Dysregulated inflammatory response to urogynecologic meshes in women with diabetes and its implications.

BACKGROUND: Diabetes is an independent risk factor for mesh complications in women undergoing mesh-augmented surgical repairs of stress urinary incontinence and/or pelvic organ prolapse. The underlying mechanism remains unclear. OBJECTIVE: This study aimed to define the diabetes-associated alterations in the host inflammatory response to mesh and correlate them with perioperative glucose management. STUDY DESIGN: Deidentified demographics and medical records of patients who underwent mesh removal and participated in a mesh biorepository study were reviewed (n=200). In patients with diagnosed diabetes (n=25), blood glucose management before initial mesh implantation and before and after mesh removal was assessed by blood glucose and hemoglobin A1c levels. Age- and body mass index-matched tissue samples excised from patients with and without diabetes were examined. Transcriptomic profiles of immune cell markers, immune mediators, key inflammatory regulators, cell senescence, and epigenetic enzymes were determined by multiplex transcriptomic assays (NanoString). Ratios of apoptotic cells to CD68+ macrophages were examined with immunofluorescence. Protein profiles of 12 molecules involved in apoptotic cell clearance were examined with a multiplex protein assay (Luminex). RESULTS: Demographic and clinical characteristics, including duration between mesh implantation and removal, reason for removal, and type of mesh, etc., were comparable between patients with and without diabetes, except for 11.6% higher body mass index in the former (P=.005). In patients with diabetes, suboptimal management of blood glucose following mesh implantation was observed, with 59% of the patients having loosely or poorly controlled glucose before and after the mesh removal. Ongoing chronic inflammatory response was observed in the excised mesh-tissue complexes in both groups, whereas markers for M2 macrophages (Mrc1 [mannose receptor C-type 1]) and helper T cells (Cd4 [CD4 molecule]) were increasingly expressed in the diabetic vs nondiabetic group (P=.023 and .047, respectively). Furthermore, the gene expressions of proinflammatory Ccl24 (C-C motif chemokine ligand 24) and Ccl13 (C-C motif chemokine ligand 13) were upregulated by 1.5- and 1.8-fold (P=.035 and .027, respectively), whereas that of Il1a (interleukin 1 alpha) was paradoxically downregulated by 2.2-fold (P=.037) in the diabetic vs nondiabetic group. Interestingly, strong positive correlations were found between the expression of Ccl13, Setdb2 (SET domain bifurcated histone lysine methyltransferase 2), and M2 macrophage markers, and between the expression of Il1a, Fosl1 (activator protein-1 transcription factor subunit), and dendritic cell markers, suggesting the involvement of macrophages and dendritic cells in the diabetes-dysregulated proinflammatory response. Supportively, apoptotic cell clearance, which is an important function of macrophages, appeared to be impaired in the diabetic group, with a significantly increased protein level of CALR (calreticulin), an "eat-me" signal on the surface of apoptotic cells (P=.031), along with an increase of AXL (AXL receptor tyrosine kinase) (P=.030), which mediates apoptotic cell clearance. CONCLUSION: Diabetes was associated with altered long-term inflammatory response in complicated mesh implantation, particularly involving innate immune cell dysfunction. Suboptimal blood glycemic control following mesh implantation may contribute to this immune dysregulation, necessitating further mechanistic studies.

The potential of glucose management indicator for the estimation of glucose disposal rate in people with type 1 diabetes.

