Continuous glucose monitoring (CGM) might be beneficial for investigating healthy aging since high glycemic variability may increase protein glycation, oxidative stress, and inflammation, resulting in vascular damage. Additionally, CGM data on the risks for hypoglycemia and hyperglycemia are scarce, have not been analyzed by individual day and night blocks, and have not been related to diet. Therefore, this study aimed to compare glucose parameters of healthy older and young adults and the relationship with diet. Participants were 34 young (age 20-35 years) and 27 older volunteers (age 60-75 years) with a normal glycated hemoglobin A1c less than 39 mmol/mol hemoglobin, free of disorders and medication. Twenty-four CGM-derived glucose parameters measured over 5 consecutive days were analyzed for whole days and for individual daytime and nighttime blocks. Dietary intake was determined by 3-day dietary record. Neither intraday nor interday glycemic variability differed between the healthy age groups. Glycemic control was good in both age groups, but somewhat poorer in older adults. The risk of hyperglycemia was higher and of hypoglycemia lower in older adults. During the daytime, mean and minimum glucose were higher in older adults. During the nighttime, age group differences were small. The carbohydrate intake correlated positively with glycemic variability in both age groups. The protein intake correlated positively with the hypoglycemic risk in young adults, but negatively in older adults. Results suggest that healthy aging does not increase glycemic variability and the risk of hypoglycemia. The effect of diet on hypoglycemic and hyperglycemic risk might change with aging.

Our previous studies have demonstrated that dairy products protect against type 2 diabetes (T2D) and improve cardiometabolic health outcomes. Given that continuous glucose monitoring (CGM) and metabolomics analysis capture different aspects of T2D, this study investigated the effects of dairy and non-dairy products on the glycemic and metabolite profiles in healthy adults following lacto-vegetarian and vegan diets.

A parallel randomized feeding trial with 30 participants compared isoenergetic vegan and lacto-vegetarian diets. All participants wore CGM sensors for 14 days to track glucose concentrations. Anthropometric and biochemical characteristics were also measured. In a subgroup of 13 individuals, fasting and postprandial blood samples were collected on days 1 and 15 for metabolomics analysis.

Our CGM data showed higher mean glucose concentrations in the vegan group over 14 days compared to the lacto-vegetarian group (p = 0.0399), after adjusting for age, sex, body mass index, and baseline glucose concentrations. Metabolomics analysis from day 1 to day 15 showed increased postprandial phenylalanine (Phe; p = 0.0189) in the vegan group, while the lacto-vegetarian group showed increased acetyl carnitine (C2; p = 0.00704) and decreased argininosuccinic acid (p = 0.0149).

Our pilot CGM data suggest a lacto-vegetarian diet may offer better glycemic control, potentially explained by our preliminary metabolomics findings. The increased Phe observed in the vegan group may be explained by a hypothetical mechanism in which higher glucose induces oxidative stress, whereas the increased C2 from dairy in the lacto-vegetarian group may protect against oxidative stress, contributing to lower glucose concentrations. However, larger, longer-term studies with more diverse populations, along with in vitro investigations into biomolecular mechanisms, are needed to confirm these findings.

The assessment of haemoglobin A1c (HbA1c) continues to play an essential role in diabetes care; however, major advances in new technologies widen the armament available to clinicians to further refine treatment for their patients. Whilst HbA1c remains a critical glycaemic marker, advances in technologies such as Continuous Glucose Monitoring (CGM) now offer real-time glucose monitoring, allowing a more instant assessment of glycaemic control. Discrepancies between laboratory-measured HbA1c and Glucose Management Indicator (GMI) values are a significant clinical issue. In this article, we present a checklist of potential sources of error for both GMI and HbA1c values and provide suggestions to mitigate these sources in order to continue to improve diabetes care.

We identified key literature pertaining to GMI measurement, HbA1c measurement, and potential factors of discordance between the two. Using these sources, we explore the potential factors leading to discordance and how to mitigate these when found.

We have constructed a quick reference checklist covering the main sources of discordance between HbA1c and GMI, with accompanying narrative text for more detailed discussion. Discordance can arise due to various factors, including CGM accuracy, sensor calibration, red blood cell turnover and other physiological conditions.

GMI will likely continue to be used in the upcoming years by both persons with diabetes and their health care providers, and so it is important for users of CGM devices to be equipped with the knowledge to understand the potential causes of discordance between GMI and HbA1c values.

To evaluate the off-label use of the MiniMed™ 780G system in children under seven years old, as clinical outcomes in this age group are less well-established, despite the improvements in glycemic control seen with MiniMed™ 780G therapy.

Children under seven years old with type 1 diabetes using MiniMed™ 780G pump therapy were compared with children of similar age and gender using MiniMed™ 640G insulin pump therapy and multiple-dose insulin therapy with continuous glucose monitoring systems (CGMs). CGM metrics, total daily insulin (TDI) dose, and hemoglobin A1c (HbA1c) levels were evaluated retrospectively at baseline and at the 3rd, 6th, and 12th months.

