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.

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.

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous glycemic monitoring systems. The aim of this study is to evaluate the GV in patients with type 1 diabetes mellitus (T1DM) and to assess its associations with other parameters used to evaluate the glycemic control. Methods: GV indexes and classical glycemic control markers were analyzed for 147 adult patients with T1DM in a multicentric cross-sectional study. Results: Stable glycemia was associated with a higher time in range (TIR) (78% vs. 63%; p < 0.001) and a lower HbA1c (6.8% vs. 7.1%; p = 0.006). The coefficient of variation (CV) was reversely correlated with TIR (Spearman's r = -0.513; p < 0.001) and positively correlated with hemoglobin A1c (HbA1c) (Spearman's r = 0.349; p < 0.001), while TIR was reversely correlated with HbA1c (Spearman's r = -0.637; p < 0.001). The composite GV and metabolic outcome was achieved by 28.6% of the patients. Conclusions: Stable glycemia was associated with a lower HbA1c, average and SD of blood glucose, and a higher TIR. A TIR higher than 70% was associated with a lower HbA1c, and SD and average blood glucose. Only 28.6% of the patients with T1DM achieved the composite GV and metabolic outcome, despite 53.7% of them achieving the HbA1c target, emphasizing thus the role of GV in the assessment of the glycemic control.

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.

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.

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.

Recent advancements in multi-omics technologies have provided unprecedented opportunities to identify biomarkers associated with prediabetes, offering novel insights into its diagnosis and management. This review synthesizes the latest findings on prediabetes from multiple omics domains, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and radiomics. We explore how these technologies elucidate the molecular and cellular mechanisms underlying prediabetes and analyze potential biomarkers with predictive value in disease progression. Integrating multi-omics data helps address the limitations of traditional diagnostic methods, enabling early detection, personalized interventions, and improved patient outcomes. However, challenges such as data integration, standardization, and clinical validation and translation remain to be resolved. Future research leveraging artificial intelligence and machine learning is expected to further enhance the predictive power of multi-omics technologies, contributing to the precision diagnosis and tailored management of prediabetes.

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

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

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

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

Time in tight range (TITR) is a novel glycemic metric assessing normoglycemia in individuals with diabetes.

To assess the attainability of the TITR (70-140 mg/dL) target in youth with diabetes using different treatment strategies during Ramadan fasting.

This prospective study included 276 non-insulin-treated type 2 diabetes mellitus (T2DM) and 426 patients with type 1 diabetes mellitus (T1DM) who were categorized into: multiple daily injections [MDI] + intermittently scanned CGM (isCGM), sensor augmented pump (SAP) and advanced hybrid closed loop (AHCL).

At the end of Ramadan, the mean TITR was 42.3 ± 6.6 % for all T1DM patients and 63.5 ± 4.0 % in T2DM (p < 0.001). The highest TITR was in T2DM group together with T1DM on AHCL (62.3 ± 11.6 %), followed by SAP group (37.7 ± 5.7 %) and MDI + isCGM group (23.6 ± 5.9 %, p < 0.001). Hypoglycemic episodes as shown by time below range (TBR) < 70 mg/dL and TBR < 54 mg/dL were minimal during Ramadan in AHCL group in comparison to before Ramadan (2.6 ± 0.7 versus 2.9 ± 0.9 %; p = 0.061 and 0.4 ± 0.1 vs 0.5 ± 0.1 %, p = 0.561, respectively) with a lower coefficient of variation (CoV) (p < 0.001) than other T1DM participants.

At the end of Ramadan, TITR was decreased in patients with T1DM except those using AHCL who had similar levels to non-insulin-treated T2DM patients. Advanced technology has the potential for achieving tight glycemic targets, along with a reduction in CoV, without increasing hypoglycemic risk compared with other insulin treatment modalities.

Even though HbA1c can be obtained easily and accurately by blood test, the cost may limit its availability for some patients and their proper follow-up. The triglyceride-glucose index has been shown to have an association with HbA1c in other populations, but it hasn't been studied in adolescents with T1DM yet. The aim of this study is to assess the association of TyG index with glycaemic control in adolescents with T1DM.

