The Glycemia Risk Index (GRI) quantifies the risk of glycemic events by considering both hypoglycemic and hyperglycemic episodes, offering a comprehensive evaluation of glycemia. While associations between GRI and various glycometric indicators have been established in clinical trials using continuous glucose monitoring (CGM), real-world assessments, particularly with intermittent scanning CGM (isCGM), are underexplored. This study examines these associations and their clinical implications in individuals with Type 1 Diabetes (T1D).

We conducted a retrospective study involving individuals with T1D undergoing intensive insulin therapy. All participants had used isCGM for at least three months. We collected clinical, metabolic, and glycemic data and calculated the GRI, with its components for hypoglycemia (CHypo) and hyperglycemia (CHyper). We then assessed the correlation between the GRI and traditional glycemic metrics in relation to the coefficient of variation (CV).

The study included 194 patients (105 males, 89 females) with a median age of 21.5 years for adults and 16 years for adolescents. Of these, 62.4% were on multiple daily injections, and 37.6% used insulin pumps. GRI showed a significant negative correlation with Time in Range (%TIR70 - 180) (p  < 0.001) and a positive association with various glycemic measures such as glycemic variability (r = 0.33, p < 0.001). Individuals with lower glycemic variability (CV< 36%) had significantly higher %TIR70 - 180 (63% vs. 39%, p < 0.01) and lower GRI (40 vs. 45.8, p < 0.01), CHyper (20 vs. 24, p = 0.01), and CHypo (2.6 vs. 3.4, p < 0.01).

GRI correlates with key glycemic metrics, indicating its potential utility in comprehensive glycemia assessment. These findings highlight the importance of individualized treatment approaches and suggest GRI's clinical relevance in optimizing glycemic management strategies for individuals with T1D.

The online version contains supplementary material available at 10.1007/s40200-025-01569-w.

Diabetes management often requires insulin therapy, yet adherence to daily injections can be challenging due to complexity, injection pain, and fear of hypoglycemia. Basal Insulin Fc (BIF) is a novel once-weekly insulin analog designed to simplify regimens, improve adherence, and enhance glycemic control. This meta-analysis evaluates the efficacy and safety of BIF compared to once-daily insulin degludec.

A systematic search of PubMed, Google Scholar, EBSCO, ScienceDirect, and the Cochrane Library, along with ClinicalTrials.gov, was conducted up to November 2024 to identify RCTs comparing BIF with insulin degludec. The search employed MeSH terms like "type 1 diabetes mellitus," "type 2 diabetes mellitus," "once weekly basal insulin Fc," and "insulin degludec." Studies were screened in accordance with PRISMA guidelines, and data on glycemic outcomes, safety, and patient demographics were extracted. Statistical analysis included pooled mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CIs) using random-effects models. Heterogeneity was assessed using the I2 statistic, and sensitivity analyses were conducted for cases of high heterogeneity. Subgroup and meta-regression analyses assessed moderators such as diabetes type, insulin status, follow-up duration, and heterogeneity.

Five RCTs with 2,562 participants (Type 1 and Type 2 diabetes) were included. BIF showed non-inferiority to degludec in HbA1c reduction (MD 0.03, p = 0.37) and percentage time in range (MD 0.56, p = 0.27). No significant differences were observed in self-monitored fasting blood glucose (MD 2.73, p = 0.40) or clinically significant hypoglycemia (RR 1.00, p = 0.95). However, BIF increased time spent below range (MD 0.30, p = 0.0004) and was associated with higher treatment-emergent adverse events (RR 1.12, p = 0.006). The subgroup analysis highlighted differences in hypoglycemia risks between Type 1 and Type 2 diabetes.

BIF offers comparable glycemic control to insulin degludec while reducing injection frequency, potentially enhancing adherence. However, increased hypoglycemia risks in certain subgroups and higher adverse event rates warrant further evaluation.

The online version contains supplementary material available at 10.1007/s40200-025-01602-y.

Continuous Glucose Monitoring (CGM) may help detect early dysglycemia in Transfusion-Dependent Thalassemia (TDT) patients, though previous reports suggest it may overestimate prediabetes prevalence. This study analyzed glucose-related metrics in TDT patients with negative diabetes screening tests, compared with healthy controls. A secondary objective was to assess the association between TAR140 > 6 % and clinical/laboratory characteristics of patients.

Patients resulted negative to the screening tests for glucose disorders were compared to healthy controls using CGM system for 7 days.

This study involved 39 participants (19 patients, 20 controls). HbA1c was falsely elevated in patients, despite normal mean glucose and GMI. Standard deviations and coefficients of variation were higher in patients than controls. No healthy control but 7/19 (37 %) TDT patients presented the interval TAR140 > 6 %. Significant differences were observed between "really euglycaemic" TDT patients (TAR140 ≤ 6 %) and "hyperglycemic" ones (TAR140 > 6 %) in terms of GMI, mean glucose and TAR140%. Comparing the glucose metrics of TDT euglycaemic patients (TAR140 ≤ 6 %) and healthy controls, no significant difference was reported. No differences in iron overload indexes were found between the hyperglycemia and euglycemia groups.

CGM reliably detects prediabetes in 37 % of TDT patients. TAR140 > 6 % may serve as a diagnostic cutoff.

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.

Background: Multiple daily injections (MDIs) have been a mainstay for insulin delivery by persons with type 1 diabetes mellitus (T1DM). "Smart" insulin pens (SIPs) offer several advantages over traditional insulin pens, such as a memory function, bolus calculator, and reminders for patients to take their insulin. SIPs can integrate with CGM, allowing for the collection of accurate insulin and glucose data, which can integrate into combined reports. Using these technologies along with telecommunication modalities provides an infrastructure to improve the way in which healthcare can be delivered to those with diabetes. Methods: Four cases of uncontrolled T1DM managed by MDIs (and not insulin pumps) and deemed to have plateaued in their management were selected to retrospectively review to identify potential advantages of SIP/CGM along with telemedicine as a method of care delivery. Results: This case series revealed potential benefits of this model of care delivery, such as the ability to identify dysglycemia patterns not discernible prior to the use of SIP/CGM, use combined reports as a visual education tool to provide targeted insulin and dietary education, and improve patient engagement in diabetes self-care behaviors. Conclusions: We described the benefits of using SIPs and CGM technologies along with telecommunication solutions, as a novel concept for a comprehensive telemedicine system, to improve management of glycemic control and diabetes self-management capabilities.