At the initiation of MiniMed™ 780G therapy, the mean age was 5.25±1.22 years (range: 2.8-6.8 years), and the mean TDI was 10.12±4.34 U/day (range: 4.5-17.0 U/day). The glucose management indicator and HbA1c remained lower in the MiniMed™ 780G group at the 3rd, 6th, and 12th months compared to baseline (p=0.009 and p<0.001, respectively). In the MiniMed™ 780G group, time above range (TAR) was significantly lower at the 3rd, 6th, and 12th months (p=0.018, p=0.017 and p=0.04, respectively) while time in range (TIR) was higher at the 3rd, and 12th months (p=0.026 and p=0.019, respectively) compared to other groups. The coefficient of variation (CV) of the sensor glucose and HbA1c were lower at the 12th month (p=0.008 and p=0.015, respectively) compared to both other groups. No instances of ketoacidosis or severe hypoglycemic events were observed in any of the children during the follow-up period.

The absence of significantly higher levels of hypoglycemia compared to other groups at any time point, along with a significant decrease in TAR across all time points, a significant increase in TIR at the 3rd and 12th months, and a significant decrease in HbA1c and CV suggests that the MiniMed™ 780G system is both safe and effective for children under seven years old.

The primary goal in managing glycogen storage disorders (GSD) is to implement dietary therapy through regular glucose monitoring while attempting to prevent complications. Self-monitoring of blood glucose is often insufficient for detecting asymptomatic hypoglycemia in patients. Therefore, Continuous glucose monitoring systems (CGMS) play a crucial role in identifying hypoglycemic episodes and providing detailed glucose profiles throughout the day. In this study, CGMS data, laboratory findings, and daily nutritional intake were examined in patients with GSDIa and GSDIII. The lack of similar studies in GSDIII patients in the literature highlights the need for further research in this field.

The glucose profiles of 12 patients (7 GSDIa and 5 GSDIII) were analyzed over a 72 h period using CGMS. Nutritional intake, biochemical parameters, and growth parameters were also evaluated.

This study demonstrated that CGMS detected both hypoglycemia (<70 mg/dL) and hyperglycemia (>150 mg/dL) in GSD patients. Growth retardation was also observed in these patients. As complications of the disease, elevated levels of liver enzymes, cholesterol, triglycerides, and creatine kinase were identified, with fatty liver and hepatomegaly detected in all patients. The patients' nutritional intake is similar to the recommendations in disease-specific treatment guidelines.

The primary dietary treatment goal for GSD patients is to maintain normoglycemia. Patients may experience asymptomatic low glucose and/or asymptomatic hypoglycemic episodes during treatment. CGMS enables a more detailed monitoring of glucose profiles, which not only facilitates the precise adjustment of dietary therapy based on detailed results but also helps prevent complications associated with the disease.

In glucose management using continuous insulin infusion, artificial pancreas systems prevent blood glucose fluctuations and severe hypoglycemia using insulin pumps and continuous glucose monitoring. Advances in both insulin pumps and continuous glucose monitoring have enabled the transition from sensor augmented pump therapy, where insulin delivery is manually adjusted, to hybrid closed-loop insulin pump therapy, which automatically adjusts basal insulin infusion. Furthermore, fully automated insulin delivery systems that adjust insulin based on variations due to meals and exercise are now possible. These systems have been primarily applied to patients with type 1 diabetes but are now expanding to all insulin-dependent patients. Wearable artificial pancreas systems measure glucose levels in subcutaneous tissue fluid, while bedside artificial pancreas systems measure glucose levels in venous blood, making them suitable for managing the highly variable blood glucose levels of perioperative and critically ill patients. Future developments are anticipated to integrate the benefits of both wearable and bedside systems.

Nicotine inhibits glucose metabolism. In this national cross-sectional analysis of 388 young adults with type 1 diabetes and above target glycemic control, vaping was the most common route of nicotine use, and heavy nicotine use plus higher type 1 diabetes distress was related to worse objective measures of glycemic control. Trial registration: ClinicalTrials.govNCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473.

Aim: This study aims to evaluate the accuracy of continuous glucose monitoring (CGM)-derived metrics, particularly those related to glycemic variability, in the presence of missing data. It systematically examines the effects of different missing data patterns and imputation strategies on both standard glycemic metrics and complex variability metrics. Methods: The analysis modeled and compared the effects of three types of missing data patterns-missing completely at random, segmental, and block-wise gaps-with proportions ranging from 5% to 50% on CGM metrics derived from 14-day profiles of individuals with type 1 and type 2 diabetes. Six imputation strategies were assessed: data removal, linear interpolation, mean imputation, piecewise cubic Hermite interpolation, temporal alignment imputation, and random forest-based imputation. Results: A total of 933 14-day CGM profiles from 468 individuals with diabetes were analyzed. Across all metrics, the coefficient of determination (R2) improved as the proportion of missing data decreased, regardless of the missing data pattern. The impact of missing data on the agreement between imputed and reference metrics varied depending on the missing data pattern. To achieve high accuracy (R2 > 0.95) in representing true metrics, at least 70% of the CGM data were required. While no imputation strategy fully compensated for high levels of missing data, simple removal outperformed others in most scenarios. Conclusion: This study examines the impact of missing data and imputation strategies on CGM-derived metrics. The findings suggest that while missing data may have varying effects depending on the metric and imputation method, removing periods without data is a general acceptable approach.

This expert consensus provides a standardized methodology for interpreting continuous glucose monitoring (CGM) data to optimize diabetes management. It aims to help healthcare professionals recognize glycemic patterns and apply targeted interventions based on real-time glucose metrics.

