Influence of glucometric 'dynamical' variables on duodenal-jejunal bypass liner (DJBL) anthropometric and metabolic outcomes
Language English Country Great Britain, England Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
CZ.02.1.01/0.0/0.0/16_019/0000765
Research Center for Informatics - International
SGS19/171/OHK3/3T/13
Biomedical data acquisition, processing and visualization - International
MH CZ - DRO ("IKEM, IN 00023001") - International
RVO VFN64165 - International
PubMed
31916665
DOI
10.1002/dmrr.3287
Knihovny.cz E-resources
- Keywords
- continuous glucose monitoring, detrended fluctuation analysis (DFA), diabesity, duodenal-jejunal bypass liner (DJBL), metabolic surgery, type 2 diabetes mellitus,
- MeSH
- Biomarkers analysis MeSH
- Diabetes Mellitus, Type 2 complications surgery MeSH
- Adult MeSH
- Duodenum surgery MeSH
- Glycated Hemoglobin analysis MeSH
- Weight Loss MeSH
- Jejunum surgery MeSH
- Blood Glucose analysis MeSH
- Middle Aged MeSH
- Humans MeSH
- Metabolic Syndrome etiology prevention & control MeSH
- Obesity, Morbid physiopathology surgery MeSH
- Follow-Up Studies MeSH
- Prognosis MeSH
- Aged MeSH
- Gastric Bypass methods MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Biomarkers MeSH
- Glycated Hemoglobin A MeSH
- hemoglobin A1c protein, human MeSH Browser
- Blood Glucose MeSH
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.
Department of Cybernetics Czech Technical University Prague Prague Czech Republic
Department of Internal Medicine Hospital Universitario 12 de Octubre Madrid Spain
Department of Internal Medicine Hospital Universitario de Móstoles Madrid Spain
Technological Institute of Informatics Universitat Politècnica de València Alcoi Spain
See more in PubMed
O'Rahilly S. Science, medicine, and the future. Non-insulin dependent diabetes mellitus: the gathering storm. Br Med J. 1997;314(7085):955-959.
World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva: World Health Organization. 2009; 62 p.
Hossain P, Kawar B, El Nahas M. Obesity and diabetes in the developing world - a growing challenge. N Engl J Med. 2007;356(3):213-215.
Ogurtsova K, da Rocha Fernandes JD, Huang Y, et al. IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40-50.
Beagley J, Guariguata L, Weil C, et al. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2014;103:150-160.
Zimmet P, Alberti KGMM, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414:782-787.
Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239-2251.
Haffner SM, Lehto S, Ronnemaa T. Mortality from coronary heart disease in subjects with and without type 2 diabetes and mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med. 1998;339:229-234.
Rubino F, Cummings DE. The coming of age of metabolic surgery. Nat Rev Endocrinol. 2012;8:702-704.
Pournaras DJ, Glicksman C, Vincent RP, et al. The role of bile after Roux-en-Y gastric bypass in promoting weight loss and improving glycaemic control. Endocrinology. 2012;153(8):3613-3619.
Cummings DE. Endocrine mechanisms mediating remission of diabetes after gastric bypass surgery. Int J Obes (Lond). 2009;33(S1):S33-S40.
Ribaric G, Buchwald JN, McGlennon TW. Diabetes and weight in comparative studies of bariatric surgery vs conventional medical therapy: a systematic review and meta-analysis. Obes Surg. 2014;24(3):437-455.
Kwok CS, Pradhan A, Khan MA, et al. Bariatric surgery and its impact on cardiovascular disease and mortality: a systematic review and meta-analysis. Int J Cardiol. 2014;173(1):20-28.
Rubino F, Nathan DM, Eckel RH, et al. Metabolic surgery in the treatment algorithm for type 2 diabetes: a joint statement by international diabetes organizations. Diabetes Care. 2016;39(6):861-877.
Afonso BB, Rosenthal R, Li KM, et al. Perceived barriers to bariatric surgery among morbidly obese patients. Surg Obes Relat Dis. 2010;6(1):16-21.
Patel SR, Mason J, Hakim N. The duodenal-Jejunal bypass sleeve (EndoBarrier gastrointestinal liner) for weight loss and treatment of type II diabetes. Indian J Surg. 2012;74(4):275-277.
Kumar N. Weight loss endoscopy: development, applications, and current status. World J Gastroenterol. 2016;22(31):7069-7079.
Sullivan S, Edmundowicz SA, Thompson CC. Endoscopic bariatric and metabolic therapies: new and emerging technologies. Gastroenterology. 2017;152(7):1791-1801.
