Glycemic Variability Research Articles

The related factors affecting the relationship between HbA1c and glucose management indicator in adult T2D patients with good glycemic control.

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.

HFpEF and glycemic variability: GHIDD Trial

Abstract Background Recently the guidelines of European Association of Preventive Cardiology reported that the prevalence of diabetes mellitus in patients with diastolic heart failure (HFpEF) is around 31%. Glunovo® is a transdermal device capable of detecting and storing the concentration of glucose in the abdominal interstitial fluid 480 times a day for up to 14 consecutive days. Purpose In our study we evaluated the non diabetic patients with HFpEF with Glunovo to evaluate the glycaemic variability in this population Methods 100 patients with HFpEF admitted to cardiology units of 4 Italian centres on the major islands were enrolled consecutively. Glunovo® was applied to each enrolled patient for 7 days. At the end of the glycaemic monitoring were calculated for each patient the glycaemic variability and the incidence of hyperglycaemia and hypoglycaemia. Glycaemic variability refers to a blood glucose value of ≥ 200 mg / dL or ≤ 59 mg / dL detected more than 3 times in a day for at least 4 days. The inclusion criterion was the presence of diastolic heart failure while the only exclusion criterion was the presence of diabetes diagnosis. Overall, 43 males and 57 females were enrolled with a mean age of 69.3 years (39-87 years). All patients underwent a timely glucose measurement at admission which excluded the presence of hyperglycaemia. No potentially hyperglycaemic drugs were added to the treatment during the hospital stay. Continuous glucose monitoring was performed as an integral part of the hospitalization diagnostic routine. Results In 94 of the 100 patients enrolled it was possible to conclude the analysis, detecting the glycaemic variability, the point glycaemia values and the estimated glycated haemoglobin value. A glycaemia ≥200 mg/dL was found in 53 patients (56%) while a high glycaemic variability was found in 51 patients (54%). A blood glucose value &amp;lt;59 mg / dl was found in 48 patients (51%). Only 5 times the estimated glycated Hb values were&amp;gt; 7%. 32 of the patients who had at least 3 punctual glucose values ≥200 mg/dL were prescribed an oral glucose load curve, which in 100% of cases confirmed the diagnosis of diabetes.No statistically significant differences were found based on age group or sex. In the control group, consisting of 10 patients without DHF undergoing continuous glucose monitoring at one of the participating cardiology units, an unknown hyperglycaemia was found in only 1 patient (10%) and a glycaemic variability in only 1 patient (10%). Conclusions Our experience suggests an incidence of hyperglycaemia and glycaemic variability more then 65% in patients affected by HFpEF. If our data were reproducible on a large scale, a so high prevalence of diabetes in patients with HFpEF cold explain the efficacy of SGLT-2 inhibitor and GLP-1 Agonist in this class of patients.

Association of long-term visit-to-visit glycemic variability with risk of adverse clinical outcomes in diabetic patients with heart failure with preserved ejection fraction

Abstract Background Previous research has linked glycemic variability (GV) with cardiovascular issues in type 2 diabetes mellitus. However, its impact on patients with heart failure with preserved ejection fraction (HFpEF) remains understudied. Purpose In this study, we aim to examine how glycemic variability influences on clinical cardiovascular outcomes in diabetic HFpEF patients. Methods We conducted a retrospective cohort analysis utilizing the electronic medical records of a tertiary medical center in Taiwan between 2014 and 2019. Diabetic patients diagnosed with HFpEF were included in the study. The coefficient of variability of fasting glucose (FGCV) was determined for each individual, and the FGCVs were categorized into tertiles. We analyzed the association between FGCV and the risks of hospitalization for heart failure (HHF), atrial fibrillation (AF), cardiac mortality, and overall mortality. Results A total of 74,835 patients were enrolled in this study. Among them, 753 diabetic patients with concomitant with HFpEF and measured FGCV were identified, with a median follow-up duration of 38.1 months. In the multivariable regression model, the third tertile of FGCV demonstrated a significant association with an elevated risk of HHF and overall mortality compared to the first tertile (hazard ratio [HR] 1.32, 95% confidence interval [CI] 1.03-1.50, p = 0.026; HR 1.64 [1.15-2.26], p = 0.006). Although a higher FGCV exhibited a trend towards increased AF and cardiac mortality, statistical significance was not reached (HR 1.19 [0.91-1.56], p=0.208; HR 1.64 [0.87-3.12], p=0.129). The Kaplan-Meier analyses revealed a significant association between higher FGCV and both HHF and overall mortality (log-rank p = 0.022 and &amp;lt;0.001, respectively). Conclusions Our study highlights a significant association between increased GV and a higher incidence of HHF as well as an overall mortality rate in diabetic individuals with HFpEF.

