Continuous Glucose Monitoring Data Research Articles

HIT4HYPOS Continuous Glucose Monitoring Data Analysis: The Effects of High-Intensity Interval Training on Hypoglycemia in People With Type 1 Diabetes and Impaired Awareness of Hypoglycemia

Aims: To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH). Methods: Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH. Results: When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, THypo(L1) 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; P = .335, THypo(L2) 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; P = .146, HIIT + CGM versus CGM, respectively, rate ( EventPerWeekHypo 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, P = .340) or duration ( DurationHypo 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; P = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, THypo(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; P = .076; THypo(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; P = .006, HIIT + CGM versus CGM, respectively. Conclusions/interpretation: Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.

Improved Glycemic Control during a One-Week Adventure Camp in Adolescents with Type 1 Diabetes-The DIACAMP Study.

Adolescence remains a crucial age associated with diabetes distress in individuals living with type 1 diabetes (T1D). The Austrian organization "Diabär" regularly hosts a one-week adventure camp for adolescents (12-18 years) living with T1D. The camp focuses on "fun activities" without a structured educational protocol in order to minimize diabetes distress and increase diabetes management skills. In contrast to educational camps, training is kept to a minimum. However, attendees analyze the glycemic data of the previous day with their medical supervisor once daily during the camp. All subjects used a standardized real-time continuous glucose monitoring (CGM) system (DexcomG7) throughout the whole study. Glycemic metrics were prospectively analyzed during three periods: week 1 = home phase, week 2 = adventure camp, and week 3 = after the camp. Safety (time below range 1 [TBR1], 69-54 mg/dL, and time below range 2 [TBR2], <54 mg/dL) and efficacy (time in range [TIR], 70-180 mg/dL) were assessed by comparing the CGM data during weeks 1-3. The CGM data of 14 participants were analyzed. The TIR was higher during the camp week versus week 1 (70.4 ± 11.1% vs. 53.1 ± 20.2%; p = 0.001). The TBR1 significantly increased during camp compared to week 1 (2.5 ±1.7% vs. 1.3 ± 1.2%; p = 0.009), whereas the TBR2 did not differ. No serious adverse events occurred. This adventure camp without a main focus on education showed feasibility and safety in adolescents with T1D.

A system of ODEs for representing trends of CGM signals

Diabetes Mellitus is a metabolic disorder which may result in severe and potentially fatal complications if not well-treated and monitored. In this study, a quantitative analysis of the data collected using CGM (Continuous Glucose Monitoring) devices from eight subjects with type 2 diabetes in good metabolic control at the University Polyclinic Agostino Gemelli, Catholic University of the Sacred Heart, was carried out. In particular, a system of ordinary differential equations whose state variables are affected by a sequence of stochastic perturbations was proposed and used to extract more informative inferences from the patients’ data. For this work, Matlab and R programs were used to find the most appropriate values of the parameters (according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) for each patient. Fitting was carried out by Particle Swarm Optimization to minimize the ordinary least squares error between the observed CGM data and the data from the ODE model. Goodness of fit tests were made in order to assess which probability distribution was best suitable for representing the waiting times computed from the model parameters. Finally, both parametric and non-parametric density estimation of the frequency histograms associated with the variability of the glucose elimination rate from blood were conducted and their representative parameters assessed from the data. The results show that the chosen models succeed in capturing most of the glucose fluctuations for almost every patient.

SARS-CoV-2 booster vaccination does not worsen glycemia in people with type 1 diabetes using insulin pumps: an observational study.

Despite an increased risk for adverse outcomes from SARS-CoV-2 infection among individuals with type 1 diabetes (T1D), vaccine hesitancy persists due to safety concerns including dysglycemia. The impact of booster vaccination on individuals using automated insulin delivery (AID) systems remains unclear. We used continuous glucose monitoring (CGM) data from 53 individuals with T1D using insulin pump therapy who received their third and/or fourth COVID-19 vaccination. CGM data from the 14 days before and 3 and 7 days after each vaccination were compared. The primary outcome was glucose time in range (TIR) (70-180mg/dL) 3 and 7 days post-vaccination compared with the 14 days prior. Secondary outcomes included other CGM metrics such as time below range (< 70mg/dL), time above range (> 180mg/dL), mean glucose, co-efficient of variation and average total daily insulin. The cohort comprised 53 adults (64% women, 64% AID), totaling 74 vaccination periods (84% Pfizer-BioNTech boosters), mean ± SD age 40.0 ± 15.9 years, duration of diabetes 26.0 ± 15.4 years. There was no significant difference between pre-vaccination TIR (61.0%±18.5) versus 3 (60.5%±22.8) and 7 days post-vaccination (60.2%±21.8; p = 0.79). Level 1 hypoglycemia, time in range 54-69mg/dL, was lower 3 (1.1%±1.7) and 7 days post-vaccination (1.1%±1.6), compared with 14 days pre-vaccination (1.4%±1.4; p = 0.021). The study provides evidence that SARS-CoV-2 booster vaccination does not acutely worsen glycemia in people with T1D receiving insulin pump therapy.

