Mean Glycemia Research Articles

Metabolic risk factors, but not vascular lesions and β‐amyloid, are associated with functional connectivity across the Alzheimer’s disease spectrum

AbstractBackgroundAlzheimer’s disease (AD) is a multifactorial disease, characterized not only by pathological protein aggregation (Ab and tau), but also by early vascular dysfunctions and functional connectivity alterations (Iturria‐Medina 2016; van der Kant 2019). The study objective was to investigate the association between different markers of vascular health as well as brain β‐amyloid burden and brain network functional connectivity across the AD spectrum.MethodThe sample included 131 participants with normal cognition, subjective cognitive decline, mild cognitive impairment and clinical AD from the IMAP cohort (Caen, France). Vascular risk factors comprised mean arterial pressure, body‐mass‐index (BMI), glycemia and HbA1c levels. White matter hyperintensities (WMH) were segmented from FLAIR‐MRI using the “lesion‐segmentation‐tool” (Schmidt, 2017). Brain β‐amyloid burden was measured by the mean of AV45‐SUVR in an AD‐related neocortex mask (LaJoie 2012). Resting‐state functional connectivity (RSFC) was determined within seven predefined functional networks (Schaefer 2018, Verfaillie 2018). Partial correlation analyses were run to assess associations of vascular risk factors, total WMH load and β‐amyloid burden with RSFC. Additionally, we tested for interaction effects with β‐amyloid status in linear regression analyses. All models were age‐ and sex‐adjusted.ResultParticipant characteristics are presented in Figure 1. Across all participants, higher BMI and higher glycemia were associated with lower RSFC within the default‐mode, salience/ventral‐attention, fronto‐parietal and dorsal‐attention networks (Figure 2 and 3). Results remained significant after correction for diagnostic group status. No associations were found between RSFC and total WMH load or β‐amyloid burden (p≥0.05). There were no significant moderation effects by β‐amyloid positivity (p≥0.05).ConclusionThese findings demonstrate that metabolic‐related vascular risk factors, but not cerebrovascular or β‐amyloid pathologies, are associated with functional connectivity specifically within cognition‐related functional networks. Our study further highlights that connectivity alterations associated with metabolic risk, previously shown in diabetes patients (Musen 2012; Chen 2015), are also present in a non‐diabetic aging and AD cohort. Given that functional connectivity supports reserve capacity, connectivity failure within higher‐order networks may limit resilience against aging and pathological processes.

Mean and visit-to-visit variability of glycemia and left ventricular diastolic dysfunction: A longitudinal analysis of 3025 adults with serial echocardiography

ObjectiveWe aimed to determine the mean glucose thresholds to increase the risk of left ventricular diastolic dysfunction (LVDD) and whether visit-to-visit variability of fasting plasma glucose (FPG) and glycated hemoglobin (A1C) could independently increase the risk in a cohort with serial echocardiography. MethodsThis was a 3.5-year (range, 0.5–8.3) retrospective longitudinal cohort study of 3025 adults (age, 55.15 ± 7.6 years; without diabetes, n = 2755) with LV ejection fraction > 50% by serial echocardiography between 2006 and 2016. Mean, standard of deviation (SD) and coefficient of variation (CV) of FPG and A1C obtained from three consecutive measurements preceding the first echocardiography. The definition of LVDD in this study was primarily based on early peak mitral inflow velocity and early diastolic mitral annulus motion velocity. ResultsLVDD developed in 611/3025 subjects (20.2%). Cox proportional hazard models showed increased adjusted hazard ratios (HRs) for incident LVDD in the highest quartile of FPG-mean (HR 1.76, 95% confidence interval [CI]; 1.36–2.30), FPG-SD (HR 1.63, 95% CI; 1.27–2.09), FPG-CV (HR 1.47, 95% CI; 1.15–1.89), and A1C-mean (HR 1.83, 95% CI; 1.41–2.38) versus the lowest quartile, which was consistent even in subjects without diabetes. Mean glucose thresholds for the increased risk were below the lower limits for pre-diabetes. ConclusionsIn terms of mean glycemia, LVDD may be initiated in the earliest diabetic continuum, and such changes could be measurable within several years. Visit-to-visit variability of FPG, but not that of A1C, predicted accelerated development of LVDD.

An Electrochemical Chip to Monitor In Vitro Glycation of Proteins and Screening of Antiglycation Potential of Drugs.