A systematic literature review informed expert panel discussions. Specialists from Latin America assessed CGM interpretation challenges, reviewed key metrics, and reached consensus through an anonymous voting process. The recommendations align with international guidelines while addressing regional limitations in technology access and healthcare infrastructure.

Reliable CGM data interpretation requires at least 70% sensor use over 14 days. The Ambulatory Glucose Profile (AGP) report serves as the primary tool, offering essential metrics such as time in range (TIR), time below range (TBR), time above range (TAR), coefficient of variation (CV), and glucose management indicator (GMI). Identifying hyperglycemia, hypoglycemia, and glucose variability allows for personalized treatment adjustments. The panel adopted international glycemic targets, adapting them to Latin American settings. The time in tight range (TITR) was considered but not included due to limited supporting evidence and regional barriers to advanced CGM technology.

Standardized CGM interpretation improves glycemic control and treatment decisions. These recommendations provide a structured approach to diabetes care, aiming to enhance clinical outcomes and address healthcare disparities in Latin America.

Glucose management indicator (GMI) may not perform equally well for different continuous glucose monitoring (CGM) systems. Thus, we aimed to develop device-specific GMI for the Guardian 3 and 4 sensors, compare them to the original GMI, and investigate the association between the glycaemic gap (=HbA1c-GMI) and HbA1c.

In a single-centre, observational study of adult type 1 diabetes patients using Guardian Sensor 3 (G3) and 4 (G4) CGM devices, we estimated HbA1c using CGM-derived mean glucose for both CGMs using linear mixed models. We compared the estimates and their residuals (G3-gap and G4-gap) to the original GMI and its residuals (Bergenstal-gap) using regression and Bland-Altman plots.

We included 120 adult type 1 diabetes patients (90 with G3 and 30 with G4) and 194 measurement points. We found that for G3 and G4 sensors, GMI significantly underestimated glycaemia in high HbA1c ranges, reaching the clinically significant 0.5 %[4.4 mmol/mol] difference at 7.6 % [60 mmol/mol] for G3 and 8.3 % [67 mmol/mol] for G4 sensors. For G4, GMI significantly overestimated glycaemia in the lower HbA1c range. We found a strong relationship between all 3 gaps and HbA1c, and the slope was steeper for the Bergenstal-gap versus the sensor-specific G3 and G4 gaps. The G3-gap was approximately half as large as the Bergenstal-gap for HbA1c > 7 % [53 mmol/mol], and the G4-gap is approximately half of the Bergenstal-gap for the whole HbA1c range.

Device-specific GMI equations could reduce the risk of clinically significant under- and overestimation of HbA1c, improving clinical decision-making.

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.

This study compares the association between real-time continuous glucose monitoring (rtCGM) and intermittently- scanned CGM (isCGM) and glycemic control in individuals with type 1 diabetes mellitus (T1DM) in a real-world setting.

Using data from the Korean National Health Insurance Service Cohort, individuals with T1DM managed by intensive insulin therapy were followed at 3-month intervals for 2 years after the initiation of CGM. The glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CVs) of rtCGM and isCGM users were compared using independent two-sample t-test and a linear mixed model.

The analyses considered 7,786 individuals (5,875 adults aged ≥19 years and 1,911 children and adolescents aged <19 years). Overall, a significant reduction in HbA1c level was observed after 3 months of CGM, and the effect was sustained for 2 years. The mean HbA1c level at baseline was higher in rtCGM users than in isCGM users (8.9%±2.7% vs. 8.6%±2.2%, P<0.001). However, from 3 to 24 months, rtCGM users had lower HbA1c levels than isCGM users at every time point (7.1%±1.2% vs. 7.5%±1.3% at 24 months, P<0.001 for all time points). In both adults and children, the greater reduction in HbA1c with rtCGM remained significant after adjusting for the baseline characteristics of the users. The CV also showed greater decrease with rtCGM than with isCGM.

In this large nationwide cohort study, the use of rtCGM was associated with a greater improvement in glycemic control, including HbA1c reduction, than the use of isCGM in both adults and children with T1DM.

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.

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.

HbA1c testing in African populations may be limited due to high prevalence of hemoglobinopathies, anaemia, malaria and renal impairment. We aimed to assess the performance of glycated albumin (GA) and fructosamine in comparison to HbA1c for determining glycaemic control in Africans living with type 2 diabetes.

We compared the relationship between fructosamine, GA, and HbA1c with mean continuous glucose monitoring (CGM) glucose and assessed the impact of sickle cell trait (SCT), anaemia and renal impairment on the relationship between each measure and CGM glucose.

The overall association of HbA1c, GA and fructosamine with CGM glucose was similar (r = 0.88 [95%CI: 0.84, 0.91], 0.84 [0.79, 0.88] and 0.84 [0.79, 0.88]), respectively. For detecting those with mean CGM glucose >8 mmol/L HbA1c had similar diagnostic accuracy to GA and fructosamine, even in those with conditions reported to affect HbA1c performance (n = 63). We found no evidence that SCT (n = 43/192) altered the relationship between HbA1c, fructosamine or GA with CGM glucose (p > 0.3 for all). However, individuals with anaemia showed an underestimation of CGM glucose by HbA1c and fructosamine compared to those without anaemia (p for interaction <0.005 for both). In contrast, GA with average CGM glucose between those with anaemia and those without were not significantly different.