From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data.

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes.

The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D). A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID. Secondary outcomes included time above/below range (TAR/TBR) and total daily insulin (TDI). Compared with PLGS, AID increased TIR on average from 63.2% to 72.6%, decreased TAR from 36.2% to 26.8%, and increased TDI from 70.2 to 76.3 U (all P < .001), without significant change to TBR. Glycemic improvements were more pronounced in those with worse glycemic control during PLGS use (P < .001). Real-life use of AID led to a rapid and sustained improvement of glycemic control in individuals with T2D.

Identifying Patterns in Long Term CGM and Insulin Data - An Explorative Study.

The increased utilization of continuous glucose monitors (CGM) and smart insulin pens (SIP) among people with type 2 diabetes generates significant health data. This study explored possible patterns in long term CGM and SIP data.

Discordance Between Mealtimes Reported by Trial Participants with Type 2 Diabetes and Healthcare Professionals.

Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients' CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient-reported meals. The results showed poor disagreement between HCP- and patient-reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study.

Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia.

Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature. Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks. Hypoglycaemic events were identified as episodes of consecutive CGM readings <70 mg/dl or <54 mg/dl for at least 15 minutes. Subsequently, the time <54 mg/dl (TB54), time below range (TBR), time in range (TIR), time above range (TAR), glucose coefficient of variation (CV), and incidence of hypoglycaemic events were calculated for diurnal and nocturnal periods. Furthermore, the effect of nocturnal hypoglycaemic events on glucose levels the following day was assessed. The incidence of hypoglycaemic events <70 mg/dl was significantly lower during the night compared to the day, with 0.8 and 3.8 events per week, respectively, while the TBR, TB54, and incidence of events with CGM readings <54 mg/dl was not significantly different. Nocturnal hypoglycaemic events <70 mg/dl were significantly longer (60 vs 35 minutes) and enveloped by less rapidly changing glucose levels. On days following nights containing hypoglycaemic events, there was a decrease in TAR, mean CGM glucose level and morning glucose levels and an increase in TB54, TBR, and CV. The results showed that nocturnal hypoglycaemic events are a common occurrence in persons with type 1 diabetes using MDI with significant differences between the characteristics of nocturnal and diurnal events.

How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions.

Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.

Accuracy of continuous glucose monitoring in the hospital setting: an observational study.

Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital. We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants' hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments. A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%). The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.

'We're taught green is good': Perspectives on time in range and time in tight range from youth with type 1 diabetes, and parents of youth with type 1 diabetes.

Continuous glucose monitoring (CGM) systems are standard of care for youth with type 1 diabetes with the goal of spending >70% time in range (TIR; 70-180 mg/dL, 3.9-10 mmol/L). We aimed to understand paediatric CGM user experiences with TIR metrics considering recent discussion of shifting to time in tight range (TITR; >50% time between 70 and 140 mg/dL, 3.9 and 7.8 mmol/L). Semi-structured interviews and focus groups with adolescents with type 1 diabetes and parents of youth with type 1 diabetes focused on experiences with TIR goals and reactions to TITR. Groups and interviews were audio-recorded, transcribed and analysed using content analysis. Thirty participants (N = 19 parents: age 43.6 ± 5.3 years, 79% female, 47% non-Hispanic White, 20 ± 5 months since child's diagnosis; N = 11 adolescents: age 15.3 ± 2 years, 55% female, 55% non-Hispanic White, 16 ± 3 months since diagnosis) attended. Participants had varying levels of understanding of TIR. Some developed personally preferred glucose ranges. Parents often aimed to surpass 70% TIR. Many described feelings of stress and disappointment when they did not meet a TIR goal. Concerns about TITR included increased stress and burden; risk of hypoglycaemia; and family conflict. Some participants said TITR would not change their daily lives; others said it would improve their diabetes management. Families requested care team support and a clear scientific rationale for TITR. The wealth of CGM data creates frequent opportunities for assessing diabetes management and carries implications for management burden. Input from people with type 1 diabetes and their families will be critical in considering a shift in glycaemic goals and targets.