Hyperglycemia and the production of advanced glycation end products (AGEs) are the primary factors for the development of chronic complications in diabetes. The level of protein glycation is proportional to the glucose concentration and represents mean glycemia. In this study, we present an electrochemical chip-based method for in vitro glycation monitoring and the efficacy evaluation of an antiglycation compound. An electrochemical chip consisting of five microchambers and embedded microelectrodes was designed for parallel measurements of capacitance signals from multiple solutions at different concentrations. The feasibility of glycation monitoring was then investigated by measuring the capacitance signal at 0.13 MHz with bovine serum albumin and gelatin samples in the presence of various glucose concentrations over 28 days. A significant change in the capacitance due to protein glycation was observed through measurements conducted within 30 s and 21 days of incubation. Finally, we demonstrated that the chip-based capacitance measurement can be utilized for the selection of an antiglycation compound by supplementing the protein solution and hyperglycemic concentration of glucose with an inhibitory concentration of the standard antiglycation agent aspirin. The lack of a significant change in the capacitance over 28 days proved that aspirin is capable of inhibiting protein glycation. Thus, a strong relationship exists between glycation and capacitance, suggesting the application of an electrochemical chip for evaluating glycation and novel antiglycation agents.

The Interaction of Acute and Chronic Glycemia on the Relationship of Hyperglycemia, Hypoglycemia, and Glucose Variability to Mortality in the Critically Ill.

To determine the relationship between preadmission glycemia, reflected by hemoglobin A1c level, glucose metrics, and mortality in critically ill patients. Retrospective cohort investigation. University affiliated adult medical-surgical ICU. The investigation included 5,567 critically ill patients with four or more blood glucose tests and hemoglobin A1c level admitted between October 11, 2011 and November 30, 2019. The target blood glucose level was 90-120 mg/dL for patients admitted before September 14, 2014 (n = 1,614) and 80-140 mg/dL or 110-160 mg/dL for patients with hemoglobin A1c less than 7% or greater than or equal to 7% (n = 3,953), respectively, subsequently. None. Patients were stratified by hemoglobin A1c: less than 6.5.(n = 4,406), 6.5-7.9% (n = 711), and greater than or equal to 8.0% (n = 450). Increasing hemoglobin A1c levels were associated with significant increases in mean glycemia, glucose variability, as measured by coefficient of variation, and hypoglycemia (p for trend < 0.0001, < 0.0001, and 0.0010, respectively). Among patients with hemoglobin A1c less than 6.5%, mortality increased as mean glycemia increased; however, among patients with hemoglobin A1c greater than or equal to 8.0%, the opposite relationship was observed (p for trend < 0.0001 and 0.0027, respectively). Increasing glucose variability was independently associated with increasing mortality only among patients with hemoglobin A1c less than 6.5%. Hypoglycemia was independently associated with higher mortality among patients with hemoglobin A1c less than 6.5% and 6.5-7.9% but not among those with hemoglobin A1c greater than or equal to 8.0%. Mean blood glucose 140-180 and greater than or equal to 180 mg/dL were independently associated with higher mortality among patients with hemoglobin A1c less than 6.5% (p < 0.0001 for each). Among patients with hemoglobin A1c greater than or equal to 8.0% treated in the second era, mean blood glucose greater than or equal to 180 mg/dL was independently associated with decreased risk of mortality (p = 0.0358). Preadmission glycemia, reflected by hemoglobin A1c obtained at the onset of ICU admission, has a significant effect on the relationship of ICU glycemia to mortality. The different responses to increasing mean glycemia support a personalized approach to glucose control practices in the ICU.

Preoperative Level of Leptin Can Be a Predictor of Glycemic Control for Patients with Diabetes Undergoing Bariatric Surgery.

Bariatric surgery is a first-line treatment for patients with obesity and diabetes. It is uncertain whether leptin has an influence on glycemia in the postoperative period. A cohort study of thirty-eight individuals with obesity and diabetes who underwent laparoscopic Roux-en-Y gastric bypass was undertaken. The levels of leptin, glucose, and glycosylated hemoglobin were verified in the preoperative period and in the first and third postoperative months. The majority of patients were women (81.6%), and the mean age was 47.3years (SD 8.8). The mean BMI was reduced by 17.75% (from 47.3 to 38.9kg/m2) and the mean glycemia by 26.76%. Preoperative leptin had a moderate positive correlation with glucose level at the third month (Pearson r = 0.46, P = 0.02), but not with HbA1c. Patients with leptin above 27.34ng/mL had a higher glucose level at the end of observation (101.9 versus 88.9, t test, P = 0.042). The search for factors that influence diabetes control after bariatric surgery is of major importance in clinical practice. Our study reported a level of leptin that can predict the prognosis of glycemic control after the intervention. This finding still needs to be validated and confirmed in other populations.

