Abstract
Context
Intermediate-term glycemic control metrics may represent a viable alternative to continuous glucose monitoring (CGM) in patients without access to CGM.
Objective
This work aimed to ...compare the relationship between CGM parameters and glycated albumin (GA), glycated hemoglobin A1c (HbA1c), and fructosamine for 24 weeks.
Methods
We conducted exploratory comparative analyses of CGM subgroup data from a previously published 24-week prospective study of assay performance in 8 US clinics. Participants included 34 individuals with type 1 (n = 18) and type 2 diabetes (n = 16) undergoing changes to improve glycemic control (n = 22; group 1) or with stable diabetes therapy (n = 12; group 2). Main outcome measures included Pearson correlations between CGM and glycemic indices and receiver operating characteristic (ROC) analysis of glycemic index values predictive of time in range (TIR) greater than 70%.
Results
At weeks 4 and 8, GA correlations with TIR were higher than HbA1c correlations in group 1. In group 2, GA correlations with TIR were statistically significant, whereas HbA1c correlations were not. In both groups over the first 12 weeks, GA correlations with TIR were higher than fructosamine-TIR correlations. In the ROC analysis, GA predicted a TIR greater than 70% during weeks 2 to 24 (area under the curve >0.80); HbA1c was predictive during weeks 12 to 24. Cutoff values for TIR greater than 70% were 17.5% (sensitivity and specificity, 0.88) for GA and 7.3% (0.86) for HbA1c.
Conclusion
GA is the most accurate predictor of TIR over 8 weeks compared with other glycemic indices, which may assist in clinical evaluation of changes in treatment where CGM is not possible and it is too early to use HbA1c (NCT02489773).
Abstract Purpose Within the past decade, many new classes of drugs have received approval from the US Food and Drug Administration for treatment of type 2 diabetes mellitus, including glucagon-like ...peptide-1agonists, dipeptidyl peptidase-4 inhibitors, and the sodium-glucose cotransporter-2 inhibitors. Many trials have been performed, and several more are currently ongoing to evaluate these drugs. This review addresses the broad therapeutic and pleiotropic effects of these drugs. The review also discusses the role of these drugs in the treatment paradigm for type 2 diabetes and identifies patients who would be suitable candidates for treatment with these drugs. Methods In this comprehensive evidence-based review, the following databases were searched from 1990 to the present: PubMed/MEDLINE, Scopus, CINAHL, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Portal, and the American Diabetes Association and European Association for the Study of Diabetes abstract databases. Randomized clinical trials (RCTs) were only included for the main therapeutic and cardiovascular (CV) effects of these drug classes. For pleiotropic effects, RCTs were included unless no RCTs exist, in which case other studies as specified in the detailed Methods section were included. Findings All 3 drug classes are effective in lowering hemoglobin A1c between 0.4% and 1.4%, depending on the drug class and population selected. These drug classes have beneficial effects on CV risk factors, such as weight, lipids, and blood pressure, in addition to lowering blood glucose levels. The CV tolerability of some drugs has been evaluated and found to be neutral; however, most trials are currently ongoing to assess CV tolerability. There are no concrete guidelines to determine where these drugs fit in the diabetes management paradigm, and there are ongoing trials to determine the best combination drug with metformin. Implications These 3 drug classes will potentially increase the armamentarium against hyperglycemia. However, the specific combinations with other antidiabetic drugs and populations that will best benefit from these drugs are still being tested. Future research is also being conducted on the use of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors in patients with type 1 diabetes.
