Continuous Glucose Monitoring Data Research Articles

An mHealth Text Messaging Program Providing Symptom Detection Training and Psychoeducation to Improve Hypoglycemia Self-Management: Intervention Development Study.

Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology. This study aims to develop a scalable, personalized mobile health (mHealth) behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology. We (a multidisciplinary team, including clinical and health psychologists, diabetes care and education specialists, endocrinologists, mHealth interventionists and computer engineers, qualitative researchers, and patient partners) jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data-connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed. The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients' own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period. A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program's efficacy for future clinical implementation.

Nocturnal Glucose Patterns with and without Hypoglycemia in People with Type 1 Diabetes Managed with Multiple Daily Insulin Injections.

Nocturnal hypoglycemia (NH) is a potentially dangerous and underestimated complication of insulin therapy. In this study, we aimed to determine which patterns of nocturnal glucose profiles are associated with NH in patients with type 1 diabetes (T1D) managed with multiple daily insulin injections. A dataset of continuous glucose monitoring (CGM) recordings obtained from 395 adult subjects with T1D was used for modeling. The clustering of CGM data was performed using a hierarchical clustering algorithm. Ten clusters without hypoglycemia and six clusters with NH episode(s) were identified. The differences among the clusters included initial and final glucose levels, glucose change during the night, and the presence of uptrends or downtrends. Post-midnight hyperglycemia was revealed in 5 out of 10 clusters without NH; in patterns with downtrends, initially elevated glucose prevented NH episodes. In clusters with initially near-normal glucose levels and downtrends, most episodes of NH were observed from midnight to 4 a.m.; if glucose was initially elevated, the episodes occurred at 2-4 a.m. or 4-6 a.m., depending on the time of the start of the downtrend. The results demonstrate the diversity of nocturnal glucose profiles in patients with T1D, which highlights the need for a differentiated approach to therapy adjustment.

Efficacy of the MiniMed™ 670G hybrid closed loop system in managing postprandial glucose excursion with high protein high fat foods in children and adolescents under free-living conditions

Objectives: High protein high fat (HPHF) meals are considered “difficult” foods because they can cause prolonged hyperglycemia after ingestion. The potential of hybrid closed loop therapy in managing postprandial glucose excursions with these difficult foods remains unknown. This pilot study aimed to explore the impact of manual mode in standard insulin pump therapy and auto mode with hybrid closed loop pump therapy in managing glucose excursions caused by HPHF foods and to obtain feedback from families about each mode. Material and Methods: Children and adolescents (8–18 years) with type 1 diabetes and using the MiniMed™ 670G were recruited to a free-living randomized cross-over study. Participants consumed a standardized lasagne or pizza meal two nights a week for 4 weeks while in auto mode and manual mode. Postprandial continuous glucose monitoring data were collected for 7 h post-meal. The primary outcomes were mean postprandial net incremental area under the glucose × time curve. User experiences were collected during end-of-study interviews administered to parents. Results: Postprandial excursions from 38 meals in seven participants were analyzed. There were no significant differences between auto mode and manual mode for the mean net incremental area under the glucose × time curve, irrespective of meal type. Semi-structured end-of-study interviews revealed that five of seven families felt more confident eating HPHF meals in auto mode. Conclusion: Although most families felt confident with auto mode for postprandial HPHF excursions, this was not reflected in the postprandial glucose levels.

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use.

Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days. Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2). For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use. We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

Understanding temporal changes and seasonal variations in glycemic trends using wearable data.

Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year's Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.

Real-World Glycemic Outcomes with Early Omnipod 5 Use in Youth with Type 1 Diabetes.