Symphysis-Fundal Height Curve in Pregnancies Complicated by Maternal Hyperglycemia: Comparison with Curves of Nondiabetic Pregnant Women.

Background Reference symphysis-fundal height (SFH) curves for pregnancies complicated by maternal hyperglycemia are not available. Objective To build an SFH curve according to gestational age for pregnant women with hyperglycemia-type 2 diabetes (T2DM), gestational diabetes mellitus (GDM), or mild gestational hyperglycemia (MGH) and compare it with three other curves in use in Brazil. Methods Prospective cohort study of 422 pregnant women with hyperglycemia attending the Perinatal Diabetes Research Center (PDRC) of Botucatu Medical School, São Paulo State University/UNESP. Between 13 and 41 weeks of pregnancy, 2470 SFH measurements were obtained (mean 5.85 per woman). For the assessment of glycemic control, 2074 glucose level measurements were taken and the glycemic mean (GM) at each gestational week was estimated. Results GM was adequate (<120 mg/dL) in 94.9% and inadequate (≥120 mg/dL) in 5.1% of the cases. The equation applied for SFH prediction was expressed as SFH = 1.082 + 0.966∗week (r2 = 84.6%). At visual analysis, P10 and P90 SFH measurements were higher in the study curve than in the three other curves. Statistical analysis confirmed that SFH median values in this study were higher than those in the reference curve of habitual risk pregnancies, especially after 19 weeks of pregnancy. Conclusion Taking into account that the maternal hyperglycemia was at strict control, our unedited results suggest that the current SFH curve can be a useful tool in prenatal care of T2DM, GDM, and MGH pregnant women.

Evaluation of the relationship between cardiac autonomic function and glucose variability and HOMA-IR in prediabetes.

Aims:The present study aims to investigate the relationship between cardiac autonomic function (CAF) and glucose variability (GV) and HOMA-IR in subjects with prediabetes and normal glucose tolerance (NGT).Material and methods:Ninety-two subjects (59 with prediabetes and 33 with NGT), of mean age 50.3 ± 11.5 years, mean BMI 30.4 ± 6.0 kg/m2, were included in this cross-sectional study. Glucose tolerance was assessed by OGTT according to WHO 2006 criteria. Glucose, HbA1c, insulin, oxLDL, and 3-Nitrotyrosine were measured. CGM was performed with a blinded sensor (FreeStyle Libre Pro). CAF was assessed by ANX-3.0 technology.Results:GV indices were increased in prediabetes. CAF was suppressed in subjects with any stage of dysglycemia. The prevalence of cardiac autonomic dysfunction was higher in prediabetes −20.3% as compared to NGT −3.0%, p = 0.028. HOMA-IR [OR 1.5 (95% CI: 1.1–2.1), p = 0.010] and time in target range [OR 0.8 (95% CI: 0.67–0.97), p = 0.021] were found to be predictive variables for impaired CAF. Sympathetic and parasympathetic activity negatively correlated with mean glycemia and GV indices and were independently related to JINDEX in prediabetes (F[1, 47] = 5.76, p = 0.021 and F[1, 47] = 5.94, p = 0.019, respectively); and to time above target range in NGT (F[1, 18] = 4.48, p = 0.049 and F[1, 18] = 4.65, p = 0.046, respectively).Conclusion:CAF is declined in prediabetes and seems to be related to GV and HOMA-IR at early stages of dysglycemia.