BACKGROUND AND AIMS: Insulin resistance is a growing feature in type 1 diabetes (T1D). It can be quantified by calculating the estimated glucose disposal rate (eGDR) with the Epstein's formula, which includes laboratory-measured glycated hemoglobin (HbA1c). We aimed the current research to assess the agreement between the conventional eGDR formula and an alternative one (eGDR-GMI) incorporating the glucose management indicator (GMI) derived from continuous glucose monitoring (CGM). We also explored the relationship between eGDR-GMI, cardiovascular risk factors, and the prevalence of diabetes-related complications. METHODS AND RESULTS: We designed a cross-sectional study that included adults with T1D. eGDR-GMI and eGDR (mg/kg/min) were calculated using GMI or HbA1c, waist circumference, and hypertensive state. Clinical data were collected from electronic medical records. The analyses encompassed 158 participants with a mean age of 39 ± 13 years. The Bland-Altman analysis showed a good agreement between eGDR-GMI and eGDR. When we divided participants in eGDR-GMI tertiles we found a higher prevalence of diabetes-related complications and a less favorable metabolic profile in the lowest eGDR-GMI tertile. The relative risk of retinopathy, nephropathy, and neuropathy significantly increased by approximately 1 unit with each decrease in eGDR-GMI, regardless of age, sex, disease duration, lipids, and smoking habit. CONCLUSIONS: eGDR-GMI represents a valid and robust alternative to the eGDR to assess insulin resistance in T1D. Low eGDR-GMI is associated with diabetes complications and a less favorable metabolic profile. Incorporating the eGDR-GMI into clinical practice can enhance the characterization of T1D people and allow for a more personalized treatment approach.

Glucose evaluation and management in the ICU (GEM-ICU): Protocol for a bi-centre cohort study.

INTRODUCTION: Hyperglycaemia is common in intensive care unit (ICU) patients. Glycaemic monitoring and effective glycaemic control with insulin are crucial in the ICU to improve patient outcomes. However, glycaemic control and insulin use vary between ICU patients and hypo- and hyperglycaemia occurs. Therefore, we aim to provide contemporary data on glycaemic control and management, and associated outcomes, in adult ICU patients. We hypothesise that the occurrence of hypoglycaemia in acutely admitted ICU patients is lower than that of hyperglycaemia. METHODS: We will conduct a bi-centre cohort study of 300 acutely admitted adult ICU patients. Routine data will be collected retrospectively at baseline (ICU admission) and daily during ICU stay up to a maximum of 30 days. The primary outcome will be the number of patients with hypoglycaemia during their ICU stay. Secondary outcomes will be occurrence of severe hypoglycaemia, occurrence of hyperglycaemia, time below blood glucose target range, time above target range, all-cause mortality at Day 30, number of days alive without life support at Day 30 and number of days alive and out of hospital at Day 30. Process outcomes include the number of in-ICU days, glucose measurements (number of measurements and method) and use of insulin (including route of administration and dosage). All statistical analyses will be descriptive. CONCLUSIONS: This cohort study will provide a contemporary overview of glucose evaluation and management practices in adult ICU patients and, thus, highlight potential areas for improvement through future clinical trials in this area.

Role perceptions and experiences of adult children in remote glucose management for older parents with type 2 diabetes mellitus: a qualitative study.

BACKGROUND: With the advent of the smart phone era, managing blood glucose at home through apps will become more common for older individuals with diabetes. Adult children play important roles in glucose management of older parents. Few studies have explored how adult children really feel about engaging in the glucose management of their older parents with type 2 diabetes mellitus (T2DM) through mobile apps. This study provides insights into the role perceptions and experiences of adult children of older parents with T2DM participating in glucose management through mobile apps. METHODS: In this qualitative study, 16 adult children of older parents with T2DM, who had used mobile apps to manage blood glucose for 6 months, were recruited through purposive sampling. Semi-structured, in-depth, face-to-face interviews to explore their role perceptions and experiences in remotely managing their older parents' blood glucose were conducted. The Consolidated Criteria for Reporting Qualitative Research (COREQ) were followed to ensure rigor in the study. The data collected were analyzed by applying Colaizzi's seven-step qualitative analysis method. RESULTS: Six themes and eight sub-themes were identified in this study. Adult children's perceived roles in glucose management of older parents with T2DM through mobile apps could be categorized into four themes: health decision-maker, remote supervisor, health educator and emotional supporter. The experiences of participation could be categorized into two themes: facilitators to participation and barriers to participation. CONCLUSION: Some barriers existed for adult children of older parents with T2DM participating in glucose management through mobile apps; however, the findings of this study were generally positive. It was beneficial and feasible for adult children to co-manage the blood glucose of older parents. Co-managing blood glucose levels in older parents with T2DM can enhance both adherence rates and confidence in managing blood glucose effectively.

