Glycemic Fluctuations Research Articles

Evaluating Time in Range as an Outcome Measure for Vildagliptin 100 mg SR OD

Background: Vildagliptin, a dipeptidyl peptidase-4 inhibitor, has shown promising effects in improving glycemic control in patients with diabetes. Glycemic variability, characterized by fluctuations in blood glucose levels, is a common challenge in diabetes management. It has been associated with an increased risk of both acute and chronic complications. Clinical studies evaluating the effects of vildagliptin 50 mg bid have demonstrated its ability to reduce glycemic variability and improve the TIR. Vildagliptin has been shown to decrease postprandial glucose excursions and nocturnal glycemic fluctuations, leading to a smoother glycemic profile. Aims and Objectives: This pilot study highlights the relationship between vildagliptin 100 mg sustained release once daily (SR OD) therapy and its potential impact on reducing glycemic variability and promoting a better time in range (TIR) for patients with diabetes. Materials and Methods: In this study, patients were enrolled in a prospective, multicentric, and observational study to assess the real-world experience of vildagliptin 100 mg SR OD in terms of glycemic variability. Results and Conclusions: At the end of the pilot study period of 14 days, the results demonstrated that in real-life experience, vildagliptin 100 mg SR OD was associated with a clinically meaningful reduction in hemoglobin A1c, and a higher proportion of patients maintained their average blood glucose levels between 70 and 180 mg/dL for almost three-fourth of a day.

Novel predictors of daily fluctuations in glycemia and self-management in adolescents and young adults with type 1 diabetes.

To understand morning biopsychosocial factors that predict glycemia, adherence, and goal attainment in adolescents and young adults (AYA) with type 1 diabetes (T1D) on a daily basis. Eight-eight AYA (mean 17.6± 2.6 years, 54% female, HbA1c 7.9± 1.4%, diabetes duration 8.5± 4.5 years) with T1D who use Continuous Glucose Monitoring (CGM) completed a 2-week prospective study. Participants chose a self-management goal to focus on during participation. For six days, participants prospectively completed a 25-item Engagement Prediction Survey to assess biopsychosocial factors to predict daily diabetes outcomes and an end-of-day Goal Survey. Lasso and mixed-model regression were used to determine items in the Engagement Prediction Survey most predictive of perceived goal attainment, CGM Time-in-Range (TIR, 70-180 mg/dl), sensor mean glucose, number of insulin boluses and hyperglycemia response (bolus within 30 min of high alert or glucose <200 mg/dl within 2hours). A 7-item model (including current glucose, planning/wanting to manage diabetes, wanting to skip self-management, feeling good about self, health perception and support needs) explained 16.7% of the daily variance in TIR, 18.6% of mean sensor glucose, 2.1% of the number of boluses, 14% of hyperglycemia response, and 28.7% of goal attainment perceptions. The mean absolute change in day-to-day TIR was 16%, sensor glucose was 30 mg/dl, and the number of boluses was 2. AYA reported more positive Engagement Prediction Survey responses on mornings when they awoke with lower glucose levels. Morning biopsychosocial state factors predict glycemic and adherence outcomes in AYA with diabetes and could be a novel intervention target for future behavioural interventions.

Discordance in the reduction rate between glycated albumin and glycated hemoglobin levels in type 2 diabetes patients receiving SGLT2 inhibitors

AimsAlthough the difference in HbA1c reduction between sodium-glucose cotransporter 2 (SGLT2) inhibitors and other oral glucose-lowering agents is relatively small, SGLT2 inhibitors exhibit beneficial cardiorenal protection. This study was based on the hypothesis that changes of HbA1c in patients treated with SGLT2 inhibitors may not accurately reflect an improved glycemic profile. MethodsTwo studies were conducted: 1) a retrospective cohort study of 3039 patients administered with either an SGLT2 or a dipeptidyl peptidase-4 (DPP4) inhibitor for 12 months comparing the changes in glycated albumin (GA) and HbA1c levels and 2) a pilot study of 10 patients whose glycemic dynamics were evaluated using flash glucose monitoring at baseline and 2 months after treatment with an SGLT2 inhibitor. ResultsSGLT2 inhibitors reduced GA more markedly than HbA1c in both studies. DPP4 inhibitors decreased both GA and HbA1c to a comparable degree. The mean glucose levels and glycemic standard deviation were significantly reduced after treatment with an SGLT2 inhibitor, in concordance with GA decline, although the lowering of HbA1c was marginal. ConclusionsChanges in HbA1c levels underestimated the glucose-lowering effect and the diminished glycemic fluctuation induced by SGLT2 inhibitors. Thus, the distinct biomarker roles of GA and HbA1c should be reevaluated.