This cross-sectional study included a sample of 36 adolescents (50% female) from the paediatric unit of the San Juan Hospital in Alicante (Spain). Data on sociodemographics, growth parameters, glycaemic control, and blood tests results were collected after routine visits.

A higher TyG index was statistically associated with a higher BMI, percentile and z-score, a higher triglyceride, HbA1c and glucose levels and with the triglycerides/HDL, the total cholesterol/HDL, the TyG-BMI and the TyG-waist circumference indexes.

The TyG index is a simple and non-invasive biomarker that could serve as a valuable adjunct to HbA1c monitoring in adolescents with T1DM. It may have a potential utility as a screening tool for early identification of patients at risk for developing metabolic complications such as insulin resistance and dyslipidemia.

Hospital Hyperglycemia (HH) is linked to poorer outcomes, including higher mortality rates, increased ICU admissions, and extended hospital stays, and occurs in both people living with diabetes or not. The prevalence of HH in non-critical patients ranges from 22 to 46%. This panel reviewed the evidence and made recommendations for the best care for hospitalized hyperglycemic patients, with or without diabetes mellitus.

The methodology was published previously and was defined by the internal institutional steering committee. The SBD Acute and Hospital Complications Department drafted the manuscript, selecting key clinical questions for a narrative review using MEDLINE via PubMed. The best available evidence was reviewed, including randomized clinical trials (RCTs), meta-analyses, and high-quality observational studies related to Hospital Hyperglycemia.

The department members and external experts developed 23 recommendations for the management of patients with HH, including screening, initial interventions, treatment adjustments, and care for potential complications. Based on the best available evidence, our article provides safe and effective management strategies for both public and private healthcare settings.

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.

Type 2 diabetes (T2D) is a large and growing epidemic. Importantly, new technologies and pharmaceutical options are improving the management of T2D. Continuous glucose monitoring (CGM) systems have advanced glucose-sensing technology, which has made it easier for users to monitor their glucose levels. Glucagon-like peptide 1-based therapies and dual agonists have similarly revolutionized the treatment of T2D. In this article, we present four cases of individuals with T2D who, in collaboration with their healthcare provider, used the data from their CGM systems to inform therapy changes, including the initiation and titration of glucagon-like peptide 1-based therapies. Combined use of CGM systems and glucagon-like peptide 1-based therapies could improve people's diabetes as well as their overall health.

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.

We evaluated the effectiveness of a community diabetes specialist nurse (cDSN) working with district nurses (DNs) to optimise insulin therapy on DN workload and patient outcomes.

This was an observational clinical improvement study of outcomes pre- and post-introduction of an intervention within a community diabetes service in an areas of England. Patients were followed up for 6 months. The intervention was a cDSN providing advice and support to DNs in safe diabetes management, with a particular focus on insulin use.

in total, 148 of 224 patients were reviewed; 130 (87.8 %) were available for follow up 6 months after their first review. Comparing pre- to post-intervention outcomes, number of patients with a hypoglycaemic event reduced from 21/129 to 1/128 (X2 = 19.71, p < 0.001) as did the number with a hyperglycaemic event; 53/129 to 23/128 (X2 = 16.48, p < 0.001). Number of DN visits and use of acute hospital services also improved significantly. Estimated cost savings through reduced DN visits, insulin usage, and hospital service use totalled £1.9 million.

Significant financial savings and reduced patient harms were identified following our intervention in this cohort. Roll-out to other sites in England is a next step.

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.