Diabetic management and infection risk in total ankle arthroplasty.

BACKGROUND: As total ankle arthroplasty (TAA) increases in popularity nationwide for the management of end-stage arthritis, it is essential to understand ways to mitigate the risk of infection. Diabetes increases the risk of infection due to compromised immunity and impaired wound-healing mechanisms. However, there is limited research on how diabetic management, inclusive of medications and glucose control, may impact infection risks post-TAA. This study aims to demonstrate the impact of diabetic management on the occurrence of periprosthetic joint infection (PJI) following TAA. METHODS: This was a retrospective study of patients who underwent a TAA at a single academic institution from March 2002 to May 2022. Patients with diabetes who developed an intraarticular infection following TAA were propensity score matched (1:3) to diabetic patients who did not. Data collection included demographics, implant types, diabetic medications, and preoperative hemoglobin A1c. PJI was diagnosed based on Musculoskeletal Infection Society (MSIS) criteria. Statistical analyses assessed differences in medication use, glucose control, and infection rates between groups. RESULTS: Of the 1863 patients who underwent TAA, 177 patients had a diagnosis of diabetes. The infection rate in patients with diabetes (2.8%) was higher than the total cohort rate (0.8%). Five patients with diabetes developed a PJI at an average of 2.2 months postoperatively. This cohort (n = 5) was compared to propensity score-matched controls (n = 15). There was no significant difference in diabetic medication use. Patients who developed PJI had higher rates of uncontrolled diabetes (60.0% vs. 6.7%) and average A1c levels (7.02% vs. 6.29%) compared to controls. CONCLUSION: Our findings suggest that the elevated risk of PJI observed in individuals with diabetes subsequent to TAA may be attributed not solely to the presence of diabetes, but to inadequate glycemic control. Effectively managing blood glucose levels is imperative for achieving favorable outcomes following TAA. LEVEL OF EVIDENCE: III.

Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes.

BACKGROUND: The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized. METHODS: We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor. RESULTS: Median wear time was 27 days (IQR: 23-29) for the Dexcom G4 and 28 days (IQR: 24-29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12-0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68-0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66-0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference >1 percentage points (>11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4. CONCLUSIONS: GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice.Clinicaltrials.gov Registration Number: NCT02454153.

A pregnancy-specific Glucose management indicator derived from continuous glucose monitoring in pregnant women with type 1 diabetes.

OBJECTIVE: Glucose management indicator (GMI) is a core metric derived from continuous glucose monitoring (CGM) and is widely used to evaluate glucose control in patients with diabetes. No study has explored the pregnancy-specific GMI. This study aimed to derive a best-fitting model to calculate GMI from mean blood glucose (MBG) obtained from CGM among pregnant women with type 1 diabetes mellitus (T1DM). METHODS: A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analysed in this study. Continuous glucose monitoring data were collected to calculate MBG, time-in-range (TIR), and glycaemic variability parameters. The relationships between the MBG and HbA1c during pregnancy and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM. RESULTS: The pregnant women had a mean age of 28.9 ± 3.8 years, with a diabetes duration of 8.8 ± 6.2 years and a mean body mass index (BMI) of 21.1 ± 2.5 kg/m2 . The HbA1c levels were 6.1 ± 1.0% and 6.4 ± 1.0% during pregnancy and at postpartum (p = 0.024). The MBG levels were lower during pregnancy than those at postpartum (6.5 ± 1.1 mmol/L vs. 7.1 ± 1.5 mmol/L, p = 0.008). After adjusting the confounders of haemoglobin (Hb), BMI, trimesters, disease duration, mean amplitude of glycaemic excursions and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* [Trimester] + 0.08 * [ BMI in kg/m2 ] + 0.01 * [Hb in g/mL] + 0.50 * [MBG in mmol/L]. CONCLUSIONS: We derived a pregnancy-specific GMI equation, which should be recommended for antenatal clinical care. CLINICAL TRIAL REGISTRY NUMBER: ChiCTR1900025955.