Usage of glucometer with mobile application in real clinical practice

Introduction. Glycemic control in patients with diabetes mellitus type 1 (DM1) can be held using glucometer with mobile application, continuous glucose monitoring (CGM), and intermediately scanned CGM (isCGM). isCGM do not need calibration with glucometer as CGM, but its usage in children and adolescents is recommended only together with glucometer.Aim: to study characteristics of glucometer usage in real clinical practice in a patient which utilizes glucometer with mobile application and is CGM simultaneously.Materials and methods. A 17-year-old girl with diabetes mellitus type 1 was advised to use isCGM together with glucometer Contour Plus One (ISO 15197:2013) and mobile application Contour Diabetes.Results. The first three months, while insulin dose titration and education, the patient used glucometer 1–9 times a day (3.0 (2.0; 4.0)), filled in diary. These led to decrease of glycaemia (10.7 (5.5; 14.7) – 7.8 (5.2; 9.5) mmol/L) and variability (56–45%), increase of percent of measurements within range (38–57%), according to glucometer; decrease of mean glycaemia (11.8–8.5 mmol/L) and increase of time in range (TIR) (14–59%), according to isCGM. The next three months, while diabetes mellitus type 1 compensation, according to glucometer (glycaemia 6.9 (4.9; 9.7) mmol/L, variability 48%, percent of measurements within range 71%) and isCGM (mean glycaemia mean glycaemia 7.3 mmol/L, TIR 67%), the patient stopped to fill in diary, decreased number of measurements by glucometer to 1.0 (1.0; 2.0) times a day. Mean month glycaemia was 5.5–9.8% lower according to glucometer than isCGM. The patient used glucometer in cases with relatively high risk of hypoglycemia more often.Conclusion. Usage of glucometer with mobile application can increase adherence to treatment. If glucometer and isCGM used simultaneously the patients measure glycaemia with glucometer in cases of decompensated diabetes mellitus type 1 and while insulin titration more often. Mean month glycaemia according to glucometer, if used every day, corresponds with isCGM data. We must discuss with patients circumstances when they use glucometer because this can influence glycemic control indicators in mobile application reports.

The Relationship Between CGM-Derived Metrics, A1C, and Risk of Hypoglycemia in Older Adults With Type 1 Diabetes.

Continuous glucose monitoring (CGM) is now commonly used in the management of type 1 diabetes (T1D). The CGM-derived coefficient of variation (CV) measures glucose variability, and the glucose management indicator (GMI) measures mean glycemia (previously called estimated A1C). However, their relationship with laboratory-measured A1C and the risk of hypoglycemia in older adults with T1D is not well studied. In a single-center study, older adults (age ≥65 years) with T1D wore a CGM device for 14 days. The CV (%) and GMI were calculated, and A1C and clinical and demographic information were collected. We evaluated 130 older adults (age 71 ± 5 years), of whom 55% were women, 97% were White, diabetes duration was 39 ± 17 years, and A1C was 7.3 ± 0.6% (56 ± 15 mmol/mol). Participants were stratified by high CV (>36%; n = 77) and low CV (≤36%; n = 53). Although there was no difference in A1C levels between the groups with high and low CV (7.3% [56 mmol/mol] vs. 7.3% [53 mmol/mol], P = 0.4), the high CV group spent more time in hypoglycemia (<70 mg/dL and ≤54 mg/dL) compared with the group with low CV (median 31 vs. 84 min/day, P < 0.0001; 8 vs. 46 min/day, P < 0.001, respectively). An absolute difference between A1C and GMI of ≥0.5% was observed in 46% of the cohort. When the A1C was higher than the GMI by ≥0.5%, a higher duration of hypoglycemia was observed (P = 0.02). In older adults with T1D, the use of CGM-derived CV and GMI can better identify individuals at higher risk for hypoglycemia compared with A1C alone. These measures should be combined with A1C for better diabetes management in older adults with T1D.

Differences in Risk Factors for Diabetic Retinopathy in Type 1 and Type 2 Diabetes Mellitus Patients in North-East Poland.

Background and Objective: Nowadays, diabetes is one of the main causes of blindness in the world. Identification and differentiation of risk factors for diabetic retinopathy depending on the type of diabetes gives us the opportunity to fight and prevent this complication. Aim of the research: To assess differences in the risk factors for diabetic retinopathy in type 1 and type 2 diabetes mellitus patients in Warmia and Mazury Region, Poland. Materials and Methods: Risk factors for diabetic retinopathy (DR) were assessed on the basis of an original questionnaire, which included: personal data, clinical history of diabetes and eye disease. Elements of clinical examination: blood pressure, BMI, waist circumference. Indicators of diabetes metabolic control: mean glycemia, glycated hemoglobin (HbA1c), total cholesterol and triglycerides, creatinine, glomerular filtration rate (GFR), albumin–creatinine ratio in urine. Results: The study group included 315 (26%) patients with DM1 and 894 (74%) patients with DM2. Risk factors were estimated on the basis of logistic regression and verified with Student’s t-test. Statistically significant dependencies were found in both groups between the occurrence of diabetic retinopathy and diabetes duration, HbA1c, triglyceride concentrations, indicators of kidney function and cigarette smoking status. In the DM2 group, the development of DR was significantly influenced by the implemented models of diabetic treatment. Conclusions: In the whole study group, the risk of DR was associated with the duration of diabetes, HbA1c, triglyceride concentrations and smoking. In DM1 patients, the risk of DR was associated with diabetic kidney disease in the G1A1/A2 stage of chronic kidney disease, and in DM2 patients with the G2 stage of chronic kidney disease. An important risk factor for DR in DM2 patients was associated with late introduction of insulin therapy.