Evaluation of real-life clinical outcomes in Australian youth with type 1 diabetes on hybrid closed-loop therapy: A retrospective study.

AimTo determine the clinical outcomes and evaluate the perspectives of children with Type 1 diabetes (T1D) and their parents managing their child on hybrid closed‐loop (HCL) therapy.MethodsChildren with T1D on HCL attending a tertiary diabetes centre between April 2019 and July 2021 were included. A retrospective analysis of glycaemic data was conducted to determine the clinical outcomes. Time spent in closed loop, time in target glucose range (TIR 3.9–10 mmol/L), hypoglycaemia and hyperglycaemia were collected at baseline, 4 weeks, 3 and 6 months post‐HCL. User experience was assessed by questionnaires administered to parents of children with T1D.ResultsSeventy‐one children, mean (SD) age of 12.2 (3.2) years were commenced on HCL. Ten (14%) discontinued HCL use, with 60% discontinuing within the first 6 months. Glycaemic outcomes were analysed in 52 children. Time spent in closed loop was 78 (21) % at 4 weeks, declined to 69 (28) % at 3 months (P = 0.037) and 63 (34) % at 6 months (P = 0.001). The mean %TIR increased from 59.8 at baseline to 67.6 at 3 months and 65.6 at 6 months with a mean adjusted difference of 7.8% points [95% CI 3.6, 11.9] and 5.5% points [95% CI 1.4, 9.5], respectively. There was a reduction in time > 10 mmol/L and time < 3.9 mmol/L from baseline to 6 months. Although families faced challenges with technology, better glucose control with reduced glycaemic fluctuations were reported.ConclusionsHCL therapy is associated with improved glycaemia; however, adequate support and education are required for best outcomes.

Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms

Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to measure the blood glucose based on chemical reactions with the blood. Unlike traditional glucometer devices, noninvasive continuous glucose monitoring (NICGM) devices aim to solve these issues by consistently monitoring users’ blood glucose levels (BGLs) without invasively acquiring a sample. In this work, we investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable sensors and learning-based models (i.e., machine learning) to predict blood glucose. Two datasets were used for this study: (1) the OhioT1DM dataset, provided by the Ohio University; and (2) the UofM dataset, created by our research team. The UofM dataset consists of fourteen features provided by six sensors for studying possible relationships between glucose and noninvasive biometric measurements. Both datasets are passed through a machine learning (ML) pipeline that tests linear and nonlinear models to predict BGLs from the set of noninvasive features. The results of this pilot study show that the combination of fourteen noninvasive biometric measurements with ML algorithms could lead to accurate BGL predictions within the clinical range; however, a larger dataset is required to make conclusions about the feasibility of this approach.

Use of Continuous Glucose Monitoring in the Assessment and Management of Patients With Diabetes and Chronic Kidney Disease.