To meet their glycated hemoglobin (HbA1c) goals, youth with type 1 diabetes (T1D) need to engage with their daily T1D treatment. The mealtime insulin Bolus score (BOLUS) is an objective measure of youth's T1D engagement which we have previously shown to be superior to other objective engagement measures in predicting youth's HbA1c. Here, to further assess the BOLUS score's validity, we compared the strengths of the associations between youth's HbA1c with their mean insulin BOLUS score and a valid, self-report measure of T1D engagement, the Self-Care Inventory (SCI).

One-hundred and five youth with T1D self-reported their T1D engagement using the SCI. We also collected two weeks of insulin pump data and a concurrent HbA1c level. We scored youth's SCI and calculated their mean insulin BOLUS score using standardized methods. For the analyses, we performed simple correlations, partial correlations, and multiple regression models.

Youth had a mean age of 15.03 ± 1.97 years, mean time since diagnosis of 8.11 ± 3.26 years, and a mean HbA1c of 8.78 ± 1.49%. The sample included n = 58 boys (55%) and n = 96 families (91%) self-identified as white. Simple correlations between youth's age, HbA1c, SCI total score, and BOLUS score were all significant. Partial correlation and regression models revealed that youth's insulin BOLUS score was more strongly associated with HbA1c than the SCI.

Youths' BOLUS score has better concurrent validity with HbA1c than the SCI. We should consider reporting the BOLUS score as an outcome metric in insulin pump data reports.

This study assessed efficacy and safety of the fixed ratio combination iGlarLixi 100/33 (insulin glargine 100 U/mL plus lixisenatide 33 μg/mL) in people with type 2 diabetes (PwT2D) in daily clinical practice.

This non-interventional, multicentre, prospective, single-arm 24-week study documented PwT2D with an HbA1c of 7.5%-10.0%, currently treated with a basal insulin supported oral therapy (BOT) in German primary care facilities, after the physician had decided to change treatment to iGlarLixi 100/33, independent of study participation. Primary end-point was the absolute change in HbA1c (%) from baseline.

Of 93 participants included, 70 comprised the full analysis set for efficacy assessment. Approximately 24 weeks after switching to iGlarLixi 100/33 HbA1c (mean ± standard deviation) changed from 8.52 ± 0.82% by -0.74 ± 0.81% to 7.74 ± 0.76%, FPG from 174.3 ± 44.6 mg/dL (9.67 ± 2.48 mmol/L) by -32.9 ± 46.3 mg/dL (-1.83 ± 2.57 mmol/L) to 141.4 ± 34.1 mg/dL (7.85 ± 1.89 mmol/L) and body weight from 104.3 ± 22.5 kg by -3.0 ± 7.5 kg to 101.3 ± 21.6 kg (all p < 0.01). Furthermore, use of DPP4 inhibitors was significantly reduced from 34.8% to 6.8% of participants. Derived (from 7-point self-measured plasma glucose) time in range (TIR) increased and time above range (TAR) decreased after 24 weeks to target ranges (all p < 0.05). Flash glucose monitoring data of 20 patients showed similar patterns for TIR and TAR, respectively, and a reduction in time below range (p = 0.007). Hypoglycaemia events did not change significantly and were low in number. No severe hypoglycaemia was reported.

Modifying antiglycaemic treatment from a BOT regimen to iGlarLixi 100/33 in suboptimal controlled PwT2D in daily clinical practice improved glycaemic control without increasing hypoglycaemia and with favourable body weight change.

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.

Complications of diabetes mellitus have significant impacts on morbidity, mortality, quality of life, and health costs for individuals. Setting and achieving glycemic targets to prevent these complications is a top priority when managing diabetes. However, patients often already have complications when diagnosed with diabetes mellitus. Therefore, methods to prevent disease progression become a crucial component of diabetes management. The purpose of this article is to review glycemic targets and methods of screening and managing diabetes-related complications.

A PubMed review of the literature pertaining to diabetes mellitus, glycemic targets, microvascular complications, and macrovascular complications was conducted. We reviewed articles published between 1993 and 2024. Guidelines published by nationally recognized organizations in the fields of diabetes, nephrology, and cardiology were referenced. Public health statistics obtained by the Center for Disease Control and Prevention and the National Kidney Foundation were used.

Achieving glycemic targets and screening for diabetes-related complications at appropriate intervals remains the key factor for early detection and intervention. An algorithmic approach to glycemic management based on individual risk factors is beneficial in choosing pharmacotherapy.

The consequences of diabetes-related complications can be detrimental. However, achieving and maintaining glycemic targets combined with diligent screening, reduction of risk factors, and prompt treatment can halt disease progression.

Most adolescents with type 1 diabetes (T1D) encounter barriers to achieving optimal glycemia, including effective planning and monitoring their T1D and problem-solving, and following through with T1D treatment decisions. Thus, the overall aim of Diabetes Journey, a randomized controlled clinical trial, was to assess the feasibility, acceptability, and preliminary efficacy of a novel, amusement park-themed, web-based mobile health (mHealth) intervention tailored for adolescents who experienced barriers to T1D self-management. Secondary aims included examining post-intervention changes in T1D health-related quality of life, T1D self-management behaviors, and hemoglobin A1c (HbA1c). This article describes the study rationale, recruitment, design, and baseline characteristics of the adolescents (aged 12-17 years) who were randomized to one of two groups: Diabetes Journey or Enhanced Standard of Care. Diabetes Journey focused on reducing challenges related to stress, burnout, time pressure, and planning through the delivery of 5-8 intervention sessions using a problem-solving framework. Enhanced Standard of Care participants attended 4 education sessions focused on similar topics through the T1D Toolkit© website (educational content and brief videos about T1D). Adolescents (N = 195) were recruited with n = 162 randomized (Mage = 14.8 ± 1.6 years; 50 % female, 88 % White; HbA1c% = 8.2 ± 1.8; 86 % on insulin pumps). Diabetes Journey was impacted by COVID-19 and modifications to the study design were warranted. Future directions include examining the impact of Diabetes Journey on primary and secondary outcomes, while accounting for the impact of COVID-19.

