Glycated haemoglobin (HbA1c) is currently the gold standard outcome measure for type 2 diabetes trials. Time in range is a continuous glucose monitoring (CGM) metric defined as the proportion of time in euglycemia (3.9-10.0 mmol/L) and may be valuable not only in type 1 diabetes clinical trials but also as an endpoint in type 2 diabetes trials. This narrative review aimed to assess the relative merits of time in range versus HbA1c as outcome measures for type 2 diabetes studies. It reviews the strengths and limitations of time in range as an outcome measure and evaluates studies in type 2 diabetes that have used time in range as a primary or secondary outcome measure. A literature search was conducted on PubMed and MEDLINE databases using key terms "time in range" AND "diabetes" OR "type 2 diabetes mellitus". Further evidence was obtained from relevant references of retrieved articles. Literature search identified 247 papers, of which 110 were included in this review. These included a broad range of articles, including 45 randomized trials using time in range as an outcome measure in patients with type 2 diabetes, as well as papers validating time in range. Time in range provides valuable and clinically relevant information and should be used as an important endpoint in type 2 diabetes in clinical trial settings, in conjunction with HbA1c.

In Soli-CGM, treatment with iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL) in insulin-naive adults with suboptimally controlled type 2 diabetes (T2D; haemoglobin A1c 9%-13% on ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapy) increased time in range (TIR; primary endpoint) from 26.4% at baseline to 52.7% at Week 16. This exploratory analysis examined the impact of treatment with iGlarLixi on patient-reported treatment satisfaction.

Soli-CGM was a single-arm, 16-week, multicentre, interventional, open-label, phase 4 study using blinded continuous glucose monitoring (CGM; FreeStyle Libre Pro) to assess glycaemic metrics (N = 124). CGM data were collected for a 2-week period before initiation of iGlarLixi, and after treatment with iGlarLixi (Weeks 14-16). Treatment satisfaction was assessed using the Diabetes Medication Treatment Satisfaction Tool (DM-SAT, which comprises four domains: well-being, medical control, lifestyle and convenience), at baseline and end-of-treatment. Association of TIR and overall satisfaction (sum of all items) was also assessed.

Overall, 118 (95.9%) and 107 (87.0%) participants completed the DM-SAT at baseline and Week 16, respectively. Mean overall score increased by 0.18, from 0.59 (baseline) to 0.78 (Week 16). A trend in improvement in score was observed in all domains. Improvement in TIR had a positive, but weak, trend of association with improvement in overall treatment satisfaction (mean r = 0.14).

In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RA, 16 weeks' treatment with iGlarLixi resulted in a trend of improvement in treatment satisfaction.

Connected insulin pens (CIPs) provide insulin dosing data that can be leveraged to drive improvements in glycaemic control. To realize this potential in routine care, insulin data need to be distilled into actionable insights for clinicians. We describe two algorithms for detecting glucose excursions using continuous glucose monitoring (CGM) data and then link excursions to CIP data to derive "insulin metrics" characterizing suboptimal bolus dosing practices.

This post hoc analysis used CGM and CIP data from a 12-week observational study (clinicaltrials.gov: NCT03368807) of 64 adults with type 1 or type 2 diabetes receiving multiple daily injection insulin therapy and glycated haemoglobin ≥8%. Two updated algorithms were applied to analyse glucose excursions associated with pre-meal boluses, missed bolus doses (MBDs), delayed boluses and correction boluses.

Glycaemic metrics obtained using both algorithms were similar. Time in range (%TIR) was lower, and time above range (%TAR) and glycaemic variability (%GV) were higher, on days with MBDs. Compared with pre-meal boluses, delayed and correction boluses were followed by glucose excursions with larger change in glucose, longer duration with glucose >180 mg/dL, lower post-excursion %TIR and higher post-excursion %TAR; excursions following MBDs showed lower %TIR, higher %TAR and lower percent recovery. Glucose excursions were larger and longer when CGM was masked than when unmasked.

This analysis demonstrates "insulin metrics"-characterizations of insulin dosing behaviour derived from integrated CGM and CIP data-and provides a foundation for future work that will expand the understanding of an individual's insulin self-administration practices and improve clinical decision-making.

Continuous glucose monitoring (CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states (MAE = 3.7 mg/dL). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task (AUROC = 0.914 for type 2 diabetes (T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics, CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling (Pearson correlation coefficient = 0.763) and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the early warning of T2D and recommendations for lifestyle modification in diabetes management.

To assess the efficacy and safety of fully closed-loop (FCL) compared with usual care (UC) glucose control in patients experiencing major abdominal surgery-related stress hyperglycemia.

Major abdominal surgery-related stress and periprocedural interventions predispose to perioperative hyperglycemia, both in diabetes and non-diabetes patients. Insulin corrects hyperglycemia effectively, but its safe use remains challenging.

In this two-centre randomized controlled trial, we contrasted subcutaneous FCL with UC glucose management in patients undergoing major abdominal surgery anticipated to experience prolonged hyperglycemia. FCL (CamAPS HX, Dexcom G6, mylife YpsoPump 1.5x) or UC treatment was used from hospital admission to discharge (max 20 d). Glucose control was assessed using continuous glucose monitoring (masked in the UC group). The primary outcome was the proportion of time with sensor glucose values in a target range of 5.6 to 10.0 mmol/L.

Thirty-seven surgical patients (54% pancreas, 22% liver, 19% upper gastrointestinal, 5% lower gastrointestinal), of whom 18 received FCL and 19 UC glucose management, were included in the analysis. The mean ± SD percentage time with sensor glucose in the target range was 80.1% ± 10.0% in the FCL and 53.7% ± 19.7% in the UC group ( P < 0.001). Mean glucose was 7.5 ± 0.5 mmol/L in the FCL and 9.1 ± 2.4 mmol/L in the UC group ( P = 0.015). Time in hypoglycemia (<3.0 mmol/L) was low in either group. No study-related serious adverse events occurred.

The FCL approach resulted in significantly better glycemic control compared with UC management, without increasing the risk of hypoglycemia. Automated glucose-responsive insulin delivery is a safe and effective strategy to minimize hyperglycemia in complex surgical populations.