Visit-to-visit HbA1c variability and risk of potentially avoidable hospitalisations in adults with type 2 diabetes receiving outpatient care at a tertiary hospital.

This study aims to investigate the relationship between long-term visit-to-visit within-person HbA1c variability and hospitalisation outcomes in adults with type 2 diabetes (T2D). We conducted a cohort study at a tertiary hospital in Singapore involving people aged 21 to 101 years with T2D who had ≥3 HbA1c tests over 2 years. HbA1c variability was assessed using coefficient of variation (CV), variability independent of the mean (VIM) and HbA1c variability score (HVS). A 1-year follow-up was performed after the last HbA1c measurement to identify all-cause and potentially avoidable hospitalisations (PAH), categorised as overall, acute, chronic and diabetes composites. The study included 14 923 patients (mean age: 62.9 ± 12.9 years; 55% male). The median HbA1c variability was 8.6% CV (IQR: 5.1-14.3). Higher quartiles of HbA1c variability were associated with greater risks of PAH and all-cause hospitalisations, independent of glycaemic control. Compared to Q1, for example, the risk ratios and 95% confidence intervals for diabetes-related PAH based on HbA1c CV were as follows: Q2, 1.32 (0.93-1.88); Q3, 1.65 (1.18-2.31) and Q4, 2.16 (1.54-3.03). For all-cause hospitalisations, they were as follows: Q2, 0.97 (0.90-1.05); Q3, 1.08 (1.00-1.17) and Q4, 1.16 (1.07-1.26). When stratified by glycaemic control, elevated risk of PAH persisted even in those with optimal glycaemic control. Consistent findings were observed using HbA1c VIM and HVS measures. In individuals receiving care at specialist outpatient clinics of a tertiary hospital, HbA1c variability is associated with a higher risk of PAH. Comprehensive diabetes management strategies addressing both glycaemic control and variability may offer benefits.

Risk factors for Relative and Absolute Hypoglycemia in Patients Treated for Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic Syndrome

Introduction: Glycemic management in the intensive care unit is an evolving practice area. This evolution has included the refinement of blood glucose targets, matching glycemic management to premorbid status, and investigations into the impact of glycemic variability and relative hypoglycemia on ICU outcomes. The interplay between these phenomena and absolute hypoglycemia has yet to be investigated in hyperglycemic emergencies. Objectives: To examine the incidence of and risk factors for relative hypoglycemia and absolute hypoglycemia in patients admitted to an intensive care unit for the management of hyperglycemic emergencies. Methods: This was a retrospective, single-center, exploratory analysis of adults admitted to the medical intensive care unit for diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome. The primary outcome was the incidence of relative hypoglycemia, defined as a blood glucose level 30% lower than baseline. The baseline was determined by the estimated average blood glucose calculated from hemoglobin A1c within 3 months of index admission. Secondary outcomes were ICU length of stay, glycemic variability, and incidence of absolute hypoglycemia.Results: Relative hypoglycemia was observed in 60% of patients in the cohort. Longer insulin infusion duration and higher hemoglobin A1c levels were found to statistically increase the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU length of stay were associated with the risk of developing absolute hypoglycemia. Conclusions: Relative hypoglycemia is a frequent occurrence in this patient population. Hemoglobin A1c and duration of the insulin infusion statistically influenced the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU stay were significantly associated with developing absolute hypoglycemia. While relative hypoglycemia is common in hyperglycemic emergencies, the clinical impact remains uncertain and warrants additional investigation.