Hemoglobin A1c (A1c) is the standard for glycemic control in type 1 diabetes. With recent increase in continuous glucose monitoring (CGM), other metrics (time in range 70-180 mg/dL [TIR]) are increasingly available. Data are limited for youth. We evaluated the association between A1c and time in range in a large pediatric cohort. We included patients from 2018 through 2020, aged <22 years with ≥70% CGM usage (n = 2393). A linear correlation between A1c and TIR was observed (correlation coefficient -0.73), similar to studies in adults. Each 1% increase in A1c was associated with a 9.1% lower TIR. The R2 for A1c versus mean sensor glucose was 0.66 (P < 0.001) and A1c versus TIR was 0.55 (P < 0.001). TIR correlates with A1c in children with type 1 diabetes. TIR should be considered alongside A1c. Further research is warranted to establish long-term outcomes associated with TIR in children.

Low-carbohydrate (LC) diets have gained popularity. We compared glycaemic and metabolic parameters following an LC versus a Mediterranean (MED) diet in adolescents and youths with type 1 diabetes.

In a six-month, open-label, randomised trial, 40 individuals were assigned to either diet. Glycaemic outcomes, based on continuous glucose monitoring, included per cent time of blood glucose in the range [3.9-10.0 mmol/L (70-180 mg/dL)] and haemoglobin A1c (HbA1c).

Twenty-eight (70%) were females. The median age was 18 years. After 6 months, the median time in range increased from 47% to 58% in the LC and from 52% to 64% in the MED diet group (p = 0.98). The delta values for the time in range were 16% and 7% for the respective groups (p = 0.09). The percentage of time >13.9 mmol/L (>250 mg/dL) improved more in the LC diet than in the MED diet group: -10% vs. -2% (p = 0.005). The percentage of time <3.0 mmol/L (<54 mg/dL) was comparable. The delta HbA1c improved in both groups: -0.7% vs. -0.1% (p = 0.02). Changes in BMI Z-score and lipid levels were similar.

Both diets improved glycaemic outcomes in adolescents and youths with type 1 diabetes, without increasing hypoglycaemia or cardiovascular risk factors, indicating comparable safety and efficacy.

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and summarizes recent advances in the technology behind glucose monitoring, and its role in glucose-responsive integrated technology that is feasible with the use of automated insulin delivery (AID) systems in children and adolescents. The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and summarizes recent advances in the technology behind glucose monitoring, and its role in glucose-responsive integrated technology that is feasible with the use of automated insulin delivery (AID) systems in children and adolescents.

Imeglimin is a novel oral antidiabetic agent that improves glucose tolerance. This study aimed to investigate the efficacy of combining imeglimin with dipeptidyl peptidase-4 inhibitor (DPP-4i), the most frequently prescribed first-line treatment for patients with type 2 diabetes (T2D) in Japan, to improve glycemic control.

Eleven patients with T2D treated with DPP-4i alone (6.5% ≤ hemoglobin A1C [HbA1c] < 10%) received 1000 mg imeglimin twice daily for 16 weeks. A meal tolerance test (MTT) was conducted on seven of these patients to assess parameters associated with islet function or insulin tolerance, such as homeostasis model assessment (HOMA)-β-cell function (HOMA-β), HOMA-insulin resistance (HOMA-IR), C-peptide immunoreactivity (CPR) index, and glucagon kinetics. Continuous glucose monitoring was conducted to evaluate parameters for glycemic variability.

Sixteen weeks after imeglimin administration, the HbA1c level improved from 7.5%±1.3% to 6.5%±0.5% (p < 0.05), the casual blood glucose level significantly improved from 168.2±55.4 to 127.8±20.0 mg/dL (p=0.027), time in range increased from 65.0%±0.34% to 90.0%±0.08% (p < 0.05), and time above range reduced from 34.0%±0.034% to 9.0%±0.08% (p < 0.05). During MTT, we observed significantly reduced area under the curve (AUC)0-180 glucose, increased AUC0-180 CPR/AUC0-180 glucose, CPR index, and HOMA-β (p<0.05). HOMA-IR and glucagon kinetics did not change with the addition of imeglimin.

The addition of imeglimin to DPP-4i significantly improved glycemic control and glycemic variability, based on increased glucose-induced insulin secretion, indicating its potential as a therapeutic option for patients with T2D.

Glycated haemoglobin (HbA1c) is currently the gold standard for assessing glycaemic control in diabetes, given the established relationship with microvascular and macrovascular complications in this condition. However, HbA1c is affected by non-glycaemic factors, while also failing to provide data on hypoglycaemic exposure and glucose variability, which are associated with adverse vascular outcomes. Continuous glucose monitoring (CGM)-derived glucose metrics provide a more comprehensive assessment of glycaemia, but their role in predicting future vascular complications remains unclear. Here, we present the protocol for a real-world cohort study, aiming to establish the relationship between CGM-derived glycaemic metrics and the incidence of macrovascular and/or microvascular complications in people with diabetes.

This cohort study will use data from all CGM new users (FreeStyle Libre system) in France who uploaded their glycaemic values onto the LibreView cloud-based system, linked with data from the French nationwide Système National des Données de Santé claims database. The study is expected to include a minimum of 70 000 individuals with diabetes with a first date of glucose data upload to the LibreView platform after 1 January 2018 and with a 6-year follow-up period. The primary outcomes are the first occurrence of new macrovascular or microvascular complications, analysed as a composite outcome and separately. Secondary outcomes will include all-cause mortality and hospital admissions for any cause. This longitudinal study will provide key data on the relationship between CGM-derived glycaemic metrics and micro/macrovascular complications in diabetes. This will have an impact on routine clinical practice by setting targets for the different glycaemic markers, based on robust outcome data, thus helping to optimise glucose management in diabetes.