To compare glycaemic outcomes for two automated insulin delivery (AID) systems, the Tandem Control IQ (CIQ) and the MiniMed 780G (MM780G).

In this observational study, we evaluated 60 days of glycaemic data from 139 persons with type 1 diabetes (CIQ: 79 persons, MM780G: 60 persons), who had an active glucose sensor time ≥ 85%.

The time with AID was median 620 (IQR, 439-755) days for CIQ users and 509 (429-744) days for MM780G users (p = 0.26). The last HbA1c before initiation of AID was 59.7 mmol/mol in CIQ and 60.1 mmol/mol in MM780G (p = 0.88). The time with an active glucose sensor was higher for CIQ than MM780G (median 98.5 (97.4-98.0)% vs. 96.5 (94.9-97.0)%, p < 0.001). Time in range (TIR, glucose 3.9-10.0 mmol/L) was lower in CIQ than MM780G (mean 68.9% ± 11.4% vs. 73.7% ± 12.0%, p = 0.02) as was time in tight range (TITR) (glucose 3.9-7.8 mmol/L) (43.0% ± 12.2% vs. 48.4% ± 12.7%, p = 0.01). The difference in TIR (4.2 (95% CI 1.0-7.5)%, p = 0.01) and TITR (5.0 (1.4-8.6)%, p < 0.01) remained statistically significant in a multiple regression model controlling for various baseline variables. Time with an absolute rate of glucose change > 1.5 mmol/L/15 min was higher in CIQ than MM780G (9.4 (IQR, 7.2-13.3)% vs. 7.4 (5.2-10.4)%, p < 0.001).

The CIQ system had a higher active glucose sensor time but a lower TIR, TITR, and a higher time with a rapid glucose rate of change than the MM780G system.

The first 26 weeks of the 2GO-CGM trial assessed the efficacy and safety of real-time continuous glucose monitoring (rtCGM) use within a supported specialist model of care in a cohort of community-based adults with insulin-requiring type 2 diabetes in New Zealand.

A 26-week randomised one-way crossover 'waitlist-controlled' trial comparing rtCGM (Dexcom G6) with self-monitoring of blood glucose (SMBG). All participants completed 2 weeks of SMBG before being randomised to 12 weeks (phase 1) use of SMBG followed by 12 weeks (phase 2) use of rtCGM (Group A) or 24 weeks of rtCGM (Group B). A time-adjusted within-subject analysis was conducted to estimate the overall treatment effect of rtCGM versus SMBG.

Sixty-seven participants were randomised to Group A or B, and all were included in the analysis (53% indigenous Māori, 57% female, median age 53 [range 16-69] years). Baseline-adjusted mean time in range (3.9-10.0 mmol/L) was 15% (95% CI 10-20; p = <0.001) higher with rtCGM use versus SMBG use. There was no evidence of a difference in Hba1c between rtCGM and SMBG use (-3.4 mmol/mol [0.31%], 95% CI -9.4 to 2.7 mmol/mol [-0.86 to 0.24%], p = 0.27). One participant withdrew in phase 2 due to unmanageable skin reactions to the CGM device. There were no severe hypoglycaemia or ketoacidosis events in either group during the study.

Use of rtCGM demonstrates safe and sustained glycaemic improvement in rtCGM use with insulin-requiring type 2 diabetes during the first 26 weeks of the 2GO-CGM study.

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Continuous glucose monitoring (CGM) has revolutionised diabetes care, with proven effect on glycaemic control, adverse diabetic events (such as hypoglycaemia and diabetic ketoacidosis) and hospitalisations in the general population. However, the evidence for CGM in older people is less robust.

We conducted a narrative review of trials reporting data comparing standard blood glucose monitoring (SBGM) and CGM in adults over 65 with type 1 or type 2 diabetes who were treated with insulin published between 1999 and 2024.

Seventeen studies were identified, including eight retrospective cohort studies and five randomised controlled trials (RCTs). Sixteen of the 17 papers were based in Europe or North America. The studies were highly heterogeneous; however, they provided clear evidence supporting the use of CGM in reducing hypoglycemia in older adults, with potential benefits for overall wellbeing and quality of life..

Current approaches to diabetes care in older adults may over-rely on HbA1c (haemoglobin A1c) as a measurement of control given accuracy may be reduced in older adults and propensity for hypoglycaemia. Although goals should be personalised, avoidance of hypoglycaemia is a key goal for many older people with diabetes. There is good evidence that CGM can improve time-in-range and reduce hypoglycaemia and glucose variability in older adults. CGM should be considered for older adults as a means of reducing hypoglycaemia and associated potential harm.

Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-based controllers offer a promising approach for improving automated insulin administration.

We propose a Dual Proximal Policy Optimization (Dual PPO) controller for personalized insulin delivery in a hybrid closed-loop system. The controller optimizes patient-specific insulin bounds through a grid search on pre-trained models to manage both hyperglycemia and hypoglycemia. A safe-control mechanism prevents insulin administration when glucose levels drop below a predefined threshold. The system was evaluated on 10 in silico adult patients using the UVA/Padova simulator, with five-day randomized meal scenarios.

The Dual PPO controller significantly improved Time in Range (TIR) (69.30% ±1.61) compared to a single PPO model (61.69% ±1.54). The system effectively reduced severe hyperglycemia while maintaining a low incidence of severe hypoglycemia. Unlike conventional open-loop methods such as Basal-Bolus (BBC) and Proportional-Integral-Derivative (PIDC) controllers, our system requires minimal patient interaction, eliminating the need for carbohydrate estimation.

The Dual PPO controller enhances personalized insulin delivery in T1DM, improving glycemic control while reducing patient burden. This approach advances precision medicine in diabetes management, with potential for future real-world applications.