Continuous glucose monitoring (CGM) in a non-Icu hospital setting: The patient's journey.

AIMS: Although consistent data support the outpatient use of continuous glucose monitoring (CGM) to improve glycemic control and reduce hypoglycemic burden, and clinical outcomes, there are limited data regarding its use in the hospital setting, particularly in the non-intensive care unit (non-ICU) setting. The emerging use of CGM in the non-critical care setting may be useful in increasing the efficiency of hospital care and reducing the length of stay for patients with diabetes while improving glycemic control. DATA SYNTHESIS: The purpose of this Expert Opinion paper was to evaluate the state of the art and provide a practical model of how CGM can be implemented in the hospital. SETTING: A patient's CGM journey from admission to the ward to the application of the sensor, from patient education on the device during hospitalization until discharge of the patient to maintain remote control. CONCLUSIONS: This practical approach for the implementation and management of CGM in patients with diabetes admitted to non-ICUs could guide hospitals in their diabetes management initiatives using CGM, helping to identify patients most likely to benefit and suggesting how this technology can be implemented to maximize clinical benefits.

Lipohypertrophy and Insulin: An Old Dog That Needs New Tricks.

OBJECTIVE: To review the current status of practical knowledge related to insulin-associated lipohypertrophy (LH) - an accumulation of fatty subcutaneous nodules commonly caused by repeated injections and/or infusions of insulin into the same site. METHODS: Review of published literature with additional contributions from leading multidisciplinary experts with the emphasis on clinical aspects including pathophysiology, clinical and economic consequences, diagnosis, prevention and treatment. RESULTS: LH is the most common dermatologic complication of insulin therapy. Risk factors for the development of lipohypertrophy include repeated delivery of large amounts of insulin into the same location over time, repeated injection trauma to the skin and subcutaneous tissue, and multiple injections using the same needle. Subcutaneous insulin injection in skin areas with lipohypertrophy is associated with reduced pain; however, this problem can interfere with insulin absorption, thereby increasing the likelihood of glucose variability, hypo- and hyperglycemia when a site is changed. Modern visualization technology of the subcutaneous space with ultrasound can demonstrate lipohypertrophy early in the course of its development. CONCLUSIONS: The physiological and psychological consequences of developing insulin lipohypertrophy can be prevented and treated with education focusing on insulin injection techniques.

Evaluation of Computer-Based Insulin Infusion Algorithm Compared With a Paper-Based Protocol in the Treatment of Diabetic Ketoacidosis.

Background: Development of computer-based software, termed electronic glucose management system (eGMS), offers an alternative strategy to manage diabetic ketoacidosis (DKA) compared with institution-specific paper protocols by integrating glucose and insulin titration into the electronic medical record. Objective: To evaluate the safety and efficacy of eGMS versus a paper-based DKA protocol in an urban academic medical center. Methods: Single-center, retrospective analysis of patients admitted for DKA. The primary objective of this study was the time to transition from intravenous to subcutaneous insulin after resolution of DKA pre- and post-eGMS implementation. Secondary outcomes included incidence of hypoglycemia while on an insulin infusion, intensive care unit (ICU) length of stay, and total hospital length of stay. Results: Time to DKA resolution was similar in both groups with a median time of 8.6 versus 8.8 hours in the paper-based (n = 133) and eGMS groups (n = 84), respectively (P = 0.43). Hypoglycemia occurred more frequently in the paper-based group compared with eGMS during insulin infusion (14 vs 3 patients, P = 0.06). The median ICU (36.5 vs 41.4 hours; P = 0.05) and hospital length of stay (67.9 vs 77.8 hours; P = 0.05) were shorter in the paper-based group compared with the eGMS group. Conclusion and Relevance: Similar rates of DKA resolution were seen for patients managed with a paper-based protocol compared with eGMS. Patients in the paper-based protocol had a shorter ICU and hospital length of stay; however, eGMS had improved clinically relevant safety outcomes.