Switching to fructosamine or GA is unlikely to improve the accuracy of laboratory glycaemic monitoring beyond that of HbA1c in a population with high prevalence of conditions reported to affect HbA1c reliability.

Eating jetlag (EJL), the difference in eating times between weekdays and weekends, disrupts circadian alignment and may affect metabolic health. However, its influence on glucose tolerance and continuous glucose monitoring (CGM) during pregnancy remains unknown.

We aimed to investigate the associations between EJL and glycemic parameters during pregnancy.

This secondary analysis was conducted on a cohort of 248 healthy pregnant females from Singapore. EJL, derived from 4-d food diaries at 20-wk of gestation, was the absolute difference in average meal times between weekdays and weekends for the first (EJLfirst) and last (EJLlast) meals and categorized as ≤1-h (reference) or >1-h. Primary outcomes at 25-wk of gestation included results from the 75-g oral glucose tolerance test, fasting insulin, homeostasis model assessment of insulin resistance (HOMA2-IR), and β-cell function (HOMA2-%B). Secondary outcomes at 20-wk of gestation included glycemic control and variability measured over 10-d using CGM. Skewed glycemic variables were log-transformed for normality, and associations between EJL and glycemic outcomes were analyzed using multivariable regressions.

After adjusting for baseline sociodemographic, lifestyle, and dietary factors, EJLlast >1-h was associated with higher fasting insulin [geometric mean ratio (95% confidence intervals): 1.21 (1.05, 1.39)], HOMA2-IR [1.21 (1.05, 1.39)], HOMA2-%B [1.11 (1.01, 1.22)], and CGM-based measures, including mean glucose [1.05 (1.00, 1.09)], J-index [1.11 (1.01, 1.22)], and glucose management indicator [1.03 (1.00, 1.06)]. EJLfirst >1-h was associated with higher CGM-based mean amplitude of glycemic excursions (MAGE) [1.09 (1.01, 1.19)]. For CGM-based glycemic variability outcomes (standard deviation, coefficient of variation [CV], MAGE), there were interactions between EJLfirst and 1) diet quality [adherence to Dietary Approaches to Stop Hypertension (DASH)] (P-interactions = 0.06-0.09), and 2) prepregnancy body mass index (BMI) (P-interaction=0.07 for CV). In females with a prepregnancy BMI ≥23 kg/m2 and low diet quality (DASH score ≤median), EJLfirst >1 h was associated with higher CGM-based glycemic variability.

EJL was associated with unfavorable glycemic parameters during pregnancy. Dietary interventions could promote consistent meal timing, especially in higher risk groups with suboptimal nutritional status. This trial was registered at clinicaltrials.gov as NCT03803345.

Heart failure (HF) is a major cause of mortality in critically ill patients and often requires intensive care. The hemoglobin glycation index (HGI), defined as the difference between predicted glycated hemoglobin (HbA1c) and measured HbA1c, may provide additional prognostic insights beyond traditional glycemic metrics.

We conducted a retrospective analysis of 8,098 adult patients with HF from the MIMIC-IV database (2008-2022). All were first-time ICU admissions with available hematologic and metabolic data. Patients were stratified into three groups (T1 ≤ - 1.26, - 1.26 < T2 < 1.74, T3 ≥ 1.74) based on HGI. Baseline characteristics were recorded within 24 h of ICU admission, including demographic data, disease severity scores, comorbidities, and medication use. Logistic regression and Cox proportional hazards models assessed the associations between HGI and in-hospital, 30-day, and 1-year all-cause mortality, adjusting for age, sex, race, comorbidities, laboratory results, and relevant treatments. Restricted cubic spline (RCS) analysis was performed to examine potential non-linear relationships. We used sensitivity analyses to increase the confidence in our primary outcome.

Patients in the lowest HGI group (T1) had significantly higher in-hospital, 30-day, and 1-year mortality than those in the other two groups. Specifically, T1 showed an 18.6% in-hospital mortality rate, compared with 12.3% and 9.7% in T2 and T3, respectively (p < 0.001). Fully adjusted models revealed that each 1-unit increase in HGI was associated with an approximate 12% reduction in in-hospital mortality risk (OR = 0.88; 95%CI: 0.83-0.93), and an 3% decreased risk of 1-year all-cause mortality (HR 0.97; 95%CI0.94~1.00). RCS analysis indicated a J-shaped relationship between HGI and mortality, underscoring the heightened risk associated with very low HGI. We conducted sensitivity analyses by separately excluding missing data, diagnosed sepsis, and diagnosed hepatic impairment, consistent with the primary analysis.

In critically ill HF patients, extremely low HGI levels correlate with poorer short- and long-term survival. These findings suggest that HGI could serve as an adjunct risk stratification tool, prompting closer monitoring and potential intervention in patients with markedly low HGI.

Musculoskeletal disorders (MSDs) are common, but overlooked, complications of type 1 diabetes mellitus (T1DM). This study aims to describe MSD phenotypes (clinical, lifestyle, socio-economic) in adults with T1DM.

We analyzed adult participants in the SFDT1 cohort study. We assessed the following MSDs: stress fractures, non-traumatic upper-limb disorders, and entrapment syndromes. We performed a cross-sectional analysis of the association between MSDs and various factors. After applying multiple imputations for missing data, we computed logistic regression models with progressive adjustments on confounding factors.

