scholarly journals Hypoglycemia in People with Type 2 Diabetes and CKD

2019 ◽  
Vol 14 (6) ◽  
pp. 844-853 ◽  
Author(s):  
Iram Ahmad ◽  
Leila R. Zelnick ◽  
Zona Batacchi ◽  
Nicole Robinson ◽  
Ashveena Dighe ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Confidence Interval ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitor ◽  
Glucose Lowering ◽  
Level 1 ◽  
Glucose Management ◽  
Level 2

Background and objectivesAmong people with diabetes mellitus, CKD may promote hypoglycemia through altered clearance of glucose-lowering medications, decreased kidney gluconeogenesis, and blunted counter-regulatory response. We conducted a prospective observational study of hypoglycemia among 105 individuals with type 2 diabetes treated with insulin or a sulfonylurea using continuous glucose monitors.Design, setting, participants & measurementsWe enrolled 81 participants with CKD, defined as eGFR<60 ml/min per 1.73 m2, and 24 control participants with eGFR≥60 ml/min per 1.73 m2 frequency-matched on age, duration of diabetes, hemoglobin A1c, and glucose-lowering medications. Each participant wore a continuous glucose monitor for two 6-day periods. We examined rates of sustained level 1 hypoglycemia (<70 mg/dl) and level 2 hypoglycemia (<54 mg/dl) among participants with CKD. We then tested differences compared with control participants as well as a second control population (n=73) using Poisson and linear regression, adjusting for age, sex, and race.ResultsOver 890 total days of continuous glucose monitoring, participants with CKD were observed to have 255 episodes of level 1 hypoglycemia, of which 68 episodes reached level 2 hypoglycemia. Median rate of hypoglycemic episodes was 5.3 (interquartile range, 0.0–11.7) per 30 days and mean time spent in hypoglycemia was 28 (SD 37) minutes per day. Hemoglobin A1c and the glucose management indicator were the main clinical correlates of time in hypoglycemia (adjusted differences 6 [95% confidence interval, 2 to 10] and 13 [95% confidence interval, 7 to 20] fewer minutes per day per 1% higher hemoglobin A1c or glucose management indicator, respectively). Compared with control populations, participants with CKD were not observed to have significant differences in time in hypoglycemia (adjusted differences 4 [95% confidence interval, −12 to 20] and −12 [95% confidence interval, −29 to 5] minutes per day).ConclusionsAmong people with type 2 diabetes and moderate to severe CKD, hypoglycemia was common, particularly with tighter glycemic control, but not significantly different from groups with similar clinical characteristics and preserved eGFR.

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

2021 ◽  
pp. 193229682199872
Author(s):  
Gregg D. Simonson ◽  
Richard M. Bergenstal ◽  
Mary L. Johnson ◽  
Janet L. Davidson ◽  
Thomas W. Martens
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Glucose Monitoring ◽  
Care Model ◽  
Time In Range ◽  
Glucose Management ◽  
The Impact ◽  
Primary Care Management

Background: Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model. Methods: Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM. Results: Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications. Conclusions: Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.

scholarly journals Network Meta-Analysis of Novel Glucose-Lowering Drugs on Risk of Acute Kidney Injury

2020 ◽  
Vol 16 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Min Zhao ◽  
Shusen Sun ◽  
Zhenguang Huang ◽  
Tiansheng Wang ◽  
Huilin Tang
Keyword(s):  
Type 2 Diabetes ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Lower Risk ◽  
Meta Analysis ◽  
Secondary Analysis ◽  
Sglt2 Inhibitors ◽  
Glucose Lowering ◽  
Event Driven