The glycemia risk index (GRI) is an emerging metric designed to quantify the risk of both hypo- and hyperglycemia, providing a combined assessment of glycemic control quality. A high GRI is associated with an increased risk of diabetic complications. In this study, we leverage long-term continuous glucose monitoring (CGM) data to develop and validate predictive models for a high GRI (>60) in individuals with T1D. We assessed over 250 000 days of measurements collected over four years from 736 patients with type 1 diabetes. Our modeling approach shows promise for predicting glycemic control quality (area under the receiver operating characteristic curve [ROC-AUC] of 0.87) six to nine months from baseline. However, additional analysis and validation are imperative to determine its full clinical utility.

Time at high risk of hypoglycemia (THRH), 3.9 to 5.6 mmol/L, is a continuous glucose monitoring (CGM)-based metric recommended for reporting in hospitalized patients. This study aims to validate THRH as a predictor of hypoglycemia.

The CGM data from 166 non-intensive care unit (non-ICU) inpatients with type 2 diabetes from the DIATEC trial were analyzed. All participants received basal-bolus insulin therapy. Of these, 82 were monitored with point-of-care glucose testing and blinded CGM, while 84 had open CGM. Linear and negative binomial regression analyses assessed the relationship between THRH and time below range (TBR) (<3.0 mmol/L, 3.0-3.9 mmol/L, and <3.9 mmol/L) and hypoglycemic events. Analyses were conducted for day (07:00-23:00), night (23:01-06:59), and 24-hour periods.

For CGM-monitored patients, every 10%-point increase in THRH was associated with a 0.13%-point increase in TBR (<3.0 mmol/L) (95% confidence interval [CI] = 0.06-0.21), 0.66%-point increase in TBR (3.0-3.9 mmol/L) (95% CI = 0.47-0.86), and 0.74%-point increase in TBR (<3.9 mmol/L) (95% CI = 0.51-0.97), all P < .001. A THRH threshold below 50% was linked to a TBR <3.9 mmol/L of less than 4%, as recommended. Similar results were observed during both day and night analyses and for point-of-care monitored patients, also for hypoglycemic events.

The THRH is strongly associated with hypoglycemia in non-ICU hospitalized patients with type 2 diabetes on basal-bolus insulin. Aiming for THRH below 50% aligns with the recommended TBR target of <3.9 mmol/L below 4%, supporting THRH's role in guiding hypoglycemia prevention strategies.

Data on culturally tailored diabetes education with and without real-time continuous glucose monitoring (RT-CGM) in Latinos with type 2 diabetes, who are not on intensive insulin management, is lacking.

This is an open-label randomized control trial of Latinos with uncontrolled (HbA1c > 8.0%) type 2 diabetes conducted in a Federally Qualified Health Center (FQHC). All participants received 12 one-hour culturally tailored education sessions. Patients were randomized (1:1) to education sessions only (blinded CGM) or cyclic (50 days wear: 10 days on, 7 days off) RT-CGM. The primary outcome was a change in HbA1c from baseline to 12 weeks in those with or without CGM. Secondary outcomes included 24-week HbA1c, CGM, and metabolic parameters.

Participants (n = 120) were 46 years old on average, 44% female, 98% preferred Spanish language, 30% with income <$25,000, 68% uninsured and 26% using basal insulin only. Mean 1-hour session attendance and RT-CGM wear was 7.0 (±4.4) and 27.9 (±20.5) days, respectively. Mean baseline HbA1c was 10.5% (±1.8). HbA1c reduced by 1.9% (95% confidence interval [CI]: 1.5-2.3) overall (P < .001). Participants in the RT-CGM group reduced HbA1c at 12 weeks by 2.3% (95% CI: 1.5-3.2) compared to 1.5% (95% CI: 0.6-2.3) in the blinded CGM group (P =.04). At 24 weeks, overall HbA1c reduction was maintained but between-group differences attenuated.

In a Latino type 2 diabetes population that was primarily noninsulin-requiring, virtually delivered, culturally tailored education improved HbA1c, with RT-CGM conferring greater improvement. RT-CGM should be an adjunctive therapy to diabetes education, irrespective of insulin use but continued cyclic CGM use may be needed for sustained effect.

Elevated glycemic variability (GV) is commonly observed in intensive care unit (ICU) patients and has been associated with clinical outcomes. However, the relationship between GV and prognosis in ICU patients with hemorrhagic stroke (HS) remains unclear. This study aims to investigate the association between GV and short- and long-term all-cause mortality.

Clinical data for hemorrhagic stroke (HS) patients were obtained from the MIMIC-IV 3.1 database. GV was quantified using the coefficient of variation (CV), calculated as the ratio of the standard deviation to the mean blood glucose level. The association between GV and clinical outcomes was analyzed using Cox proportional hazards regression models. Additionally, restricted cubic spline (RCS) curves were employed to examine the nonlinear relationship between GV and short- and long-term all-cause mortality.

A total of 2,240 ICU patients with HS were included in this study. In fully adjusted models, RCS analyses revealed a U-shaped association between the CV and both short- and long-term all-cause mortality (P for nonlinearity < 0.001 for all outcomes). Two-piecewise Cox regression models were subsequently applied to identify CV thresholds. The thresholds for all-cause mortality in ICU, during hospitalization, and at 30, 90, and 180 days were determined to be 0.14, 0.16, 0.155, 0.14, and 0.14, respectively. These findings were consistent in sensitivity and subgroup analyses.

In HS patients, higher GV is associated with an increased risk of both short- and long-term all-cause mortality. Our findings suggest that stabilizing GV may improve the prognosis of HS patients.

Individuals with diabetes frequently encounter sleep disturbances, which can detrimentally impact glycaemic management. We reviewed the relationship between sleep outcomes and glycaemic variability in adults with diabetes.

We systematically searched Medline, EMBASE and Cochrane Library (2002-March 2023) for studies evaluating sleep and glycaemic variability in adults with type 1 and type 2 diabetes. Among the 3049 records, 27 met the inclusion criteria (type 1 diabetes studies = 22). Due to methodological heterogeneity, a qualitative analysis was conducted.

