Iryna Sihinevich (1), Christiane Dings (1), Nina Scherer (1), Valerie Nock (2), Anita M. Hennige (2), Violeta Raverdy (3), Francois Pattou (3) and Thorsten Lehr (1) for the IMI DIRECT consortium
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany, (3) University Hospital of Lille, Lille, France
Introduction/Objectives: Type 2 diabetes mellitus (T2DM) is a complex multifactorial disorder, impacted by several genetic and environmental factors. Overweight and obesity combined with an unhealthy diet and lack of physical activity are considered to be the strongest risk factors for developing T2DM [1]. For patients with severe obesity-related T2DM who could not achieve the recommended treatment targets otherwise, bariatric surgery can be considered an appropriate option [2]. After Roux-en-Y Gastric Bypass (RYGB) surgery a large number of diabetic patients demonstrate normalization of blood glucose levels and disappearance of diabetes symptoms within days, even before weight loss occurs [3,4]. To gain more insight into the underlying mechanisms, the main research goal was to develop a mathematical model that simultaneously describes individual changes in weight (WGT), fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) levels in morbidly obese diabetic patients over the first year after the bariatric surgery.
Methods: The model was developed using WGT, FPG, HbA1c and other data (n=78) from the prospective bariatric cohort (University of Lille, France) of the Diabetes Research on Patient Stratification (DIRECT) study [5]. This cohort consists of 79 patients with T2DM and severe obesity (BMI ≥ 35 kg/m2 with comorbidities or BMI ≥ 40 kg/m2) who underwent RYGB surgery. Plasma levels of biomarkers for T2DM as well as weight and other demographic and laboratory parameters were obtained at baseline and 1, 3 and 12 months after the surgery. The mathematical model was developed using the nonlinear mixed-effects (NLME) modeling approach implemented in NONMEM® (version 7.3.0) [6] using PiranaTM (version 2.9.5) as a modeling environment. Data analysis and visualization were performed in R (version 3.2.5) using RStudio (version 1.0.44).
Results: Individual changes in WGT, FPG and HbA1c levels after the bariatric surgery were well described using turnover models with zero-order production rates (kin) and first-order elimination rates (kout). Change in WGT was best described by a factor of 0.492 on kinWGT. Additional incorporation of a time-dependent long-term effect with an exponent of 0.0245 on kinWGT (49.9%CV IIV) allowed depicting different individual trajectories in the first year after the RYGB. Postsurgical changes in FPG were best described by a factor of 0.644 on kinFPG (26.5 %CV IIV). In addition, one month after the surgery the ratio of actual to baseline weight was a significant factor on the production rate of fasting plasma glucose (kinFPG) with a factor of 0.622 multiplied by the weight ratio. The formation of HbA1c from hemoglobin (Hb) was described by a second-order process dependent on Hb and FPG concentrations. The degradation of Hb and HbA1c was described by a first-order process assuming an erythrocyte half-life of 120 days. Overall, the developed model showed a very good descriptive performance of the individual time profiles and good precision of parameters estimates (relative standard error < 25%).
Conclusions: A mathematical model has been developed simultaneously describing individual changes in WGT, FPG and HbA1c occurring before and up to 12 months after the RYGB surgery. The initial effect of the surgery is possibly related to combined effects of caloric restriction, hormonal and metabolic changes (elevation of incretins, peptide YY, ghrelin levels and an increase in lipolysis [7] along with others) after RYGB surgery and less dependent on the weight loss. Starting one month after the surgery, the biological system approaches new homeostasis, in which the variation in FPG and HbA1c seems to be mainly triggered by body weight changes. Next steps will be to investigate the effect of covariates and especially antidiabetic medication on FPG, HbA1c and body weight.
References:
[1] Global Report on Diabetes. World Health Organization (2016) 978: 92–4.
[2] Dixon JB, Zimmet P, Alberti KG, et al. Bariatric surgery: an IDF statement for obese Type 2 diabetes. Diabet Med (2011) 28(6): 628–642.
[3] Buchwald H, Estok R, Fahrbach K, et al. Weight and Type 2 Diabetes after Bariatric Surgery: Systematic Review and Meta-analysis. Am J Med (2009) 122(3): 248–256.e5.
[4] Chang SH, Stoll CR, Song J, et al. The Effectiveness and Risks of Bariatric Surgery: An Updated Systematic Review and Meta-analysis, 2003-2012. JAMA Surg (2014) 149(3): 275-287
[5] DIRECT – Innovative Medicines Initiative: Diabetes Research on Patient Stratification. http://www.direct-diabetes.org/
[6] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA.
[7] Hansen M, Lund M, Gregers E, et al. Adipose tissue mitochondrial respiration and lipolysis before and after a weight loss by diet and RYGB. Obesity (2015) 23(10):2022-9
Reference: PAGE 28 (2019) Abstr 9087 [www.page-meeting.org/?abstract=9087]
Poster: Drug/Disease Modelling - Endocrine