CGM in the Hospital: Is It Ready for Prime Time?

PURPOSE OF REVIEW: The use of continuous glucose monitoring (CGM) in the hospital setting is growing with more patients using these devices at home and when admitted to the hospital, especially during the COVID-19 pandemic. RECENT FINDINGS: Historically, most evidence for CGM use in the inpatient setting was limited to small studies utilizing outdated CGM technology and analyzing accuracy of sensor measurements. Previous studies have shown reduced sensor accuracy during extreme hypo- or hyperglycemia, rapid fluctuations of glucose, compression of the sensor itself, and in those who are critically ill. Studies that are more recent have shown CGM to have adequate accuracy and may be effective in reducing hypoglycemia in hospitalized patients; some studies have also showed improvement in time in target glycemic range. Furthermore, CGM may reduce nursing workload, cost of inpatient care, and use of personal protective equipment and face-to-face patient care especially for patients during the COVID-19 pandemic. This review will describe the evidence for use of CGM in hospitalized critically ill or non-critically ill patients, address accuracy and safety considerations, and outline paths for future implementation.

Pharmacological management of patients undergoing total pancreatectomy with auto-islet transplantation.

Chronic pancreatitis results in permanent parenchymal destruction of the pancreas gland leading to anatomical and physiological consequences for patients. Surgical management varies, and some patients require total pancreatectomy with autologous islet cell transplantation (TPIAT). Patients undergoing TPIAT require complex and diligent management after surgery. This encompasses the management of glucose control (endocrine function of the pancreas) and supplementing loss of exocrine function of the pancreas with digestive enzymes. Other areas of management include optimizing pain relief while reducing narcotic usage, providing antimicrobial prophylaxis, and reducing loss of islet cells by improving its integrity through anticoagulation and use of anti-inflammatory agents. Each aspect of care is unique to this population. However, comprehensive reviews on its pharmacological management are scarce. This review will discuss the available literature to date surrounding all aspects of pharmacological management of patients undergoing TPIAT.

Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin.

Laboratory measured glycated haemoglobin (HbA1c) is the gold standard for assessing glycaemic control in people with diabetes and correlates with their risk of long-term complications. The emergence of continuous glucose monitoring (CGM) has highlighted limitations of HbA1c testing. HbA1c can only be reviewed infrequently and can mask the risk of hypoglycaemia or extreme glucose fluctuations. While CGM provides insights in to the risk of hypoglycaemia as well as daily fluctuations of glucose, it can also be used to calculate an estimated HbA1c that has been used as a substitute for laboratory HbA1c. However, it is evident that estimated HbA1c and HbA1c values can differ widely. The glucose management indicator (GMI), calculated exclusively from CGM data, has been proposed. It uses the same scale (% or mmol/mol) as HbA1c, but is based on short-term average glucose values, rather than long-term glucose exposure. HbA1c and GMI values differ in up to 81% of individuals by more than ±0.1% and by more than ±0.3% in 51% of cases. Here, we review the factors that define these differences, such as the time period being assessed, the variation in glycation rates and factors such as anaemia and haemoglobinopathies. Recognizing and understanding the factors that cause differences between HbA1c and GMI is an important clinical skill. In circumstances when HbA1c is elevated above GMI, further attempts at intensification of therapy based solely on the HbA1c value may increase the risk of hypoglycaemia. The observed difference between GMI and HbA1c also informs the important question about the predictive ability of GMI regarding long-term complications.