Novel Protocol for the Use of Advanced Hybrid Closed-Loop System in Adolescents Engaged in Contact Sports.

Managing exercise remains challenging for adolescent athletes with type 1 diabetes (T1D), especially in contact sports. Even the use of hybrid closed loops can cause problems due to the need to disconnect the pump during some training or competitions. This study evaluated the efficacy of a novel protocol for the use of an Advanced Hybrid Closed-Loop System in adolescent football players with T1D during a sports camp. Eleven boys aged 14.9 years (25-75th percentile: 14-15.5), with a diabetes duration of 5.7 years (5.2-7) and regular training schedules in junior football leagues, participated in the study. They started AHCL (MiniMed780G, Medtronic) therapy a month before a week-long sports camp and were observed during the sports camp and the preceding week. Daily camp activities included two 1.5-hour training sessions. Protocol included a 90-minute temporary target of 150 mg/dL before and insulin pump disconnection during training. Physical activity was tracked using wGT3X-BT Actigraph monitors. The camp provided conditions of demanding physical activity (6.6[6-6.9] hours/day of moderate-to-vigorous intensity). After starting AHCL, the average participant time spent in the target glucose range (70-180mg/dL) was 79.34±8.46%, and no significant change was observed during the camp (mean difference +0.79±8.24%, p=0.7581). Median glucose levels dropped by 10.91±12.08mg/dL (p=0.0134), and time in the tight target range increased by 11.41±11.60% (p=0.0008) without increasing the time below range (&lt;70mg/dL) or glycemic variability. During the camp, daily insulin dose and basal/bolus ratio remained comparable with baseline, but the relative amount of automated bolus insulin decreased by 14.24±4.65% (p&lt;0.0001). The predefined regimen, including a temporary target before and disconnection of AHCL during football training, was safe and may provide satisfactory glucose control in active adolescents with T1D. This protocol could be adapted for use in other intensive contact sports.

The Relation of Diabetes Complications to a New Interpretation of Glycaemic Variability from Continuous Glucose Monitoring in People with Type 1 Diabetes.

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 18months. The mean age of participants was 43 years and the mean duration of diabetes was 18years. A total of 3.22 million readings were analysed, giving an average of 10.3mmol/L blood glucose. Those with the largest change in glucose from reading to reading, summated over time, showed the greatest change in eGFR of 3.12ml/min/1.73 m2 (p = 0.007). People with a higher proportion of glucose readings > 18mmol/L showed a fall in eGFR of 2.8ml/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 > 18mmol/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.

Unveiling the Spectrum of Glucose Variability: A Novel Perspective on FreeStyle Libre Monitoring Data.

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 42.6years, and the mean duration of T1D was 18.4years. A total of 3.22 million readings were analysed, yielding an average blood glucose level of 10.3mmol/l and a GMI of 57.2mmol/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 60mmol/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 18months. The %AVTD of GMI (overall average 57.2mmol/mol) increased after midday, dipped at 18:00, and peaked at 22:00. The %AVTD of AGF (overall average 0.60mmol/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 18mmol/l (top 5% of distribution) increased exponentially above 54mmol/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.

Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems

This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data from 122 adults with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. This study comprehensively analyzed the effects of hypoglycemia on glycemic variability, covering time periods before and after hypoglycemia. Results: Glucose variability normalization post-hypoglycemia can take up to 48 h, with severe hypoglycemia (41–50 mg/dL) linked to prolonged normalization. A cyclical pattern was observed where hypoglycemia predisposes individuals to further hypoglycemia, even with AID system use. A rise in glucose levels often precedes hypoglycemia, followed by an elevated mean time above range (TAR) post-hypoglycemia, indicating a ‘rebound’ effect. The experimental results are further validated on T1DEXI data (n = 222), originating from commercial AID systems. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. The level of hypoglycemia does influence the pattern of subsequent glucose fluctuations. Conclusion: Hypoglycemia, especially at lower levels, significantly impacts subsequent glycemic variability, even with the use of open-source AID systems. This should be studied further with a broader set of commercial AID systems to understand if these patterns are true of all types of AID systems. If these patterns occur in all types of AID systems, it underscores potential opportunities for enhancements in AID algorithms and highlights the importance of educating healthcare providers and people with diabetes about post-hypoglycemia glucose variability.