The study data-collection protocol is approved by the French National Commission for Informatics and Liberties, including approval from the Comité Ethique et Scientifique pour les Recherches, les Etudes et les Evaluations dans le domaine de la Santé. This study complies with French and European regulations, including those relating to the General Data Protection Regulation. This study uses pseudonymous information, not requiring informed consent. Dissemination plans include full publication of the study outcomes in peer-reviewed journal(s) with open access and presentations at national and international diabetes and cardiovascular conferences.

To elucidate the fluctuations in glucose levels measured using CGM-metrics during the four distinct seasons of the year in individuals with type 1 diabetes mellitus (T1DM) using an intermittently scanned CGM (isCGM) device or sensor augmented pump (SAP).

This retrospective, single-center study enrolled 93 individuals with T1DM who were equipped with an isCGM device or SAP at Kobe University Hospital. The subjects had a median age of 47.0 years [interquartile range, 37.0-62.0 years], 25 individuals (26.9%) were male, median body mass index was 22.0 kg/m2 [20.8-23.8 kg/m2], and median hemoglobin A1c level was 7.4% [6.9-8.0%]. CGM data were reviewed from January to December 2019, and the mean sensor glucose (SG) value, time above range (TAR), time in range (TIR), time below range (TBR), and standard deviation (SD) of SG were calculated for each season (spring, March-May; summer, June-August; autumn, September-November; winter, December-February).

Seasonal fluctuations were detected for mean SG, TAR, TIR, and SD, with TIR being lower and mean SG, TAR, and SD being higher in cold seasons (spring or winter) than in warm seasons (summer or autumn).

Seasonal fluctuations in CGM metrics should be taken into account in future studies performed to evaluate the favorable impact of CGM on glycemic management in individuals with T1DM.

The aim of this single-center observational study was to assess the real-world performance of first- and second-generation automated insulin delivery (AID) systems in a cohort of children and adolescents with type 1 diabetes over a one-year follow-up.

Demographic, anamnestic, and clinical data of the study cohort were collected at the start of automatic mode. Data on continuous glucose monitoring metrics, system settings, insulin requirements, and anthropometric parameters at three different time points (start period, six months, 12 months) were retrospectively gathered and statistically analyzed.

Fifty-four individuals (55.6% of females) aged 7 to 18 years switching to AID therapy were included in the analysis. Two weeks after starting automatic mode, subjects using advanced hybrid closed-loop (AHCL) showed a better response than hybrid closed-loop (HCL) users in terms of time in range (P = .016), time above range 180 to 250 mg/dl (P = .022), sensor mean glucose (P = .047), and glycemia risk index (P = .012). After 12 months, AHCL group maintained better mean sensor glucose (P = .021) and glucose management indicator (P = .027). Noteworthy, both HCL and AHCL users achieved the recommended clinical targets over the entire study period. The second-generation AID system registered longer time spent with automatic mode activated and fewer shifts to manual mode at every time point (P < .001).

Both systems showed sustained and successful glycemic outcomes in the first year of use. However, AHCL users achieved tighter glycemic targets, without an increase of hypoglycemia risk. Improved usability of the device may also have contributed to optimal glycemic outcomes by ensuring better continuity of the automatic mode activation.

Diabetes mellitus (DM) is a chronic disease with high morbidity and mortality, and glycemic control is key to avoiding complications. Technological innovations have led to the development of new tools to help patients with DM manage their condition.

This consensus assesses the current perspective of physicians on the potential benefits of using smart insulin pens in the glycemic control of patients with type 1 diabetes (DM1) in Spain.

The Delphi technique was used by 110 physicians who were experts in managing patients with DM1. The questionnaire consisted of 94 questions.

The consensus obtained was 95.74%. The experts recommended using the ambulatory glucose profile report and the different time-in-range (TIR) metrics to assess poor glycemic control. Between 31% and 65% of patients had TIR values less than 70% and were diagnosed based on glycosylated hemoglobin values. They believed that less than 10% of patients needed to remember to administer the basal insulin dose and between 10% and 30% needed to remember the prandial insulin dose.

The perception of physicians in their usual practice leads them to recommend the use of ambulatory glucose profile and time in range for glycemic control. Forgetting to administer insulin is a very common problem and the actual occurrence rate does not correspond with clinicians' perceptions. Technological improvements and the use of smart insulin pens can increase treatment adherence, strengthen the doctor-patient relationship, and help improve patients' education and quality of life.

We assessed the impact of initiating intermittently scanned continuous glucose monitoring (isCGM) compared with capillary blood glucose monitoring (BGM) on HbA1c levels and hospitalisations for diabetes-related complications in adults with insulin-treated type 2 diabetes in Sweden.

This retrospective comparative cohort study included adults with type 2 diabetes who had a National Diabetes Register initiation date for isCGM after 1 June 2017. Prescribed Drug Register records identified subgroups treated with multiple daily insulin injections (T2D-MDI) or basal insulin (T2D-B), with or without other glucose-lowering drugs. The National Patient Register provided data on hospitalisation rates.

We identified 2876 adults in the T2D-MDI group and 2292 in the T2D-B group with an isCGM index date after 1 June 2017, matched with 33,584 and 43,424 BGM control participants, respectively. The baseline-adjusted difference in the change in mean HbA1c for isCGM users vs BGM control participants in the T2D-MDI cohort was -3.7 mmol/mol (-0.34%) at 6 months, and this was maintained at 24 months. The baseline-adjusted difference in the change in HbA1c for isCGM users vs BGM control participants in the T2D-B cohort was -3.5 mmol/mol (-0.32%) at 6 months, and this was also maintained at 24 months. Compared with BGM control participants, isCGM users in the T2D-MDI cohort had a significantly lower RR of admission for severe hypoglycaemia (0.51; 95% CI 0.27, 0.95), stroke (0.54; 95% CI 0.39, 0.73), acute non-fatal myocardial infarction (0.75; 95% CI 0.57, 0.99) or hospitalisation for any reason (0.84; 95% CI 0.77, 0.90). isCGM users in the T2D-B cohort had a lower RR of admission for heart failure (0.63; 95% CI 0.46, 0.87) or hospitalisation for any reason (0.76; 95% CI 0.69, 0.84).