In developed countries, diabetes is the leading cause of chronic kidney disease (CKD) and accounts for 50% of incidence of end stage kidney disease. Despite declining prevalence of micro- and macrovascular complications, there are rising trends in renal replacement therapy in diabetes. Optimal glycemic control may reduce risk of progression of CKD and related death. However, assessing glycemic control in patients with advanced CKD and on dialysis (G4-5) can be challenging. Laboratory biomarkers, such as glycated haemoglobin (HbA1c), may be biased by abnormalities in blood haemoglobin, use of iron therapy and erythropoiesis-stimulating agents and chronic inflammation due to uraemia. Similarly, glycated albumin and fructosamine may be biased by abnormal protein turnover. Patients with advanced CKD exhibited heterogeneity in glycemic control ranging from severe insulin resistance to ‘burnt-out’ beta-cell function. They also had high risk of hypoglycaemia due to reduced renal gluconeogenesis, frequent use of insulin and dysregulation of counterregulatory hormones. Continuous glucose monitoring (CGM) systems measure glucose in interstitial fluid every few minutes and provide an alternative and more reliable method of glycemic assessment, including asymptomatic hypoglycaemia and hyperglycaemic excursions. Recent international guidelines recommended use of CGM-derived Glucose Management Index (GMI) in patients with advanced CKD although data are scarce in this population. Using CGM, patients with CKD were found to experience marked glycemic fluctuations with hypoglycemia due to loss of glucose and insulin during haemodialysis (HD) followed by hyperglycemia in the post-HD period. On the other hand, during peritoneal dialysis, patients may experience glycemic excursions with influx of glucose from dialysate solutions. These undesirable glucose exposure and variability may accelerate decline of residual renal function. Although CGM may improve the quality of glycemic monitoring and control in populations with CKD, further studies are needed to confirm the accuracy, optimal mode and frequency of CGM as well as their cost-effectiveness and user-acceptability in patients with advanced CKD and dialysis.

Social Isolation of Older Adults With Diabetes

We aimed to conduct a scoping review of social isolation in elderly patients with diabetes and to clarify current knowledge and gaps and future challenges. A literature search was conducted using Medline, Web of Science, CINAHL, CiNii, and Ichushi, and included studies with an eligibility criterion of a survey of social isolation in elderly patients with diabetes and aged ≥60 years. Social isolation was defined as limited or non-face-to-face contact with family and community. A data extraction form describing characteristics of studies incorporated in the present review was prepared. A total of six studies met eligibility criterion (sample size, 451–3,500). Subjects’ age averaged 67 years, and 42% were female. Social isolation ranged from 9% to 49%. Factors related to social isolation included vascular complications,decreased activities of daily living, death, dementia, glycemic fluctuation, disturbance of lifestyle habits, and poor self-management and -rated health. However, research on the cause and mechanism of the relationship and impact of sex-based differences was lacking. In conclusion, additional research is needed on the definition of social isolation in elderly patients with diabetes, the causal relationship with related factors and their mechanisms, and the relationship with other outcomes.

Definition of the dependence of QTc interval prolongation on glycemic control in patients with type 2 diabetes mellitus

The aim of the study: to assess the impact of glycemic variability on the duration of QTc interval in patients with diabetes mellitus type 2. 68 patients with type 2 diabetes mellitus (DM) and glycosylated hemoglobin (HbA1c) level ≤10% were examined. Of them – 37 (54.4%) men and 31 (45.6%) women. The average age – 46.0 (43.0; 54.0) years, the duration of DM type 2 – 7.0 (5.0; 9.0) years. Patients were divided into 2 groups according to HbA1c level: group 1 (n=31) with HbA1c &lt;7% and group 2 (n=37) with HbA1c ≥7%. The control group consisted of 10 practically healthy people, compared by gender and age. The duration of the QTc interval was calculated automatically by Bazett's formula during 24-hour Holter electrocardiogram (ECG) recordings. Additionally, the percentage of cases of exceeding the QTc threshold over 450 ms (QTc&gt;450) was also calculated. Simultaneously with 24-hour Holter monitoring, the continuous glucose monitoring was performed, using iPro2 system (Medtronic MiniMed, USA). The maximum value of glycemia (Gmax), the minimum value of glycemia (Gmin), as well as indicators of glycemia variability (GV) were analyzed: standard deviation of mean glycemia (SD) and glycemia range (GR). The duration of daily QTc and the value of QTc &gt;450 in patients with type 2 DM were significantly greater compared with the control group (p&lt;0.05) and did not depend on the HbA1c level. In type 2 DM patients without recorded hypoglycemic episodes, the characteristics of QTc did not differ from the results of the control group (p&gt;0.05). At the time of the hypoglycemic episode, the QTc duration in patients with type 2 DM significantly increased compared with the average daily value of QTc in the same patients – 487 (466; 519.5) ms against 436.5 (431; 452) ms (p&lt;0.001). A strong correlation between QTc duration and the presence of hypoglycemia was determined (rs=0.78; p=0.023). QTc duration also correlated with GR (rs=0.23; p=0.016) and SD (rs=0.21; p=0.021). Therefore, it was found that in patients with type 2 diabetes, the prolongation of QTc duration is associated with high glycemic fluctuations and hypoglycemia (p&lt;0.05) regardless of the HbA1c level.

