, and dulaglutide 0.6%), matched 1:1 on age, sex, and propensity score to a new user of the active comparator, dipeptidyl peptidase 4 (DPP-4) inhibitors. The main outcome was serious renal events, a composite including renal replacement therapy, death from renal causes, and hospitalization for renal events. Secondary outcomes were the individual components of the main outcome. Hazard ratios (HRs) were estimated using Cox models and an intention-to-treat exposure definition. Mean (SD) follow-up time was 3.0 (1.7) years.

Mean (SD) age of the study population was 59 (10) years, and 18% had cardiovascular disease. A serious renal event occurred in 570 users of GLP-1 receptor agonists (incidence rate 4.8 events per 1,000 person-years) and in 722 users of DPP-4 inhibitors (6.3 events per 1,000 person-years, HR 0.76 [95% CI 0.68–0.85], absolute difference –1.5 events per 1,000 person-years [–2.1 to –0.9]). Use of GLP-1 receptor agonists was associated with a significantly lower risk of renal replacement therapy (HR 0.73 [0.62–0.87]) and hospitalization for renal events (HR 0.73 [0.65–0.83]) but not death from renal causes (HR 0.72 [0.48–1.10]). When we used an as treated exposure definition in which patients were censored at treatment cessation or switch to the other study drug, the HR for the primary outcome was 0.60 (0.49–0.74).

In this large cohort of patients seen in routine clinical practice in three countries, use of GLP-1 receptor agonists, as compared with DPP-4 inhibitors, was associated with a reduced risk of serious renal events.

In this phase 3, open-label, multicenter trial, 321 patients with HbA_1c≥7.5 to ≤10.0% (58–86 mmol/mol) and fasting plasma glucose (FPG) ≤13.8 mmol/L (250 mg/dL) were randomized 1:1 to iGlarLixi or Lixi for 52 weeks. The primary end point was change in HbA_1c at week 26.

Change in HbA_1c from baseline to week 26 was significantly greater with iGlarLixi (–1.58% [–17.3 mmol/mol]) than with Lixi (–0.51% [–5.6 mmol/mol]), confirming the superiority of iGlarLixi (least squares [LS] mean difference –1.07% [–11.7 mmol/mol], P < 0.0001). At week 26, significantly greater proportions of patients treated with iGlarLixi reached HbA_1c <7% (53 mmol/mol) (65.2% vs. 19.4%; P < 0.0001), and FPG reductions were greater with iGlarLixi than Lixi (LS mean difference –2.29 mmol/L [–41.23 mg/dL], P < 0.0001). Incidence of documented symptomatic hypoglycemia (≤3.9 mmol/L [70 mg/dL]) was higher with iGlarLixi (13.0% vs. 2.5%) through week 26, with no severe hypoglycemic events in either group. Incidence of gastrointestinal events through week 52 was lower with iGlarLixi (36.0% vs. 50.0%), and rates of treatment-emergent adverse events were similar.

This phase 3 study demonstrated superior glycemic control and fewer gastrointestinal adverse events with iGlarLixi than with Lixi, which may support it as a new treatment option for Japanese patients with T2DM that is inadequately controlled with OADs.

We included 84,285 postmenopausal women without a history of diabetes from the national Women’s Health Initiative Observational Study (WHI-OS). Replication analysis was then conducted among 62,338 women who participated in the WHI-Clinical Trial (WHI-CT). Additionally, data from a case-control study of 3,749 women nested in the WHI-OS with information on biomarkers of inflammation and endothelial dysfunction were examined using mediation analysis to determine the relative contributions of these known biomarkers by which manganese affects type 2 diabetes risk.

Compared with the lowest quintile of energy-adjusted dietary manganese, WHI-OS participants in the highest quintile had a 30% lower risk of type 2 diabetes (hazard ratio [HR] 0.70 [95% CI 0.65, 0.76]). A consistent association was also confirmed in the WHI-CT (HR 0.79 [95% CI 0.73, 0.85]). In the nested case-control study, higher energy-adjusted dietary manganese was associated with lower circulating levels of inflammatory biomarkers that significantly mediated the association between dietary manganese and type 2 diabetes risk. Specifically, 19% and 12% of type 2 diabetes risk due to manganese were mediated through interleukin 6 and hs-CRP, respectively.

Higher intake of manganese was directly associated with a lower type 2 diabetes risk independent of known risk factors. This association may be partially mediated by inflammatory biomarkers.

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.

All pregnant women in the Australian Capital Territory (ACT) are recommended for GDM testing with a 75-g OGTT using the World Health Organization diagnostic criteria. From January 2015 to May 2017, OGTT samples were collected into sodium fluoride (NaF) tubes and kept at room temperature until completion of the test (delayed centrifugation). From June 2017 to October 2018, OGTT samples in NaF tubes were centrifuged within 10 min (early centrifugation).