The Association of Glycemic Control Medication Regimens and Preoperative Fructosamine Among Total Joint Artrhoplasty Patients.

Although glycated hemoglobin A1C (HbA1c) has classically been used for glycemic control screening before surgery, fructosamine, a short-term glucose variability indicator, has been reported to be a more accurate predictor of postoperative periprosthetic joint infection among patients with diabetes mellitus (DM). Given the variability of diabetic medication management, this study aims to identify the associated effect of glycemic control medication regimen (GCMR) on the incidence rate and associated odds of abnormal preoperative fructosamine levels among diabetic primary total knee arthroplasty or total hip arthroplasty patients. Between 2017 and 2018, consecutive series of total hip arthroplasty and total knee arthroplasty patients were identified, and the final cohort included only diabetic patients. All patients reported preoperative HbA1c and fructosamine levels. GCMR categories included insulin, metformin, and other. Independent risk of GCMR and abnormal fructosamine levels (>293 µmol/L) were identified using multivariable logistic regression, while controlling for preoperative baseline factors including HbA1C. Among 420 patients, 15.7% (66/420) were diabetic, of whom 22.7% (15/66) reported an abnormal fructosamine level. Among patients requiring GCMR, 24.0% (18/75), 56.0% (42/75), and 77.7% (58/75) reported using insulin, other, and metformin, respectively. Multivariable logistic regressions demonstrated that insulin-dependent patients with DM reported a 1.71 (95% confidence interval [CI], 0.096 to 30.213, P = 0.716) increased odds of abnormal fructosamine levels compared with nonactive GCMR patients, whereas patients managed with metformin and other glycemic control medications reported a protective 0.48 (95% CI, 0.418 to 5.407, P = 0.549) and 0.32 (95% CI, 0.216 to 4.508, P = 0.393) decreased odds of abnormal fructosamine levels, respectively. In this study, insulin and other GCMR medications exhibited a trend for increased and decreased odds of having abnormal preoperative fructosamine levels while controlling for baseline HbA1c level compared with patients with DM without active GCMR. This association may be explained by multifactorial short-term glucose variability in insulin users, indicating the continued need and optimization of short-term glycemic variations instead of HbA1c.

Glycemic management in the stroke unit and its relationship with morbidity and mortality

To determine the treatment of hyperglycemia in the stroke unit, and to compare the morbidity and mortality of patients treated with an intravenous (iv) insulin therapy protocol compared to subcutaneous (sc) insulin when reaching glycemia levels of = 155 mg/dL. We performed a prospective observational study of patients admitted to our stroke unit between July and October 2022. Demographic, glycemic and prognostic variables were collected. Glycemic variability was defined as the standard deviation (SD) of the mean individual glycemia during the first 24-72 hours. Acute complications during admission and mortality at discharge and at 3 months were determined. The variables were analysed by subgroup according to the insulin regime in patients with type 2 diabetes mellitus (DM2) or stress hyperglycemia. The sample consisted of 181 patients, of whom 63.5% were male, with a mean age of 74.2 (SD: 11.6) years. 25.4% required insulin due to glycemia = 155 mg/dL (18 patients iv and 28 sc). 31.5% had DM2 (82.6% of the group receiving insulin and 14% of group without insulin). The group receiving insulin presented higher levels of glycemic variability, at 33.3 (SD: 21.7) mg/dL vs. 11.7 (SD: 7) mg/dL (p < 0.01), more acute complications (43.5% vs. 19.2%; p < 0.01) and higher mortality at 3 months (19.5% vs. 6.6%; p = 0.04) than the group without insulin, and no differences were observed between the type of insulin regime in the subgroups with DM2 or stress hyperglycemia. The patients with glycemia = 155 mg/dL presented higher levels of glycemic variability, acute complications and mortality at 3 months, and no differences were observed in the type of insulin regime, regardless of whether they had DM2.