Of 1832 participants (53 % men, median age 38 (IQR 22) years), 34 % reported at least one personal history of MSD: 8 % for stress fractures, 24 % for upper-limb disorders and 15 % for entrapment syndromes. A higher prevalence of MSDs was found in women, with aging and with diabetes duration. In a fully adjusted model, we observed a positive association between current smoking (OR [95 %CI] = 1.50 [1.14;1.97]), non-excessive alcohol consumption (1.45 [1.14;1.85]), neuropathy (1.70 [1.35;2.15]), retinopathy (1.30 [1.02;1.65]), use of automated insulin delivery systems (1.53 [1.06;2.21]) and MSDs. MSDs were associated with reduced global quality of life (0.97 [0.95;0.98]). MSDs were not associated with HbA1c, social vulnerability or physical activity.

We have shown that MSDs are found in 1 in 3 people with T1DM. They are associated with several lifestyle factors, diabetes complications and the use of automated insulin delivery systems. MSDs should be considered in the T1DM assessment to optimize quality of life.

The hemoglobin glycation index (HGI) represents the discrepancy between the glucose management indicator (GMI) based on mean blood glucose levels and laboratory values of glycated hemoglobin (HbA1c). The HGI is a promising indicator for identifying individuals with excessive glycosylation, facilitating personalized evaluation and prediction of diabetic complications. However, the factors influencing the HGI in patients with type 1 diabetes (T1D) remain unclear. Autoimmune destruction of pancreatic β cells is central in T1D pathogenesis, yet insulin resistance can also be a feature of patients with T1D and their coexistence is called "double diabetes" (DD). However, knowledge regarding the relationship between DD features and the HGI in T1D is limited.

To assess the association between the HGI and DD features in adults with T1D.

A total of 83 patients with T1D were recruited for this cross-sectional study. Laboratory HbA1c and GMI from continuous glucose monitoring data were collected to calculate the HGI. DD features included a family history of type 2 diabetes, overweight/obesity/central adiposity, hypertension, atherogenic dyslipidemia, an abnormal percentage of body fat (PBF) and/or visceral fat area (VFA) and decreased estimated insulin sensitivity. Skin autofluorescence of advanced glycation end products (SAF-AGEs), diabetic complications, and DD features were assessed, and their association with the HGI was analyzed.

A discrepancy was observed between HbA1c and GMI among patients with T1D and DD. A higher HGI was associated with an increased number of SAF-AGEs and a higher prevalence of diabetic microangiopathy (P = 0.030), particularly retinopathy (P = 0.031). Patients with three or more DD features exhibited an eight-fold increased risk of having a high HGI, compared with those without DD features (adjusted odds ratio = 8.12; 95% confidence interval: 1.52-43.47). Specifically, an elevated PBF and/or VFA and decreased estimated insulin sensitivity were associated with high HGI. Regression analysis identified estimated insulin sensitivity and VFA as factors independently associated with HGI.

In patients with T1D, DD features are associated with a higher HGI, which represents a trend toward excessive glycosylation and is associated with a higher prevalence of chronic diabetic complications.

Youth with type 1 diabetes (T1D) and Medicaid must demonstrate they have self-monitored their blood glucose level at least four times daily to receive continuous glucose monitors (CGMs). New California Medicaid policies eliminated this requirement and, thus, CGM access has increased. This study examines whether infrequent baseline self-monitored blood glucose (SMBG) checks result in suboptimal outcomes or nonadherence with CGM use.

This retrospective study included youth with T1D and Medicaid who started CGM after January 2019, when newer models no longer needed calibration, at two large health care systems. Patients were stratified by data on baseline SMBG frequency (<4 vs. ≥4 checks daily) collected at the clinic visit prior to starting CGM. Differences between SMBG groups in CGM adherence and HbA1c over time were assessed by a mixed-effects linear regression model and fixed-effect interaction term. Patients were surveyed to explore individual impact of CGM on diabetes management.

We followed 78 youth for 6 months. CGM adherence was similar between SMBG frequency groups at 3 months (68.7% vs. 68.4%; P = 0.97) and sustained at 6 months. HbA1c values improved in both groups at 3 months, with a larger improvement in those with SMBG <4 daily checks (1.3% vs. 0.4%), and sustained at 6 months. Patient surveys (n = 35) reported high engagement with CGM and increased insulin boluses after initiation.

Patients using CGM demonstrated improvement in HbA1c regardless of prior SMBG. Increased engagement with CGM likely promoted increased insulin boluses. Therefore, restriction of CGM to those with SMBG ≥4 daily checks is an unnecessary barrier, excluding those who could potentially benefit the most.

Type 2 diabetes (T2D) imposes significant personal challenges and societal costs. Continuous glucose monitoring (CGM) is recognised as a state-of-the-art tool, but remains underutilised. Adoption of CGM in primary care should be informed by a broader understanding of the technology's capabilities and limitations.

An expert panel was convened to review current literature and clinical experience to provide practical approaches to CGM for primary care practitioners and discuss the technology's value in the routine management of T2D. The goals were to review and reach consensus on the current state of CGM in non-specialist practice settings and on strategies for successfully initiating and maintaining people on CGM.