Rates of type 2 diabetes (T2D) continue to rise in the United States, with many patients failing to achieve glycemic targets. Primary care providers often serve as the sole clinician managing diabetes. Continuous glucose monitors (CGMs) have shown promise in diabetes management, yet their adoption in primary care settings, especially among patients with T2D not using intensive insulin therapy, remains limited. We sought to evaluate the impact of CGM use on glycemic control in patients with T2D not using insulin and those using basal but not bolus insulin in a primary care setting. CGM use was associated with a significantly greater reduction in HbA1c (-0.62%, p < 0.01) compared with matched controls at 3 months (n = 182). Patients showed improvements in time in range (39.7-61.9%, p < 0.0001), time > 180 mg/dL (60.1-37.9%, p < 0.001), time > 250 mg/dL (27.6-8.5%, p < 0.001), mean estimated glucose value (212 mg/dL to 173 mg/dL, p < 0.001) and glucose management indicator (8.39-7.46%, p < 0.001). CGM use in a primary care setting compared to usual care significantly improved glycemic control in T2D patients not on bolus insulin, irrespective of treatment with non-insulin or basal insulin. This suggests potential for broader CGM integration in primary 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 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).

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.

Microvascular and macrovascular complications in type 1 diabetes (T1D) may be linked to endothelial stress due to glycaemic variability. Continuous glucose monitoring systems (CGMs) provide new opportunities to quantify this variability, utilising the amplitude of glucose change summated over time. The aim of this study was to examine whether this determination of glucose variability (GV) is associated with microvascular clinical sequelae.

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).

Results for the 89 individuals (44 men and 45 women) were analysed over 18 months. The mean age of participants was 43 years and the mean duration of diabetes was 18 years. A total of 3.22 million readings were analysed, giving an average of 10.3 mmol/L blood glucose. Those with the largest change in glucose from reading to reading, summated over time, showed the greatest change in eGFR of 3.12 ml/min/1.73 m2 (p = 0.007). People with a higher proportion of glucose readings > 18 mmol/L showed a fall in eGFR of 2.8 ml/min/1.73 m2 (p = 0.009) and experienced higher rates of sight-threatening retinopathy (44% of these individuals) (p = 0.01) as did 39% of individuals in the highest tertile of glucose levels (p = 0.008).

Those individuals with T1D in the highest tertile of reading-to-reading glucose change showed the greatest change in eGFR. Those with a higher proportion of glucose readings > 18 mmol/L also showed a fall in eGFR and experienced higher rates of sight-threatening retinopathy, as did people with higher mean glucose. Discussions with T1D individuals could reflect on how the percentage recorded glucose above a critical level and degree of change in glucose are important in avoiding future tissue complications.

The hypothalamic-pituitary-adrenal axis is thought to play a vital role in glucose homeostasis and diabetes. This study investigated the association between morning serum cortisol and time in range (TIR), including daytime TIR, in type 2 diabetes (T2DM).

310 patients with T2DM had serum cortisol measured at 8 a.m. All participants underwent continuous glucose monitoring (CGM) for three consecutive days, then TIR and glycemic variability (GV) parameters were evaluated. Using 100 g standard steamed bread meal test, blood glucose, C peptide and insulin at different points were collected to assess insulin sensitivity and islet function.

Patients with higher serum cortisol exhibited lower TIR and TITR (P < 0.001). Spearman correlation analysis showed that the negative correlation between cortisol and daytime TIR (r=-0.231, P < 0.001) was stronger than that of overnight TIR (r=-0.134, P = 0.028). Similarly, there existed a negative correlation between cortisol and pancreatic function indicators such as HOMA-β, insulinogenic index (IGI), area under the curve of C-peptide within half an hour (AUCCp0.5 h) and three hours (AUCCp3h) (r=-0.248, -0.176, -0.140, -0.185, respectively, P < 0.05). In contrast, cortisol was positively associated with TAR (r = 0.217, P < 0.001) and GV parameters including MBG, MAGE, LAGE, HBGI, MODD, ADDR (P of MAGE and MODD > 0.05). Multiple stepwise regression revealed that cortisol was an independent contributor of TIR, TITR and diurnal TIR, with diurnal TIR of stronger relevance.

Morning serum cortisol is negatively correlated with TIR, especially diurnal TIR and positively associated with GV parameters. Inappropriate cortisol secretion may have an adverse influence on glucose homeostasis in T2DM.

