A Predictive Mathematical Model of Nutrient-Stimulated Hormone Dynamics (NUSH) and Their Impact on Body Weight Regulation

IntroductionNutrient-stimulated hormones (NUSH) play a critical role in regulating energy metabolism. While dysregulation of NUSH signaling is associated with obesity, there is a lack of quantitative models to investigate the complex dynamics of NUSH signaling and its impact on obesity development. ObjectiveTo develop and validate a predictive quantitative mathematical framework for elucidating the complex dynamics of NUSH and their regulatory impact on body weight homeostasis, with particular emphasis on mechanism-based therapeutic interventions. MethodsData on elevated body mass index (BMI) were collected from meta-analysis studies available on PubMed, focusing on incretin-based therapies. A multi-compartmental mathematical model was developed using Python with SciPy and NumPy computational libraries to integrate the complex interactions between NUSH levels, nutrient intake, and BMI variations. The model utilized systems of differential equations to capture the intricate dynamics and regulatory feedback mechanisms governing hormonal control of obesity. Parameter estimation was performed through meta-analytical data optimization to minimize discrepancies between model predictions and experimental observations. ResultsThe developed mathematical framework, following calibration with datasets from 15 meta-analyses of incretin-based therapeutic regimens (liraglutide, semaglutide, tirzepatide), yielded a predictive formula characterizing the temporal dynamics of NUSH - (NUSH(t) = N{square} * (1 - e^(-kt)) + I * [1 - e^(-{beta}t)] / {beta}), integrating baseline concentrations, decay kinetics, nutrient intake influence, and response velocity parameters. The model achieved an R2 of XX and RMSE of YY when validated against observed outcomes from meta-analyses, indicating strong predictive accuracy. ConclusionThis mathematical model provides a quantitative framework for understanding the association between NUSH and increased body weight, offering insights into therapeutic strategies and obesity prevention offering insights into therapeutic strategies and obesity prevention, and providing a quantitative tool for the prediction of weight loss outcomes with incretin-based therapies.