Initiation and maintenance of CGM therapy can be successfully conducted in primary care settings. CGM therapy should include proper patient selection, proper setting of expectations, and evidence-based adjustments to therapy. Most patients are likely to see quick, meaningful, and lasting improvements in their diabetes, along with a better understanding of their condition and greater motivation for successful management. Retrospective report interpretation is feasible and intuitive. Barriers to adoption and sustained use include cost, technological limitations, behavioural or psychological factors, and therapeutic inertia. Addressing these barriers is critical to enable better access to CGM. Continuous glucose monitoring can be leveraged by primary care teams to inform treatment decisions and also by patients to inform diabetes self-management.

CGM should be considered for all people with T2D. The recommendations provided here should simplify adoption and maintenance use of CGM in primary care and maximise the glycaemic and psychosocial benefits of the technology.

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.

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.

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.

To analyze the sociodemographic characteristics and trends in clinical and analytical parameters among individuals with chronic kidney disease (CKD) in Aragon (Spain), who remain uninfected with COVID-19 during the first year of pandemic. The secondary objectives were to identify the associated comorbidities and their evolution throughout the pandemic, as well as to determine the cases that got worse and their possible relationship with the control of the main risk factors.

CKD is a major public health problem worldwide. Studies encompassing national, European, and global contexts, show a rise in the prevalence of CKD, with a significant decrease in life quality, high morbidity and mortality, and increased healthcare costs. In this scenario, primary care is a cornerstone for the early detection of CKD and for the management of progression factors. To date, there are few publications regarding the evolution of the CKD population throughout the pandemic that are not related to hospitalizations or complications due to COVID-19.

We conducted a retrospective longitudinal study with real-world data from the population over 16 years of age registered in Aragon (Spain), collecting data from electronic health records. The variables included were sociodemographic, analytical and clinical (glomerular filtration rate, cholesterol, triglycerides, glycated haemoglobin, and blood pressure) and comorbidities (hypertension, dyslipidemia, obesity, diabetes, and smoking). The data were archived and processed using the SPSS v22.0 software package.

During the first six months of COVID-19 pandemic, the clinical parameters of people with CKD were poorly controlled, although there was a later improvement which could be related to the progressive recovery of health services. The glycated haemoglobin value found was low, which makes us suspect possible overtreatment. There is a high prevalence of high blood pressure, diabetes, dyslipidemia, obesity and smoking. Interventions targeting these factors could help reduce the burden of CKD.

Diabetes presents a significant challenge to healthcare due to the short- and long-term complications associated with poor blood sugar control. Computer simulation platforms have emerged as promising tools for advancing diabetes therapy by simulating patient responses to treatments in a virtual environment. The University of Virginia Virtual Lab (UVLab) is a new simulation platform engineered to mimic the metabolic behavior of individuals with type 2 diabetes (T2D) using a mathematical model of glucose homeostasis in T2D and a large population of 6062 virtual subjects. This work proposes a statistical method - the Distribution-based sub-population selection (DSPS) method - for selecting subsets of virtual subjects from this large initial pool, ensuring that the selected group possesses the desired characteristics necessary to reproduce and predict the outcomes of a clinical trial. DSPS formulates the sub-population selection as a linear programming problem, identifying the largest virtual cohort to closely resemble the statistical properties (moments) of key outcomes from real-world clinical trials. The method was applied to the insulin degludec arm of a 26-week phase 3 clinical trial, evaluating the efficacy and safety of insulin degludec and liraglutide combination therapy. DSPS selected a sub-population that mirrored clinical trial data across key metrics, including glycemic efficacy, insulin dosages, and cumulative hypoglycemia events, with a relative sum of square errors of 0.33 and a percentage error of 1.07 %. This approach bridges the gap between large population simulation platforms and clinical trials, enabling the selection of virtual sub-populations with specific properties required for targeted studies.

This study explored ways in which the COVID-19 pandemic impacted adolescents' diabetes management and psychosocial functioning, and how adolescents, parents, and providers viewed telemedicine. We present data from three studies: (1) a comparison of psychosocial functioning and glycemic levels before and after pandemic onset (n = 120 adolescents; 89% with type 1 diabetes), (2) an online survey of parents about pandemic-related stressors (n = 141), and (3) qualitative interviews with adolescents, parents, and medical providers about the pandemic's impacts on adolescents' diabetes care and mental health (n = 13 parent-adolescent dyads; 7 medical providers). Results suggested some adverse effects, including disrupting routines related to health behaviors and psychosocial functioning and impairing adolescents' quality of life. Despite these challenges, most participants did not endorse significant impacts. Some even noted benefits, such as increased parental supervision of diabetes management that can be leveraged beyond the pandemic. Furthermore, telemedicine offers benefits to continuity of diabetes care but presents challenges to care quality. These findings underscore the varied and unique impacts of the COVID-19 pandemic on adolescents with diabetes.

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.

The integration of continuous glucose monitoring (CGM) into clinical practice has rapidly emerged in the last decade, changing the evaluation of long-term glucose regulation in patients with diabetes. When using CGM-derived metrics to evaluate long-term glucose regulation, it is essential to determine the minimal observation period necessary for a reliable estimate. The approach of this study was to calculate mean absolute errors (MAEs) for varying window lengths, with the goal of demonstrating how the CGM observation period influences the accuracy of the estimation of 90-day glycemic control.