Background: Individuals with diabetes face a higher risk of mobility disabilities, frailty, and sarcopenia (physical health agility). Studies have shown an association between HbA1C levels and physical health agility status. There is less information available about the relationship with time in range (%) (TIR). The aim of this study was to evaluate the cross-sectional relationship between time in range % (TIR %) and physical health agility status among older adults with type 2 diabetes (aerobic capacity, gait speed, strength, balance, and frailty). Methods: A cross-sectional study was conducted among individuals with diabetes over the age of 60. Participants were equipped with a blinded continuous glucose monitor (CGM) system (Medtronic iPro™2 and CareLink™, Medtronic, Northridge, CA, USA)) for 1 week and underwent an elaborate physical functional assessment (physical health agility) at the beginning and end of that week. The relationship between various physical health agility indices and both TIR (%) and HbA1c was evaluated using linear regression, with adjustments made for age and sex. Results: This analysis pertains to 139 individuals over the age of 60 with type 2 diabetes. After adjustment for age and sex, a 1% higher TIR (70-180 mg/dL) was associated with a 0.169 better score on aerobic capacity and endurance (p-value = 0.023), 0.119 better score on muscle strength of the upper limb (p-value = 0.039), 0.164 better score on dynamic balance (p-value = 0.039), and 0.165 better score on the evaluation of fall risk and balance (p-value = 0.037). Conclusions: In older individuals with diabetes, a higher percentage of time in range (TIR) is linked to better physical agility status indices. Future research is needed to determine whether glucose levels are simply a marker of disease severity or if there is a potential causal relationship.

Glycemic variability and hypoglycemia during diabetes treatment may impact therapeutic effectiveness and safety, even when glycated hemoglobin (HbA1c) reduction is comparable between therapies.

We employed masked continuous glucose monitoring (CGM) during a randomized trial of dapagliflozin plus saxagliptin (DAPA + SAXA) vs insulin glargine (INS) to compare glucose variability and patient-reported outcomes (PROs).

24-week substudy of a randomized, open-label, 2-arm, parallel-group, phase 3b study.

Multicenter study (112 centers in 11 countries).

283 adults with type 2 diabetes (T2D) inadequately controlled with metformin ± sulfonylurea.

DAPA + SAXA vs INS.

Changes in CGM profiles, HbA1c, and PROs.

Changes from baseline in HbA1c with DAPA + SAXA were similar to those observed with INS, with mean difference [95% confidence interval] between decreases of -0.12% [-0.37 to 0.12%], P = .33. CGM analytics were more favorable for DAPA + SAXA, including greater percent time in range (> 3.9 and ≤ 10 mmol/L; 34.3 ± 1.9 vs 28.5 ± 1.9%, P = .033), lower percent time with nocturnal hypoglycemia (area under the curve ≤ 3.9 mmol/L; 0.6 ± 0.5 vs 2.7 ± 0.5%, P = .007), and smaller mean amplitude of glycemic excursions (-0.7 ± 0.1 vs -0.3 ± 0.1 mmol/L, P = .017). Improvements in CGM were associated with greater satisfaction, better body weight image, less weight interference, and improved mental and emotional well-being.

DAPA + SAXA and INS were equally effective in reducing HbA1c at 24 weeks, but people with T2D treated with DAPA + SAXA achieved greater time in range, greater reductions in glycemic excursions and variability, less time with hypoglycemia, and improved patient-reported health outcomes.

Diabetic peripheral neuropathy (DPN) is a prevalent chronic complication of diabetes which is linked to chronic hyperglycemia and glycemic variability. This study aimed to investigate the association between the glycemia risk index (GRI) and DPN in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) data.

From 2019 to 2023, 862 adults diagnosed with T2DM were enrolled at a tertiary care diabetes center in Ningbo, China. The medical history and laboratory parameters were recorded. Neurophysiological examinations were performed to evaluate DPN. The CGM data were recorded for 14 days, and the GRI was calculated based on these data. Multivariate logistic regression analyses were conducted to assess the odds ratio (OR) for DPN with an increased GRI.