Relationship between hemoglobin glycation index and risk of hypoglycemia in type 2 diabetes with time-in-range in target

BACKGROUND In patients with type 2 diabetes mellitus (T2DM), the risk of hypoglycemia also occurs in at a time-in-range (TIR) of &gt; 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. AIM To investigate the relationship between HGI and hypoglycemia and the implications of HGI on hypoglycemia in T2DM with TIR &gt; 70%. METHODS 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 &lt; 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. RESULTS 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. CONCLUSION High HGI was significantly associated with greater glycemic excursions and increased hypoglycemia in patients with TIR &gt; 70%. Our findings indicate that HGI is a reliable predictor of hypoglycemia in this population.

Clinical implications and pharmacological considerations of glycemic variability in patients with type 2 diabetes mellitus

Glycemic variability (GV), independently of glycemic control, has emerged as a prognostic marker in patients with type 2 diabetes mellitus (DM). In this study, we assessed the prognostic value of long-term GV for predicting major adverse cardiovascular events (MACE) in our local population. We also assessed its prognostic value for diabetic microvascular complications (DMC) and its relationships with antidiabetic medications. This was a retrospective cohort study that recruited 680 patients with type 2 DM across 2015–2017. MACE were defined as: the composite of; total death, myocardial infarction (MI), stroke, hospitalization due to heart failure, and revascularization. GV was calculated for two glycemic control markers: glycated hemoglobin (G-Hb) and fasting blood sugar (FBS); via three metrics- standard deviation (SD), coefficient of variation (CV), and variability independent from the mean (VIM). Cox proportional hazard models and Kruskal-Wallis tests were used in the statistical analysis. 105 events classified as MACE were identified in 86 patients and 104 DMC in 98 patients in an average follow-up period of 78.43 months. Long-term GV was found to be an independent predictor of MACE, particularly for FBS-CV but not a predictor of DMC. FBS-CV ≥ 17.51% as compared with < 17.51% was a significant and independent predictor of MACE, with HR 1.589 (95% CI; 1.022, 2.472) (P = 0.040). DMC were predicted mainly by the duration of type 2 DM, and by the glycemic control; similarly represented by G-Hb and FBS. Patients on metformin, and dipeptidyl peptidase (DPP) 4 inhibitors, had the lowest GV, as compared with patients whose treatments included insulin/sulphonylureas (P < 0.001). In our population, long-term GV predicted MACE: with FBS-CV superior to the “gold standard” glycemic control marker G-Hb. Further, GV may be explained, partially at least, by the choice of antidiabetic medications: this finding might contribute to the cardiovascular protection attributed to one class rather than another.

Long-term Glycemic Variability Predicts Adverse Outcomes in Diabetic Heart Failure with Preserved Ejection Fraction.

Previous studies have shown associations between glycemic variability (GV) and cardiovascular outcomes in patients with type 2 diabetes. However, the impact of GV on outcomes in patients with heart failure with preserved ejection fraction (HFpEF) has not been investigated. Between 2014 and 2019, we conducted a retrospective cohort analysis using the electronic medical records of a tertiary medical center in Taiwan. Diabetic patients with HFpEF were enrolled. Each individual's coefficient of variability of fasting glucose (FGCV) was determined and the FGCVs were categorized into tertiles. Multivariable Cox regression models and the Kaplan-Meier with log-rank test were used to assess the association between the FGCV and the risk of hospitalization for heart failure (HHF), atrial fibrillation (AF), cardiovascular mortality, and overall mortality. In a cohort comprising 74,835 individuals diagnosed with diabetes, a subset of 753 patients was identified with HFpEF and measurement of FGCV. The median follow-up duration 38.1 months. In the model of full adjustment, the third FGCV tertile was significantly associated with an increased risk of HHF compared to the first tertile (hazard ratio [HR] = 1.32, 95% confidence interval [CI] 1.04-1.69, p = 0.025). Likewise, the highest FGCV tertille was associated with an increased risk of death (HR 1.65, 95% CI: 1.16-2.35, p = 0.005) while it was not associated with increased of AF and cardiovascular mortality. Kaplan-Meier analyses revealed a significant association between FGCV and both HHF and overall mortality (log-rank p = 0.022 and <0.001, respectively). Our study highlights a significant association between increased GV and a higher incidence of HHF as well as an elevated overall mortality rate in individuals with diabetes and HFpEF.

Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.

The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.

12596 Characterizing Glycemic Variability In Novel Subphenotypes Of Individuals At Elevated Risk For Type 2 Diabetes Using Continuous Glucose Monitoring

Abstract Disclosure: A.J. Kretowski: None. P. Konopka: None. A. Janucik: None. A. Citko: None. A. Paszko: None. M. Klak: None. A. Golonko: None. A. Szklaruk: None. L. Szczerbinski: None. Background: The heterogeneity among individuals at elevated risk for Type 2 Diabetes (T2D) recently has led to the identification of six subphenotypes, each with distinct risks for developing T2D, its complications, and mortality. Our study leveraged Continuous Glucose Monitoring (CGM) to offer a detailed characterization of these subphenotypes, aiming to uncover differences in glycemic variability and control. Methods: CGM data from 616 individuals without diabetes, from the Polish Registry of Diabetes (PolReD) study, classified into six clusters, were analyzed. The PolReD used Freestyle Libre 2 (Abbott) as the CGM device. CGM-derived measures of glucose control and glucose variability were calculated using the iglu R package. We utilized Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA), adjusting for age and sex, to identify significant differences in CGM parameters across the clusters. Results: Our analysis identified significant differences in several CGM-derived parameters among the clusters, notably in the Glycemic Risk Assessment Diabetes Equation (GRADE), Standard Deviation of weekly glucose values (SDw), Mean Amplitude of Glycemic Excursions (MAGE), Maximum glucose value, Glucose range, and Mean Absolute Glucose (MAG). Clusters 3 and 5, characterized by beta-cell dysfunction and high insulin resistance respectively, and associated with the highest risk of T2D, exhibited the greatest glucose variability, with elevated risk for glycemic excursions. In contrast, Cluster 4, indicative of a metabolically healthy obesity profile with a low risk of T2D, demonstrated the most stable glycemic control and the lowest variability among the clusters. Conclusions: This study shows that subtypes of patients at elevated risk for T2D exhibit distinct patterns in CGM-derived parameters, indicating variations in glucose control and stability that extend beyond traditional fasting and post-challenge glucose assessments. Notably, we found that subtypes facing the highest T2D risk display increased glycemic variability, despite their differing underlying mechanisms of dysglycemia. These findings highlight the utility of CGM as a tool for identifying individuals at an increased risk of T2D, reducing the need for extensive phenotyping typically required for cluster assignment. Presentation: 6/1/2024

12491 Follow Up Of Glycemic Control In Patients Users Of Sensor Augmented Insulin Pump (sap) After Multi Dairy Injection (mdi) Or Change Of Technology