Insulin for hyperglycemia prevention and management during postgastrectomy nutrition support in gastric cancer: Reduced complications in a retrospective cohort study in China.

To evaluate the effectiveness of insulin addition to the total nutrition admixture (TNA) for glycemic control among patients with gastric cancer (GC) receiving supplementary parenteral nutrition (SPN) after gastrectomy. A retrospective cohort study was conducted among 208 noncritical ill patients who underwent gastrectomy for GC from 2017 to 2019 at a tertiary teaching hospital in Lanzhou, China. All the included patients received individualized SPN and enteral nutrition treatment after gastrectomy. The patients were randomly divided into insulin and noninsulin groups based on the TNA composition. Blood glucose (BG) measurements, glycemic fluctuation, and hypoglycemia incidence during SPN were compared between the two groups. The postoperative comprehensive complications index (CI) and infections were compared according to insulin regimen and postoperative glycemic status. The mean BG was significantly lower and fluctuated less in the insulin group than in the noninsulin group (p<0.05). One unit of insulin per 6 g of parenteral nutrition glucose addition to TNA did not increase hypoglycemia incidence (p>0.05). Comparing CI and the infection rate, no significance was observed between the insulin and noninsulin groups, but a higher postoperative CI was observed in patients with hyperglycemia than in euglycemic patients (p<0.05). Appropriate insulin addition to TNA has an overall positive effect on glycemic management in patients with noncritical GC who received SPN after gastrectomy. Postoperative glycemic status was associated with the incidence of relevant complications. Further research is needed for conclusive recommendations.

The Impact and Clinical Prediction of Hyperglycemia During Parenteral Nutrition for Nondiabetic Patients After Gastrectomy for Gastric Cancer.

Background and PurposeHyperglycemia (HG) is associated with increased postoperative complications. This study aims to evaluate the effect of HG during supplemental parenteral nutrition (SPN) on short-term prognosis in non-diabetic patients undergoing gastrectomy for cancer and to analyse the risk factors and prevention methods for HG.MethodsA total of 359 patients were divided into three groups according to blood glucose (BG) during SPN: normoglycemic patients ( ≤ 125 mg/dL), mild HG (125~200 mg/dL), and severe HG (>200 mg/dL). The effect of BG on postoperative short-term outcomes was analyzed. Multivariate regression was performed to investigate influencing factors for severe HG. The safety and efficacy of insulin addition to total nutrient admixture (TNA) for the prevention and management of HG were assessed by propensity score matching (PSM). In addition, regression analysis was performed in the noninsulin group to investigate the predictive factors of severe HG, and a nomogram was plotted.ResultsThe postoperative complication rate was 18.9%, but it was significantly higher in patients with severe HG than in mild HG and normoglycemic patients (25.2, 15.0, and 10.0%, respectively, p < 0.05). Multivariate logistic regression analysis showed that anemia, myosteatosis, higher postoperative capillary blood glucose (CBG) before TNA infusion, and insulin in the TNA were independent influencing factors for severe HG. Based on the above factors, 75 pairs of patients (insulin group and non-insulin group) with comparable baseline data were successfully matched by PSM. The HG incidence and the glycemic fluctuation were significantly improved through 1 U insulin/6 g glucose (1/6 scheme) to TNA. A nomogram containing hemoglobin, skeletal muscle radiodensity, pre-SPN CBG, and pTNM stage with good predictive efficacy (C-index: 0.750) was constructed based on the noninsulin group.ConclusionPoor postoperative glycemic control was related to worse outcomes in non-diabetic patients undergoing gastrectomy for cancer. Pre-operative anemia, myosteatosis, and high postoperative CBG before TNA infusion are risk factors for severe HG. Insulin in TNA can improve the blood glucose control of patients. Our proposed nomogram rendered an individualized predictive tool for HG during SPN, which helps screen high-risk patients requiring insulin therapy. Future studies with larger samples are needed to develop a complete insulin application protocol for SPN.

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal.