The prevalence of DPN in the ascending GRI quartiles was 41.6%, 47.9%, 49.1%, and 59.5%, respectively (P for trend < 0.001). In the multivariable logistic analysis, the highest GRI quartile exhibited a 63% greater risk of DPN (OR 1.631, 95% CI: 1.071 to 2.484, P = 0.023) than the lowest quartile after adjusted for age, sex, body mass index, diabetes duration, blood pressure, creatinine, urinary albumin-to-creatinine ratio, lipid profile and glycated hemoglobin.

High GRI levels, as measured by CGM, were associated with a greater likelihood of DPN in T2DM patients.

Background: There is a need for accurate glycemic control metrics in patients with diabetes and end-stage kidney disease (ESKD). Hence, we assessed the relationship of continuous glucose monitoring (CGM) metrics and laboratory-measured hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2D) treated by hemodialysis. Methods: This prospective observational study included adults (age 18-80 years) with T2D (HbA1c 5%-12%), treated by hemodialysis (for at least 90 days). Participants used a Dexcom G6 Pro CGM for 10 days. Correlation analyses between CGM metrics [mean glucose, glucose management indicator (GMI), and time-in-range (TIR 70-180 mg/dL)] and HbA1c were performed. Results: Among 59 participants (mean age was 57.7 ± 9.3 years, 58% were female, 86% were non-Hispanic blacks), the CGM mean glucose level was 188.9 ± 45 mg/dL (95% CI: 177.2, 200.7), the mean HbA1c and GMI were 7.1% ± 1.3% and 7.8% ± 1.1%, respectively (difference 0.74% ± 0.95). GMI had a strong negative correlation with TIR 70-180 mg/dL (r = -0.96). The correlation between GMI and HbA1c (r = 0.68) was moderate. Up to 29% of participants had a discordance between HbA1c and GMI of <0.5%, with 22% having a discordance between 0.5% and 1%, and 49% having a discordance of >1%. Conclusions: In patients with diabetes and ESKD treated by hemodialysis, the GMI has a strong correlation with TIR, while HbA1c underestimated the average glucose and GMI. Given the limitations of HbA1c in this population, GMI or mean glucose and TIR may be considered as more appropriate glucose control markers.

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.

Objective: To investigate the prevalence of elevated arterial stiffness and associations to known and potentially novel risk factors in a modern European technology-based cohort of children and adolescents with type 1 diabetes. Research Design and Methods: Cross-sectional study, including 127 children recruited from Pediatric Diabetes Departments across Eastern Denmark between May 2022 and January 2024. Arterial stiffness was assessed as carotid-femoral pulse-wave-velocity (cfPWV) using the Sphygmocor XCEL system. Unadjusted and adjusted linear regression models explored associations between cfPWV and other risk factors. Adjustments included age, sex, diabetes duration, time-in-range, hemoglobin A1c (HbA1c), body mass index (BMI) z-score, low-density lipoprotein (LDL)-cholesterol, and mean arterial pressure (MAP). Results: Median (interquartile range [IQR]) age was 14.2 years (12.0, 16.4), diabetes duration was 4.7 years (2.7, 8.4), HbA1c level was 7.0% (6.5, 7.9), (53 mmol/l: 48-63), time-in-range was 63% (53-75), and 52% were male. The majority were treated with continuous-subcutaneous-insulin-infusion (82%), and all (except two) used continuous-glucose-monitors. The prevalence of elevated arterial stiffness (cfPWV z-score over the 90th percentile) was 16%. Unadjusted analyses demonstrated higher cfPWV was associated with longer diabetes duration, higher age, HbA1c, MAP, and liver stiffness, and lower time-in-range and insulin sensitivity. Higher cfPWV remained associated with higher age (standardized β (confidence interval (CI) 95%): 0.38 (0.27, 0.48); p  < 0.001) and lower time-in-range (-0.15 ((-0.26), (-0.03)); p  < 0.011) after adjustment. Conclusions: Despite modern treatment technology and better overall metabolic control, children and adolescents with type 1 diabetes present with a high prevalence of elevated arterial stiffness. Higher arterial stiffness was associated with higher age and lower time-in-range, independent of other risk factors, including HbA1c.

