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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.

[EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a hig

Juan Churruca, Luis Vigil, Esther Luna, Julian Ruiz-Galiana, Manuel VarelaServicio de Medicina Interna, Hospital de Móstoles, Mostoles, Madrid, SpainAims: In many physiologic systems, the evolution from health to disease correlates with a loss of complexity in the system’s output. We analyze the difference in complexity of the glycemic profile in healthy volunteers (H), patients with the metabolic syndrome (MS), and patients with type 2 diabetes mellitus (DM).Methods: We measured interstitial fluid glucose every 5 minutes for 3 days in 10 H, 10 MS, and 10 DM. Complexity of the glycemic profile was evaluated by means of detrended fluctuation analysis (DFA). Mean amplitude of glycemic excursions (MAGE) was also calculated.Results: Glucose profile was more complex (lower DFA) in healthy subjects than in patients with MS or DM (mean DFA [SD]: H: 1.25 (0.10), MS: 1.39 (0.07), DM: 1.42 (0.10). ANOVA: F2,27 = 9.94, p = 0.001). DM had also a less complex profile than MS, but this difference was not statistically significant. There was an inverse relation between complexity (lower DFA) and the number of MS defining criteria (rho = 0.55, p = 0.002) and between complexity and MAGE (r = 0.68, p < 0.0001).Conclusions: There is a progressive loss of complexity in the glycemic profile from health, through the metabolic syndrome to type 2 diabetes mellitus. This loss of complexity precedes hyperglycemia and correlates with other markers of disease progression. Complexity analysis may be a useful tool to track the evolution from health to type 2 diabetes. Furthermore, it may provide a way to measure glycemic control in real-life situations and has some distinct advantages over other conventional variability metrics.Keywords: complexity, metabolic syndrome, type 2 diabetes mellitus, detrended fluctuation analysis, glycemic profile

BNE cat. (bibliogr.), 12/07/2005 da como fecha probable para esta ed. ca. 1950