CGM data were collected from the DIABASE cohort (ZGT hospital, The Netherlands). Trailing aggregates (TAs) were calculated for four CGM-derived metrics: time in range (TIR), time below range (TBR), glucose management indicator (GMI) and glycemic variability (GV). Arbitrary MAEs for each patient were compared between the TAs of window lengths from 1 to 89 days and a reference TA of 90 days, which is assumed to reflect long-term glycemic regulation.

Using 14 days of CGM data resulted in 65% of subjects having their TIR estimation being below a MAE threshold of 5%. In order to have 90% of the subjects below a TIR MAE threshold of 5%, the observation period needs to be 29 days.

Although there is currently no consensus on what is an acceptable MAE, this study provides insight into how MAEs of CGM-derived metrics change according to the used observation period within a population and may thus be helpful for clinical decision-making.

Continuous glucose monitoring (CGM) data stored in data warehouses often include duplicated or time-shifted uploads from the same patient, compromising data quality and accuracy of resulting CGM metrics. We developed a processing algorithm to detect and resolve these errors. We validated the algorithm using two weeks of CGM data from 2038 patients with diabetes. Duplication errors were identified in 528 patients, with 25.7% showing significant differences in at least one metric (Time in Range, Coefficient of Variation, Glycemic Management Indicator, or Glycemic Episode counts) between raw and processed data. Eleven patients crossed clinically meaningful thresholds in one or more metrics after processing. Our results underscore the importance of real-world CGM data processing to maintain accurate and reliable CGM metrics for research and clinical care.

Since the arrival of continuous glucose monitoring (CGM), the relationship between the glucose management indicator (GMI) and HbA1c has been a topic of considerable interest in diabetes research. This study aims to explore the association between the GMI/HbA1c ratio and the presence of preclinical carotid atherosclerosis in type 1 diabetes (T1D).

Individuals with T1D and no prior history of cardiovascular disease were recruited from two centers. Carotid ultrasonography was performed using a standardized protocol and carotid plaques were defined as intima-media thickness ≥ 1.5 mm. CGM-derived data were collected from a 14-day report. A GMI/HbA1c ratio < 0.90 was selected to identify "fast-glycator" phenotype.

A total of 584 participants were included (319 women, 54.6%), with a mean age of 48.8 ± 10.7 years and a mean diabetes duration of 27.5 ± 11.4 years. Carotid plaques were present in 231 subjects (39.6%). Approximately 43.7% and 13.4% of participants showed absolute differences of ≥ 0.5 and ≥ 1.0 between 14-day GMI and HbA1c, respectively. Among patients ≥ 48 years, the fast-glycator phenotype was independently associated with presence of plaques (OR 2.27, 95%CI: 1.06-4.87), even after adjusting for non-specific and T1D-specific risk factors and statin treatment. No significant association was observed in younger subjects (p for interaction < 0.05).

Fast-glycator phenotype is independently associated with atherosclerosis in T1D individuals aged ≥ 48 years, suggesting an age-related increase in the glycation risk. These findings highlight the potential of the GMI/HbA1c ratio for cardiovascular risk stratification in this population.

Background: Modeling techniques in the field of pediatrics present unique challenges beyond traditional model limitations, and sometimes difficulties in faithfully simulating the condition's evolution over time. Objective: This study aimed to identify whether economic modeling approaches in diabetes in pediatric patients align with the recommendations of clinical practice guidelines and the latest scientific evidence. Methods: A literature review was performed in March 2023 to identify modeling-based economic evaluations in diabetes in pediatric patients. Data were extracted and synthesized from eligible studies. Clinical practice guidelines for diabetes were gathered to compare their alignment with modeling strategies. Two endocrinology specialists provided insights on the latest findings in diabetes that are not yet included in the guidelines. A multidisciplinary group of experts agreed on the relevant themes to conduct the comparative analysis: parameter informing on glycemic control, diabetic ketoacidosis/hypoglycemia, C-peptide as prognostic biomarker, metabolic memory, age at diagnosis, socioeconomic status, pediatric-specific sources of risk equations, and pediatric-specific sources of utilities/disutilities. Results: Nineteen modeling-based studies (7 de novo, 12 predesigned models) and 34 guidelines were selected. Hemoglobin A1c was the main parameter to model the glycemic control; however, guidelines recommend the usage of complementary measures (eg, time in range) which are not included in economic models. Eight models included diabetic ketoacidosis (42.1%), 16 included hypoglycemia (84.2%), 2 included C-peptide (1 of those as prognostic factor) (10.5%) and 1 included legacy effect (5.3%). Neither guidelines nor models included recent findings, such as age at diagnosis or socioeconomic status, as prognostic factors. The lack of pediatric-specific sources for risk equations and utility/disutility values were additional limitations. Discussion: Economic models designed for assessing interventions in diabetes in pediatric patients should be based on pediatric-specific data and include novel adjuvant glucose-monitoring metrics and latest evidence on prognostic factors (C-peptide, legacy effect, age at diagnosis, socioeconomic status) to provide a more faithful reflection of the disease. Conclusions: Economic models represent useful tools to inform decision making. However, further research assessing the gaps is needed to enhance evidence-based health economic modeling that best represents reality.

To explore the relationship between glucose management indicator (GMI) and HbA1c and find the affecting factors in adult T2D patients with good glycemic control.