Most studies measuring sleep quality (5 out 7; 71%) reported a significant association with glycaemic variability in type 1 and type 2 diabetes. Sleep duration was not significantly associated with glycaemic variability in type 1 diabetes, whereas other sleep metrics yielded inconclusive results. Hybrid closed-loop pump interventions (n = 12) demonstrated varying sleep outcomes with improved glycaemic variability. Similarly, sleep interventions (n = 3) consistently enhanced sleep but not glycaemic variability. Limitations included moderate to high risk of study bias, confounders, methodological heterogeneity and limited type 2 diabetes data.

A potential association between sleep quality and glycaemic variability exists. However, associations with other sleep metrics remain elusive, with no discernible association between sleep duration and glycaemic variability in type 1 diabetes. Despite advancements in continuous glucose monitoring and ambulatory sleep monitoring, standardised sleep assessment methodologies are lacking in real-world studies. Establishing standard protocols for sleep assessment and defining optimal sleep targets are crucial for meaningful comparisons between studies. Understanding the complex interplay between sleep and glycaemic variability holds promise in improving diabetes management and sleep health.

Currently, there is a lack of evidence regarding time in tight range (TITR) and long-term adverse outcomes. We aimed to investigate the association between TITR and the risk of all-cause and cardiovascular mortality among patients with type 2 diabetes.

A total of 6061 patients with type 2 diabetes were prospectively recruited in a single centre. TITR was measured with continuous glucose monitoring (CGM) at baseline and was defined as the percentage of time in the target glucose range of 3.9-7.8 mmol/L (70-140 mg/dL) during a 24-h period. Cox proportion hazard regression models were used to examine the association between TITR and the risk of all-cause and cardiovascular mortality.

During a median follow-up period of 10.9 years, 1898 (31.3%) death events were confirmed, with 689 (11.4%) due to cardiovascular mortality. The restricted cubic spline revealed significant linear relationships between lower TITR and higher risks of all-cause and cardiovascular mortality (p for linearity <0.01). In the fully adjusted model including glycated haemoglobin A1c, each 10% decrease in TITR was associated with 4% (95% confidence interval, 1.01-1.06) increased risk of all-cause mortality and 4% (95% confidence interval, 1.00-1.08) increased risk of cardiovascular mortality. Subgroup analyses showed that the linear relationship between TITR and all-cause mortality risk was sustained in patients with haemoglobin A1c <7.0% and patients with fasting plasma glucose <7.0 mmol/L.

Lower TITR is associated with an increased risk of all-cause and cardiovascular mortality in patients with type 2 diabetes, indicating that tight glycaemic control within the physiological range may be crucial for reducing long-term mortality risk, especially in those with seemingly well-controlled diabetes.

Type 1 and type 2 diabetes are metabolic disorders that require one to manage one's blood glucose levels on a daily basis through a series of behaviorally complex tasks. Research shows that psychosocial factors, including mood, stress, and social relationships, have a significant influence on one's ability to maintain these disease management routines and achieve healthy blood glucose levels. However, researchers have typically approached these questions from a between-person perspective. Here, we argue for greater consideration of short-term, within-person links of psychosocial factors-including mood, stress, and social interactions-to glucose outcomes. Drawing from existing social and health psychology theories, we put forth an organizing theoretical framework describing how psychosocial experiences may operate on glucose outcomes over subsequent hours. We then review the small but burgeoning literature of intensive longitudinal studies that have examined the short-term effects of negative affect, positive affect, stress, and social interactions on glucose outcomes. Findings showed somewhat stronger links for negative affect and stress compared to positive affect and social interactions, but studies varied greatly in their methodologies, making direct comparisons challenging. A number of findings, particularly in the social interaction literature, depended on dispositional or contextual factors, further complicating interpretation. There was little investigation of the mechanistic pathways that may connect psychosocial factors to glucose outcomes, and few studies conducted lagged analyses to probe the directionality of these links. We conclude by proposing best practices for future research that will address the key weaknesses in the extant literature.

Growing evidence suggests the exercise timing, time-of-day it is performed, is important for maximizing glycemic benefits in type 2 diabetes (T2D). This randomized controlled trial investigated the impact of utilizing continuous glucose monitoring to personalise exercise timing on peak hyperglycaemia and cardiometabolic health in people with T2D.

Adults with T2D (HbA1c: 7.2 ± 0.8 %; Age: 63 ± 12 y; BMI: 29 ± 5 kg/m2) were randomized to eight weeks: i) waitlist control (CTL, eight week CTL then re-randomized to interventions), ii) 22-min daily exercise beginning ∼ 30 min before peak hyperglycemia (ExPeak) or iii) 22-min daily exercise ∼ 90 min after peak hyperglycemia (NonPeak). Time of peak hyperglycemia was pre-determined for each participant using the median of a 14-d habitual continuous glucose monitoring (CGM) period. Glycemic control (HbA1c [primary outcome], CGM), vascular function (flow-mediated dilation [FMD]), arterial stiffness, blood pressure) and body composition were assessed. Linear mixed models compared changes across time between groups.

There was no intervention effect for HbA1c, however there was a significant interaction for changes in 24-h peak glucose and %FMD between groups. Compared to CTL, both intervention groups significantly lowered peak glucose (ExPeak: 95 %CI: -2.0 to -0.3 mmol/L, NonPeak: CI: -2.3 to -0.6 mmol/L) and %FMD increased (ExPeak: 95 %CI: 0.6 to 1.5 %, NonPeak: 95 %CI: 0.0 to 1.1 %). Adherence to interventions was high for both intervention groups (>90 %).

Prescribing exercise to target peak hyperglycemia did not improve HbA1c; however cardiometabolic health outcomes improved in both groups prescribed an exercise time compared to control. Personalizing exercise prescription by prescribing a time to exercise may be a novel approach to improve health outcomes and physical activity participation.

Underestimating hyper-/hypoglycemia or failure to perceive hyperglycemia hinders optimal glucose management in diabetes care. Our study investigated individuals who, while aware of their hyper-/hypoglycemia, may not perceive them as problematic. Also, we clarified the factors contributing to discrepancies between these individuals' perceptions and the objective measurements.