Background: Pivotal trials of diabetes technologies have demonstrated glycemic improvements; however, these trials include patients of limited diversity and ranges of glycemic control. We assessed changes in glycemic control during the first 90 days of Omnipod 5 use in a real-world cohort of youth with type 1 diabetes (T1D). Methods: Youth 2-21 years with T1D initiating Omnipod 5 at two pediatric academic centers were included. Fourteen days of baseline (BL) continuous glucose monitoring (CGM) data were compared against data from the first 90 days of Omnipod 5 use. Outcome measures included changes in time in range (TIR), hemoglobin A1c (HbA1c), and CGM and insulin pump metrics based on the duration of Omnipod 5 use. Results: Among 195 youth (78.9% non-Hispanic White, 15.4% publicly insured, age 11.7 years, T1D duration 3.3 years) TIR increased 11%-points, from 49% to 61% (P < 0.001), and HbA1c decreased 0.5%-points, from 7.5% to 6.9% (P < 0.001). TIR improved within the first 9 days of Omnipod 5 use (p < 0.001) and did not change significantly thereafter (P = 0.1) despite decreases in user-initiated boluses (5.1 vs. 5.0, P = 0.01) and carbohydrate entries (4.2 vs. 4.1, P = 0.005) from days 1-9 to days 1-90. TIR improved 15%-points among youth with BL TIR <60% compared to a 5%-point increase for youth with BL TIR ≥60% (P < 0.001). Conclusions: Glycemic control improved within 9 days of Omnipod 5 initiation in this real-world cohort, and improvements were sustained over the first 90 days of use despite concomitant decreases in user-initiated boluses. These improvements were comparable to those observed in the pivotal trial.

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis.

Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range. Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range. Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.

Safety Event Outcomes and Glycemic Control with a Hybrid Closed-Loop System Used by Chinese Adolescents and Adults with Type 1 Diabetes Mellitus.

Background: While evidence supports glycemic control benefits for individuals with type 1 diabetes mellitus (T1DM) using hybrid closed-loop (HCL) systems, HCL automated insulin delivery therapy in China has not been assessed. This study evaluated safety events and effectiveness during HCL system use by Chinese adolescents and adults with T1DM. Methods: Sixty-two participants (n = 12 adolescents with a mean ± standard deviation [SD] of 15.5 ± 1.1 years and n = 50 adults [mean ± SD of 37.6 ± 11.1 years]) with T1DM and baseline A1C of 7.1% ± 1.0% underwent a run-in period (∼2 weeks) using open-loop Manual Mode (sensor-augmented pump) insulin delivery with the MiniMed™ 770G system with the Guardian™ Sensor (3) glucose sensor, followed by a study period (4 weeks) with HCL Auto Mode enabled. Analyses compared continuous glucose monitoring data and insulin delivered during the run-in versus study period (Wilcoxon signed-rank test or t-test). Safety events included rates of severe hypoglycemia and diabetic ketoacidosis (DKA). Results: Compared to baseline run-in, overall Auto Mode use increased time in range (TIR, 70-180 mg/dL) from 75.3% to 80.9% (P < 0.001) and reduced time below range (TBR, <70 mg/dL) from 4.7% to 2.2% (P < 0.001). Subgroup analysis demonstrated that participants (n = 29) with baseline A1C <7.0% had TBR that reduced from 5.6% to 2.0%, while participants (n = 21) with baseline A1C ≥7.5% had time above range (TAR, >180 mg/dL) that reduced from 31.6% to 20.8%. Auto Mode use also increased the percentage achieving combined recommendations for time at sensor glucose ranges (i.e., TIR of >70%, TBR of <4% and TAR of <25%) from 24.2% at baseline to 77.4% at study end. Total daily insulin dose reduced from 42.8 ± 19.8 to 40.7 ± 18.9 U (P = 0.013). There were no severe hypoglycemic, DKA, or serious adverse events. Conclusions: Chinese adolescents and adults, some of whom met target A1C at baseline, safely achieved significantly improved glycemia with 1 month of MiniMed 770G system use when compared to open-loop insulin delivery. ClinicalTrials.gov ID: NCT04663295.

Real-life data of hypoglycemic events in children and adolescents with type 1 diabetes

IntroductionHypoglycemia composes an always present risk in the treatment of type 1 diabetes (T1D) and can be a fatal complication. Many studies on hypoglycemic events are based on self-reported data...