This study shows that Swedish adults with type 2 diabetes on insulin who are using isCGM have a significantly reduced HbA1c and fewer hospital admissions for diabetes-related complications compared with BGM control participants.

The glucose management indicator (GMI) and time-in-range (TIR) are important glycemic parameters calculated from continuous glucose monitoring (CGM) values. KARAZ, a hybrid Internet of things-artificial intelligence platform, was developed in Saudi Arabia to help manage diabetes mellitus. The complex nature of self-care and behavior changes associated with diabetes mellitus requires breaking large behaviors into achievable ones and related incentives.

This study explored how tiny habits as a behavioral intervention and incentive system affect glycemic control among KARAZ Platform users with diabetes mellitus in various age groups in Saudi Arabia.

This retrospective study included KARAZ Platform users and analyzed the effects of behavioral interventions and incentives on GMI and TIR as glycemic control parameters.

Of 296 active users, 118 (40%) and 148 (50%) maintained a desirable TIR and GMI, respectively. Adult females aged ≥ 26 years who consistently followed tiny habits and behavior changes exhibited a significant reduction in the GMI (5%). Intrinsic motivation through behavioral modification was more effective than external incentives for maintaining glucose control.

The findings highlight how behavioral interventions can impact GMI, suggesting their effectiveness in promoting better health behaviors and improving glycemic control in the Saudi Arabian context. Further research should investigate how these habits and behaviors can be maintained sustainably without relying on external incentives. Recommendations discussed how children with Type 1 diabetes mellitus would benefit from CGM connection to KARAZ Platform iteration and the integration of a comprehensive diabetes care program within the Saudi health system.

Hyperglycemia is a common adverse event (AE) associated with phosphatidylinositol-3-kinase inhibitors (PI3Kis) and considered an on-target effect. Presence of hyperglycemia is associated with poor outcomes in patients with cancer, and there is need for further refinement of hyperglycemia prevention and mitigation strategies in patients receiving PI3Kis. In this review, the authors highlight effective strategies for preventing PI3Ki-induced hyperglycemia before and during treatment as well as hyperglycemia management. Prior to initiating treatment with PI3Ki, identify baseline risk factors of patients at increased risk for developing hyperglycemia, which include older age, obesity, and glycosylated hemoglobin (HbA1c) 5.7%-6.4% (prediabetes or Type 2 diabetes). To prevent new-onset hyperglycemia, optimize blood glucose, and recommend a low-carbohydrate (60-130 g/day) diet along with regular exercise to all patients prior to initiating the PI3Ki. Prophylactic metformin may be considered in all patients starting a PI3Ki with HbA1c ≤6.4%. Although existing recommendations support monitoring fasting blood glucose (FBG) once weekly (twice-weekly for intermediate-risk, daily for high-risk patients) and HbA1c every 3 months upon initiation of PI3Ki, more frequent FBG monitoring may be considered for prompt detection of hyperglycemia. Experts also recommend considering postprandial glucose monitoring because it is an early indicator of glucose intolerance. If hyperglycemia develops, metformin (first-line) and/or sodium glucose co-transporter 2 inhibitors or thiazolidinediones (second-/third-line) are the preferred agents; consider early referral to an endocrinologist. In conclusion, hyperglycemia is a common but manageable AE associated with PI3Kis. Multidisciplinary approach to the prevention, monitoring, and management of hyperglycemia optimizes patient care and allows patients to maintain therapy on PI3Ki.

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and updates recommendations on the glycemic targets for children and adolescents living with diabetes. A new target for hemoglobin A1c (HbA1c) of ≤6.5% (48 mmol/mol) is recommended for those who have access to advanced diabetes technologies like continuous glucose monitoring and automated insulin delivery. This target should be encouraged for all children and adolescents living with diabetes when safely achievable. In other settings, the HbA1c target is ≤7.0% (53 mmol/mol). The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and updates recommendations on the glycemic targets for children and adolescents living with diabetes. A new target for hemoglobin A1c (HbA1c) of ≤6.5% (48 mmol/mol) is recommended for those who have access to advanced diabetes technologies like continuous glucose monitoring and automated insulin delivery. This target should be encouraged for all children and adolescents living with diabetes when safely achievable. In other settings, the HbA1c target is ≤7.0% (53 mmol/mol).

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.

Since the introduction of insulin therapy, it has become apparent that type 1 diabetes (T1D) is accompanied by long-term microvascular and macrovascular complications. In the context of the many benefits of continuous glucose monitoring (CGM), there remain opportunities to study the large amount of data now available in order to maximise its potential in the endeavour to reduce the occurrence of diabetes tissue complications in the longer term.

Continuous glucose monitoring values were downloaded for 89 type 1 diabetes mellitus (T1D) individuals for up to 18 months from 2021 to 2023. Data for patient demographics was also taken from the patient record which included Sex, Date of Birth, and Date of Diagnosis. The recorded laboratory glycated haemoglobin (HbA1c) test results were also recorded. The glucose management index (GMI) was calculated from average glucose readings for 18 months using the formula GMI (%) = (0.82 - (Average glucose/100)). This was then adjusted to give GMI (mmol/mol) = 10.929 * (GMI (%) - 2.15). Average Glucose Fluctuation (AGF) was calculated by adding up the total absolute change value between all recorded results over 18 months and dividing by the number of results minus one. The % Above Critical Threshold (ACT) was calculated by summing the total number of occurrences for each result value. A cumulative 95% limit was then applied to identify the glucose value that only 5% of results exceeded in the overall population. Using this value, we estimated the percentage of total tests that were above the Critical Threshold (ACT).