Rohde U, Hedbäck N, Gluud LL, et al. Effect of the EndoBarrier gastrointestinal liner on obesity and type 2 diabetes: a systematic review and meta-analysis. Diabetes Obes Metab. 2016;18(3):300-305.
Rodriguez-Grunert L, Galvao Neto MP, Alamo M, et al. First human experience with endoscopically delivered and retrieved duodenal-jejunal bypass sleeve. Surg Obes Relat Dis. 2008;4(1):55-59.
Rodriguez L, Reyes E, Fagalde P, et al. Pilot clinical study of an endoscopic, removable duodenal-Jejunal bypass liner for the treatment of type 2 diabetes. Diabetes Technol Ther. 2009;11(11):725-732.
Escalona A, Pimentel F, Sharp A, et al. Weight loss and metabolic improvement in morbidly obese subjects implanted for 1 year with an endoscopic duodenal-jejunal bypass liner. Ann Surg. 2012;255(6):1080-1085.
de Jonge C, Rensen SS, Koek GH, et al. Endoscopic duodenal-jejunal bypass liner rapidly improves type 2 diabetes. Obes Surg. 2013;23:1354-1360.
Cohen R, le Roux CW, Papamargaritis D, et al. Role of proximal gut exclusion from food on glucose homeostasis in patients with type 2 diabetes. Diabet Med. 2013;30(12):1482-1486.
Haluzík M, Kratochvílová H, Haluzíková D, et al. Gut as an emerging organ for the treatment of diabetes: focus on mechanism of action of bariatric and endoscopic interventions. J Endocrinol. 2018;237(1):R1-R17.
El Khoury L, Chouillard E, Chahine E, et al. Metabolic surgery and Diabesity: a systematic review. Obes Surg. 2018;28(7):2069-2077.
Thaler JP, Minireview CDE. Hormonal and metabolic mechanisms of diabetes remission after gastrointestinal surgery. Endocrinology. 2009;150(6):2518-2525.
Kaválková P, Mráz M, Trachta P, et al. Endocrine effects of duodenal-jejunal exclusion in obese patients with type 2 diabetes mellitus. J Endocrinol. 2016;231(1):11-22.
Mingrone G, Panunzi S, De Gaetano A, et al. Bariatric surgery versus conventional medical therapy for type 2 diabetes. N Engl J Med. 2012;366(17):1577-1585.
Mingrone G, Panunzi S, De Gaetano A, et al. Bariatric-metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-Centre, randomised controlled trial. Lancet. 2015;386:964-973.
Sjöström L, Peltonen M, Jacobson P, et al. Association of bariatric surgery with long-term remission of type 2 diabetes and with microvascular and macrovascular complications. JAMA. 2014;311(22):2297-2304.
Monnier L, Mas E, Ginet C, et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006;295(14):1681-1687.
Ceriello A, Esposito K, Piconi L, et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes. 2008;57(5):1349-1354.
Di FA, Picconi F, Di Stefano P, et al. Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care. 2011;34:1605-1609.
Nusca A, Tuccinardi D, Albano M, et al. Glycemic variability in the development of cardiovascular complications in diabetes. Diabetes Metab Res Rev. 2018;34(8):1-10.
Dungan KM, Binkley P, Nagaraja HN, Schuster D, Osei K. The effect of glycaemic control and glycaemic variability on mortality in patients hospitalized with congestive heart failure. Diabetes Metab Res Rev. 2011;27(1):85-93.
Monnier L, Colette C, Owens DR. Integrating glycaemic variability in the glycaemic disorders of type 2 diabetes: a move towards a unified glucose tetrad concept. Diabetes Metab Res Rev. 2009;25:393-402.
Zaccardi F, Pitocco D, Ghirlanda G. Glycemic risk factors of diabetic vascular complications: the role of glycemic variability. Diabetes Metab Res Rev. 2009;25:199-207.
Frontoni S, Di Bartolo P, Avogaro A, Bosi E, Paolisso G, Ceriello A. Glucose variability: an emerging target for the treatment of diabetes mellitus. Diabetes Res Clin Pract. 2013;102(2):86-95.
Service FJ, Molnar GD, Rosevear JW, et al. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970;19(9):644-655.
Freire AX, Editorial MLC. How “sweet” complexity is and how “bitter” variability can be; the new aspect of intensive care unit hyperglycemia. Crit Care Med. 2010;38(3):996-997.
Lundelin K, Vigil L, Bua S, Gomez-Mestre I, Honrubia T, Varela M. Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: a pilot study. Crit Care Med. 2010;38(3):849-854.