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study.

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients. We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making. The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated. A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss. With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics. ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Early Dysglycemia Is Detectable Using Continuous Glucose Monitoring in Very Young Children at Risk of Type 1 Diabetes.

Continuous glucose monitoring (CGM) can detect early dysglycemia in older children and adults with presymptomatic type 1 diabetes (T1D) and predict risk of progression to clinical onset. However, CGM data for very young children at greatest risk of disease progression are lacking. This study aimed to investigate the use of CGM data measured in children being longitudinally observed in the Australian Environmental Determinants of Islet Autoimmunity (ENDIA) study from birth to age 10 years. Between January 2021 and June 2023, 31 ENDIA children with persistent multiple islet autoimmunity (PM Ab+) and 24 age-matched control children underwent CGM assessment alongside standard clinical monitoring. The CGM metrics of glucose SD (SDSGL), coefficient of variation (CEV), mean sensor glucose (SGL), and percentage of time >7.8 mmol/L (>140 mg/dL) were determined and examined for between-group differences. The mean (SD) ages of PM Ab+ and Ab- children were 4.4 (1.8) and 4.7 (1.9) years, respectively. Eighty-six percent of eligible PM Ab+ children consented to CGM wear, achieving a median (quartile 1 [Q1], Q3) sensor wear period of 12.5 (9.0, 15.0) days. PM Ab+ children had higher median (Q1, Q3) SDSGL (1.1 [0.9, 1.3] vs. 0.9 [0.8, 1.0] mmol/L; P < 0.001) and CEV (17.3% [16.0, 20.9] vs. 14.7% [12.9, 16.6]; P < 0.001). Percentage of time >7.8 mmol/L was greater in PM Ab+ children (median [Q1, Q3] 8.0% [4.4, 13.0] compared with 3.3% [1.4, 5.3] in Ab- children; P = 0.005). Mean SGL did not differ significantly between groups (P = 0.10). CGM is feasible and well tolerated in very young children at risk of T1D. Very young PM Ab+ children have increased SDSGL, CEV, and percentage of time >7.8 mmol/L, consistent with prior studies involving older participants.

Novel Glucose Metric "Latest Spike Time" Correlated with Weight Loss at Six Months in People with Obesity Using the Signos System.

The rise of digital health applications utilizing continuous glucose monitoring (CGM) allows for novel assessments of glucose management and weight changes in people without diabetes. The Signos System incorporates a digital health app paired with a CGM to provide information and prompts aimed to help people without diabetes to manage weight. The primary objective of this study was to determine whether the average timing of the latest chronological glucose excursion ("spike") was correlated with amount of weight loss. This was a retrospective analysis of prospectively obtained glucose and weight data from people without diabetes who enrolled in the Signos System from November 2021 to August 2023. Participants were provided CGMs as well as encouraged to use the Signos app with personalized advice and logging capabilities for weight, food, physical activity, heart rate, sleep, and activities. "Latest Spike Time" was retrospectively derived from CGM data and compared to weight changes at six months. Nine hundred and twenty-six subjects met the inclusion criteria including sufficient days wearing a CGM and a weight log within fifteen days of six months from their first weight log. There was a strong correlation between an earlier spike time and increased weight loss. The top quintile of subjects, with an average latest spike time before 5:41 PM, lost over three times as much weight as the bottom quintile of users, with latest spike time after 8:40 PM; this separation was predictable within one month of data. In a large population of obese people without diabetes, continuous glucose data, specifically a novel metric "Latest Spike Time," was highly correlated with percentage of total body weight loss at six months. This research suggests that for people attempting weight loss, review and alteration of behaviors relating to later glucose excursions may be of specific benefit.

Continuous Glucose Monitoring Prediction of Gestational Diabetes Mellitus and Perinatal Complications.

Objective: To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. Research Methods: In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. Results: The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. Conclusion: CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24-34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data.

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Glycaemic patterns during breastfeeding with postpartum use of closed-loop insulin delivery in women with type 1 diabetes.