In patients with type 2 diabetes mellitus (T2DM), the risk of hypoglycemia also occurs in at a time-in-range (TIR) of > 70%. The hemoglobin glycation index (HGI) is considered the best single factor for predicting hypoglycemia, and offers new perspectives for the individualized treatment of patients with well-controlled blood glucose levels that are easily ignored in clinical settings.

To investigate the relationship between HGI and hypoglycemia and the implications of HGI on hypoglycemia in T2DM with TIR > 70%.

All participants underwent a 7-days continuous glucose monitoring (CGM) using a retrospective CGM system. We obtained glycemic variability indices using the CGM system. We defined HGI as laboratory hemoglobin A1c minus the glucose management indicator. Patients were categorized into low HGI (HGI < 0.5) and high HGI groups (HGI ≥ 0.5) according to HGI median (0.5). Logistic regression and receiver operating characteristic curve analyses were used to determine the risk factors for hypoglycemia.

We included 129 subjects with T2DM (54.84 ± 12.56 years, 46% male) in the study. Median TIR score was 90%. The high HGI group exhibited lower TIR and greater time below range with higher hemoglobin A1c than the low HGI group; this suggests more glycemic excursions and an increased incidence of hypoglycemia in the high HGI group. Multivariate analyses revealed that mean blood glucose, standard deviation of blood glucose and HGI were independent risk factors for hypoglycemia. Receiver operating characteristic curve analysis indicated that the HGI was the best predictor of hypoglycemia. In addition, the optimal cut-off points for HGI, mean blood glucose, and standard deviation of blood glucose in predicting hypoglycemia were 0.5%, 7.2 mmol/L and 1.4 mmol/L respectively.

High HGI was significantly associated with greater glycemic excursions and increased hypoglycemia in patients with TIR > 70%. Our findings indicate that HGI is a reliable predictor of hypoglycemia in this population.

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.

Objective: This meta-analysis aimed to compare the effect of the real-time continuous glucose monitoring (rt-CGM) and flash glucose monitoring (FGM) on glycemic control in adults with type 1 diabetes mellitus (T1DM). Methods: A systematic literature search of all relevant studies comparing the clinical effectiveness of rt-CGM and FGM in adults with T1DM on Cochrane Library, PubMed, Embase, Web of Science, and Scopus from January 2015 to June 2023 was performed. The primary endpoints were glycated hemoglobin (HbA1c) and TIR (time in range). Secondary endpoints included time below range [TBR (<3.9 mmol/L) and (<3.0 mmol/L)], time above range [TAR (>10.0 mmol/L) and (>13.9 mmol/L)], mean glucose, and glycemic variability (GV) [standard deviations (SD) and coefficient of variation (CV)]. Results: Six studies with 1516 TIDM patients, including three randomized controlled trials and three observational studies, were enrolled in this meta-analysis. Compared to FGM, rt-CGM led to greater glycemic control, represented by higher TIR (%, 3.9 ∼ 10 mmol/L) (SMD = 0.59, 95%CI: 0.37 ∼ 0.81, p < 0.001), decreased TBR (%, <3.9 mmol/L) (SMD = -1.45, 95%CI: -2.33 ∼ -0.57, p = 0.001), decreased TAR [(%, >10.0 mmol/L) (SMD = -0.38, 95%CI: -0.71 ∼ -0.04, p = 0.03) and (%, >13.9 mmol/L) (SMD = -0.42, 95%CI: -0.79 ∼ -0.04, p = 0.03), respectively], lower mean glucose (SMD = -0.18, 95%CI: -0.31 ∼ -0.06, p = 0.003), decreased SD (SMD = -0.70, 95%CI: -1.09 ∼ -0.31, p < 0.001), and decreased CV (SMD = -0.76, 95%CI: -1.05 ∼ -0.47, p < 0.001). However, there was no difference in lowering HbA1c and TBR (%, <3.0 mmol/L) between groups. Conclusion: The rt-CGM outperformed FGM in improving several key CGM metrics among adults with T1DM, but there is no significant difference in HbA1c and TBR (<3.0 mmol/L).