The mean age of the participants was 42.6 years, and the mean duration of T1D was 18.4 years. A total of 3.22 million readings were analysed, yielding an average blood glucose level of 10.3 mmol/l and a GMI of 57.2 mmol/mol. There was a strong correlation between GMI and measured HbA1c (r2 = 0.82). However, there were patients who had an above-critical threshold (ACT) of 4-10% at a GMI of 60 mmol/mol or less. The percentage average value at the time of day (%AVTD) was applied to all blood glucose readings at each 15-min interval throughout the day, averaged over 18 months. The %AVTD of GMI (overall average 57.2 mmol/mol) increased after midday, dipped at 18:00, and peaked at 22:00. The %AVTD of AGF (overall average 0.60 mmol/l) showed higher change rates after 09:00 declining at the end of the day. The %AVTD of ACT peaked at 22:00, with those having the highest %ACT showing an additional peak at 15:00.

We have shown here that the percentage glucose results above 18 mmol/l (top 5% of distribution) increased exponentially above 54 mmol/mol HbA1c. The %AVTD is introduced as a useful measure. Our data indicate that over the 24-h period, improvement in metabolic control could be focussed on the afternoon and evening, when there are higher-than-average levels of GMI, a higher-than-average degree of glucose change, and higher-than-average risks of being above the critical threshold. In conclusion, a measure of glycaemic variation based on the amplitude of glucose change to a population mean could be used to provide valuable clinical insights into glucose change over a 24-h period.

We assessed the impact of intermittently scanned continuous glucose monitoring (isCGM) compared with blood glucose monitoring (BGM) on rates of hospitalization for metabolic and vascular complications of diabetes and on HbA1c levels for adults with type 1 diabetes.

This retrospective study using data from the Swedish National Diabetes Register and the Swedish National Patient Register comprised adults with type 1 diabetes and an isCGM initiation date after 1 June 2017 and matched control individuals using BGM. Hospital admission rates were calculated per 100 person-years of follow-up.

We identified 11,822 adults with type 1 diabetes and an isCGM index date after 1 June 2017 and HbA1c baseline values 3-8 months prior to the index date. Compared with 3,007 BGM users, isCGM users had a significantly lower relative risk of hospitalization for hypoglycemia (0.32; 95% CI 0.14, 0.74), diabetic ketoacidosis (0.55; 0.35, 0.87), stroke (0.48; 0.37, 0.64), acute myocardial infarction (0.64; 0.46, 0.91), atrial fibrillation (0.59; 0.38, 0.94), heart failure (0.25; 0.16, 0.39), peripheral vascular disease (0.21; 0.07, 0.62), kidney disease (0.48; 0.35, 0.66), or hospitalization for any reason (0.32; 0.29, 0.35). Compared with BGM users, change in mean HbA1c for isCGM users was -0.30% (-3.3 mmol/mol) at 6 months and -0.24% (-2.6 mmol/mol) at 24 months (both P < 0.001).

This study shows that adults with type 1 diabetes in Sweden who initiate isCGM have significantly reduced hospitalization rates for acute diabetes events, kidney disease, and cardiovascular complications, along with improved glucose control, compared with BGM users.

Managing glycemia during Ramadan is challenging for individuals with type 1 diabetes (T1D) due to prolonged fasting and altered eating patterns. While many are exempt from fasting, some choose to fast, necessitating careful monitoring. The glycemia risk index (GRI) is valuable for assessing glycemic quality and interpreting continuous glucose monitoring (CGM) data to identify individuals needing closer clinical attention. This study investigates the effects of Ramadan fasting on glycemic control in T1D, focusing on GRI and its components for hypoglycemia (CHypo) and hyperglycemia (CHyper).

An ambispective study involved 186 individuals with T1D using intermittent scanning CGM (isCGM). Data were retrospectively collected for one month before Ramadan and prospectively during and one month after Ramadan. Clinical, metabolic, and glycemic data were collected, with GRI calculated alongside its components.

During Ramadan, GRI improved by 54.6% (from 56.4 to 25.6), CHypo decreased by 60% (from 6 to 2.4), and CHyper dropped by 40.5% (from 21 to 12.5). However, these benefits were temporary, as glycemic measures increased after Ramadan, reflecting a return to pre-Ramadan patterns once normal routines resumed. No participants were admitted for diabetes emergencies during Ramadan. Adolescents and patients on insulin pumps had more favorable outcomes. GRI and its components significantly correlated with other CGM metrics, with these relationships maintained during and after Ramadan.

Ramadan fasting significantly improved GRI and its components in individuals with T1D. Incorporating GRI as a novel metric alongside classical CGM metrics could enhance glycemic control, highlighting the need for personalized diabetes management strategies.

Considerable efforts to standardize continuous glucose monitoring (CGM) have occurred in recent years. The aim was to perform an analysis of clinical studies in clinicaltrials.gov to evaluate trends in CGM endpoint adoption.

Clinicaltrials.gov was searched for studies of drugs, devices and combination products containing CGM terms posted from 2012 to 2023. 1269 studies were returned and 954 were excluded. 315 studies were divided into two periods (P1 [2012-2017] and P2 [2018-2023]) and differences analyzed using descriptive statistics and two-tailed t tests.

There was a significant 60.3% increase in total clinical studies from P1 (121) to P2 (194). Phase 2 and Phase 3 Studies both saw significant increases of 125.8 and 169.2%, respectively, in P2. Adult-only studies predominated in both periods, with a 40.4% increase in P2. Studies that included pediatric populations, although smaller in number, increased significantly. Most studies were nonindustry-funded, and studies in this category saw a significant 80.0% increase in P2. However, industry-only funded studies also increased significantly by 78.4% in P2 in the same period. Studies of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) increased by 55.8% and 26.9%, respectively, but increases were not statistically significant. Studies of nondiabetes-related indications did increase significantly (233.3%). 27.6% of studies used CGM-derived metrics as primary endpoints (PE). Studies that used time in range (TIR) increased by 222.4% in P2, which was significant. Conversely studies that used mean amplitude of glycemic excursions (MAGE) decreased significantly by 71.3%.