Predictors of time in target glucose range in real-world users of the MiniMed 780G system.

AIM: Automated insulin delivery systems have improved glycaemic control in people with type 1 diabetes mellitus. The analysis investigated predictors of improved sensor glucose time-in-range (TIR; 70-180 mg/dl) based on real-world use of the MiniMed 780G advanced hybrid closed-loop (AHCL) system. METHODS: Data uploaded by MiniMed 780G system users from August 2020-July 2021 were analysed using univariate and multivariable models to identify baseline, demographic and system use characteristics associated with TIR after AHCL initiation (post-AHCL). System settings associated with improved TIR post-AHCL were identified and their impact on time below range (TBR, <70 mg/dl) post-AHCL was explored. RESULTS: In total, 12 870 users were included, of which 2977 had baseline sensor glucose data. Baseline TIR and time in AHCL (defined as the percentage of time the system was in Auto-mode) were positively associated with TIR post-AHCL with larger values predicting greater mean TIR post-AHCL. Characteristics inversely associated with TIR post-AHCL included the percentage of daily basal insulin dose, daily autocorrection dose, number of daily AHCL exits triggered by the system and number of daily alarms, wherein larger values of these characteristics predicted lower mean TIR post-AHCL. System settings that predicted the largest mean TIR post-AHCL were active insulin time of 2 h and glucose target of 100 mg/dl. Active insulin time was not associated with TBR post-AHCL. CONCLUSION: Modifiable factors, including optimized pump settings, can allow users to achieve glycaemic targets with >80% TIR. The findings from this analysis will potentially guide the optimal use of the MiniMed 780G system and facilitate meaningful improvements in safe glycaemic control.

Comparison of MiniMed 780G system performance in users aged younger and older than 15 years: Evidence from 12 870 real-world users.

AIM: To investigate real-world glycaemic outcomes and goals achieved by users of the MiniMed 780G advanced hybrid closed loop (AHCL) system aged younger and older than 15 years with type 1 diabetes (T1D). MATERIALS AND METHODS: Data uploaded by MiniMed 780G system users from 27 August 2020 to 22 July 2021 were aggregated and retrospectively analysed based on self-reported age (≤15 years and >15 years) for three cohorts: (a) post-AHCL initiation, (b) 6-month longitudinal post-AHCL initiation and (c) pre- versus post-AHCL initiation. Analyses included mean percentage of time spent in AHCL and at sensor glucose ranges, insulin delivered and the proportion of users achieving recommended glucose management indicator (GMI < 7.0%) and time in target range (TIR 70-180 mg/dl > 70%) goals. RESULTS: Users aged 15 years or younger (N = 3211) achieved a GMI of 6.8% ± 0.3% and TIR of 73.9% ± 8.7%, while spending 92.7% of time in AHCL. Users aged older than 15 years (N = 8874) achieved a GMI of 6.8% ± 0.4% and TIR of 76.5% ± 9.4% with 92.3% of time in AHCL. Time spent at less than 70 mg/dl was within the recommended target of less than 4% (3.2% and 2.3%, respectively). Similar outcomes were observed for each group (N = 790 and N = 1642, respectively) in the first month following AHCL initiation, and were sustained over the 6-month observation period. CONCLUSIONS: This real-world analysis shows that more than 75% of users with T1D aged 15 years or younger using the MiniMed 780G system achieved international consensus-recommended glycaemic control, mirroring the achievements of the population aged older than 15 years.

Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes.