Abstract Disclosure: A. Medina: None. S. Tabares: None. C. Coronado: None. L. Burgos: None. A. Ely: None. Introduction: Sensor augmented insulin pump (SAP) is considered an efective therapy in type 1 diabetes (T1D) that combines continuous subcutaneous insulin infusion (CSII) with continuous glucose monitoring (CGM). A new metric of glycemic control is the time in range (TIR). More time in the expected range means less severe highs and lows. Objective: To describe glycemic control of a cohort of patients treated with multi-daily injection (MDI) or sensor-augmented insulin pump (SAP) VEO /640G who switched to a new therapy with 640G, 670G and 780G at 3 and 6 months. Methodology: A cohort study was analyzed with patients with T1D receiving SAP therapy in an university hospital. Patients with insulin therapy with MDI or SAP (VEO /640G) prior to SAP (670G /780G) were included and were followed up with glycemic control trough A1c, time in range (TIR), time in hypoglycemia and hyperglycemia, glycemic variability (GV) using continuous glucose monitoring (CGM) for 14 days, at 3 and 6 months after starting a new technology. Results: 40 patients were analyzed from 2022 to 2023, who attended check-ups every 3 months. The median age was 32 y and 65% were male. At baseline data, 40%, 22.5%, 37.5% were on MDI, VEO and 640G respectively, before changing therapy to 670G or 780G. The TIR increased by 97.6% of the patients and 75% reached the TIR (&amp;gt;70% between 70 and 180 mg/dl of glucose).Hypoglycemia decreased in 75% of patients, and of 27 patients in hyperglycemia, only 3, did not decrease glucose levels due to: lack of follow-up, sensor failure and misuse of the pump. In this cohort were included 12 patients in special conditions. The average initial GV was 37,2%, the normal being &amp;lt;36%, with an improvement at 3 and 6 months of 32,8 and 32,9%, respectively.The mean basal A1c was 7,32% . A1c decreased to 6,8 and 6,9% at 3 and 6 months after new therapy. Discusion: Regardless of the basal therapy (MDI, SAP), an improvement in TIR and A1c as measures of glycemic control is observed, as well as a decrease in the time in hypo and hyperglycemia and GV. The few patients who did not experience improvement had poor pump management, inappropriate carbohydrate counting, or poor adherence at checkups. Conclusion: SAP is an effective therapy in patients with (T1D) to reach metabolic goals, without increasing the rate of hypoglycemia. The most recent technologies in insulin pumps demonstrate better glycemic control. Presentation: 6/1/2024

6521 Continuation of Continuous Glucose Monitors in Hospitalized Diabetic Patients Decreased the Incidence of Hypoglycemia

Abstract Disclosure: C.E. DeClue: None. S. Bao: None. Objective: Glycemic control improves patient outcomes in hospitalized patients, and this is generally achieved by monitoring point-of-care (POC) capillary glucose levels. In the outpatient setting, continuous glucose monitoring (CGM) devices have shown improved glycemic control in diabetic patients, but their evidence in hospitalized patients is lacking. Methods: To evaluate CGM accuracy and safety of use in the inpatient setting, 567 consults were analyzed in a single-center inpatient diabetes service between November 2022 and January 2023. Patients were included if managed with multiple daily injections or insulin pump therapy and separated into four groups based on CGM continuation and type of diabetes. POC glucose and incidence of hypoglycemia were recorded over 72 hours from the time of consultation. Results: Forty-six Type-1 (T1DM) and 72 Type-2 (T2DM) diabetic patients were included in this study, with average hemoglobin A1c of 8.6% and 9.0%, respectively. Both diabetic groups showed an average POC glucose that was not statistically different based on CGM continuation (185 mg/dL for T1DM and 187 mg/dL for T2DM continued on CGM versus 183 mg/dL for T1DM and 188 mg/dL for T2DM not using CGM). However, only 2 of the 18 T1DM patients continued on CGM experienced hypoglycemia, whereas 17 of the 28 T1DM patients not using CGM experienced at least 1 hypoglycemic event. Similarly, only 2 of the 20 T2DM patients continued on CGM experienced hypoglycemia, whereas 13 of the 52 patients not using CGM experienced at least 1 hypoglycemic event. Additionally, less glucose variability was observed in both T1DM and T2DM patients continued on CGM (average standard deviation of 64 mg/dL and 55 mg/dL, respectively) compared to those not using CGM (average standard deviation of 84 mg/dL and 62 mg/dL, respectively). Conclusion: CGM has great potential to improve patient care and nursing workload in the hospital, but the safety and accuracy are not yet well-established. In this study, there seemed to be a statistically insignificant trend to less glycemic variability for CGM users despite no glycemic difference on average POC testing. Importantly, significantly fewer hypoglycemic events were observed in patients continued on CGM devices regardless of type of diabetes or baseline glycemic control. This study adds further evidence to the growing body of work supporting improved safety of CGM utilization in hospitalized diabetic patients. Presentation: 6/1/2024

12503 An Observational Study Of The Glycemic Variability In Type 1 Diabetes Mellitus Women Preconception And During Pregnancy And Its Associations With Adverse Maternal And Neonatal Outcomes