Background and objectivesLittle is known about the comparative effects of dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), or sodium glucose cotransporter-2 (SGLT2) inhibitors on risk of AKI. This study aimed to compare the effects of these three novel classes of glucose-lowering drugs on AKI risk in patients with or without type 2 diabetes, by network meta-analysis of event-driven cardiovascular or kidney outcome trials.Design, setting, participants, & measurementsWe systematically searched electronic databases up to September 2020, and included 20 event-driven cardiovascular or kidney outcome trials (18 trials included patients with type 2 diabetes only, and two trials included patients with or without type 2 diabetes). A network meta-analysis using a frequentist approach was performed to compare the effects of DPP-4 inhibitors, GLP-1RAs, or SGLT2 inhibitors on risk of AKI, and estimate the probability for each intervention as the safest one. The primary analysis included 18 trials with type 2 diabetes only, and a secondary analysis included 20 trials.ResultsIn the 18 trials with a total of 2051 AKI events (range: 1–300) among 156,690 patients with type 2 diabetes only, our network meta-analysis showed that SGLT2 inhibitors were associated with a lower risk of AKI compared with placebo (odds ratio, 0.76; 95% confidence interval, 0.66 to 0.88), whereas both DPP-4 inhibitors and GLP-1RAs had neutral effects on risk of AKI. Moreover, SGLT2 inhibitors were significantly associated with a lower risk in AKI than both GLP-1RAs (odds ratio, 0.79; 95% confidence interval, 0.65 to 0.97) and DPP-4 inhibitors (odds ratio, 0.68; 95% confidence interval, 0.54 to 0.86). SGLT2 inhibitors have the highest probability of being the safest intervention (84%). The results were similar in the secondary analysis.ConclusionsCurrent evidence indicates that SGLT2 inhibitors have a lower risk of AKI than both DPP-4 inhibitors and GLP-1RAs.

The Accuracy of Hemoglobin A1c and Fructosamine Evaluated by Long-Term Continuous Glucose Monitoring in Patients with Type 2 Diabetes Undergoing Hemodialysis

2021 ◽  
pp. 1-9
Author(s):  
Tobias Bomholt ◽  
Marianne Rix ◽  
Thomas Almdal ◽  
Filip K. Knop ◽  
Susanne Rosthøj ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Plasma Glucose ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Control Group ◽  
Glucose Levels ◽  
Sensor Glucose

<b><i>Introduction:</i></b> The accuracy of hemoglobin A1c (HbA1c) as a glycemic marker in patients with type 2 diabetes (T2D) receiving hemodialysis (HD) remains unknown. To assess accuracy, we compared HbA1c and fructosamine levels with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with T2D receiving HD. <b><i>Methods:</i></b> Thirty patients in the HD group and 36 patients in the control group (T2D and an estimated glomerular filtration rate &#x3e;60 mL/min/1.73 m<sup>2</sup>) completed the study period of 17 weeks. CGM (Ipro2<sup>®</sup>, Medtronic) was performed 5 times for periods of up to 7 days (with 4-week intervals) during a 16-week period. HbA1c (mmol/mol), the estimated mean plasma glucose from HbA1c (eMPGA1c [mmol/L]) and fructosamine (μmol/L) was measured at week 17 and compared with mean sensor glucose levels from CGM. <b><i>Findings:</i></b> In the HD group, mean sensor glucose was 1.4 mmol/L (95% confidence interval [CI]: 1.0–1.8) higher than the eMPGA1c, whereas the difference for controls was 0.1 mmol/L (95% CI: −0.1–[0.4]; <i>p</i> &#x3c; 0.001). Adjusted for mean sensor glucose, HbA1c was lower in the HD group (−7.3 mmol/mol, 95% CI: −10.0–[−4.7]) than in the control group (<i>p</i> &#x3c; 0.001), with no difference detected for fructosamine (<i>p</i> = 0.64). <b><i>Discussion:</i></b> HbA1c evaluated by CGM underestimates plasma glucose levels in patients receiving HD. The underestimation represents a clinical challenge in optimizing glycemic control in the HD population. Fructosamine is unaffected by the factors affecting HbA1c and appears to be more accurate for glycemic monitoring. CGM or fructosamine could thus complement HbA1c in obtaining more accurate glycemic control in this patient group.

scholarly journals A Virtual Type 2 Diabetes Clinic Using Continuous Glucose Monitoring and Endocrinology Visits

2019 ◽  
Vol 14 (5) ◽  
pp. 908-911 ◽  
Author(s):  
Ronald F. Dixon ◽  
Howard Zisser ◽  
Jennifer E. Layne ◽  
Nathan A. Barleen ◽  
David P. Miller ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Management ◽  
Medication Management ◽  
Glucose Monitoring ◽  
Mobile App ◽  
Office Visits ◽  
Continuous Glucose Monitor ◽  
Diabetes Clinic ◽  
Clinical Support