The Advanced Hybrid Closed Loop Improves Glycemia Risk Index, Continuous Glucose Monitoring Index, and Time in Range in Children with Type 1 Diabetes: Real-World Data from a Single Center Study.

Introduction: The Glycemia Risk Index (GRI) and Continuous Glucose Monitoring Index (COGI) are newly defined composite metric parameters derived from continuous glucose monitoring (CGM) data. GRI is divided into five separate risk zones (from lowest to highest: A-E). In this study, the effect of the advanced hybrid closed loop (AHCL) system on GRI and COGI in children with type 1 diabetes was evaluated. Materials and Methods: Forty-five children who had started using the AHCL and whose baseline and sixth-month CGM data were available were analyzed in terms of achievement of CGM consensus goals and changes in GRI scores and zones. The paired t-test was used for the analyses. Results: The mean age and duration of diabetes of the participants were 10.95 ± 3.41 and 3.85 ± 2.67 years, respectively. The mean GRI score significantly decreased from 35.66 ± 17.46 at baseline to 22.83 ± 9.08 at 6 months (P < 0.001). Although the proportion of those in the A zone was 20% at baseline, it increased to 42% at 6 months. AHCL also improved COGI from 72.59 ± 12.44 to 82.90 ± 7.72 (P < 0.001). Time in range (TIR) increased significantly from 70.54% to 80.51% (P < 0.001) at 6 months. Conclusion: AHCL provides not only an improvement in TIR but also a significant improvement in both GRI and COGI at 6 months. The incorporation of GRI and COGI alongside TIR may enhance the assessment of the glycemic profile by providing a more comprehensive and in-depth analysis.

Integration of Continuous Glucose Monitoring Data into an Electronic Health Record System: Single-Center Implementation.

Managing data from continuous glucose monitoring (CGM) systems presents challenges to health care provider teams that rely on the electronic health record (EHR) during patient visits. A method of integrating CGM data with the EHR that relies on the Dexcom API was developed by Northwestern Medicine and Dexcom to address these challenges. Here, we describe the data management steps and user interface of the integrated system. Providers can access patients' historical and latest daily CGM data in the form of modal day plots and stacked columns showing time in various glucose concentration ranges. The integration facilitates the acquisition, storage, analysis, and display of CGM data within an EHR system and may be appropriate for deployment in other health care facilities.

Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes.

The glycemia risk index (GRI) is a new composite metric derived from continuous glucose monitoring (CGM) data to assess the quality of glycemia. This study investigates the association between the GRI and albuminuria. Professional CGM and urinary albumin-to-creatinine ratio (UACR) data from 866 individuals with type 2 diabetes were retrospectively reviewed. Albuminuria and macroalbuminuria were defined as one or more UACR measurements ≥30 mg/g and ≥300 mg/g, respectively. The overall prevalence of albuminuria and macroalbuminuria was 36.6% and 13.9%, respectively. Participants with a higher UACR had a significantly higher hyperglycemia component and GRI score than those with a lower UACR (all p <0.001), although the hypoglycemia component did not differ among the groups. Multiple logistic regression analyses that adjusted for various factors affecting albuminuria revealed that the OR of albuminuria was 1.13 (95% CI: 1.02-1.27, p = 0.039) per increase in the GRI zone. The results were similar for the risk of macroalbuminuria (OR: 1.42 [95% CI: 1.20-1.69], p <0.001), and that association remained after adjusting for HbA1c (OR: 1.31 [95% CI: 1.10-1.58], p = 0.004). GRI is strongly associated with albuminuria, especially macroalbuminuria, in type 2 diabetes.

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals.

The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D. At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

Real-world Accuracy of CGM in Inpatient Critical and Noncritical Care Settings at a Safety-Net Hospital.