This study aimed to describe the relationship between breastfeeding episodes and maternal glucose levels, and to assess whether this differs with closed-loop vs open-loop (sensor-augmented pump) insulin therapy. Infant-feeding diaries were collected at 6 weeks, 12 weeks and 24 weeks postpartum in a trial of postpartum closed-loop use in 18 women with type 1 diabetes. Continuous glucose monitoring (CGM) data were used to identify maternal glucose patterns within the 3 h of breastfeeding episodes. Generalised mixed models adjusted for breastfeeding episodes in the same woman, repeat breastfeeding episodes, carbohydrate intake, infant age at time of feeding and early pregnancy HbA1c. This was a secondary analysis of data collected during a randomised trial (ClinicalTrials.gov registration no. NCT04420728). CGM glucose remained above 3.9 mmol/l in the 3 h post-breastfeeding for 93% (397/427) of breastfeeding episodes. There was an overall decrease in glucose at nighttime within 3 h of breastfeeding (1.1 mmol l-1 h-1 decrease on average; p=0.009). A decrease in nighttime glucose was observed with open-loop therapy (1.2±0.5 mmol/l) but was blunted with closed-loop therapy (0.4±0.3 mmol/l; p<0.01, open-loop vs closed-loop). There is a small decrease in glucose after nighttime breastfeeding that usually does not result in maternal hypoglycaemia; this appears to be blunted with the use of closed-loop therapy.

Nocturnal Glucose Profile According to Timing of Dinner Rapid Insulin and Basal and Rapid Insulin Type: An Insulclock® Connected Insulin Cap-Based Real-World Study.

A study to assess the glucose levels of people with type 1 diabetes (T1D) overnight, based on the insulin type and timing. A real-world, retrospective study of T1D, using multiple daily insulin injections. Continuous glucose monitoring and insulin injection data were collected for ten hours after dinner using the Insulclock® connected cap. Meal events were identified using the ROC detection methodology. The timing of the rapid insulin, second injections, and the type of insulin analogs used, were evaluated. The nocturnal profiles (n = 775, 49 subjects) were analyzed. A higher glucose AUC of over 180 mg/dL was observed in subjects with delayed injections (number; %; mg/dL × h): -45-15 min (n = 136; 17.5%, 175.9 ± 271.0); -15-0 min (n = 231; 29.8%, 164.0 ± 2 37.1); 0 + 45 min (n = 408; 52.6%, 203.6 ± 260.9), (p = 0.049). The use of ultrarapid insulin (FiAsp®) (URI) vs. rapid insulin (RI) analogs was associated with less hypoglycemia events (7.1 vs. 13.6%; p = 0.005) and TBR70 (1.7 ± 6.9 vs. 4.6 ± 13.9%; p = 0.003). Users of glargine U300 vs. degludec had a higher TIR (70.7 vs. 58.5%) (adjusted R-squared: 0.22, p < 0.001). The use of a correction injection (n = 144, 18.6%) was associated with a higher number of hypoglycemia events (18.1 vs. 9.5%; p = 0.003), TBR70 (5.5 ± 14.2 vs. 3.0 ± 11.1%; p = 0.003), a glucose AUC of over 180 mg/dL (226.1 ± 257.8 vs. 178.0 ± 255.3 mg/dL × h; p = 0.001), and a lower TIR (56.0 ± 27.4 vs. 62.7 ± 29.6 mg/dL × h; p = 0.004). The dinner rapid insulin timing, insulin type, and the use of correction injections affect the nocturnal glucose profile in T1D.

Continuous Glucose Monitoring for Diabetes Management During Pregnancy: Evidence, Practical Tips, and Common Pitfalls.

Continuous glucose monitoring (CGM) has the potential to revolutionize diabetes management during pregnancy by providing detailed and real-time data to patients and clinicians, overcoming many of the limitations of self-monitoring of blood glucose. Although there are limited data on the role of CGM to improve pregnancy outcomes in patients with type 2 diabetes or gestational diabetes, CGM has been shown to reduce pregnancy complications in patients with type 1 diabetes. Despite the limited data in some populations, given its ease of use and recent U.S. Food and Drug Administration approval with expanding insurance coverage, CGM has gained widespread popularity among pregnant patients with all types of diabetes. It is critical for obstetric clinicians to understand how CGM can be successfully integrated into clinical practice. We present a practical, step-wise approach to CGM data interpretation that can be incorporated into diabetes management during pregnancy and common CGM pitfalls and solutions. Although technology will continue to advance with newer-generation CGM devices and diabetes technology such as automated insulin delivery (not covered here), these key principles form a basic foundation for understanding CGM technology and its utility for pregnant people.