Abstract
Poor glycemic control in type 2 diabetes has been associated with accentuated age-related cognitive decline, although the underlying neural mechanisms are not well understood. The current ...study sought to identify the impact of glycemic control on the neural dynamics serving working memory in adults with type 2 diabetes. Participants (n = 34, ages = 55–73) performed a working memory task while undergoing MEG. Significant neural responses were examined relative to poorer (A1c > 7.0%) or tighter glycemic control (A1c < 7.0%). Those with poorer glycemic control showed diminished responses within left temporal and prefrontal regions during encoding and showed diminished responses within right occipital cortex during maintenance but showed an enhanced activity in the left temporal, occipital, and cerebellar regions during maintenance. Notably, left temporal activity in encoding and left lateral occipital activity in maintenance significantly predicted performance on the task such that diminished temporal activity led to longer reaction times, which were driven by the poorer glycemic control group. Greater lateral occipital activity during maintenance was associated with both lower accuracy and longer reaction times across all participants. These findings suggest that glycemic control has a robust impact on the neural dynamics serving working memory, with distinct effects by subprocess (e.g. encoding vs. maintenance) and direct effects on behavior.
Observational studies suggest an association between a low 25-hydroxyvitamin D level and the risk of type 2 diabetes; whether vitamin D supplements reduce the risk is unknown. In this trial in which ...high-risk persons received either vitamin D
3
(4000 IU per day) or placebo, such supplementation did not significantly decrease the risk of type 2 diabetes.
Abstract
Context
Intermediate-term glycemic control metrics fulfill a need for measures beyond hemoglobin A1C.
Objective
Compare glycated albumin (GA), a 14-day blood glucose measure, with other ...glycemic indices.
Design
24-week prospective study of assay performance.
Setting
8 US clinics.
Participants
Subjects with type 1 (n = 73) and type 2 diabetes (n = 77) undergoing changes to improve glycemic control (n = 98) or with stable diabetes therapy (n = 52).
Interventions
GA, fructosamine, and A1C measured at prespecified intervals. Mean blood glucose (MBG) calculated using weekly self-monitored blood glucose profiles.
Main Outcome Measures
Primary: Pearson correlation between GA and fructosamine. Secondary: magnitude (Spearman correlation) and direction (Kendall correlation) of change of glycemic indices in the first 3 months after a change in diabetes management.
Results
GA was more concordant (60.8%) with changes in MBG than fructosamine (55.5%) or A1C (45.5%). Across all subjects and visits, the GA Pearson correlation with fructosamine was 0.920. Pearson correlations with A1C were 0.655 for GA and 0.515 for fructosamine (P < .001) and with MBG were 0.590 and 0.454, respectively (P < .001). At the individual subject level, Pearson correlations with both A1C and MBG were higher for GA than for fructosamine in 56% of subjects; only 4% of subjects had higher fructosamine correlations with A1C and MBG. GA had a higher Pearson correlation with A1C and MBG in 82% and 70% of subjects, respectively.
Conclusions
Compared with fructosamine, GA correlates significantly better with both short-term MBG and long-term A1C and may be more useful than fructosamine in clinical situations requiring monitoring of intermediate-term glycemic control (NCT02489773).
Objective
In this secondary analysis of the Rural Engagement in Primary Care for Optimizing Weight Reduction (RE‐POWER) randomized trial, the authors determined the effectiveness of weight‐loss ...interventions in people with diabetes compared with those without diabetes living in rural areas.
Methods
The RE‐POWER study was a randomized trial designed to determine the effectiveness of nonpharmacological behavioral weight‐loss interventions in rural participants with obesity, comparing the individual in‐clinic visit model to in‐person group sessions and phone group sessions over 24 months. In this secondary analysis, weight loss was compared in participants with and without diabetes. The effects of factors such as medications, insulin, and behavioral factors were compared.
Results
Participants with diabetes were less likely to lose weight during the study compared with those without diabetes up to 18 months (4.12% vs. 5.31%; net difference = 1.46%; 95% CI: 0.63%‐2.28%). Participants with diabetes on insulin lost less weight than patients with diabetes not on insulin at 6 months (4.52% vs. 6.88%; net difference = 2.35%; 95% CI: 0.55%‐4.16%). The group with diabetes had significantly lower changes in blood pressure and lipid parameters versus the group without diabetes.
Conclusions
Patients with diabetes in rural areas were less likely to lose weight, and metabolic parameters were less responsive to weight loss, compared with patients without diabetes.
