scholarly journals Ketogenic Diet in a Patient With Type 1 Diabetes Mellitus With Hypoglycemia Unawareness

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A460-A460
Author(s):  
Mohamad Anas Sukkari ◽  
Lucia Cotten ◽  
Murtaza Alam ◽  
Emily Temponi ◽  
Priya D John ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Ketogenic Diet ◽  
Severe Hypoglycemia ◽  
Glycemic Variability ◽  
Glucose Variability ◽  
Continuous Glucose Monitor ◽  
Hypoglycemia Unawareness ◽  
The Impact

Abstract Introduction: The high fat, low carbohydrate ketogenic diet has become increasingly popular in recent years for weight loss and glycemic control in patients with type 2 diabetes. Although prior studies have suggested this diet can improve glycemic control and decrease glucose variability, the impact of a ketogenic diet on rates of hypoglycemia in patients with hypoglycemia unawareness is not well described. Case Description: Our patient is a 37 year-old woman with Type 1 diabetes for 13 years complicated by hypoglycemia unawareness with HbA1c of 7.7%. Her insulin treatment regimen included insulin glargine 22 units daily, insulin aspart using a 1:15 carbohydrate ratio for prandial insulin dosing with a correction factor of 90. She had 5 episodes of severe hypoglycemia within the previous 3 months. The patient decided to resume a ketogenic diet given her previous improvement in glycemic control. Ketosis was confirmed using urine ketone strips performed by the patient. After 2 weeks on the ketogenic diet, a professional blinded continuous glucose monitor (CGM) was used for 4 weeks to monitor glycemic control. CGM data for weeks 1 and 2 showed overall stability of time in target glucose range [TIR, 60% and 69%, respectively], with a slight increase in time spent below range [TBR, 13% and 17%, respectively]. During week 3, the patient experienced a significant decline in TIR to 31%, and associated increase in hypoglycemia (TBR, 13% to 28%). In addition, glycemic variability increased during this time [CV (coefficient of variation), 40.6% during week 1 to 58.1% during week 3]. Patient did not experience symptoms concerning for DKA, and continued to have asymptomatic hypoglycemia despite reductions in her insulin doses during week 3. Following these dose adjustments, CGM data during week 4 were similar to week 1 (TIR 65%, TBR 10%, CV 35%). Patient stopped following the ketogenic diet after 6 weeks due to social factors. Conclusion: A ketogenic diet was associated with increased frequency of hypoglycemic events. In a patient with Type 1 diabetes and hypoglycemia unawareness, use of ketogenic diet may further increase the risk of severe hypoglycemia.

scholarly journals Anxiety, depression, and glycemic control during Covid-19 pandemic in youths with type 1 diabetes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Maria Cusinato ◽  
Mariangela Martino ◽  
Alex Sartori ◽  
Claudia Gabrielli ◽  
Laura Tassara ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Psychological Impact ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Well Being ◽  
Glucose Variability ◽  
Threshold Score ◽  
The Impact

Abstract Objectives Our study aims to assess the impact of lockdown during the coronavirus disease 2019 pandemic on glycemic control and psychological well-being in youths with type 1 diabetes. Methods We compared glycemic metrics during lockdown with the same period of 2019. The psychological impact was evaluated with the Test of Anxiety and Depression. Results We analyzed metrics of 117 adolescents (87% on Multiple Daily Injections and 100% were flash glucose monitoring/continuous glucose monitoring users). During the lockdown, we observed an increase of the percentage of time in range (TIR) (p<0.001), with a significant reduction of time in moderate (p=0.002), and severe hypoglycemia (p=0.001), as well as the percentage of time in hyperglycemia (p<0.001). Glucose variability did not differ (p=0.863). The glucose management indicator was lower (p=0.001). 7% of youths reached the threshold-score (≥115) for anxiety and 16% for depression. A higher score was associated with lower TIR [p=0.028, p=0.012]. Conclusions Glycemic control improved during the first lockdown period with respect to the previous year. Symptoms of depression and anxiety were associated with worse glycemic control; future researches are necessary to establish if this improvement is transient and if psychological difficulties will increase during the prolonged pandemic situation.

scholarly journals 1020 Sleep and Glycemic Control in Adults With Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A387-A388
Author(s):  
S K Malone ◽  
A J Peleckis ◽  
A I Pack ◽  
N Perez ◽  
G Yu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Sleep Onset ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Sleep Efficiency ◽  
Hypoglycemia Unawareness ◽  
Time In Bed ◽  
Hba1c Levels