Adult T2D patients with both HbA1c < 7% and time in range (TIR) > 70% were retrospectively analyzed. A significant difference between GMI and HbA1c was defined as an absolute value of hemoglobin glycation index (|HGI|, HbA1c minus GMI) ≥ 0.5%. Factors associated with high |HGI| were determined by logistic regression analysis. The performance of possible factors in predicting high |HGI| was verified by ROC curve analysis. And the linear relationship between GMI and HbA1c was also investigated.

Of all the 94 patients (median HbA1c 6.18%, mean GMI 6.34%) included, 28.72% had an |HGI | ≥ 0.5% and only 15.96% had an |HGI | < 0.1%. Standard deviation of blood glucose (SDBG), a glycemic variability index, affected |HGI| (OR = 3.980, P = 0.001), and showed the best performance in predicting high |HGI| (AUC = 0.712, cutoff value = 1.63 mmol/L, P = 0.001). HbA1c was linearly correlated with GMI (β = 0.295, P = 0.004). Their correlation weakened after further adjusting for SDBG (β = 0.232, P = 0.012). Linear correlation between them was closer in patients with smaller SDBG ( < 1.63 mmol/L) than those with larger SDBG (P = 0.004).

Even in adult T2D patients with good glycemic control, the discrepancy between GMI and HbA1c existed. Their relationship was affected by glycemic variability. SDBG mainly accounted for this consequence.

Chinese clinical trial registry ( www.chictr.org.cn ), ChiCTR2000034884, 2020-07-23.

Background Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disease in Saudi Arabia, with a prevalence rate of approximately 25%. Traditional blood glucose monitoring methods, such as finger stick tests, provide limited insights into blood glucose measurements and fluctuations contributing to clinical inertia. The advent of continuous glucose monitoring (CGM) systems, such as the FreeStyle Libre glucose monitoring system, has transformed diabetes management by offering comprehensive exposure of glucose data to healthcare providers. Objective This study aims to evaluate the impact of the FreeStyle Libre glucose monitoring system on diabetes management intensification and treatment progression provided by healthcare specialists among T2DM patients in primary healthcare settings in Riyadh, Saudi Arabia. Methods An observational, retrospective, 24-week, two-arm study was conducted at Prince Sultan Military Medical City. The study involved 188 T2DM patients who were either using standard capillary glucose monitoring or transitioned to the FreeStyle Libre system, and it was based on clinical discretion rather than randomization. The primary outcomes were to evaluate the effect of FreeStyle Libre on treatment intensification provided by healthcare providers and changes in HbA1c levels. Data analysis included descriptive statistics and hypothesis testing using R software. Results Participants using the FreeStyle Libre glucose monitoring system experienced higher rates of medication intensification, and the use of insulin correction doses, and a significant reduction in median HbA1c levels was observed at three months (9.19% vs. 9.6%, p=0.047). However, at six months, the median HbA1c further reduced to 9.07%, though the difference between groups was not statistically significant. Despite these improvements, healthcare provider visits due to hyperglycemia were higher in the FreeStyle Libre group (p<0.001). There were no significant differences in hypoglycemia-related visits between the two groups (p=0.09). Conclusion The FreeStyle Libre glucose monitoring system was associated with increased treatment intensification and a significant reduction in HbA1c at three months compared to standard glucometers. However, by six months, the reduction in HbA1c was no longer statistically significant between groups. The increased healthcare provider visits in the FreeStyle Libre group may be attributed to heightened glucose monitoring awareness rather than the true worsening of hyperglycemia. While CGM offers advantages in diabetes management, its impact on long-term glycemic control remains uncertain. Further research is needed to confirm these findings, assess patient adherence, and evaluate the long-term effectiveness of continuous glucose monitoring in diabetes care.

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.

In this study, we assessed whether the ratio of glucose management index (GMI) to glycated albumin (GA) was linked to microvascular complications in patients with type 2 diabetes mellitus (T2DM) who also possessed a shortened erythrocyte lifespan.

This study encompassed individuals from the Tianjin Diabetic Retinopathy Screening Cohort who completed continuous glucose monitoring and had an erythrocyte lifespan of under 90 days. Differences in GMI/GA were compared between the T2DM patients with or without microvascular complications, including diabetic kidney disease (DKD) and diabetic retinopathy (DR). The relationship between GMI/GA and microvascular complications (DKD and/or DR) was assessed by dividing GMI/GA into three groups based on tertiles.

Our study comprised 140 participants with T2DM (62 men and 78 women, with a median age of 67 years) with a median DM duration of 9.68 years, a mean glycated hemoglobin A1c (HbA1c) value of 7.10%, and a median GA value of 16.10%. As expected, the lower GMI/GA group exhibited higher HbA1c and GA (P < 0.001) with similar mean glucose levels (P = 0.099). GMI/GA values were significantly higher in participants without microvascular complications than in those with microvascular complications, including DKD and/or DR (P < 0.05). After adjusting for confounders, the lowest GMI/GA group (T1) had a 3.601-fold increased risk of microvascular complications (95% CI, 1.364-9.508, P = 0.010) and a 3.830-fold increased risk of DKD, specifically (95% CI, 1.364-12.222, P = 0.023) relative to the highest group (T3).

GMI/GA serves as a novel risk indicator for microvascular complications in T2DM, independent of HbA1c.