The Onduo Virtual Diabetes Clinic (VDC) telehealth technology/care model for adults with type 2 diabetes (T2D) combines connected devices, remote lifestyle coaching, and clinical support with a mobile App. Key differentiating program features are the availability of live video consultations with board-certified endocrinologists for medication management and real-time continuous glucose monitor use for higher-risk participants. Preliminary data ( n = 740) suggest that participation was associated with a significant improvement in HbA1c with up to 6 months follow-up in those not meeting treatment targets. HbA1c decreased by 2.3% ± 1.9%, 0.7% ± 1.0%, and 0.2% ± 0.8% across baseline categories of >9.0%, 8.0%-9.0% and 7.0% to <8.0%, respectively (all P < .001). These findings suggest that the VDC has potential to support individuals with T2D and their clinicians in diabetes management between office visits.

scholarly journals Impact of COVID-19 Vaccination on Glycemia in Individuals With Type 1 and Type 2 Diabetes: Substudy of the COVAC-DM Study

2022 ◽  
Author(s):  
Felix Aberer ◽  
Othmar Moser ◽  
Faisal Aziz ◽  
Caren Sourij ◽  
Haris Ziko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Immune Response ◽  
Type 1 Diabetes ◽  
Side Effects ◽  
Glucose Monitoring ◽  
Time In Range ◽  
The Mean ◽  
Glucose Management

Vaccination and potentially related side effects might impact glucose management in people with diabetes. In this study, we investigated effects of COVID-19 vaccination on glycemia assessed by continuous glucose monitoring (CGM) in people with type 1 and type 2 diabetes. <br> 74 participants of the ongoing multicenter prospective COVAC-DM-study, investigating the immune response to COVID-19 vaccines in people with diabetes, were willing to participate in this CGM sub-study. Time spent in glycemic ranges (time in range [TIR] 70-180 mg/dL; time below range [TBR] <70 mg/dL and time above range [TAR] >180 mg/dL) was assessed daily from two days prior to three days after the first COVID-19 vaccination. Participants were asked to document side effects in response to vaccination, insulin injections, and carbohydrate intake.<br> 58 participants with type 1 (27 female, mean age 39.5 years, HbA1c 57 ± 12 mmol/mol) and 16 with type 2 diabetes (9 females, mean age 60.6 years, HbA1c 63 ± 11mmol/mol) were included in this study. The mean TIR did not change on the day of the vaccination and the following 3 days (p>0.05). In people with type 1 diabetes, the TIR (p=0.033) and the TAR (p= 0.043) deteriorated on days with side effects as compared to those without. <br> Side effects occurring after COVID-19 vaccination significantly reduce the TIR and increase the TAR in people with type 1 diabetes, while no impact was observed in people with type 2 diabetes.

scholarly journals Impact of COVID-19 Vaccination on Glycemia in Individuals With Type 1 and Type 2 Diabetes: Substudy of the COVAC-DM Study

2022 ◽  
Author(s):  
Felix Aberer ◽  
Othmar Moser ◽  
Faisal Aziz ◽  
Caren Sourij ◽  
Haris Ziko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Immune Response ◽  
Type 1 Diabetes ◽  
Side Effects ◽  
Glucose Monitoring ◽  
Time In Range ◽  
The Mean ◽  
Glucose Management

Vaccination and potentially related side effects might impact glucose management in people with diabetes. In this study, we investigated effects of COVID-19 vaccination on glycemia assessed by continuous glucose monitoring (CGM) in people with type 1 and type 2 diabetes. <br> 74 participants of the ongoing multicenter prospective COVAC-DM-study, investigating the immune response to COVID-19 vaccines in people with diabetes, were willing to participate in this CGM sub-study. Time spent in glycemic ranges (time in range [TIR] 70-180 mg/dL; time below range [TBR] <70 mg/dL and time above range [TAR] >180 mg/dL) was assessed daily from two days prior to three days after the first COVID-19 vaccination. Participants were asked to document side effects in response to vaccination, insulin injections, and carbohydrate intake.<br> 58 participants with type 1 (27 female, mean age 39.5 years, HbA1c 57 ± 12 mmol/mol) and 16 with type 2 diabetes (9 females, mean age 60.6 years, HbA1c 63 ± 11mmol/mol) were included in this study. The mean TIR did not change on the day of the vaccination and the following 3 days (p>0.05). In people with type 1 diabetes, the TIR (p=0.033) and the TAR (p= 0.043) deteriorated on days with side effects as compared to those without. <br> Side effects occurring after COVID-19 vaccination significantly reduce the TIR and increase the TAR in people with type 1 diabetes, while no impact was observed in people with type 2 diabetes.

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