To identify a clinical judgment of susceptibility referring to the development of glycemic fluctuations in adults with Diabetes Mellitus undergoing treatment. Theoretical study with construct analysis. The exploration of the phenomena of glycemic variations provided clues for the description of the blood glucose pattern construct and the proposal of a new risk Nursing diagnosis as a judgment of susceptibility to the balance of this pattern. The risk factors for "Risk for Imbalanced Blood Glucose Pattern" are cognitive dysfunction; excessive alcohol consumption; excessive daily exercise; inadequate follow through with treatment regimen; increased frequency of self-monitoring of blood glucose; inadequate knowledge of disease process; inadequate management of amount of food; inadequate regularity of meal consumption; obesity; overweight; smoking; underweight. The elaboration of "Risk for Imbalanced Blood Glucose Pattern" Nursing diagnosis contributes to the advancement in the Nursing classifications and to the elaboration of planning actions and specific interventions.

Long-Term Glycemic Variability and Risk of Cardiovascular Events in Type 2 Diabetes: A Meta-Analysis.

Long-term glycemic fluctuation has been associated with cardiovascular risk in patients with type 2 diabetes mellitus (T2DM). However, the findings are inconsistent. We performed a meta-analysis to summarize the association between parameters of long-term glycemic variability and risk of cardiovascular events in T2DM patients. Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of cardiovascular events in T2DM patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies was used to pool the results. Twelve follow-up studies with 146 653 T2DM patients were included. The mean follow-up duration was 4.9 years. Pooled results showed that compared to those with the lowest glycemic variability, patients with the highest glycemic variability had significantly increased risk of cardiovascular events, as evidenced by the standard deviation of glycated hemoglobin [HbA1c-SD: relative risk (RR)=1.44, 95% confidence interval (CI): 1.23 to 1.69, p<0.001; I2=70%], HbA1c coefficient of variation (HbA1c-CV: RR=1.46, 95% CI: 1.19 to 1.79. p<0.001; I2=83%), standard deviation of fasting plasma glucose (FPG-SD: RR=1.33, 95% CI: 1.07 to 1.65, p=0.009; I2=0%), and FPG coefficient of variation (FPG-CV: RR=1.29, 95% CI: 1.01 to 1.64, p=0.04; I2=47%). In conclusion, increased long-term glycemic variability may be an independent risk factor for cardiovascular events in T2DM patients.

Metabolites and Genes behind Cardiac Metabolic Remodeling in Mice with Type 1 Diabetes Mellitus.

Metabolic remodeling is at the heart of diabetic cardiomyopathy. High glycemic fluctuations increase metabolic stress in the type 1 diabetes mellitus (T1DM) heart. There is a lack of understanding on how metabolites and genes affect metabolic remodeling in the T1DM heart. We hypothesize that differential expression of metabolic genes and metabolites synergistically influence metabolic remodeling preceding T1DM cardiomyopathy. To test our hypothesis, we conducted high throughput analysis of hearts from adult male hyperglycemic Ins2+/− (Akita) and littermate normoglycemic Ins2+/+ (WT) mice. The Akita mouse is a spontaneous, genetic model of T1DM that develops increased levels of consistent glycemic variability without the off-target cardiotoxic effects present in chemically- induced models of T1DM. After validating the presence of a T1DM phenotype, we conducted metabolomics via LC-MS analysis and genomics via next-generation sequencing in left ventricle tissue from the Akita heart. Ingenuity Pathway Analyses revealed that 108 and 30 metabolic pathways were disrupted within the metabolomics and genomics datasets, respectively. Notably, a comparison between the two analyses showed 15 commonly disrupted pathways, including ketogenesis, ketolysis, cholesterol biosynthesis, acetyl CoA hydrolysis, and fatty acid biosynthesis and beta-oxidation. These identified metabolic pathways predicted by the differential expression of metabolites and genes provide the foundation for understanding metabolic remodeling in the T1DM heart. By limited experiment, we revealed a predicted disruption in the metabolites and genes behind T1DM cardiac metabolic derangement. Future studies targeting these genes and metabolites will unravel novel therapies to prevent/improve metabolic remodeling in the T1DM heart.