This study was a prospective observational study comprising 284 Japanese individuals with type 2 diabetes who underwent ambulatory blinded professional continuous glucose monitoring (CGM) and self-administered the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Individuals with a time above range (TAR; > 180 mg/dL) ≥ 25% and those who answered 0 ("never") or + 1 ("almost never") for the frequency of hyperglycemia in the DTSQ were defined as having no-perception of hyperglycemia. Individuals with a time below range (TBR; < 70 mg/dL) ≥ 4% with an answer of 0 or + 1 for the frequency of hypoglycemia were labeled as having no-perception of hypoglycemia. Multivariate logistic regression analysis was performed to analyze clinical characteristics associated with the discrepancies between failure to perceive hyper-/hypoglycemia and TAR ≥ 25% or TBR ≥ 4%.

Insulin-use (odds ratio [OR] = 0.29, p < 0.05) and older age (OR = 1.05, p < 0.05) were independent determinants of no-perception of hyperglycemia. Low eGFR was an independent determinant of no-perception of hypoglycemia (OR = 0.94, p < 0.05).

No-insulin-use, being an older adult, and renal dysfunction are linked to the discrepancy between the perception of hyper-/hypoglycemia and actual blood glucose. These results will help create personalized diabetes care.

A low 1-hour glucose challenge test (GCT) result (<10th percentile for population) has been associated with neonatal morbidity, including small-for-gestational-age birth weight, and it is hypothesized that underlying maternal hypoglycemia may contribute to this neonatal morbidity. We sought to assess whether eligible patients would undergo continuous glucose monitoring to allow comparison of maternal hypoglycemia between those with a low GCT result versus controls.This exploratory study enrolled patients who completed a GCT between 24 and 30 weeks' gestation from June to September 2022. English- or Spanish-speaking participants aged ≥18 years wore a blinded continuous glucose monitor (CGM) for 10 days. There were 10 participants each in the low GCT (<82 mg/dL) and normal GCT group. Proportions were calculated to determine recruitment rates and describe the low versus normal glycemic groups across clinical and sociodemographic characteristics. Maternal hypoglycemia, defined using various proposed thresholds, was analyzed as continuous data (time duration) with Student's t-tests and categorical data (number of episodes) with chi-square tests and bivariate analyses were performed comparing participants with a low versus normal GCT. Primary outcome measures were recruitment, enrollment, and adherence rates, and overall glycemic values for each group.Of 64 eligible patients, 58 (91%) were approached, and of them, 20 (35%) were enrolled. All 20 participants had CGM data to review with 100% adherence. Average glucose values were similar between participants in the low GCT and normal GCT groups (111.7 ± 18.0 vs. 111.6 ± 11.7 mg/dL, p = 0.99), and participants with a low GCT value did not demonstrate more hypoglycemia than those with a normal GCT value across five proposed thresholds on CGM analysis.In this pilot study, participants wore blinded CGMs to collect glycemic data, and those with a low GCT result did not experience more hypoglycemia than those with a normal GCT on CGM analysis. · Study participants wore continuous glucose monitors in blinded mode to gather glycemic data with 100% adherence.. · Participants with a low GCT result (<82 mg/dL) as compared with those with a normal GCT result were not more likely to demonstrate maternal hypoglycemia using several thresholds on CGM analysis.. · In our cohort, there were few participants in either glycemic group who reported food insecurity or lived in a food desert..

People with type 2 diabetes (T2D) and glycated haemoglobin (HbA1c) ≥9% may benefit from fixed-ratio combination therapies such as iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL). Use of continuous glucose monitoring (CGM) is recommended, but data are lacking to assess the impact of iGlarLixi in individuals with HbA1c ≥9%.

Soli-CGM (NCT05114590) was a 16-week, multicentre, open-label study evaluating the efficacy of once-daily iGlarLixi using blinded CGM-based metrics in insulin-naive adults with HbA1c ≥9%-13% who were receiving ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The primary outcome was the change from baseline to week 16 in percent time in range (TIR; 70-180 mg/dL). Secondary outcomes included change in mean daily blood glucose (BG), maximum postprandial glucose 4 h post-breakfast (PPG-4 h), and time above range (TAR; >180 mg/dL). On-treatment hypoglycaemia was assessed.

The study enrolled 124 participants (mean age, 55.6 years; HbA1c, 10.2%). Sixteen weeks of treatment with iGlarLixi improved TIR (+26.2%), mean BG (-52.5 mg/dL), maximum PPG-4 h (-73.7 mg/dL), and TAR (-28.7%); all p < 0.001. Rates of American Diabetes Association level 1 (BG <70 but ≥54 mg/dL) and level 2 (BG <54 mg/dL) hypoglycaemia were reported as 1.4 and 0.6 events per person-year, respectively. No level 3 events (requiring assistance) were reported.

In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RAs, 16 weeks of treatment with iGlarLixi significantly improved TIR and reduced TAR without severe hypoglycaemia.

Objective: To compare glycemic outcomes during and following moderate-intensity exercise (MIE), high-intensity interval exercise (HIE), and resistance exercise (RE) in adolescents with type 1 diabetes (T1D) using a hybrid closed-loop (HCL) insulin pump while measuring additional physiological signals associated with activity. Methods: Twenty-eight adolescents (average age 16.3 ± 2.1 years, 50% females, average duration of T1D 9.4 ± 4 years) using HCL (Medtronic MiniMed 670G) undertook 40 min of MIE, HIE, and RE. A temporary glucose target (8.3 mmol/L, 150 mg/dL) was set for 2 h prior and during exercise. Heart rate, accelerometer, venous glucose, lactate, ketones, and counter-regulatory hormones were measured for 280 min postexercise commencement. The primary outcome was glucose percentage time in range (TIR): 3.9-10 mmol/L (70-180 mg/dL) for 14 h from exercise onset. Results: Median (interquartile range) TIR for HIE was 88 (78, 96)%, MIE 79 (63, 88)%, and RE 86 (72, 95)% for 14 h from exercise onset. For MIE compared with HIE, TIR was lower (P = 0.012) and time above range (TAR) was greater (18 [2.4, 28] vs. 6.9 [0.0, 14]%, P = 0.041). Hypoglycemia occurred in 13 (46%), 11 (39%), and 14 (50%) of participants for HIE, MIE, and RE, respectively, the majority following the meal after exercise. There were higher levels of lactate (P = 0.001), growth hormone (P = 0.001), noradrenaline (P = 0.001), and heart rate (P = 0.01) during HIE and RE compared with MIE. Conclusions: HCL use in adolescents with T1D results in excellent TIR during different forms of exercise when a temporary target is set 2 h before. Extending the temporary target after exercise may also be needed to help minimize postexercise hypoglycemia.

