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[EN] Background The endoscopically implanted duodenal-jejunal bypass liner (DJBL) is an attractive alternative to bariatric surgery for obese diabetic patients. This article aims to study dynamical aspects of the glycaemic profile that may influence DJBL effects. Methods Thirty patients underwent DJBL implantation and were followed for 10 months. Continuous glucose monitoring (CGM) was performed before implantation and at month 10. Dynamical variables from CGM were measured: coefficient of variation of glycaemia, mean amplitude of glycaemic excursions (MAGE), detrended fluctuation analysis (DFA), % of time with glycaemia under 6.1 mmol/L (TU6.1), area over 7.8 mmol/L (AO7.8) and time in range. We analysed the correlation between changes in both anthropometric (body mass index, BMI and waist circumference) and metabolic (fasting blood glucose, FBG and HbA1c) variables and dynamical CGM-derived metrics and searched for variables in the basal CGM that could predict successful outcomes. Results There was a poor correlation between anthropometric and metabolic outcomes. There was a strong correlation between anthropometric changes and changes in glycaemic tonic control ( increment BMI- increment TU6.1: rho = - 0.67, P < .01) and between metabolic outcomes and glycaemic phasic control ( increment FBG- increment AO7.8: r = .60, P < .01). Basal AO7.8 was a powerful predictor of successful metabolic outcome (0.85 in patients with AO7.8 above the median vs 0.31 in patients with AO7.8 below the median: Chi-squared = 5.67, P = .02). Conclusions In our population, anthropometric outcomes of DJBL correlate with improvement in tonic control of glycaemia, while metabolic outcomes correlate preferentially with improvement in phasic control. Assessment of basal phasic control may help in candidate profiling for DJBL implantation., Research Center for Informatics, Grant/Award Number: CZ.02.1.01/0.0/0.0/16_019/0000765; Biomedical data acquisition, processing and visualization, Grant/Award Number: SGS19/171/OHK3/3T/13; MH CZ - DRO ("IKEM, IN 00023001"); RVO VFN64165

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (β coefficient = 0.002, 95% confidence interval: 0.001-0.004, P = .001) and abdominal circumference (β coefficient = 0.002, 95% confidence interval: 0.000015-0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes. Sin financiación 2.606 JCR (2014) Q3, 31/60 Peripheral Vascular Disease UEM

Background: One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well-accepted method to measure this phenomenon is detrended fluctuation analysis (DFA). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus (T2DM). Methods: We report on the clinical and glucometric characteristics of a sample of 103 patients at increased risk of developing T2DM. All patients had HbA1c levels 5%‐6.4% and met at least one of the following criteria: body mass index (BMI) > 30 kg/m 2 , essential hypertension or a first-degree relative with T2DM. For each patient, a 24-h glucose time series was obtained, and the clinical and glucometric variables were compared. Results: There was a significant correlation between the number of National Cholesterol Education Program ‐ Adult Treatment Panel (ATP III) metabolic syndrome (MS)-defining criteria and DFA ( ρ= 0.231, P = 0.019), and DFA differed significantly between patients meeting or not the ATP III definition of MS (1.443 vs 1.399, respectively; P = 0.018). The DFA was not correlated with HbA1c. Depending on how it was calculated, the area under the log(Fn)∼log(n) curve correlated with HbA1c levels or the number of MS criteria. Conventional variability metrics (mean amplitude of glycemic excursions) did not differ between patients complying or not with the definition of MS. Conclusions: Complexity analysis is capable of detecting differences in variables related to the risk of developing T2DM and could be a useful tool to study the initial phases of glucoregulatory dysfunction leading to T2DM.

Aim Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes. Material and methods Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow-up was 32 months (6-72 mo), and there were 17 new cases of T2DM. Results In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes-defining criteria and the 2 CGMS-derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54-0.88; P 5.7%" had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development. Conclusions Continuous glucose monitoring system allows for a better T2DM risk-development categorization than fasting glucose and HbA1c in a high-risk population. Continuous glucose monitoring system-derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA1c categorization. These differences may correlate with distinct pathophysiological mechanisms.