Altered muscle mitochondrial, inflammatory and trophic markers, and reduced exercise training adaptations in type 1 diabetes.

Growing evidence of impaired skeletal muscle health in people with type 1 diabetes points toward the presence of a mild myopathy in this population. However, this myopathic condition is not yet well characterised and often overlooked, even though it might affect the whole‐body glucose homeostasis and the development of comorbidities. This study aimed to compare skeletal muscle adaptations and changes in glycaemic control after 12 weeks of combined resistance and aerobic (COMB) training between people with type 1 diabetes and healthy controls, and to determine whether the impaired muscle health in type 1 diabetes can affect the exercise‐induced adaptations. The COMB training intervention increased aerobic capacity and muscle strength in both healthy and type 1 diabetes sedentary participants, although these improvements were higher in the control group. Better glucose control, reduced glycaemic fluctuations and fewer hypoglycaemic events were recorded at post‐ compared to pre‐intervention in type 1 diabetes. Analysis of muscle biopsies showed an alteration of muscle markers of mitochondrial functions, inflammation, ageing and growth/atrophy compared to the control group. These muscular molecular differences were only partially modified by the COMB training and might explain the reduced exercise adaptation observed in type 1 diabetes. In brief, type 1 diabetes impairs many aspects of skeletal muscle health and might affect the exercise‐induced adaptations. Defining the magnitude of diabetic myopathy and the effect of exercise, including longer duration of the intervention, will drive the development of strategies to maximise muscle health in the type 1 diabetes population.Key points Type 1 diabetes negatively affects skeletal muscle health; however, the effect of structured exercise training on markers of mitochondrial function, inflammation and regeneration is not known.Even though participants with type 1 diabetes and healthy control were comparable for cardiorespiratory fitness (V˙O2max) and muscle strength at baseline, molecular markers related to muscle health were decreased in type 1 diabetes.After training, both groups increased V˙O2max and muscle strength; however, a larger improvement was achieved by the control group.The training intervention decreased glucose fluctuations and occurrence of hypoglycaemic events in type 1 diabetes, while signs of mild myopathy found in the muscle of participants with type 1 diabetes only partially improved after trainingImproving muscle health by specific exercise protocols is of considerable clinical interest in therapeutic strategies for improving type 1 diabetes management and preventing or delaying long‐term complications.

Serum vitamin B12 levels and glycemic fluctuation in patients with type 2 diabetes mellitus.

Purpose:The aim of the study was to explore the correlation between serum vitamin B12 levels and glycemic fluctuation in patients with type 2 diabetes mellitus (T2DM).Methods:This study included 202 T2DM patients in whom blood glucose levels were recorded using a continuous glucose monitoring system retrospectively. Glycemic fluctuation was determined using the average daily risk range (ADRR), a diabetes-specific measure of the risk for hyper- and hypoglycemia.Results:Serum vitamin B12 levels were higher in T2DM patients with wider glycemic fluctuations than in those with minor glycemic fluctuations (p < 0.001). We observed a positive correlation between serum vitamin B12 levels and ADRR in both T2DM patients who received and did not receive metformin therapy (r = 0.388, p < 0.001 and r = 0.280, p = 0.004, respectively). Multiple linear regression analysis showed that serum vitamin B12 levels were independently correlated with ADRR in T2DM patients who received and did not receive metformin therapy (beta = 0.367, p < 0.001 and beta = 0.410, p < 0.001, respectively).Conclusions:Serum vitamin B12 levels are correlated with glycemic fluctuation in patients with T2DM and may serve as an underlying useful biomarker of glycemic fluctuation in T2DM patients, treated with or without metformin therapy.