Higher glucose variability is linked to cognitive impairment in older adults with type 2 diabetes. While physical activity can reduce glucose variability and improve cognitive function, these relationships remain unexplored using continuous glucose monitoring. This study examined associations between physical activity, glucose variability, and cognitive function through secondary data analysis of 87 older adults with type 2 diabetes using self-reported questionnaires, computerized cognitive assessments, and continuous glucose monitoring data. Subgroup analysis showed that physical activity was associated with better cognitive function in individuals with lower cognitive function but not in those with higher cognitive function. This suggests that the effects of physical activity may vary depending on cognitive status. Future research should incorporate objective physical activity measures and longer-duration continuous glucose monitoring to explore how activity intensity, type, and timing influence glucose variability and cognitive outcomes, informing targeted interventions for this population.

Background/Objectives: International guidelines recommend that all children and adolescents with type 1 diabetes (T1D) receive education on the glycaemic impact of fat and protein from diagnosis. In addition, the insulin strategy should be adjusted to compensate for fat and protein excursions. Data from continuous glucose monitoring (CGM) can guide insulin adjustment. This study sought to determine whether the current practices of dietitians in Australia and New Zealand align with guidelines. Methods: An anonymous, online survey of paediatric T1D dietitians working in tertiary centres (n = 20; Australia, n = 14, New Zealand, n = 6) was undertaken from February to March 2023. The Australian and New Zealand Society for Paediatric Endocrinology and Diabetes (ANZSPED) disseminated the survey link. The questionnaire covered three content domains: demographic information about the clinic and practitioner, the health professionals' education practices regarding fat and protein, and the use of CGM. Results: This pilot study had a 100% response rate, with a dietitian representative from all eligible centres responding on behalf of the diabetes team. Only 10% (n = 2) of respondents both (i) provided education on the glycaemic impact of fat and protein to all families at diagnosis and (ii) always provided insulin strategies to manage fat and protein where it impacted glycemia, as per guidelines. Barriers to education included a lack of procedure (47%, n = 7), consumer resources (40%, n = 6), and time (33%, n = 5). Reasons for not recommending strategies to manage fat and protein were perceptions that the family was overwhelmed (100%, n = 10) or not interested (60%, n = 6), and uncertainty of the best strategy (40%, n = 4). CGM was used by "almost all" respondents to educate and adjust the insulin strategy (90%, n = 18). Conclusions: Most dietitians surveyed were not consistently providing fat and protein education and management strategies to children with T1D in line with guidelines. CGM is a key tool routinely used by dietitians in nutrition education to help guide insulin adjustment. Dietitians need greater support through educational resources for families and training in evidence-based strategies to manage deglycation from dietary fat and protein to align with guidelines.

Introduction: Continuous glucose monitoring (CGM) is unaffordable in sub-Saharan Africa, and providers rely heavily on hemoglobin A1c (A1c) to guide insulin adjustment. The relationship between A1c and mean glucose (MG) varies between individuals and populations. We assessed this relationship in Ugandan youth of age 4-26 years with type 1 diabetes, and evaluated whether calculation of the personalized A1c (pA1c), which only requires a brief initial sensor wear, is clinically useful. Materials and Methods: CGM data were averaged across three blinded sensor wears (31-41 days). We calculated individual apparent glycation ratios using A1c after the second sensor, and applied these to A1cs collected after the third sensor to determine pA1c. Participants were evaluated for clinical factors that influence red blood cell (RBC) lifespan (malaria, G6PD deficiency, sickle-cell trait, hemolysis, iron deficiency). Results: Patients across the A1c spectrum experienced substantial time in both hyper- and hypoglycemia; average coefficient of variation was 44%. MG was >250 mg/dL (13.9 mmol/L) in 50% of participants, and 55% of participants spent ≥4% time with glucose <70 mg/dL (3.9 mmol/L). There was considerable variability in the A1c-MG relationship. The pA1c more accurately represented MG by significantly reducing variation in this relationship (R2 = 0.84 vs. 0.40; r = 0.92 vs. 0.63), but MG is not useful in individuals with the wide glucose fluctuations seen in this population. Clinical factors did not impact the A1c-MG relationship. Conclusions: Neither the measured A1c nor the calculated pA1c provided reliable guidance for insulin adjustment in this population. No matter how accurately MG is measured or estimated, it is just an average, with limited clinical application in individuals with wide glycemic variation. These measures cannot replace the information available from CGM about glycemic excursion, daily glucose patterns, or percent time in various glucose ranges. Our data suggest that it is essential to find a way to make CGM at least periodically affordable in low-resource settings.

Objective: Evaluate the adequacy of the coefficient of variation (CV) and standard deviation (SD) as metrics of glucose variability (GV) across mean glucose (MG) levels in individuals with type 1 diabetes. Methods: Data from the GOLD and SILVER trials were analyzed. Glucose metrics were derived from continuous glucose monitoring (CGM). Generalized estimating equations were used to assess the relationship between SD and MG, considering intraindividual correlations. Nonlinear associations were evaluated using restricted cubic splines, and glucose values outside the CGM detection range (<2.22 mmol/L and >22.2 mmol/L) were handled using a censored Gamma model. Results: In total, 158 individuals with an MG of 10.6 (SD 1.7) mmol/L were included. The SD of glucose values exhibited a nonlinear relationship with the MG during CGM and self-monitoring of blood glucose (SMBG) (both P < 0.001 vs. linear model). The lack of fit of the constant CV model was most distinct at high glucose levels >12 mmol/L. During SMBG, a 25% reduction in MG from 12 to 9 mmol/L was associated with a 16% (95% confidence interval [CI] 10%-21%) reduction in the SD of glucose values. Similar associations were observed during CGM. This deviation was attributed to the censoring of glucose values outside the detection range. After adjusting for censoring, the lack of fit was resolved. When transitioning from SMBG to CGM, the ordinary CV and SD underestimated the treatment effect on GV by 30% and 27%, respectively, compared to estimates adjusted for censoring. Similarly, ordinary CV underestimated the treatment effect by 11% compared with CV adjusted for the nonlinear SD-MG relationship in the GOLD study. Conclusion: The SD of glucose values does not increase linearly with the MG during glucose-lowering therapy, suggesting that CV is not an optimal measure of GV. After adjusting for censored glucose values, CV remains reliable. Alternatively, nonlinear SD adjustments relative to MG effectively evaluate glucose-lowering therapies' impact on GV.