Abstract Introduction Nocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counterregulation. These individuals also show disturbed sleep, which may result from glycemic dysregulation. Whether use of a hybrid closed loop (HCL) insulin delivery system with integrated continuous glucose monitoring (CGM) designed for improving glycemic control, relates to better sleep across time in this population remains unknown. Methods Six adults (median age=58y,T1D duration=41y) participated in an 18-month ongoing clinical trial assessing the effectiveness of an HCL system. Sleep and glycemic control were measured concurrently using wrist actigraphs and CGM at baseline (1 week) and months 3 and 6 (3 weeks) following HCL initiation. BMI and hemoglobin A1c (HbA1c) were collected at all timepoints. Spearman’s correlations modeled associations between sleep, BMI, and glycemic control at each time point. Repeated ANOVAs modeled sleep and glycemic control changes from baseline to 3 months and to 6 months. Results Sleep and glycemic control indices showed significant associations at baseline and 3 months. More time-in-bed and later sleep offset related to higher HbA1c levels at baseline. Later sleep onset, midpoint and offset, and greater sleep efficiency associated with greater %time with hyperglycemia (glucose &gt;180 mg/dL) or hypoglycemia (glucose &lt;70 mg/dL) at baseline and 3 months. Longer sleep duration and greater sleep efficiency related to greater %time with hyperglycemia at 3 months. At 3 months, more wake after sleep onset associated with lower HbA1c levels and longer nocturnal awakenings and more sleep fragmentation associated with less glycemic variability. While both sleep and glycemic control improved from baseline to 3 and 6 months, these were not statistically significant. Conclusion Various dimensions of actigraphic sleep related to concurrently estimated glycemic indices indicative of poorer glycemic control and HbA1c across time in adults with long-standing T1D and hypoglycemia unawareness. Support This work was supported by NIH R01DK117488 (NG), R01DK091331 (MRR), and K99NR017416 (SKM).

scholarly journals Post-exercise glycemic control in type 1 diabetes is associated with residual ꞵ-cell function

2020 ◽  
Author(s):  
Guy S Taylor ◽  
Kieran Smith ◽  
Tess E Capper ◽  
Jadine H Scragg ◽  
Ayat Bashir ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Acute Exercise ◽  
Cell Function ◽  
Glycemic Variability ◽  
Free Living ◽  
Continuous Glucose Monitor ◽  
Post Exercise ◽  
C Peptide ◽  
The Impact

Objective<b> </b> <p>To investigate the impact of residual ꞵ-cell function on continuous glucose monitor (CGM) outcomes following acute exercise in people with Type 1 diabetes.</p> <p>Research<b> </b>Design and Methods<b> </b></p> <p>Thirty participants with type 1 diabetes for ≥3 years were recruited. Firstly, participants wore a blinded CGM for 7 days of free-living data capture. Secondly, a 3 hour mixed meal test, assessed stimulated C-peptide and glucagon. Peak C-peptide was used to allocate participants into undetectable (Cpep<sub>und</sub> <3 pmol/L), low (Cpep<sub>low</sub> 3–200 pmol/L) or high C-peptide groups (Cpep<sub>high</sub> >200 pmol/L). Finally, participants completed 45 minutes of incline treadmill walking at 60%VO<sub>2peak</sub> followed by a further 48 hours’ CGM capture.</p> <p>Results<b> </b></p> <p>CGM parameters were comparable across groups during the free-living observation week. In the 12 (12hr) and 24 hours (24hr) post-exercise periods the Cpep<sub>high</sub> group had significantly greater amount of time spent with glucose 3.9-10 mmol/L (12hr: 73.5±27.6%, 24hr: 76.3±19.2%) compared to Cpep<sub>low</sub> (12hr: 43.6±26.1%, p=0.027, 24hr: 52.3±25.0%, p=0.067) or Cpep<sub>und</sub> (40.6±17.0%, p=0.010, 24hr: 51.3±22.3%, p=0.041). Time spent in hyperglycemia (12hr and 24hr glucose >10 and >13.9 mmol/L, p<0.05) and glycemic variability (12hr and 24hr SD, p<0.01) were significantly lower in the Cpep<sub>high</sub> group compared to Cpep<sub>und</sub> and Cpep<sub>low</sub>. Change in CGM outcomes from pre to 24hr post-exercise was divergent: Cpep<sub>und</sub> and Cpep<sub>low</sub> experienced worsening (glucose 3.9-10 mmol/L: -9.1% and -16.2% respectively), with Cpep<sub>high</sub> experiencing improvement (+12.1%)(p=0.017). </p> <p>Conclusions<b> </b></p> <p>Residual ꞵ-cell function may partially explain the inter-individual variation in the acute glycemic benefits of exercise in individuals with type 1 diabetes. Quantifying C-peptide could aid in providing personalized and targeted support for exercising patients.</p>

scholarly journals Impact of COVID-19 Lockdown on Glycemic Control in Adults with Type 1 Diabetes Mellitus