Syndrome of Congenital Insulin Resistance Caused by a Novel INSR Gene Mutation

Mutations in the INSR gene result in rare inherited syndromes causing insulin resistance, such as leprechaunism (Donohue syndrome), Rabson-Mendenhall syndrome and insulin resistance type A. Leprechaunism is an autosomal recessive disorder associated with extreme insulin resistance that leads to hyperinsulinemia, impaired glucose homeostasis, fasting hypoglycemia and postprandial hyperglycemia. Impaired insulin action causes prenatal and postnatal growth retardation. Lipoatrophy, dysmorphic facies, hypertrichosis, macrogenitosomia and hypertrophy of internal organs are also present. A male infant with congenital insulin resistance was born at term after a normal pregnancy with a weight of 1905 g (<3 c), a length of 48 cm (<3 c), and an Apgar score of 10. Intrauterine growth retardation, transient hypoglycemia, pneumonia, urinary tract infection and heart defects [patent foramen ovale (PFO); patent ductus arteriosus (PDA)] were diagnosed after birth. At 5 weeks of age, he was admitted to the regional hospital with severe fever, diarrhea and dehydration. Hyperglycemia was observed (672 mg/dL), and insulin was administered. He was referred to a hospital at 7 weeks of age for suspected neonatal diabetes and hypertrophic cardiomyopathy. The physical examination revealed a loud systolic heart murmur, tachycardia, tachypnea, dysmorphic facies, hypertrichosis, acanthosis nigricans, hypotonia, swollen nipples and enlarged testicles. Glycemic fluctuations (50-250 mg/dL) were observed. The serum insulin concentration was high (maximum 1200 IU/mL) at normoglycemia. Ultrasound of the heart confirmed progressive hypertrophic cardiomyopathy. Leprechaunism was confirmed by genetic analysis of INSR, in which a novel c.320C>G; p. Thr107Arg homozygous missense mutation was found in exon 2.

Abstract 12380: 1,5-anhydroglucitol is a Novel and Independent Predictor of Mortality in Patients With COVID-19

Background: Fasting hyperglycemia and diabetes are the independent predictors of morbidity and mortality in patients with COVID-19. Glycemic fluctuations may be independently associated with poor prognosis in adult patients hospitalized for COVID-19. Hypothesis: We hypothesize that 1,5-anhydroglucitol (1,5-AG), a marker of glycemic variability, is strongly associated with in-hospital mortality in patients with COVID-19. Methods: Medical history, demographic variables, and laboratory measurements were collected within 48 hours of hospitalization in COVID-19 patients (n=64), including 9 patients who died in the hospital. Serum 1,5AG was measured by an enzymatic colorimetric assay (GlycoMark, Precision Diabetes, Inc. Raleigh, NC). Multivariate regression analysis was performed to assess the relation between fasting glucose, hemoglobin (Hb)A1c and 1,5-AG and mortality. Results: Baseline mean ± SD for fasting blood glucose,1,5-AG and HbA1c (available in 40 patients) were 143 ± 69 mg/dL and 14.7 ± 8.8 ug/mL and 6.9±2.3%, respectively. Only 1,5-AG was an independent predictor of mortality (n=64, AUC = 0.69, p=0.017), but not fasting glucose (n=64, AUC =0.60, p=0.32) or HbA1c (n=40, AUC=0.58, p =0.46), respectively. With respect to clinical variables, a combination of BMI and Age was predictive of mortality (AUC = 0.77, p=0.004). Interestingly, when 1,5-AG was added to BMI and Age, the AUC increased to 0.94 (p &lt;0.0001). When fasting glucose was added to BMI and Age the AUC was 0.79 (p =0.001). A cox regression analysis demonstrated that 1,5-AG &lt; 10 ug/mL was associated with an odds ratio of 0.44 (95% CI =0.11-1.85) for mortality. Conclusions: This analysis demonstrated that 1,5-AG is a novel and independent predictor of COVID-19 mortality. 1,5-AG may provide important and unique information in the COVID-19 clinical setting. An algorithm of BMI, Age, and 1,5-AG can be used clinically to predict mortality. These findings warrant further investigation in larger studies specific for diabetic and non-diabetic populations.

Congenital insulin resistance in the practice of the pediatrician and pediatric endocrinologist -path to diagnosis