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

Continuous glucose monitoring (CGM) metrics, such as time in range (TIR) and glycemic risk index (GRI), have been linked to various diabetes-related complications, including diabetic foot (DF).

To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus (T2DM).

A total of 591 individuals with T2DM (297 with DF and 294 without DF) were enrolled. Relevant clinical data, complications, comorbidities, hematological parameters, and 72-hour CGM data were collected. Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.

Individuals with DF exhibited higher mean blood glucose (MBG) levels and increased proportions of time above range (TAR), TAR level 1, and TAR level 2, but lower TIR (all P < 0.001). Patients with DF had significantly lower rates of achieving target ranges for TIR, TAR, and TAR level 2 than those without DF (all P < 0.05). Logistic regression analysis revealed that GRI, MBG, and TAR level 1 were positively associated with DF risk, while TIR was inversely correlated (all P < 0.05). Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels (P < 0.05). Additionally, achieving TAR was influenced by fasting plasma glucose, body mass index, diabetes duration, and antidiabetic medication use.

CGM metrics, particularly TIR and GRI, are significantly associated with the risk of DF in T2DM, emphasizing the importance of improved glucose control.

Clinical use of continuous glucose monitoring (CGM) is increasing storage of CGM-related documents in electronic health records (EHR); however, the standardization of CGM storage is lacking. We aimed to evaluate the sensitivity and specificity of CGM Ambulatory Glucose Profile (AGP) classification criteria.

We randomly chose 2244 (18.1%) documents from NYU Langone Health. Our document classification algorithm: (1) separated multiple-page documents into a single-page image; (2) rotated all pages into an upright orientation; (3) determined types of devices using optical character recognition; and (4) tested for the presence of particular keywords in the text. Two experts in using CGM for research and clinical practice conducted an independent manual review of 62 (2.8%) reports. We calculated sensitivity (correct classification of CGM AGP report) and specificity (correct classification of non-CGM report) by comparing the classification algorithm against manual review.

Among 2244 documents, 1040 (46.5%) were classified as CGM AGP reports (43.3% FreeStyle Libre and 56.7% Dexcom), 1170 (52.1%) non-CGM reports (eg, progress notes, CGM request forms, or physician letters), and 34 (1.5%) uncertain documents. The agreement for the evaluation of the documents between the two experts was 100% for sensitivity and 98.4% for specificity. When comparing the classification result between the algorithm and manual review, the sensitivity and specificity were 95.0% and 91.7%.

Nearly half of CGM-related documents were AGP reports, which are useful for clinical practice and diabetes research; however, the remaining half are other clinical documents. Future work needs to standardize the storage of CGM-related documents in the EHR.

Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.

This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).

Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: -4.6%, 95% confidence interval [CI]: -9.1, -0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: -1.7% [95% CI: -2.9, -0.5], P < .05) and level 2 (<54 mg/dL) (ETD: -1.3% [95% CI: -2.1, -0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: -3.4% [95% CI: -5.6, -1.2], P < .05).

For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.

The effects of postprandial resistance and combined exercise on blood glucose profiles, particularly in patients with type 2 diabetes, remain unclear. Comprehending these responses may aid in diabetes management.

Three trials were conducted: trial A examined aerobic, resistance, and combined exercise; trials B and C focused on three intensities of resistance and combined exercise. Participants including patients with type 2 diabetes and healthy adults completed a randomized crossover experiment with two arrangements of three interventions and continuous glucose monitoring. Blood glucose iAUC and slope were analyzed via repeated measures two-way ANOVA.

A total of 21 patients with type 2 diabetes (47.81±11.88 years) and 26 healthy adults (31.77±6.66 years) were assigned. In trials A-C, the main effect of subject group on iAUC/min was significant (p＜0.001, p = 0.003, and p＜0.001). The exercise in trial A (p = 0.006) and subject group in trial C (p = 0.005) significantly impacted the blood glucose slope.

Resistance and combined exercise reduce postprandial hyperglycemia in type 2 diabetes patients. Monitoring glucose before exercise may help prevent extreme events.

Objective: To compare the accuracy of commonly used continuous glucose monitoring (CGM) analysis programs with ambulatory glucose profile (AGP) and Dexcom Clarity (DC) in analyzing CGM metrics in patients with type 1 diabetes (T1D). Research Methods: CGM data up to 90 days from 152 adults using the same CGM and automated insulin delivery system with T1D were collected. Six of the 19 CGM analysis programs (CDGA, cgmanalysis, Glyculator, iglu, EasyGV, and GLU) were selected to compare with AGP and DC. Metrics were compared etween all tools with two one-sided t-tests equivalence testing. For the equivalence test, the acceptable range of deviation was set as ±2 mg/dL for mean glucose, ±2% for time in range (TIR), ±1% for time above range (TAR), time above range level 1 (TAR1), time above range level 2 (TAR2), and coefficient of variation (CV). Results: All packages were compared with each other for all CGM metrics, and most of them had statistically significant differences for at least some metrics. All tools were equivalent to AGP for mean glucose, TIR, TAR, TAR1, and TAR2 within ±2 mg/dL, ±2%, ±1%, ±1% and 1%, respectively. CDGA, Glyculator, cgmanalysis, and iglu were not equivalent to AGP for CV within ±1%. All tools were equivalent to DC for mean glucose, TIR, and TAR2 within ±2 mg/dL, ±2%, and ±1%, respectively. Glyculator was not equivalent for TAR1, TAR, and CV. CGDA, cgmanalysis, and iglu were not equivalent to DC for TAR1 and TAR. EasyGV and GLU were not equivalent for TAR within ±1%. Conclusions: CGM analysis programs reported CGM metrics statistically differently, but these differences may not be applicable in clinical practice. The equivalence test also confirmed that the differences are negligible for TIR and mean glucose, while they can be important for hyperglycemic ranges and CV. A standardization for CGM data handling and analysis is necessary for clinical studies reporting CGM-generated outcomes.