The burden of diabetes is rising in developing countries, and this is significantly linked to the increasing prevalence of poor glycemic control. The cost of glycated haemoglobin (HbA1c) testing is a barrier to timely glycemic assessments, but newer tests such as glycated albumin may be cheaper and tempting alternatives. Additional research must ascertain if glycated albumin (GA) can act as a viable supplement or alternative to conventional HbA1c measurements for glycemic control in diabetic individuals. GA as a biomarker is an emerging area of interest, particularly for those who display unreliable HbA1c levels or cannot afford the test. This study aims to investigate the prevalence of poor glycemic control in outpatient diabetic patients and the utility of glycated albumin in this population's monitoring of glycemic control. Method. A cross-sectional study of 203 diabetic patients will be conducted at the Dodoma Regional Referral Hospital and Benjamin Mkapa Hospital from August 1st, 2023, to August 31st, 2024. Patients diagnosed with diabetes mellitus for over six months will be screened for eligibility. Informed consent, history, clinical examination, and voluntary blood sample collection will be obtained from all eligible patients. Glycated Albumin levels will be obtained from the same blood samples collected. The glycemic status of all patients will be defined as per HbA1c, and a level of greater than 7% will considered as a poor control. The analysis will be computed with SPSS version 28.0, and a predictor variable, P<0.05, will be regarded as statistically significant, with the utility of GA determined by plotting the area under the ROC curve and the confusion matrix.

Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain.

The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain.

A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future.

By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

The aim of this study was to evaluate the association of chronic complications with time in tight range (TITR: 3.9-7.8 mmol/l) and time in range (TIR: 3.9-10.0 mmol/l) in people with type 1 diabetes.

The prevalence of microvascular complications (diabetic retinopathy, diabetic nephropathy and diabetic peripheral neuropathy [DPN]) and macrovascular complications according to sensor-measured TITR/TIR was analysed cross-sectionally in 808 adults with type 1 diabetes. Binary logistic regression was used to evaluate the association between TITR/TIR and the presence of complications without adjustment, with adjustment for HbA1c, and with adjustment for HbA1c and other confounding factors (sex, age, diabetes duration, BMI, BP, lipid profile, smoking, and use of statins and renin-angiotensin-aldosterone system inhibitors).

The mean TITR and TIR were 33.9 ± 12.8% and 52.5 ± 15.0%, respectively. Overall, 46.0% had any microvascular complication (34.5% diabetic retinopathy, 23.8% diabetic nephropathy, 16.0% DPN) and 16.3% suffered from any macrovascular complication. The prevalence of any microvascular complication, diabetic retinopathy, diabetic nephropathy and a cerebrovascular accident (CVA) decreased with increasing TITR/TIR quartiles (all ptrend<0.05). Each 10% increase in TITR was associated with a lower incidence of any microvascular complication (OR 0.762; 95% CI 0.679, 0.855; p<0.001), diabetic retinopathy (OR 0.757; 95% CI 0.670, 0.856; p<0.001), background diabetic retinopathy (OR 0.760; 95% CI 0.655, 0.882; p<0.001), severe diabetic retinopathy (OR 0.854; 95% CI 0.731, 0.998; p=0.048), diabetic nephropathy (OR 0.799; 95% CI 0.699, 0.915; p<0.001), DPN (OR 0.837; 95% CI 0.717, 0.977; p=0.026) and CVA (OR 0.651; 95% CI 0.470, 0.902; p=0.010). The independent association of TITR with any microvascular complication (OR 0.867; 95% CI 0.762, 0.988; p=0.032), diabetic retinopathy (OR 0.837; 95% CI 0.731, 0.959; p=0.010), background diabetic retinopathy (OR 0.831; 95% CI 0.705, 0.979; p=0.027) and CVA (OR 0.619; 95% CI 0.426, 0.899; p=0.012) persisted after adjustment for HbA1c. Similar results were obtained when controlling for HbA1c and other confounding factors.