The Calculation of the Glucose Management Indicator Is Influenced by the Continuous Glucose Monitoring System and Patient Race.

Objective: To determine whether the glucose management indicator (GMI), an estimate of hemoglobin A1c (HbA1c) derived from continuous glucose monitoring (CGM) mean glycemia, differs by CGM system and patient race. Methods: One hundred three patients with prediabetes or stable diabetes and a minimum of 10 days of CGM data collected with the FreeStyle Libre CGM system immediately before measurement of HbA1c were included in this clinic-based observational study that used data from electronic health records in an academic endocrinology clinic. HbA1c and Libre CGM-measured mean glucose were plotted to derive a race-agnostic and race-specific regression equations to calculate a Libre-specific GMI (GMILi). The mean GMI derived from the published formula (GMIP) was compared with GMILi. Results: Mean ± SD (standard deviation) age of patients was 61.9 ± 13.3 years; 50% were of nonwhite race and 77% had type 2 diabetes; mean HbA1c was 62 mmol/mol (7.8%). The mean (range) number of days with available CGM data was 26 (10-90). The mean ± SD GMILi was higher than the GMIP in the entire cohort (7.9% ± 1.0% vs. 7.5% ± 1.0%, respectively; P = 0.01) and among Asian patients (7.9% ± 0.9% vs. 7.2% ± 1.0%, respectively; P = 0.03). Conclusions: In a cohort with prediabetes or stable diabetes, the regression equation to calculate GMI varied by CGM system and patient race. The development of device- and race-specific regression equations for GMI may be warranted.

RELATIONSHIP BETWEEN THE ALLOY FREQUENCY OF THE T102C POLYMORPHISM OF THE 5HT2A GENE WITH ANTHROPOMETRIC AND CARDIOMETABOLIC MEASURES

Obesity is a disease characterized by excessive body fat. Among the neurobiological factors associated with obesity, the serotonergic system stands out. The neurotransmitter of these circuits is serotonin (5-HT). Decreased levels of 5-HT have been linked to increased desire to ingest sweets and carbohydrates. This study aims to verify the relationship of the 5HT2A T102C gene polymorphism with obesity and anthropometric parameters. Blood samples and anthropometric data were collected from 150 volunteers, divided between the control and obese groups. The results showed that for individuals with diastolic pressure (p = 0.3681), Estimated Mean Glycemia (GMe) (p = 0.0228) and Homeostatic Model Assessment (HOMA-IR-IR) (p = 0.001) all altered, a higher frequency of C alleles was observed. For normal GMe (p = 0.0270), a higher frequency of the T allele was observed. There was no difference for the allele distribution between the normal and altered groups for the parameters of Triglycerides, HDL cholesterol, total cholesterol, glycated hemoglobin and Insulin. The distribution of the alleles between the groups, normal diastolic pressure (p = 0.3681) and HOMA-IR (p = 0.9203) groups presented no difference. From the genotypic analyzes of the T102C polymorphism of the 5HT2A gene, it was possible to demonstrate a relationship between the presence of the C allele with biochemical and anthropometric markers related to obesity and hypertension.

Visit-to-visit variability of glycemia and vascular complications: the Hoorn Diabetes Care System cohort

BackgroundGlycemic variation has been suggested to be a risk factor for diabetes-related complications. Previous studies did not address confounding of diabetes duration, number of visits and length of follow-up. Here, we characterize glycemic variability over time and whether its relation to diabetes-related complications and mortality is independent from diabetes- and follow-up duration.Materials and methodsIndividuals with type 2 diabetes (n = 6770) from the Hoorn Diabetes Care System cohort were included in this study. The coefficient of variation (CV) was calculated over 5-year sliding intervals. People divided in quintiles based on their CV. Cox proportional hazard models were used to investigate the role of glycemic CV as risk factor in diabetes-related complications and mortality.ResultsThe coefficient of variation of glucose (FG-CV) increased with time, in contrast to HbA1c (HbA1c-CV). People with a high FG-CV were those with an early age of diabetes onset (ΔQ5–Q1 = − 2.39 years), a higher BMI (ΔQ5–Q1 = + 0.92 kg/m2), an unfavorable lipid profile, i.e. lower levels of HDL-C (ΔQ5–Q1 = − 0.06 mmol/mol) and higher triglycerides (ΔQ5–Q1 =+ 1.20 mmol/mol). People with the highest FG-CV in the first 5-year interval showed an increased risk of insulin initiation, retinopathy, macrovascular complications and mortality independent of mean glycemia, classical risk factors and medication use. For HbA1c, the associations were weaker and less consistent.ConclusionsIndividuals with a higher FG-CV have an unfavorable metabolic profile and have an increased risk of developing micro- and macrovascular complications and mortality. The association of HbA1c-CV with metabolic outcomes and complications was less consistent in comparison to FG-CV.