Abstract Disclosure: S. Avula: None. A.R. Ankireddypalli: None. K. Tessier: None. E.R. Seaquist: None. Background: Pregnancy in T1 diabetes mellitus (T1DM) women is associated with increased risk of maternal/fetal complications. Despite glycemic optimization during pregnancy in T1DM, offspring are at increased risk for neonatal complications. Aim: To examine glycemic variability by continuous glucose monitor (CGM) preconception and during pregnancy by trimester and assess the association with adverse maternal/fetal outcomes. Methods: This was a retrospective cohort study of pregnant patients with T1DM who used real-time CGM and delivered at a U.S. tertiary center (2012-2023). Multiple gestations, Cystic fibrosis-related diabetes (CFRD), twin or multiple births, and no valid CGM data for at least one trimester were excluded. 63 met the inclusion criteria. The primary outcome was composite neonatal morbidity (CNM), including large or small for gestational age (LGA/SGA), NICU admission, neonatal hypoglycemia, shoulder dystocia, respiratory distress syndrome, APGAR 1 or 5 minutes &amp;lt; 7, prematurity (&amp;lt;37 weeks), intrauterine growth restriction (IUGR), CPAP, and ventilator. We also collected data about maternal outcomes. Results:54 patients (86%) met at least one outcome included in the CNM. 20 patients (32%) were LGA, 3 (5%) were SGA, 32 (51%) premature, 29 (46%) required NICU admission, 44 (70%) had neonatal hypoglycemia, 3 (5%) had shoulder dystocia, 23 (37%) had respiratory distress, 22 (35%) needed CPAP, 3 (5%) were on ventilator, 11 (18%) had APGAR 1 minute &amp;lt;7. Among maternal outcomes, 18 (29%) developed pre-eclampsia, and 46 (73%) had a cesarean delivery. Overall, individuals with neonatal morbidity had a lower target in range (TIR) and higher target above range(TAR), average glucose, standard deviation(SD), and coefficient of variation (CV) compared with those without the primary composite, but statistical significance was seen for average glucose and TAR. For every 5 mg/dl increase in average glucose (mean of the 3-trimester values), there was a 25% increase in odds of neonatal morbidity (OR 1.25; 95% CI 1.04-1.56; p=0.028). There was a significant association between 1st trimester SD and neonatal morbidity (OR 1.11; 95% CI 1.02-1.24; p=0.027). There was a trend toward significance between the 3rd-trimester average blood glucose and neonatal morbidity (OR 1.26; 95% CI 1.02-1.63; p=0.051). Conclusion: In our single-center cohort, we found maternal average glucose and TAR, as well as first trimester SD, were significantly associated with CNM. This confirms that maternal hyperglycemia and demonstrates that first trimester SD are associated with poorer neonatal outcomes. Presentation: 6/1/2024

Perioperative Glycemic Variability Influences Infection Rates Differently Following Revision Hip and Knee Arthroplasty

IntroductionRecent investigations have determined that abnormal postoperative glycemia following primary total joint arthroplasty is associated with adverse events. Our study aimed to determine if hyperglycemia and glycemic variability following aseptic revision total joint arthroplasty (rTJA) were associated with periprosthetic joint infection (PJI) within two years postoperatively. MethodsA retrospective review was performed of 2,208 patients within a single institution undergoing aseptic rTJA from 2012 to 2019. Postoperative glucose values were recorded. Glycemic variability was measured via three parameters: coefficient of variation (%CV), mean amplitude of glycemic excursions (MAGE), and J-index. Logistic regression analyses were performed to examine associations with PJI at 90-day, 1-, and 2-year follow-up. ResultsIn revision hips, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses, except for MAGE, which predicted PJI at one year (P = 0.045); body mass index (BMI) was the only factor associated with PJI at all timepoints in all models. In revision knees, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses; however, PJI rates differed between diabetics and non-diabetics at all time-points (P < 0.05). ConclusionsOur findings illustrate that decreasing preoperative BMI and postoperative glycemic variability may be critical in reducing PJI rates in revision hips. Furthermore, patients who have diabetes should be counseled that they remain at higher risk of PJI regardless of perioperative glucose control after revision knee surgery.