BACKGROUND: Glucose management indicator (GMI) is a useful metric for the clinical management of diabetic patients using continuous glucose monitoring (CGM). In adults, a marked discordance between HbA1c and GMI has been reported. To date, no studies have evaluated this discordance in children/adolescents with type 1 diabetes (T1D). METHODS: HbA1c and real-life CGM data of the 12 weeks preceding HbA1c measurement were collected from 805 children/adolescents. The absolute difference between HbA1c and GMI was calculated for both the 12-week and 4-week periods preceding HbA1c measurement and the proportion of discordant patients was defined according to specific thresholds in the entire study population and in subjects stratified by type of CGM, insulin therapy, gender, age and puberty. Regression analyses were performed with HbA1c-GMI discordance as dependent variable and patients' characteristics as independent ones. A new GMI equation for children and adolescent was derived from the linear regression analysis between mean glucose and HbA1c. RESULTS: HbA1c-GMI discordance calculated on the 12-week period was <0.1, ≥0.5 and ≥1.0 in 24.8, 33.9 and 9.2% of the subjects, respectively. No significant differences in the proportion of discordant patients were found comparing patients stratified by type of CGM, insulin therapy, gender, age and puberty. GMI-HbA1c discordance was not significantly explained by age, gender, BMI, type of CGM, insulin therapy, hemoglobin, anemia and autoimmune diseases (R2  = 0.012, p = 0.409). HbA1c-GMI discordance calculated on the 4-week period was comparable. GMI (%) equation derived for this cohort was: 3.74 + 0.022x (mean glucose in mg/dl). CONCLUSIONS: GMI could be meaningfully discordant respect to HbA1c in more than a third of children/adolescents with T1D. This discrepancy should be taken into careful consideration when the two indices are directly compared in daily clinical practice.

Glucose management team significantly improves glycaemic care and commitment to in-hospital guidelines within arthroplastic patients.

BACKGROUND: Perioperative dysglycaemias are a risk for harm but guidelines to improve glucose management are poorly adhered to. AIM: To determine whether a specialized team and diabetes education improves the implementation of guidelines and glucose values. METHODS: We conducted a prospective study of 611 nonselected, consecutive patients attending for elective hip or knee arthroplasty. The first 209 patients received conventional care and the following 402 patients received intervention (Acute Glucose Service, AGS) in two chronological groups; either perioperatively (AGS1) or also preoperatively (AGS2). The AGS-team provided diabetes education, identified the patients with diabetes risk and adjusted the medication when needed. Capillary plasma glucose (CPG) was repeatedly measured and glycated haemoglobin (HbA1c) obtained before and after the surgery. The study objectives were to evaluate the staff actions when hyperglycaemia was severe (CPG >10 mmol/L), and to assess improvement of the glycaemic values and the complication rate within 3 months. RESULTS: None of the severely hyperglycaemic events in the reference group were treated according to guidelines. In the AGS 1 group, 50% and in the AGS2 group, 53% were appropriately managed (p < .001). The events of hyperglycaemia (CPG >7.8 mmol/L at least twice) and of severe hyperglycaemia (CPG >10 mmol/L) decreased in all patient groups. The medians of the highest, mean and variability of CPG values improved. The mean HbA1c improved significantly within AGS 2. There was no association between improved glycaemic care and early complications. CONCLUSIONS: AGS intervention significantly improves adherence to guidelines and glucose values.

Successful treatment of diabetic ketoacidosis secondary to fulminant type 1 diabetes mellitus using a closed-loop artificial pancreas in a pediatric patient.

Diabetic ketoacidosis (DKA) is a life-threatening complication of pediatric diabetes mellitus (DM). A bedside closed-loop artificial pancreas (AP) (STG-55; NIKKISO, Tokyo, Japan) maintains the blood glucose (BG) levels within the target range via automatic infusion of insulin and glucose. We report the application of the closed-loop AP to safely control the BG levels of a pediatric patient with DKA. A 12-year-old child with an unremarkable medical history presented with fever and restlessness. The patient was diagnosed with DKA secondary to fulminant type 1 DM and was treated with insulin infusion. He presented with Glasgow Coma Scale of E2V3M4. Arterial blood gas analysis revealed metabolic acidosis and BG levels of 489 mg/dL. His urine test was positive for ketones. Along with infusion therapy, automatic BG control using a closed-loop AP was initiated after ICU admission. This was adjusted to maintain BG levels within 100 mg/dL/6 h or less. After 24 h in the ICU, the patient regained consciousness and recovered from the metabolic acidosis. His general condition improved, and he was prescribed a diet treatment. The treatment was shifted to continuous insulin infusion, and he was transferred to the general ward, and was discharged on the 33rd day of hospitalization. The closed-loop AP prevented repetitive blood extractions, achieved prompt glycemic control, and prevented cerebral edema in a pediatric patient with DKA. This is the first report of successful treatment of DKA using a bedside closed-loop AP.