PDB21 PREDICTING HEALTHCARE COSTS OF DIABETES USING MACHINE LEARNING MODELS

To design a predictive model of the in-hospital pharmaceutical expenditure of type II diabetes mellitus, derived from the risk index determined by the associated comorbidities, in a high-complexity hospital in Colombia. Data are collected from patients hospitalized in the years 2017 and 2018 at one University Hospital in Bogotá. A cross-sectional descriptive study of costs by typology is performed, then the total risk index derived from the number and severity of comorbidities is calculated, and its results from laboratory indicators. Factorial analysis establishes the prevalence of co-occurrence of chronic disease and associated morbidities. Based on these data, we use different Machine Learning models (such as logistic regression and supervised learning/random forest) that allows to identify the main variables related with the prediction of the pharmaceutical cost or expenditure. Diabetic patients account for approximately 25% and have an average stay of 5 days. Differences in the number of comorbidities and variability of clinical severity were observed. Laboratory results were available for indicators such as: glycosylated haemoglobin, creatinine and uric acid, as well as lipid profile and 24-hour mean glycemia. The total estimated expense for diabetic patients, with and without associated comorbidities, was US$ 2,204,860 (30.1%) by 2017 and US$ 2,192,365 (27.2%) by 2018. The main co-morbidities associated with diabetes are: Urinary Tract Infection, Hypertension, Heart Failure and Coronary Ischemic Disease. There is an observable relationship in the increasing of costs with the number of associated comorbidities. Based on the aforementioned data, simulations are being carried out for the development of the model, which is expected to be able to predict the variations that emerge from the existing relationship between the risk index and pharmaceutical expenditure.

Searching for an Ideal Preclinical Model to Analyze Oncologic Safety of Breast Lipofilling: Preliminary Results

INTRODUCTION: Preclinical studies aiming to evaluate the microenvironment of breast cancer (BC) are very important for analysis of risk and the behavior of this disease to treatments proposed in humans, such as breast lipofilling. Laboratorial studies used so far for this purpose present serious methodologic problems. They are based on models that use cancer-induced carcinogens that have a residual systemic effect or through the use of nonluminal human BC implanted in immunosuppressed murine hosts. This article, although, presents preliminary results of a big project aiming to develop a preclinical studies capable of assessing risks. The primary objective here was to analyze the effectiveness of cafeteria diet (CD)—a known risk factor for BC in humans—to stimulate the mammary gland and the time required for this to trigger some effect over the murine breast tissue. METHODS: Eighteen Sprague-Dawley rats with 28 days of life were randomly divided into 4 groups: 2 controls (C1 and C2), where rats were fed with standard diet, and 2 groups that received CD (D1 and D2). CD was introduced at rats’ age of 6 weeks, what is similar to the human age (HA) of 7 years old. The following variables were collected and analyzed: weight, naso-anal length, Lee index—what is similar to the human body mass index, fasting glycemia, perigonadal fat pad weight, and groin fat pad volume. Six thoracic breasts, adipose tissue of omentum and subcutaneous of each rat were harvested for analysis. These samples were studied through histologic analysis with hematoxylin/eosin staining. Statistical analyses were performed on SPSS software using paired t tests, analysis of variance (analysis of variance, 1 way) for ordinal variables, and McNemar’s test for categorical variables. RESULTS: Ten rats (C1 e D1) were analyzed with 17 weeks old (HA = 20 years old) and 8 (C2 e D2) with 26 weeks old (HA = 32 years old). The mean weight in C1 was 250.14 g and in D1 was 332.65 g (P = 0.01); in C2, it was 263.09 g and in D2, 426.76 g (P < 0.001). The mean Lee index was, respectively, 303.37, 313.43, 298.12, 332.63 in groups C1, D1, C2 e D2 (P = 0.45 between C1 and D1; P = 0.002 between C2 and D2). The mean fasting glycemia value was 76.06 mg/dl (P = 0.26); the mean perigonadal fat pad weight was 8.21 g (P < 0.001 between control and CD groups in both times). The mean groin fat pad volume was 5.08 ml, with significant difference between C1 and D1 (P = 0.007) and C2 and D2 (P = 0.001). Regarding the mammary microenvironment, it was observed 20% of duct ectasia in D1 versus 8% in C1 (P < 0.001) within 11 weeks under CD. One adenofibroma was observed in D2 at 12th week after CD onset and other at the 13th. CONCLUSION: This study showed that the CD was an effective method to induce changes in the breast microenvironment even in young rats without compromising the glycemic status (which might be a bias). These data suggest that CD can be an effective inducer for tumorigenesis in older rats. These preliminary results can lead to the development of future preclinical models for the assessment of BC risk.