2020 ◽  
Vol 4 (12) ◽  
Author(s):  
Begoña Pla ◽  
Alfonso Arranz ◽  
Carolina Knott ◽  
Miguel Sampedro ◽  
Sara Jiménez ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Type 1 Diabetes Mellitus ◽  
Glycosylated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Glucose Management ◽  
The Impact

Abstract Aim To examine the impact of the lockdown caused by the COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (flash CGMs). Methods Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes. Results We observed a reduction of average glucose values: 160.26 ± 22.55 mg/dL vs 150 ± 20.96 mg/dL, P = .0009; estimated glycosylated hemoglobin: 7.21 ± 0.78% vs 6.83 ± 0.71%, P = .0005; glucose management indicator 7.15 ± 0.57% vs 6.88 ± 0.49%; P = .0003, and glycemic variability: 40.74 ± 6.66 vs 36.43 ± 6.09 P &lt; .0001. Time in range showed an improvement: 57.46 ± 11.85% vs a 65.76 ± 12.09%, P &lt; .0001, without an increase in percentage of time in hypoglycemia. Conclusions COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs.

scholarly journals Impact of the COVID-19 Pandemic on Management of Children and Adolescents with Type 1 Diabetes

2021 ◽  
Author(s):  
Abha Choudhary ◽  
Soumya Adhikari ◽  
Perrin White
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Teaching Hospital ◽  
Patient Characteristics ◽  
Office Visits ◽  
Continuous Glucose Monitor ◽  
Commercial Insurance ◽  
Hospitalization Rates ◽  
The Impact

Abstract Background. The coronavirus disease-2019 (COVID-19) pandemic had widespread impacts on the lives of parents and children. We determined how the pandemic affected Type 1 diabetes patients at a large urban pediatric teaching hospital.Methods. We compared patient characteristics, glycemic control, PHQ9 depression screen, in person and virtual outpatient encounters, hospitalizations and continuous glucose monitor (CGM) utilization in approximately 1600 patients in one -year periods preceding and following the local imposition of COVID-related restrictions on 3/15/2020 (“2019” and “2020” groups, respectively) Results. In a generalized linear model, increasing age, non-commercial insurance, Black and Hispanic race/ethnicity, and non-utilization of CGMs were all associated with higher hemoglobin A1c (HbA1c), but there was no difference between the 2019 and 2020 groups. The time in range in CGM users was lower with non-commercial insurance and in Black and Hispanic patients; it improved slightly from 2019 to 2020. CGM utilization by patients with non-commercial insurance increased markedly. In 2020, patients with commercial insurance had fewer office visits, but insurance status did not influence utilization of the virtual visit platform. There was no change in hospitalization rates from 2019 to 2020 in either commercially or non-commercially insured patients, but patients with non-commercial insurance were hospitalized at markedly higher rates in both years. PHQ9 scores were unchanged.Conclusions. Hospitalization rates, glycemic control and depression screening were unchanged in our large urban pediatric teaching hospital during the COVID pandemic. Increased utilization of CGM and rapid adoption of telemedicine may have ameliorated the impact of the pandemic on disease management.

scholarly journals Glycemic Variability and Hypoglycemic Excursions With Continuous Glucose Monitoring Compared to Intermittently Scanned Continuous Glucose Monitoring in Adults With Highest Risk Type 1 Diabetes

2019 ◽  
Vol 14 (3) ◽  
pp. 567-574 ◽  
Author(s):  
Parizad Avari ◽  
Vanessa Moscardo ◽  
Narvada Jugnee ◽  
Nick Oliver ◽  
Monika Reddy
Keyword(s):  
Type 1 Diabetes ◽  
High Risk ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Glycemic Variability ◽  
Clinical Trial Registration ◽  
Freestyle Libre ◽  
The Impact