A total of 7,509 women were tested with the delayed centrifugation protocol and 4,808 with the early centrifugation protocol. The mean glucose concentrations for the fasting, 1-h and 2-h OGTT samples were, respectively, 0.24 mmol/L (5.4%), 0.34 mmol/L (4.9%), and 0.16 mmol/L (2.3%) higher using the early centrifugation protocol (P < 0.0001 for all), increasing the GDM diagnosis rate from 11.6% (n = 869/7,509) to 20.6% (n = 1,007/4,887).

The findings of this study highlight the critical importance of the preanalytical processing protocol of OGTT blood samples used for diagnosing GDM. Delay in centrifuging of blood collected into NaF tubes will result in substantially lower rates of diagnosis than if blood is centrifuged early.

A total of 4,699 people with diabetes who enrolled in the National Health and Nutrition Examination Survey from 2003 to 2014 were recruited for this study. Energy and macronutrient intake was measured by a 24-h dietary recall. The differences () in energy and macronutrient intake between dinner and breakfast ( = dinner – breakfast) were categorized into quintiles. Death information was obtained from the National Death Index until 2015. Cox proportional hazards regression models were developed to evaluate the survival relationship between and diabetes, cardiovascular disease (CVD), and all-cause mortality.

Among the 4,699 participants, 913 deaths, including 269 deaths due to diabetes and 314 deaths due to CVD, were documented. After adjustment for potential confounders, compared with participants in the lowest quintile of in terms of total energy and protein, participants in the highest quintile were more likely to die due to diabetes (hazard ratio [HR]_energy 1.92, 99% CI 1.08–3.42; HR_protein 1.92, 99% CI 1.06–3.49) and CVD (HR_energy 1.69, 99% CI 1.02–2.80; HR_protein 1.96, 99% CI 1.14–3.39). The highest quintile of total fat was related to CVD mortality (HR 1.67, 99% CI 1.01–2.76). Isocalorically replacing 5% of total energy at dinner with breakfast was associated with 4% and 5% lower risk of diabetes (HR 0.96, 95% CI 0.94–0.98) and CVD (HR 0.95, 95% CI 0.93–0.97) mortality, respectively.

Higher intake of energy, total fat, and protein from dinner than breakfast was associated with greater diabetes, CVD, and all-cause mortality in people with diabetes.

The Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial (ACCORD BP), a two-by-two factorial randomized controlled trial, examined effects of SBP (<120 vs. <140 mmHg) and glycemic (HbA_1c <6% vs. 7.0–7.9% [<42 vs. 53–63 mmol/mol]) control on cardiovascular events in T2DM (N = 4,731). We examined whether effects of SBP control on cardiovascular composite were modified by baseline DBP and glycemic control.

Intensive SBP lowering decreased the risk of the cardiovascular composite (hazard ratio [HR] 0.76 [95% CI 0.59–0.98]) in the standard glycemic arm but not in the intensive glycemic arm (HR 1.06 [95% CI 0.81–1.40]). Spline regression models relating the effects of the intervention on the cardiovascular composite across the range of baseline DBP did not show evidence of effect modification by low baseline DBP for the cardiovascular composite in the standard or intensive glycemic arms. The relation between the effect of the intensive SBP intervention and baseline DBP was similar between glycemic arms for the cardiovascular composite three-way interaction (P = 0.83).

In persons with T2DM, intensive SBP lowering decreased the risk of cardiovascular composite end point irrespective of baseline DBP in the setting of standard glycemic control. Hence, low baseline DBP should not be an impediment to intensive SBP lowering in patients with T2DM treated with guidelines recommending standard glycemic control.

Blood, medical history, and personal data were collected in a substudy of 888 participants in the Sleep Apnea Cardiovascular End Points (SAVE) trial in which patients with OSA and stable CVD were randomized to receive CPAP plus usual care, or usual care alone. Serum glucose and glycated hemoglobin A_1c (HbA_1c) were measured at baseline, 6 months, and 2 and 4 years and incident diabetes diagnoses recorded.

Median follow-up was 4.3 years. In those with preexisting diabetes (n = 274), there was no significant difference between the CPAP and usual care groups in serum glucose, HbA_1c, or antidiabetic medications during follow-up. There were also no significant between-group differences in participants with prediabetes (n = 452) or in new diagnoses of diabetes. Interaction testing suggested that women with diabetes did poorly in the usual care group, while their counterparts on CPAP therapy remained stable.

Among patients with established CVD and OSA, we found no evidence that CPAP therapy over several years affects glycemic control in those with diabetes or prediabetes or diabetes risk over standard-of-care treatment. The potential differential effect according to sex deserves further investigation.