Is it time to abandon glucose control in critically ill adult patients?

To summarize the advances in literature that support the best current practices regarding glucose control in the critically ill. There are differences between patients with and without diabetes regarding the relationship of glucose metrics during acute illness to mortality. Among patients with diabetes, an assessment of preadmission glycemia, using measurement of Hemoglobin A1c (HgbA1c) informs the choice of glucose targets. For patients without diabetes and for patients with low HgbA1c levels, increasing mean glycemia during critical illness is independently associated with increasing risk of mortality. For patients with poor preadmission glucose control the appropriate blood glucose target has not yet been established. New metrics, including stress hyperglycemia ratio and glycemic gap, have been developed to describe the relationship between acute and chronic glycemia. A 'personalized' approach to glycemic control in the critically ill, with recognition of preadmission glycemia, is supported by an emerging literature and is suitable for testing in future interventional trials.

P1.03: Intravenous glucose nocturnal infusion reduces growth hormone secretion in children on total parenteral nutrition

Introduction: Most children with intestinal failure (IF) benefit from home parenteral nutrition (HPN). It allows a familial life with normal children activities such as going to school, sports, and being at home. In order to do so, PN is provided with cyclic infusion at night. Children with IF on HPN should have normal growth. However, some children grow under their genetic target height and present with decreased height velocity. Methods: We report the case of two children on HPN who presented with decreased height velocity while their weight gain was normal. After ruling out usual etiology (insufficient protein intake, acidosis, sodium deficiency, IFALD…), growth hormone (GH) deficiency was considered. Extensive testings were performed such as IgF1 levels, GH secretion stimulation test and nocturnal GH secretion. PN dependency was assessed using the PN/REE index (parenteral nutrition non-protein energy intake divided by resting energy expenditure calculated using Schofield equation, expressed in percentage). Results: These two children needed extremely high PN caloric intake to achieve a normal weight gain with a PN dependency index of 163% and 150%. Table 1 shows their clinical characteristics. Table 2 shows the results of the biological testings. These two children showed a normal GH peak under stimulation, but a low nocturnal GH secretion meaning they were able to produce GH under stimulation, but their nocturnal physiological secretion was inhibited. Their mean glycemia during PN perfusion was high compared to fasting glycemia which should normally be observed at night in children. Hyperglycemia is a well known inhibitor of GH secretion. Conclusion: Children with very high PN dependency are at risk of developing reduced height. In some children, GH secretion seems to be inhibited by nocturnal high glucose infusion. A longer PN infusion time can be proposed and/or a decrease in glucose intake with an increase in lipid intake. Also, growth hormone supplementation might be discussed and should be evaluated in this setting.

106-LB: Correlation between CGM-Derived Mean Glycemia and Measured Hemoglobin A1c in the Real World