Churruca J, Vigil L, Luna E, et al. The route to diabetes: loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes Metab Syndr Obes. 2008;1:3-11.
Peng CK, Havlin S, Stanley HE, et al. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82-87.
Ogata H, Tokuyama K, Nagasaka S, et al. The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism. 2012;61(7):1041-1050.
Rodríguez de Castro C, Vigil L, Vargas B, et al. Glucose time series complexity as a predictor of type 2 diabetes. Diabetes Metab Res Rev. 2017;33:e2831.
Abdul-Ghani MA, Williams K, DeFronzo R, Stern M. Risk of progression to type 2 diabetes based on relationship between postload plasma glucose and fasting plasma glucose. Diabetes Care. 2006;29(7):1613-1618.
Nathan DM, Davidson MB, DeFronzo RA, et al. Impaired fasting glucose and impaired glucose tolerance: implications for care. Diabetes Care. 2007;30(3):753-759.
Abdul-Ghani MA, Tripathy D, DeFronzo RA. Contributions of ß-cell dysfunction and insulin resistance to the pathogenesis of impaired glucose tolerance and impaired fasting glucose. Diabetes Care. 2006;29(5):1130-1139.
Colas A, Vigil L, Rodriguez de Castro C, et al. New insights from continuous glucose monitoring into the route to diabetes. Diabetes Metab Res Rev. 2018;34:e3002.
Meyer C, Pimenta W, Woerle HJ, et al. Different mechanisms for impaired fasting glucose and impaired postprandial glucose tolerance in humans. Diabetes Care. 2006;29(8):1909-1914.
Charles MA, Fontbonne A, Thibult N, Warnet JM, Rosselin GE, Eschwege E. Risk factors for NIDDM in white population: Paris prospective study. Diabetes. 1991;40(7):796-799.
Staimez LR, Weber MB, Ranjani H, et al. Evidence of reduced β-cell function in Asian Indians with mild dysglycemia. Diabetes Care. 2013;36(9):2772-2778.
Danne T, Nimri R, Battelino T, et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40(12):1631-1640.
American Diabetes Association. Diabetes technology: standards of medical Care in Diabetes-2019. Diabetes Care. 2019;42(Suppl.1):S71-S80.
Lu J, Ma X, Zhou J, et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care. 2018;41(11):2370-2376.
Lu J, Ma X, Shen Y, et al. Time in range is associated with carotid intima-media thickness in type 2 diabetes. [published online ahead of print, 2019 Oct 11]. Diabetes Technol Ther. 2019. https://doi.org/10.1089/dia.2019.0251.
Dixon JB, O'Brien PE. Health outcomes of severely obese type 2 diabetic subjects 1 year after laparoscopic adjustable gastric banding. Diabetes Care. 2002;25(2):358-363.
Beck RW, Bergenstal RM, Riddlesworth TD, et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care. 2019;42(3):400-405.
Alexander CM, Amiel S, Beck R, et al. Need for regulatory change to incorporate beyond A1C glycemic metrics. Diabetes Care. 2018;41(6):e92-e94.
Advani A. Positioning time in range in diabetes management. Diabetologia. 2020;63:242-252. https://doi.org/10.1007/s00125-019-05027-0.
Kovatchev BP. Metrics for glycaemic control-from HbA1c to continuous glucose monitoring. Nat Rev Endocrinol. 2017;13(7):425-436.
Narayan KM. Type 2 diabetes: why we are winning the battle but losing the war? 2015 Kelly west award lecture. Diabetes Care. 2016;39(5):653-663.
Bonora E, Targher G, Alberiche M, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity. Diabetes Care. 2000;23:57-63.
Schouten R, Rijs CS, Bouvy ND, et al. A multicenter, randomized efficacy study of the endobarrier gastrointestinal liner for presurgical weight loss prior to bariatric surgery. Ann Surg. 2010;251(2):236-243.
Wood GC, Mirshahi T, Still CD, Hirsch AG. Association of DiaRem score with cure of type 2 diabetes following bariatric surgery. JAMA Surg. 2016;151(8):779-781.
Klonoff DC. Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care. 2005;28(5):1231-1239.
Rodbard D. Continuous glucose monitoring: a review of successes, challenges, and opportunities. Diabetes Technol Ther. 2016;18(S2):S3-S13.
Buchwald H, Avidor Y, Braunwald E, et al. Bariatric surgery: a systematic review and meta-analysis. JAMA. 2004;292:1724-1737.
Koehestanie P, Betzel B, Aarts EO, et al. Is reimplantation of the duodenal-jejunal bypass liner feasible? Surg Obes Relat Dis. 2015;11(5):1099-1104.