Real-world performance of the MiniMed™ 670G system in Europe.

AIM: To evaluate the real-world performance of the MiniMed 670G system in Europe, in individuals with diabetes. MATERIALS AND METHODS: Data uploaded from October 2018 to July 2020 by individuals living in Europe were aggregated and retrospectively analysed. The mean glucose management indicator (GMI), percentage of time spent within (TIR), below (TBR) and above (TAR) glycaemic ranges, system use and insulin consumed in users with 10 or more days of sensor glucose data after initial Auto Mode start were determined. Another analysis based on suboptimally (GMI > 8.0%) and well-controlled (GMI < 7.0%) glycaemia pre-Auto Mode initiation was also performed. RESULTS: Users (N = 14 899) spent a mean of 81.4% of the time in Auto Mode and achieved a mean GMI of 7.0% ± 0.4%, TIR of 72.0% ± 9.7%, TBR less than 3.9 mmol/L of 2.4% ± 2.1% and TAR more than 10 mmol/L of 25.7% ± 10%, after initiating Auto Mode. When compared with pre-Auto Mode initiation, GMI was reduced by 0.3% ± 0.4% and TIR increased by 9.6% ± 9.9% (P < .0001 for both). Significantly improved glycaemic control was observed irrespective of pre-Auto Mode GMI levels of less than 7.0% or of more than 8.0%. While the total daily dose of insulin increased for both groups, a greater increase was observed in the latter, an increase primarily due to increased basal insulin delivery. By contrast, basal insulin decreased slightly in well-controlled users. CONCLUSIONS: Most MiniMed 670G system users in Europe achieved TIR more than 70% and GMI less than 7% while minimizing hypoglycaemia, in a real-world environment. These international consensus-met outcomes were enabled by automated insulin delivery meeting real-time insulin requirements adapted to each individual user.

Pitfalls of intermittent continuous glucose monitoring in patients with steroid diabetes.

Only a few studies of continuous glucose monitoring (CGM) in patients with steroid diabetes have been published. Therefore, we investigated all patients with type 2 diabetes (n = 121) and steroid diabetes (n = 40) who used the FreeStyle Libre Pro® device (Abbott Japan) at Gunma University Hospital between 2017 and 2019. Glycated hemoglobin (HbA1c), mean sensor glucose (SG), and glucose management indicator values were similar in both groups. However, the indices for glycemic variabilities, expressed as standard deviations and percent coefficients of variation, were higher in patients with steroid diabetes than in those with type 2 diabetes. The associations between HbA1c, mean SG, and time in range (TIR) when glucose values were 70-180, <70, or >180 mg/dL were assessed using Pearson's product-moment correlation coefficient, which demonstrated good correlations in both patient groups. However, patients with steroid diabetes had a higher SG and lower TIR than did counterparts with type 2 diabetes who had similar HbA1c levels. To examine the effect of prednisolone on CGM data, we divided patients with steroid diabetes into 2 subgroups according to prednisolone dose (≤5 and >5 mg), and found that the dose of this steroid impacted the associations between HbA1c and CGM data, mean SG, and TIR. In summary, our data highlight the importance of cautiously interpreting CGM data and the optimal HbA1c level in patients with steroid diabetes to prevent diabetes-related complications. Further analyses using other CGM devices are necessary to further validate our findings.