Our data provide evidence of significant increases in the application of CGM endpoints in clinical studies in the last six years, including studies with TIR as the PE. Increases have been driven largely by academia, but our data show that industry is starting to follow suit. The significant increase in studies that included pediatrics is encouraging.

prediabetes is a significant risk factor for the development of type 2 diabetes, cardiovascular diseases, chronic kidney disease, and other complications. Early diagnosis of prediabetes, coupled with education on lifestyle changes that support blood glucose management, are crucial for the prevention or delay of type 2 diabetes and related complications. This study aimed to evaluate the impact of incorporating real-time feedback from continuous glucose monitoring (CGM) into individualized nutrition therapy (INT) on blood glucose control in individuals with prediabetes who are overweight or obese.

participants (mean age ± SD: 55 ± 6 years; BMI: 31.1 ± 4.1 kg/m²) were randomly assigned to either the treatment group (n = 15) or the control group (n = 15). Both groups received INT and CGM, but the control group was blinded to the CGM data until the end of this study. Participants were followed for 30 days and visited the lab every 10 days for CGM replacement, study measurements, and dietary consultations.

the treatment group showed a significant increase in the percentage of time spent in the target blood glucose range (p = 0.02) and a significant decrease in the mean blood glucose concentration (p < 0.05), glucose management indicator (p = 0.02), percent coefficient of variation for blood glucose (p = 0.01), and percent time spent in the high or very high blood glucose ranges (p = 0.04). These changes were not statistically significant for the control group.

adding CGM feedback to INT resulted in better management of blood glucose levels in overweight or obese individuals with prediabetes.

Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.

Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.

1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), P < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), P < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both P < .001, with no significant difference (P = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.

Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).

This study investigated the association of Time In Range (TIR) obtained from Blood Glucose Monitoring (BGM) with Cognitive Impairment (CI) inpatients with middle-aged Type 2 Diabetes Mellitus (T2DM) and further explored whether a TIR goal for T2DM in adults with > 70% possess a protective effect on cognitive function.

A total of 274 inpatients with T2DM aged 40-64 years, who underwent seven-point BGM ( pre meals and 120 min post meals and at bedtime) were recruited in this cross-sectional study. TIR was defined as the percentage of blood glucose within the target range of 3.9-10.0mmol/L. Subjects were divided into Normal Cognitive Function (NCF) (n = 160) and CI (n = 114) groups according to the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The association of TIR and other glycemic metrics, calculated from seven-point BGM data, with cognitive dysfunction was analyzed.

The prevalence of CI was 41.6% in patients with middle-aged T2DM (median age 58 years). TIR was lower in CI group than in NCF group (28.6% vs. 42.9%, P = 0.004). The prevalence of CI decreased with ascending tertiles of TIR (p for trend < 0.05). Binary logistic regression analysis showed a significant association between TIR and CI (odds ratio [OR] = 0.84, p < 0.001) after adjusting for confounders (age, education, marital status, age at Diabetes Mellitus (DM) onset, cerebrovascular disease). Further adjustment of Standard Deviation (SD)(OR = 0.84, p = 0.001) or Coefficient of Variation (CV)(OR = 0.83, p < 0.001), TIR was still associated with CI. While a TIR goal of > 70% probably possessed independent protective effect on cognitive function (OR = 0.25, p = 0.001) after controlling for confounders above.

TIR obtained from BGM was related to CI in middle-aged T2DM individuals and a TIR goal of > 70% probably possessed a protective effect on cognitive function for middle-aged T2DM .

Time in Tight Range (TITR), defined as the percentage of time within the glucose range of 70 to 140 mg/dL, is anticipated to be challenging to maintain without causing hypoglycemia, especially in individuals with type 1 diabetes (T1D). This study aimed to investigate the TITR target value in individuals with T1D on multiple daily injections (MDI).

The study included 101 individuals with T1D on MDI aged 15 to 75 who were hospitalized at Jikei University School of Medicine from September 2006 to November 2013 to conduct Continuous Glucose Monitoring (CGM). The cutoff values of TITR for predicting the attainment of GMI < 7.0%, and TBR < 4% were determined using Receiver Operating Characteristic (ROC) curves.

The TITR cutoff value was calculated to be 41% (sensitivity 81%, specificity 88%) and 40% (54%,72%) for predicting GMI < 7.0% and TBR < 4%.

In individuals with T1D on MDI without devices capable of preventing hypoglycemia, it is recommended to target TITR at 40% to address the risk of increased hypoglycemia sufficiently.

To analyze the time in tight range (TITR), and its relationship with other glucometric parameters in patients with type 1 diabetes (T1D) treated with advanced hybrid closed-loop (AHCL) systems.

A prospective observational study was conducted on pediatric and adult patients with T1D undergoing treatment with AHCL systems for at least 3 months. Clinical variables and glucometric parameters before and after AHCL initiation were collected.

A total of 117 patients were evaluated. Comparison of metabolic control after AHCL initiation showed significant improvements in HbA1c (6.9 ± 0.9 vs. 6.6 ± 0.5%, p < 0.001), time in range (TIR) (68.2 ± 11.5 vs. 82.5 ± 6.9%, p < 0.001), TITR (43.7 ± 10.8 vs. 57.3 ± 9.7%, p < 0.001), glucose management indicator (GMI) (6.9 ± 0.4 vs. 6.6 ± 0.3%, p < 0.001), time below range (TBR) 70-54 mg/dl (4.3 ± 4.5 vs. 2.0 ± 1.4%, p < 0.001), and time above range (TAR) > 180 mg/dl (36.0 ± 7.6 vs. 15.1 ± 6.4%, p < 0.001). Coefficient of variation (CV) also improved (36.3 ± 5.7 vs. 30.6 ± 3.7, p < 0.001), while time between 140-180 mg/dl remained unchanged. In total, 76.3% achieved TITR > 50% (100% pediatric). Correlation analysis between TITR and TIR and GRI showed a strong positive correlation, modified by glycemic variability.

