Influence of glucometric 'dynamical' variables on duodenal‐jejunal bypass liner (DJBL) anthropometric and metabolic outcomes. Issue 4 (5th February 2020)

• Record Type: Journal Article
• Authors: Colás, Ana; Varela, Manuel; Mraz, Milos; Novak, Daniel; Cuesta‐Frau, David; Vigil, Luis; Benes, Marek; Pelikanova, Terezie; Haluzik, Martin; Burda, Vaclav; Vargas, Borja
• Is Part Of: Diabetes/metabolism research and reviews. Volume 36:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-05
• Subjects: diabesity -- type 2 diabetes mellitus -- continuous glucose monitoring -- duodenal‐jejunal bypass liner (DJBL) -- metabolic surgery -- detrended fluctuation analysis (DFA); Diabetes -- Periodicals; Metabolism -- Periodicals; 616.642
• DOI: 10.1002/dmrr.3287
• Languages: English
• ISSNs: 1520-7552

Abstract

Abstract: 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 (∆BMI‐∆TU6.1: rho = − 0.67, P < .01) and between metabolic outcomes and glycaemic phasic control (∆FBG‐∆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.