We sought to determine real-world accuracy of inpatient continuous glucose monitoring (CGM) at multiple levels of acuity in a large safety-net hospital. We analyzed records from hospitalized patients on Dexcom G6 CGM, including clinical, point of care (POC), and laboratory (Lab) glucose, and CGM data. POC/Lab values were matched to the closest timed CGM value. Encounters were divided into not critically ill (NCI) versus critically ill (CI). CGM accuracy was evaluated. Paired readings (2,744 POC-CGM; 3,705 Lab-CGM) were analyzed for 233 patients with 239 encounters (83 NCI, 156 CI). POC-CGM aggregated and average mean absolute relative differences (MARD) were 15.1% and 17.1%. Lab-CGM aggregated and average MARDs were 11.4% and 12.2%. Accuracy for POC-CGM and Lab-CGM was 96.5% and 99.1% in Clarke Error Grid zones A/B. Real-world accuracy of inpatient CGM is acceptable for NCI and CI patients. Further exploration of conditions associated with lower CGM accuracy in real-world settings is warranted.

The effects of early time restricted eating plus daily caloric restriction compared to daily caloric restriction alone on continuous glucose levels.

The median eating duration in the U.S. is 14.75h, spread throughout the period of wakefulness and ending before sleep. Food intake at an inappropriate circadian time may lead to adverse metabolic outcomes. Emerging literature suggests that time restricted eating (TRE) may improve glucose tolerance and insulin sensitivity. The aim was to compare 24-h glucose profiles and insulin sensitivity in participants after completing 12weeks of a behavioral weight loss intervention based on early TRE plus daily caloric restriction (E-TRE+DCR) or DCR alone. Eighty-one adults with overweight or obesity (age 18-50years, BMI 25-45kg/m2) were randomized to either E-TRE+DCR or DCR alone. Each participant wore a continuous glucose monitor (CGM) for 7days and insulin sensitivity was estimated using the homeostatic model assessment of insulin resistance (HOMA-IR) at Baseline and Week 12. Changes in CGM-derived measures and HOMA-IR from Baseline to Week 12 were assessed within and between groups using random intercept mixed models. Forty-four participants had valid CGM data at both time points, while 38 had valid glucose, insulin, HOMA-IR, and hemoglobin A1c (A1c) data at both timepoints. There were no significant differences in sex, age, BMI, or the percentage of participants with prediabetes between the groups (28% female, age 39.2±6.9years, BMI 33.8±5.7kg/m2, 16% with prediabetes). After adjusting for weight, there were no between-group differences in changes in overall average sensor glucose, standard deviation of glucose levels, the coefficient of variation of glucose levels, daytime or nighttime average sensor glucose, fasting glucose, insulin, HOMA-IR, or A1c. However, mean amplitude of glycemic excursions changed differently over time between the two groups, with a greater reduction found in the DCR as compared to E-TRE+DCR (p=0.03). There were no major differences between E-TRE+DCR and DCR groups in continuous glucose profiles or insulin sensitivity 12weeks after the intervention. Because the study sample included participants with normal baseline mean glucose profiles and insulin sensitivity, the ability to detect changes in these outcomes may have been limited.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c.

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Defining a Continuous Glucose Baseline to assess the impact of nutritional interventions.

Accurate and robust estimation of individuals' basal glucose level is a crucial measure in nutrition research but is typically estimated from one or more morning fasting samples. The use of Continuous Glucose Monitoring (CGM) devices presents an opportunity to define more robust basal glucose levels, which estimates can be generalized to any time of the day. However, to date, no standardized method has been delineated. The current paper seeks to define a reliable algorithm to characterize the individual's basal glucose level over 24 h from CGM measurements. Data drawn from four nutritional intervention studies performed on adults free from chronic diseases were used to define that basal glucose levels were optimally estimated using the 40th percentile of the previous 24 h CGM data. This simple algorithm provides a Continuous Glucose Baseline over 24 h (24 h-CGB) that is an unbiased and highly correlated estimator (r = 0.86, p-value < 0.01) of standard fasting glucose. We conclude that 24-CGB can provide reliable basal glucose estimates across the day while being more robust to interference than standard fasting glucose, adaptable to evolving daily routines and providing useful reference values for free-living nutritional intervention research in non-diabetic individuals.

Association between pre-exercise food ingestion timing and reactive hypoglycemia: Insights from a large database of continuous glucose monitoring data

ABSTRACT Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.