962-P: Continuous Glucose Monitor Readings Lag Interstitial Glucose by Several Minutes

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 962-P ◽  
Author(s):  
PENN MASON MCCLATCHEY ◽  
ETHAN S. MCCLAIN ◽  
IAN M. WILLIAMS ◽  
JUSTIN M. GREGORY ◽  
DAVID CLIFFEL ◽  
...  
Keyword(s):  
Continuous Glucose Monitor ◽  
Interstitial Glucose

scholarly journals In a Free-Living Setting, Obesity Is Associated With Greater Food Intake in Response to a Similar Premeal Glucose Nadir

2019 ◽  
Vol 104 (9) ◽  
pp. 3911-3919
Author(s):  
Janice Kim ◽  
Wai Lam ◽  
Qinxin Wang ◽  
Lisa Parikh ◽  
Ahmed Elshafie ◽  
...  
Keyword(s):  
Food Intake ◽  
Hemoglobin A1c ◽  
Normal Weight ◽  
Free Living ◽  
Continuous Glucose Monitor ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Interstitial Glucose ◽  
Glucose Profiles ◽  
Low Glucose

Abstract Purpose Changes in blood glucose levels have been shown to influence eating in healthy individuals; however, less is known about effects of glucose on food intake in individuals who are obese (OB). The goal of this study was to determine the predictive effect of circulating glucose levels on eating in free-living OB and normal weight (NW) individuals. Methods Interstitial glucose levels, measured with a continuous glucose monitor (CGM) system, were obtained from 15 OB and 16 NW volunteers (age: 40 ± 14 and 37 ± 12 years; weight: 91 ± 13 and 68 ± 12 kg; hemoglobin A1c: 5.1% ± 0.7% and 5.2% ± 0.4%, respectively). While wearing the CGM, participants filled out a food log (mealtime, hunger rating, and amount of food). Glucose profiles were measured in relation to their meals [macro program (CGM peak and nadir analysis) using Microsoft® Excel]. Results OB and NW individuals showed comparable CGM glucose levels: mean [OB = 100 ± 8 mg/dL; NW = 99 ± 13 mg/dL; P = nonsignificant (NS)] and SD (OB = 18 ± 5 mg/dL, NW = 18 ± 4 mg/dL; P = NS). Obesity was associated with slower postprandial rate of changing glucose levels (P = 0.04). Preprandial nadir glucose levels predicted hunger and food intake in both groups (P < 0.0001), although hunger was associated with greater food intake in OB individuals than in NW individuals (P = 0.008 for group interaction). Conclusions Premeal glucose nadir predicted hunger and food intake in a group of free-living, healthy, nondiabetic NW and OB individuals; however for a similar low glucose level stimulus, hunger-induced food intake was greater in OB than NW individuals.

Improved Time-in-Range (TIR) on Continuous Glucose Monitor (CGM) with Technosphere Inhaled Insulin (TI) Compared with Insulin Aspart in T1D Patients—STAT Study

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1017-P ◽  
Author(s):  
JANET K. SNELL-BERGEON ◽  
HALIS K. AKTURK ◽  
AMANDA REWERS ◽  
BRUCE W. BODE ◽  
LESLIE J. KLAFF ◽  
...  
Keyword(s):  
Insulin Aspart ◽  
Continuous Glucose Monitor ◽  
Inhaled Insulin ◽  
Time In Range

A New Analysis Tool for Continuous Glucose Monitor Data

2021 ◽  
pp. 193229682110289
Author(s):  
Evan Olawsky ◽  
Yuan Zhang ◽  
Lynn E Eberly ◽  
Erika S Helgeson ◽  
Lisa S Chow
Keyword(s):  
Glucose Monitoring ◽  
Glycemic Variability ◽  
R Package ◽  
Analysis Tool ◽  
Continuous Glucose Monitor ◽  
Analysis Tools ◽  
R Shiny ◽  
Primary Driver ◽  
Rich Information ◽  
Web App

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

2021 ◽  
pp. 193229682110182
Author(s):  
Aaron P. Tucker ◽  
Arthur G. Erdman ◽  
Pamela J. Schreiner ◽  
Sisi Ma ◽  
Lisa S. Chow
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Patient Specific ◽  
Sensor Location ◽  
Effective Care ◽  
Freestyle Libre ◽  
Patients With Diabetes ◽  
Interstitial Glucose ◽  
Continuous Glucose Monitors ◽  
Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

scholarly journals Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Brinnae Bent ◽  
Peter J. Cho ◽  
Maria Henriquez ◽  
April Wittmann ◽  
Connie Thacker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Data Driven ◽  
Self Management ◽  
Noninvasive Method ◽  
Medical Interventions ◽  
Interstitial Glucose ◽  
Using Data ◽  
Glucose Prediction ◽  
Digital Biomarkers

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

Some pearl millet-based foods promote satiety or reduce glycaemic response in a crossover trial

2020 ◽  
pp. 1-31
Author(s):  
Anna M.R. Hayes ◽  
Fanny Gozzi ◽  
Aminata Diatta ◽  
Tom Gorissen ◽  
Clay Swackhamer ◽  
...  
Keyword(s):  
Gastric Emptying ◽  
Pearl Millet ◽  
Octanoic Acid ◽  
Crossover Trial ◽  
Normal Weight ◽  
Glycaemic Response ◽  
Continuous Glucose Monitor ◽  
Gastric Emptying Rate ◽  
White Rice ◽  
Traditionally Prepared

Abstract In a previous trial in Mali, we showed traditional pearl millet couscous and thick porridge delayed gastric emptying (~5 h half-emptying times) in a normal weight population compared to non-traditional carbohydrate-based foods (pasta, potatoes, white rice; ~3 h half-emptying times), and in a gastric simulator we showed millet couscous had slower digestion than wheat couscous. In light of these findings, we tested the hypothesis in a normal weight U.S. population (n=14) that millet foods would reduce glycaemic response (continuous glucose monitor), improve appetitive sensations (Visual Analog Scale ratings), as well as reduce gastric emptying rate (13C octanoic acid breath test). Five carbohydrate-based foods (millet couscous – commercial and self-made, millet thick porridge, wheat couscous, white rice) were fed in a crossover trial matched on available carbohydrate basis. Significantly lower overall glycaemic response was observed for all millet-based foods and wheat couscous compared to white rice (p≤0.05). Millet couscous (self-made) had significantly higher glycaemic response than millet couscous (commercial) and wheat couscous (p<0.0001), but as there were no differences in peak glucose values (p>0.05) an extended glycaemic response was indicated for self-made couscous. Millet couscous (self-made) had significantly lower hunger ratings (p<0.05) and higher fullness ratings (p<0.01) than white rice, millet thick porridge, and millet couscous (commercial). A normal gastric emptying rate (<3 h half-emptying times) was observed for all foods, with no significant differences among them (p>0.05). In conclusion, some traditionally prepared pearl millet foods show the potential to reduce glycaemic response and promote satiety.

Sign in / Sign up

Export Citation Format

Share Document