Objective: Continuous glucose monitoring (CGM) has emerged as a key component of diabetes care but there are issues with data synthesis and application. A published regression equation converts CGM-derived mean glycemia into an estimate of laboratory-measured HbA1c, termed glucose management indicator (GMI). How the GMI formula defers by CGM device is not known. We compared the GMI derived from published formula vs. GMI derived from FreeStyle Libre specific regression equation. Research Design and Methods: We conducted an observational study using EHR data from a single academic endocrinology practice (Tufts Medical Center, Boston, MA). We included data from patients with diabetes and a minimum of 10-days of sensor data collected with FreeStyle Libre Pro or Personal, immediately prior to measurement of HbA1c. We plotted HbA1c and Libre average glucose and derived a Libre specific regression equation. We compared the GMI derived from the published formula (GMIp) with the Libre-specific GMI (GMIL). Results: Data were available from 59 patients (age 62 [range 22-90], 36% women, BMI 29 Kg/m2; 80% had type 2 diabetes; 70% had diabetes ≥10 years disease; HbA1c 8.1% (range 4.8-13.7). The mean number of days with CGM data was 28 (range 10-90). The Libre specific regression equation formula was GMI (%) = 5.04 + 0.0173*(mean glucose in mg/dL)]. Mean GMIp was 8.1% (SD 0.9) and mean GMIL was 7.6% (SD 1.3)(p=0.004). Conclusion: The GMI derived from a device-specific regression equation differs from the published GMI. The development of a device specific GMI may be warranted. Disclosure E. Angellotti: None. S. Muppavarapu: None. R. Siegel: None. A.G. Pittas: None.

Glycated albumin is correlated with glycated hemoglobin in type 2 diabetes.

Background and aimsGlycated hemoglobin (HbA1c) retrospectively evaluates mean glycemia in the preceding 2–3 months and is the gold standard for assessing glycemic control, while glycated albumin (GA) is currently considered a short to intermediate term integrated glycemic control marker, since it reflects glycemic status over the last 3 weeks. We aimed to investigate the levels of GA, HbA1c and fasting glycemia in a group of patients with type 2 diabetes.MethodsThe observational study included adult type 2 diabetes patients (n=135) according to inclusion and exclusion criteria, randomly selected from Clinical Centre of Diabetes, Cluj-Napoca, Romania. Fasting glycemia, GA, HbA1c and creatinine were measured using commercially available methods.ResultsOf the whole group, 62 (45.9%) were men. Mean age was 62.1±8.6 years old, body mass index was 31.8±6.1 kg/m2 and diabetes duration was 10.0 (4.0; 15.0) years. Fasting glycemia was 162±13.7 mg/dl, GA was 28.0 (21.0; 40.0)% and HbA1c 8.9±2.3%. We found GA was significantly correlated with HbA1c (r=0.19; p=0.029) and fasting glycemia (r=0.32; p<0.001), while HbA1c was significantly correlated with fasting glycemia (r=0.40; p<0.001).ConclusionsGA was significantly correlated with both HbA1c and fasting glycemia in our patients with type 2 diabetes. While HbA1c is recognized as being the reference test for diabetes control monitoring, GA might a useful biomarker for assessing short to intermediate term glycemic control, particularly important in situations when HbA1c test cannot be reliable or earlier clinical decision making is mandatory.

Acute hyperglycemia increases renal tissue oxygenation as measured by BOLD-MRI in healthy overweight volunteers

AimAnimal studies have suggested that acute hyperglycemia induces transient renal hypoxia and kidney damage, yet this has not been tested in humans. Therefore, we assessed in human subjects the effect of acute hyperglycemia on renal tissue oxygenation as measured with blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI). MethodsIn this single center prospective interventional study, healthy overweight subjects were recruited. BOLD-MRI was performed before and immediately after the intravenous administration of 0.15 g/kg of glucose in a 20% solution under standard hydration and fasting conditions. R2* maps were analyzed using the twelve layer concentric objects (TLCO) technique, a semi-automatic procedure which divides the kidney parenchyma in 12 equal layers at increasing depth. R2* is a measure of local desoxyhemoglobin concentrations, with high R2* values corresponding to low oxygenation. ResultsNineteen overweight subjects were enrolled (age 37 ± 10 years, BMI 28.9 ± 3 kg/m2, HbA1c 5.4 ± 0.3%, 57.9% women): 5 were glucose intolerant, none had diabetes. The mean glycemia rose from 4.5 ± 0.3 mmol/l to 9.0 ± 0.9, 8.9 ± 0.7, 7.7 ± 0.6 and 6.8 ± 0.8 mmol/l at respectively 1, 10, 20 and 30 min after IV glucose. Circulating insulin levels quadrupled. The mean R2* values decreased significantly in all kidney layers, irrespective of glucose intolerance. The lower BMI, the larger the decrease in R2*(spearman’s r = 0.41, p = 0.035). ConclusionThese data show that acute hyperglycemia decreases the R2* signal in humans, suggesting an acute increase in renal tissue oxygenation. The precise mechanism of this observation remains unknown, and whether this phenomenon also occurs in patients with diabetes needs additional studies.