Objective: Using a multistep machine-learning procedure, add virtual continuous glucose monitoring (CGM) traces to the original sparse data of the landmark Diabetes Control and Complications Trial (DCCT). Assess the association of CGM metrics with the microvascular complications of type 1 diabetes observed during the DCCT and establish time-in-range (TIR) as a viable marker of glycemic control. Research Design and Methods: Utilizing the DCCT glycated hemoglobin data obtained every 1 or 3 months plus quarterly 7-point blood glucose (BG) profiles in a multistep procedure: (i) utilized archival BG traces to model interday BG variability and estimate glycated hemoglobin; (ii) trained across the DCCT BG profiles and associated each profile with an archival BG trace; and (iii) used previously identified CGM "motifs" to associate a CGM trace to a BG trace, for each DCCT participant. Results: TIR (70-180 mg/dL) computed from virtual CGM data over 14 days prior to each glycated hemoglobin measurement reproduced the observed glycemic control differences between the intensive and conventional DCCT groups, with TIR generally >60% and <40% in these groups, respectively. Similar to glycated hemoglobin, TIR was associated with the risk of development or progression of retinopathy, nephropathy, and neuropathy (all P-values <0.0001). Poisson regressions indicated that TIR predicted retinopathy and microalbuminuria similarly to the original glycated hemoglobin data. Conclusions: The landmark DCCT was revisited using contemporary data science methods, which allowed adding individual CGM traces to the original data. Fourteen-day CGM metrics predicted microvascular diabetes complications similarly to glycated hemoglobin. Clinical Trials Registration: Not a clinical trial.

To explore the interplay among sleep quality, dinner timing, and glycemic control in adults with type 1 diabetes (T1D) using advanced diabetes technologies.

T1D adults on automated (AID, n = 122) or non-automated (CSII, n = 67) insulin delivery systems completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Two-week CGM-metrics, HbA1c, and post-dinner glucose control were compared by independent T-test in Good vs. Bad-sleepers (PSQI-score above 5) or in Early vs. Late-eaters (above median of the cohort's dinner time).

Time-below-range (TBR)70-54 (2.1 ± 2.0 vs. 1.3 ± 1.2 %), TBR54 (0.7 ± 1.0 vs. 0.2 ± 0.4 %), and coefficient of variation (34.4 ± 5.3 vs. 31.8 ± 5.2 %) were significantly higher in Bad-sleepers than Good-sleepers (p < 0.05 for all). Late-eaters, particularly among AID users, showed higher HbA1c and lower TBR70-54, and, after dinner, higher TAR180-250 and lower Time-in-range70-180 than Early-eaters (p < 0.05 for all). At multiple regression analysis, dinner time was a main predictor of HbA1c, and TBR54 a main predictor of sleep quality.

The rate of hypoglycemia and dinner timing are key factors affecting both sleep quality and glycemic control in adults with T1D. Addressing lifestyle habits, including dinner timing and fear of hypoglycemia, may still be needed in users of AID.

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.

Clinical guidelines in the UK and elsewhere do not specifically address hybrid closed loop (HCL) use in the postpartum period when the demands of caring for a newborn are paramount. Our aim was to evaluate the safety and efficacy of HCL use during the first 6 months postpartum compared with standard care.

In this prespecified extension to a multicentre, randomised controlled trial, pregnant women with type 1 diabetes at nine UK sites were followed up for 6 months postpartum. Eligible participants (AiDAPT participants recruited after the implementation of the postpartum protocol amendment approval, those still pregnant or within six months of delivery at the time of amendment implementation and still using HCL or continuous glucose monitoring [CGM] therapy) continued their randomly assigned treatment, either standard insulin therapy with CGM or HCL therapy (CamAPS FX system version 0.3.1, CamDiab, Cambridge, UK). Participants were randomised in a 1:1 ratio with stratification by clinical site using randomly permuted block sizes of 2 or 4. The primary outcome was the between-group difference in percentage time in range ([TIR] 3·9-10·0 mmol/L [70-180mg/dL]), measured during the periods of month 0 up to 3, months 3 to 6, and over 6 months postpartum. The study is registered at ClinicalTrials.gov (ISRCTN56898625) and is complete.

Of the 124 AiDAPT trial participants, 66 (53%) were ineligible for inclusion in the postpartum extension, and 57 participants consented to continue their treatment per original random allocation. The mean age was 31 years (SD 4), and all participants had early pregnancy HbA1c 59·4 mmol/mol (SD 10·5 [7·6% SD 1·0%]). In the 6 months postpartum, mean time with glucose levels within the target range was higher in the HCL group compared with the standard care group (72% [SD 12%] vs 54% [17%]), with an adjusted treatment difference of 15% (95% CI 7 to 22). Results for hyperglycaemia (>10·0 mmol/L) and mean CGM glucose also favoured HCL (-14% [95% CI -23% to -6%] and -1·3 mmol/L [-2·3 to -0·3], respectively). Hypoglycaemia rates were low, with no between-group differences (2·4% vs 2·6%). There were no treatment effect changes depending on postpartum period (0 up to 3 months vs 3 to 6 months) and no unanticipated safety problems.

Participants in the HCL group maintained 70% TIR during the first 6 months postpartum, supporting continued use of HCL rather than standard insulin therapy for people with diabetes once they have given birth.

National Institute for Health Research, Juvenile Diabetes Research Foundation, and Diabetes Research & Wellness Foundation. CGM devices were provided by Dexcom at a discounted price.