TITR and TIR are inversely associated with the presence of microvascular complications and CVA in people with type 1 diabetes. Although this study was not designed to establish a causal relationship, this analysis adds validity to the use of TITR and TIR as key measures in glycaemic management.

ClinicalTrials.gov NCT02601729 and NCT02898714.

Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA1c.

Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data.

In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA1c, MG explained 63% of the variation in HbA1c (r=0.79, p<0.001). The variation in HbA1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG-HbA1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA1c based on the overall association between MG and TIR with HbA1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA1c when accounting for MG.

Inter-individual variations exist between MG and HbA1c, as well as between TIR and HbA1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions.

Although several studies have shown the association between continuous glucose monitoring (CGM)-derived glycemic variability (GV) and diabetic peripheral neuropathy, no studies have focused on outpatients or used NC-stat®/DPNCheck™, a new point-of-care device for nerve conduction study (NCS). We investigated the association between CGM-derived GV and NCS using DPNCheck™ in outpatients with type 2 diabetes, and further analyzed the difference in results between patients with and without well-controlled HbA1c levels.

All outpatients with type 2 diabetes using the CGM device (FreeStyle Libre Pro®) between 2017 and 2022 were investigated. Sural nerve conduction was evaluated by sensory nerve action potential (SNAP) amplitude and sensory conduction velocity (SCV) using DPNCheck™. Associations of CGM-derived GV metrics with SNAP amplitude and SCV were investigated.

In total, 304 outpatients with type 2 diabetes were included. In a linear regression model, most CGM-derived GV metrics except for the mean amplitude of glucose excursion and low blood glucose index were significantly associated with SCV, but not with SNAP amplitude. The significant associations of most CGM-derived GV metrics with SCV remained after adjustment for possible confounding factors, but not after adjustment for glycated hemoglobin (HbA1c). Most CGM-derived GV metrics were significantly associated with SCV after adjustment for HbA1c in patients with a HbA1c ≤ 6.9%, but not in those with a HbA1c ≥ 7.0%.

In outpatients with type 2 diabetes, multiple CGM-derived GV metrics were significantly associated with SCV obtained by DPNCheck™. GV may have independent impacts on peripheral nerve function, particularly in patients with well-controlled HbA1c levels.

This study assessed experiences, attitudes, and behaviors of people with diabetes (PwD) regarding diabetes self-management and glucose control, and their level of awareness, knowledge, and attitudes toward time in range (TIR).

This quantitative survey was conducted using an online questionnaire across seven countries. Respondents were PwD classified into three subgroups: type 1 (T1), type 2 insulin (T2 insulin), and type 2 not on insulin (T2 N/insulin).

Respondents included 621 people in the T1, 780 people in the T2 insulin, and 735 people in the T2 N/insulin subgroups. Awareness of TIR was low, particularly in the T2 N/insulin subgroup (T1 53%, T2 insulin 29%, T2 N/insulin 9%). Despite a lower current use of continuous glucose monitoring (CGM) among the T2 insulin and T2 N/insulin participants (38% and 9%, respectively), versus T1 participants (64%), most (> 70%) were positive toward utilizing new tools and measures to self-manage blood glucose. Recommendations from their healthcare professionals (HCPs) were cited as a strong motivator to try new measures for analyzing glucose levels. The main barriers cited were limited access to CGM and lack of understanding of TIR benefits. Cost was the main reason given by ≥ 40% of respondents for stopping CGM use.

There is an unmet need in diabetes management, and TIR and CGM offer a potential solution. PwD are motivated to manage their blood glucose levels and are positive toward utilizing new tools and measures to achieve this goal. HCPs play a pivotal role in informing and guiding PwD on new measures for analyzing glucose.

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.