Introduction. Hyperinsulinemic hypoglycemia in children is most commonly due to congenital hyperinsulinism. When hyperinsu-linemia is accompanied by fasting hypoglycemia and postprandial hyperglycemia, rare syndromes of severe insulin resistance, which include Rabson - Mendenhall syndrome, should be suspected. This article provides an analytical review of current data on this rare genetic pathology and presents a clinical case of a previously undescribed combination of Rabson-Mendenhall syndrome with mutations in the insulin receptor gene INSR in the compound heterozygous state with multiple congenital anomalies of other organs.Clinical case. Patient N, 5.5 months old boy, with suspected congenital hyperinsulinism due to episodes of frequent severe hypoglycemia from the first day of life. At the age of 5 months, an episode of hypoglycemia up to 2.2 mmol/L was registered at an appointment with a pediatric endocrinologist. An examination was ordered, which found that against a background decrease in blood glucose to 1.9 mmol/L, C-Peptide level &gt;5000 ng/mL, insulin level &gt;300 lU/mL, cortisol - 971 nmol/L, TSH -3.88 mlU/L, free T4 - 10.53 pmol/L (10-23.2).The importance of early diagnosis of severe insulin resistance to prevent developmental disorders in children is emphasized. The issue of organizing multiple effective monitoring of a patient’s glycemia required special attention in this clinical case. Due to the features of metabolism in young children, we abandoned flash glucose monitoring systems and used a modern glucose meter with an integration program with a mobile application and the ability to generate reports for subsequent analysis as a reliable means of glycemic control.Summary. Based on the results of the genetic study in association with the clinical phenotype, age of debut, the patient was clinically diagnosed with Rabson-Mendenhall syndrome.Discussion. The paradoxical nature of glycemic fluctuations (severe fasting hypoglycemia and postprandial diabetic hyperglycemia) is quite typical for syndromes of severe insulin resistance and should draw the attention of an informed primary care physician.Conclusion. Careful attention to the symptoms of hypoglycemia, especially with a debut in the neonatal period, recurrent episodes, and the severity of the decrease in blood glycemia. If normal or elevated levels of insulin and C-peptide are detected against the background of hypoglycemia, the first thing to think about is congenital hyperinsulinism.

Novel Continuous Glucose Monitoring Metrics and Large-for-Gestational-Age Risk: An Exploratory Retrospective Cohort Study in Pregnancies with Type 1 Diabetes

Background: Continuous glucose monitoring (CGM) improves pregnancy outcomes in patients with type 1 diabetes (T1D). We aimed to assess the between-group differences in glycated hemoglobin (HbA1c) and the incidence of large-for-gestational-age (LGA) neonates in CGM and glucometer users and analyze the potential association of novel CGM metrics with LGA risk in T1D pregnancies. Materials and Methods: Our retrospective study cohort included 134 women with T1D treated with insulin pumps-75 of them used CGM and 59 patients measured their glucose concentrations using glucometers only. As part of our study, we matched the CGM users and patients who preferred the self-monitoring of blood glucose (SMBG) according to their baseline HbA1c and White's diabetes class at a 1:1 ratio. After the matching, both groups included 42 pregnancies. Results: We did not find any difference in changes in HbA1c and perinatal outcomes between CGM and SMBG users; however, we achieved a limited statistical power, and there were more cases of diabetic nephropathy in the SMBG group. Mothers of LGA infants had higher first-trimester HbA1c, time above target, and mean glucose concentrations in each trimester of pregnancy. Other CGM metrics reflecting glucose fluctuations attributed to hyperglycemia were associated with an increased risk of LGA. Despite optimal maternal HbA1c, 39% of neonates demonstrated LGA. Conclusions: Although participants reached the target HbA1c concentrations, mothers of LGA newborns had higher first-trimester HbA1c, as well as higher time above target range, higher mean glucose concentrations, and more glycemic fluctuations, suggesting that several CGM metrics associated with maternal hyperglycemia are associated with LGA in pregnancies with T1D.

Childhood diabetes and sleep.

Sleep modulates glucose metabolism, both in healthy states and in disease. Alterations in sleep duration (insufficient and excessive) and obstructive sleep apnea may have reciprocal ties with obesity, insulin resistance and Type 2 diabetes, as demonstrated by emerging evidence in children and adolescents. Type 1 diabetes is also associated with sleep disturbances due to the influence of wide glycemic fluctuations upon sleep architecture, the need to treat nocturnal hypoglycemia, and the need for glucose monitoring and insulin delivery technologies. In this article, we provide an extensive and critical review on published pediatric literature regarding these topics, reviewing both epidemiologic and qualitative data, and provide an overview of the pathophysiology linking sleep with disorders of glucose homeostasis.