Background: These studies were designed to evaluate the accuracy of a microdialysis-based subcutaneous glucose sensor (GlucoDay, A. Menarini Diagnostics, Firenze, Italy) compared with a standard reference method of plasma glucose measurement during insulin-induced hypoglycemia.
Research Design And Methods: Nine subjects without diabetes were studied in eu-, hypo-, and hyperglycemia (clamp technique). The GlucoDay was calibrated against one arterialized plasma glucose measurement (Glucose Analyzer, Beckman, Brea, CA), and plasma glucose estimates every 3 min were compared with paired plasma glucose values.
Results: Accuracy of glucose estimates was not homogeneously distributed among subjects and depended on stability of the sensor's current signal during spontaneous euglycemia (R +/- -0.68). Linear regression analysis showed a good correlation between the two methods of measurement (R = 0.9), Deming regression showed the inclusion of the unit in the confidence interval of the slope (slope 0.95, 95% confidence interval 0.87-1.02), and the accuracy of the GlucoDay reached 40 +/- 15% (American Diabetes Association criteria). The mean relative difference was 6 +/- 8% in euglycemia, 13 +/- 14% during plasma glucose fall, 5 +/- 22% in the hypoglycemic plateau, and -14 +/- 16% during recovery from hypoglycemia. The Bland-Altman analysis indicated a bias of -1.9 +/- 16.6 mg/dL, whereas the Error Grid Analysis showed 94% of the Gluco- Day measurements in the acceptable zones of the grid. The time to reach the glycemic nadir was longer when measured with the GlucoDay (90 +/- 5 vs. 72.5 +/- 9 min, P < 0.05). However, absolute values of glycemic nadir, time spent in hypoglycemia, and the rate of fall of glycemia and the rate of recovery from the hypoglycemia were not statistically different.
Conclusions: GlucoDay closely monitors changes in plasma glucose before, during, and after hypoglycemia. However, these results can be achieved only if calibration of the GlucoDay is performed under conditions of sensor signal stability. Similar studies have to be performed in subjects with diabetes to validate the GlucoDay system.
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http://dx.doi.org/10.1089/dia.2006.8.326 | DOI Listing |
BMC Public Health
January 2025
School of Public Health, Southeast University, Nanjing. 87 Dingjiaqiao Road, Nanjing, China.
Background: Triglyceride-glucose (TyG) index was regarded as a cost-efficient and reliable clinical surrogate marker for insulin resistance (IR), which was significantly correlated with cardiovascular disease (CVD). However, the TyG index and incident CVD in non-diabetic hypertension patients remains uncertain. The aim of study was to explore the impact of TyG index level and variability on risk of CVD among non-diabetic hypertension patients.
View Article and Find Full Text PDFPurpose: To evaluate the effect of osilodrostat and hypercortisolism control on blood pressure (BP) and glycemic control in patients with Cushing's disease.
Methods: Pooled analysis of two Phase III osilodrostat studies (LINC 3 and LINC 4), both comprising a 48-week core phase and an optional open-label extension. Changes from baseline in systolic and diastolic BP (SBP and DBP), fasting plasma glucose (FPG), and glycated hemoglobin (HbA) were evaluated during osilodrostat treatment in patients with/without hypertension or diabetes at baseline.
NPJ Aging
January 2025
Division of Cardiovascular Medicine, Department of Medicine, Kurume University School of Medicine, 67 Asahi-machi, Kurume, Japan.
We investigated clinical factors and biochemical markers associated with amygdalar metabolic activity evaluated by [F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) in 346 subjects without a history of malignant neoplasms. Univariate regression analysis revealed significant relationships between amygdalar metabolic activity and fasting plasma glucose (FPG), glycated hemoglobin, coronary artery disease (CAD) history, aspirin use, oral hypoglycemic agents (OHAs) use, and asymmetric dimethylarginine (ADMA). In multiple stepwise regression analysis, FPG and CAD history were independently associated with amygdalar metabolic activity.
View Article and Find Full Text PDFChin Med J (Engl)
January 2025
Department of Metabolism and Endocrinology, National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
Background: Approximately 40% of individuals with diabetes worldwide are at risk of developing diabetic kidney disease (DKD), which is not only the leading cause of kidney failure, but also significantly increases the risk of cardiovascular disease, causing significant societal health and financial burdens. This study aimed to describe the burden of DKD and explore its cross-country epidemiological status, predict development trends, and assess its risk factors and sociodemographic transitions.
Methods: Based on the Global Burden of Diseases (GBD) Study 2021, data on DKD due to type 1 diabetes (DKD-T1DM) and type 2 diabetes (DKD-T2DM) were analyzed by sex, age, year, and location.
J Prev Alzheimers Dis
February 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine - Nanyang Technological University, Singapore. Electronic address:
Background: Cardiovascular risk factors (CRFs) like hypertension, high cholesterol, and diabetes mellitus are increasingly linked to cognitive decline and dementia, especially in cerebral small vessel disease (cSVD). White matter hyperintensities (WMH) are closely associated with cognitive impairment, but the mechanisms behind their development remain unclear. Blood-brain barrier (BBB) dysfunction may be a key factor, particularly in cSVD.
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