AHCL systems achieve significant improvements in metabolic control (TIR > 70% in 93.9% patients). The increase in TIR was not related to an increase in TIR 140-180 mg/dl. Despite being closely related to TIR, TITR allows for a more adequate discrimination of the achieved control level, especially in a population with good initial metabolic control. The correlation between TIR and TITR is directly influenced by the degree of glycemic variability.

To evaluate the efficacy of a fixed-ratio combination of insulin glargine 100 U/mL plus lixisenatide (iGlarLixi) in people with type 2 diabetes (T2D) using derived time-in-range (dTIR).

Participant-level data from LixiLan-L, LixiLan-O and LixiLan-G were pooled and dTIR (70-180 mg/dL), derived time-above-range (> 180 mg/dL) and derived time-below-range (dTBR; < 70 mg/dL) were calculated from participant seven-point self-monitored blood glucose profiles.

This pooled analysis included data from 2420 participants receiving iGlarLixi (n = 1093), iGlar (n = 836), Lixi (n = 234) or a glucagon-like peptide-1 receptor agonist (GLP-1 RA) (n = 257). Numerically greater improvements in least square (LS) means dTIR were seen from baseline to end of treatment (EOT) with iGlarLixi (25.7%) versus iGlar (15.8%), Lixi (11.7%) or GLP-1 RA (16.2%). At EOT, the mean (standard deviation) dTBR was 0.71% ± 3.4%, 0.61% ± 3.2%, 0.08% ± 1.0% and 0.0% ± 0.0% for iGlarLixi, iGlar, Lixi and GLP-1 RA, respectively. In a subgroup analysis, participants aged younger than 65 years (n = 1690) and 65 years or older (n = 713) showed numerically greater improvements in LS means dTIR from baseline to EOT with iGlarLixi versus iGlar, Lixi or GLP-1 RA.

iGlarLixi achieved improvements in dTIR, with low dTBR values, providing further evidence to inform clinical outcomes with the use of iGlarLixi.

We report the 1-year results from one patient as the preliminary analysis of a first-in-human phase I clinical trial (ChiCTR2300072200) assessing the feasibility of autologous transplantation of chemically induced pluripotent stem-cell-derived islets (CiPSC islets) beneath the abdominal anterior rectus sheath for type 1 diabetes treatment. The patient achieved sustained insulin independence starting 75 days post-transplantation. The patient's time-in-target glycemic range increased from a baseline value of 43.18% to 96.21% by month 4 post-transplantation, accompanied by a decrease in glycated hemoglobin, an indicator of long-term systemic glucose levels at a non-diabetic level. Thereafter, the patient presented a state of stable glycemic control, with time-in-target glycemic range at >98% and glycated hemoglobin at around 5%. At 1 year, the clinical data met all study endpoints with no indication of transplant-related abnormalities. Promising results from this patient suggest that further clinical studies assessing CiPSC-islet transplantation in type 1 diabetes are warranted.

Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.

Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m2) and 30 adults without DR (age of 41.8±14.7 years, HbA1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m2) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.

Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.

Insulin resistance (IR) is increasingly more prevalent in people with type 1 diabetes (T1D).

We investigated whether IR is associated with continuous glucose monitor (CGM)-derived parameters (glucometrics), such as time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (CV).

This is a retrospective analysis of 2 databases: IR was quantified according to the estimated glucose disposal rate (eGDR) (NCT04664036) and by performing a hyperinsulinemic-euglycemic clamp (HEC) (NCT04623320). All glucometrics were calculated over 28 days.

A total of 287 subjects were included. Mean age was 46 ± 17 years, 55% were male, TIR was 57% ± 14%, and eGDR was 7.6 (5.6-9.3) mg/kg/min. The tertile of people with the lowest eGDR (highest level of IR) had a higher TAR compared to the tertile with the highest eGDR (39% ± 15% vs 33% ± 14%, P = .043). Using logistic regression, a higher eGDR was associated with a higher chance to fall in a higher TIR-tertile (odds ratio [OR] 1.251, P < .001), a lower TAR-tertile (OR 1.281, P < .001), and a higher TBR-tertile (OR 0.893, P = .039), adjusted for age, sex, diabetes duration, smoking status, and alcohol intake. In the 48 people undergoing a HEC, no significant association between glucometrics and the HEC-determined glucose disposal rate (M-value) was observed.

In people with T1D, an association between IR, measured by eGDR, and worse CGM profiles was observed.

The glycemia risk index (GRI) is a new composite continuous glucose monitoring (CGM) metric for weighted hypoglycemia and hyperglycemia. We evaluated the association between the GRI and cardiovascular autonomic neuropathy (CAN) and compared the effects of the GRI and conventional CGM metrics on CAN.

For this cross-sectional study, three-month CGM data were retrospectively analyzed before autonomic function tests were performed in 165 patients with type 1 diabetes. CAN was defined as at least two abnormal results of parasympathetic tests according to an age-specific reference.

The overall prevalence of CAN was 17.1%. Patients with CAN had significantly higher GRI scores, target above range (TAR), coefficient of variation (CV), and standard deviation (SD) but significantly lower time in range (TIR) than those without CAN. The prevalence of CAN increased across higher GRI zones (P for trend <.001). A multivariate logistic regression analysis, adjusted for covariates such as HbA1c, demonstrated that the odds ratio (OR) of CAN was 9.05 (95% confidence interval [CI]: 2.21-36.96, P = .002) per 1-SD increase in the GRI. TIR and CV were also significantly associated with CAN in the multivariate model. The area under the curve of GRI for the prediction of CAN (0.85, 95% CI: 0.76-0.94) was superior to that of TIR (0.80, 95% CI: 0.71-0.89, P for comparison = .046) or CV (0.71, 95% CI: 0.57-0.84, P for comparison = .049).

The GRI is significantly associated with CAN in patients with type 1 diabetes and may be a better CGM metric than TIR for predicting CAN.