Background: The I-HART CGM study has shown that real-time continuous glucose monitoring (rtCGM) has greater beneficial impact on hypoglycemia than intermittently scanned continuous glucose monitoring (iscCGM) in adults with type 1 diabetes at high risk (Gold score ≥4 or recent severe hypoglycemia using insulin injections). In this subanalysis, we present the impact of rtCGM and iscCGM on glycemic variability (GV). Methods: Forty participants were recruited to this parallel group study. Following two weeks of blinded rtCGM (DexcomG4), participants were randomized to rtCGM (Dexcom G5; n = 20) or iscCGM (Freestyle Libre; n = 20) for eight weeks. An open-extension phase enabled participants on rtCGM to continue for a further eight weeks and those on iscCGM to switch to rtCGM over this period. Glycemic variability measures at baseline, 8- and 16-week endpoints were compared between groups. Results: At the eight-week endpoint, between-group differences demonstrated significant reduction in several GV measures with rtCGM compared to iscCGM (GRADE%hypoglycemia, index of glycemic control [IGC], and average daily risk range [ADRR]; P < .05). Intermittently scanned continuous glucose monitoring reduced mean average glucose and glycemic variability percentage and GRADE%hyperglycemia compared with rtCGM ( P < .05). At 16 weeks, the iscCGM group switching to rtCGM showed significant improvement in GRADE%hypoglycemia, personal glycemic status, IGC, and ADRR. Conclusion: Our data suggest most, but not all, GV measures improve with rtCGM compared with iscCGM, particularly those measures associated with the risk of hypoglycemia. Selecting appropriate glucose monitoring technology to address GV in this high-risk cohort is important to minimize the risk of glucose extremes and severe hypoglycemia. Clinical trial registration: ClinicalTrials.gov NCT03028220

scholarly journals Post-exercise glycemic control in type 1 diabetes is associated with residual ꞵ-cell function

2020 ◽  
Author(s):  
Guy S Taylor ◽  
Kieran Smith ◽  
Tess E Capper ◽  
Jadine H Scragg ◽  
Ayat Bashir ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Acute Exercise ◽  
Cell Function ◽  
Glycemic Variability ◽  
Free Living ◽  
Continuous Glucose Monitor ◽  
Post Exercise ◽  
C Peptide ◽  
The Impact

Objective<b> </b> <p>To investigate the impact of residual ꞵ-cell function on continuous glucose monitor (CGM) outcomes following acute exercise in people with Type 1 diabetes.</p> <p>Research<b> </b>Design and Methods<b> </b></p> <p>Thirty participants with type 1 diabetes for ≥3 years were recruited. Firstly, participants wore a blinded CGM for 7 days of free-living data capture. Secondly, a 3 hour mixed meal test, assessed stimulated C-peptide and glucagon. Peak C-peptide was used to allocate participants into undetectable (Cpep<sub>und</sub> <3 pmol/L), low (Cpep<sub>low</sub> 3–200 pmol/L) or high C-peptide groups (Cpep<sub>high</sub> >200 pmol/L). Finally, participants completed 45 minutes of incline treadmill walking at 60%VO<sub>2peak</sub> followed by a further 48 hours’ CGM capture.</p> <p>Results<b> </b></p> <p>CGM parameters were comparable across groups during the free-living observation week. In the 12 (12hr) and 24 hours (24hr) post-exercise periods the Cpep<sub>high</sub> group had significantly greater amount of time spent with glucose 3.9-10 mmol/L (12hr: 73.5±27.6%, 24hr: 76.3±19.2%) compared to Cpep<sub>low</sub> (12hr: 43.6±26.1%, p=0.027, 24hr: 52.3±25.0%, p=0.067) or Cpep<sub>und</sub> (40.6±17.0%, p=0.010, 24hr: 51.3±22.3%, p=0.041). Time spent in hyperglycemia (12hr and 24hr glucose >10 and >13.9 mmol/L, p<0.05) and glycemic variability (12hr and 24hr SD, p<0.01) were significantly lower in the Cpep<sub>high</sub> group compared to Cpep<sub>und</sub> and Cpep<sub>low</sub>. Change in CGM outcomes from pre to 24hr post-exercise was divergent: Cpep<sub>und</sub> and Cpep<sub>low</sub> experienced worsening (glucose 3.9-10 mmol/L: -9.1% and -16.2% respectively), with Cpep<sub>high</sub> experiencing improvement (+12.1%)(p=0.017). </p> <p>Conclusions<b> </b></p> <p>Residual ꞵ-cell function may partially explain the inter-individual variation in the acute glycemic benefits of exercise in individuals with type 1 diabetes. Quantifying C-peptide could aid in providing personalized and targeted support for exercising patients.</p>

Short and long-term glycemic variability associated with severe hypoglycemia and retinopathy in type 1 diabetes mellitus

2020 ◽  
Author(s):  
Martín Borja Sanz ◽  
Gimeno Sergio Roman ◽  
Peteiro Miranda Carlos Miguel ◽  
Ortez Toro Jose Jorge ◽  
Ana Agudo ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Severe Hypoglycemia ◽  
Glycemic Variability ◽  
Short And Long Term

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

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