To investigate the efficacy and safety of once-daily semaglutide in comparison with once-daily liraglutide and placebo in patients with type 2 diabetes.
This 26-week, multicenter, double-blind trial ...involved patients diagnosed with type 2 diabetes with HbA
7.0-10.0% (53-86 mmol/mol) and treated with diet and exercise with or without metformin. Patients were randomized 2:2:1 to once-daily semaglutide, liraglutide, or placebo in one of four volume-matched doses (semaglutide 0.05, 0.1, 0.2, or 0.3 mg and liraglutide 0.3, 0.6, 1.2, or 1.8 mg, with both compared within each volume-matched dose group). Primary end point was change in HbA
from baseline to week 26.
In total, 705 randomized patients were exposed to trial products. At week 26, a dose-dependent change in HbA
was observed with semaglutide from -1.1% (0.05 mg) to -1.9% (0.3 mg) and with liraglutide from -0.5% (0.3 mg) to -1.3% (1.8 mg) (all
< 0.001 in favor of volume-matched semaglutide dose). Change with pooled placebo was -0.02% (
< 0.0001 vs. semaglutide). Gastrointestinal (GI) disorders were the most common adverse events (AEs) with semaglutide and liraglutide, occurring in 32.8-54.0% and 21.9-41.5% of patients, respectively.
Once-daily semaglutide at doses up to 0.3 mg/day resulted in greater reductions in HbA
compared with liraglutide or placebo but with a higher frequency of GI AEs.
Abstract Endothelial progenitor cells (EPCs) are vital for the maintenance and repair of the endothelium. Decreased EPC number and function have been associated with increased cardiovascular (CVD) ...risk. Patients with diabetes have decreased number of circulating EPCs and decreased EPC function. This may account for some of the increased CVD risk seen in patients with diabetes that is not explained by traditional risk factors such as glycemic control, dyslipidemia and hypertension. Recent studies seem to indicate that drugs commonly used in diabetes patients such as metformin, thiazolidinediones, GLP-1 agonists, DPP-4 inhibitors, insulin, statins and ACE inhibitors may increase EPC number and improve EPC function. The mechanisms by which these drugs modulate EPC function may involve reduction in inflammation, oxidative stress and insulin resistance as well as an increase in nitric oxide (NO) bioavailability. This review will discuss the evidence in the literature regarding the above mentioned topics.
Abstract
Objective
Fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG) from a 75-g oral glucose tolerance test (OGTT) and glycated hemoglobin (HbA1c) can lead to different results when ...diagnosing prediabetes and diabetes. The Hemoglobin Glycation Index (HGI) quantifies the interindividual variation in glycation resulting in discrepancies between FPG and HbA1c. We used data from the Vitamin D and Type 2 Diabetes (D2d) study to calculate HGI, to identify HGI-associated variables, and to determine how HGI affects prediabetes and diabetes diagnosis.
Measurements
A linear regression equation HbA1c (%) = 0.0164 × FPG (mg/dL) + 4.2 was derived using the screening cohort (n = 6829) and applied to calculate predicted HbA1c. This was subtracted from the observed HbA1c to determine HGI in the baseline cohort with 2hPG data (n = 3945). Baseline variables plus prediabetes and diabetes diagnosis by FPG, HbA1c, and 2hPG were compared among low, moderate, and high HGI subgroups.
Results
The proportion of women and Black/African American individuals increased from low to high HGI subgroups. Mean FPG decreased and mean HbA1c increased from low to high HGI subgroups, consistent with the HGI calculation; however, mean 2hPG was not significantly different among HGI subgroups.
Conclusions
High HGI was associated with Black race and female sex as reported previously. The observation that 2hPG was not different across HGI subgroups suggests that variation in postprandial glucose is not a significant source of population variation in HGI. Exclusive use of HbA1c for diagnosis will classify more Black individuals and women as having prediabetes compared with using FPG or 2hPG.
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