The assessment of insulin secretion is a key aspect in analyzing the functionality of pancreatic β cells and determining the amount of insulin produced in response to glucose levels. However, the Oral Minimal Model of C-peptide (OCMM), currently considered the state of the art to describe C-peptide kinetics and secretion, does not accurately represent the physiological reality of patients undergoing gastric bypass surgery. In particular, it fails to adequately predict the complex dynamics of insulin secretion after surgery, which are influenced by profound metabolic alterations that can lead to postprandial hyperinsulinemic hypoglycemia, causing a rapid decline in blood glucose levels below basal. This phenomenon compromises the reliability of the model, which was originally designed for individuals with glucose levels mostly above basal. Consequently, the OCMM tends to inaccurately predict both the initial peaks and the tail phase of the C-peptide concentration curve measured after an oral glucose challenge. The aim of this study is to adapt the existing model to this specific population through two main strategies. The first involves a mathematical redefinition of the β parameter, which is used to describe the sensitivity of insulin secretion to glucose concentration. The second consists of revising the secretion model itself by introducing a nonlinear dynamic between secretion and glucose, allowing for a more physiologically accurate representation while avoiding the approximations imposed by the original model. The implementation of these strategies leads to the development of three new models, designed to improve the prediction of insulin secretion and to provide a more realistic representation of β-cell function in patients who have undergone gastric bypass surgery. The results obtained from the different models will be compared to evaluate the ability to accurately describe β-cell responsiveness in this population and identify the most effective model.
La stima della secrezione di insulina è un aspetto fondamentale per analizzare la funzionalità delle cellule β pancreatiche e determinare la quantità di insulina prodotta in relazione ai livelli glicemici. Tuttavia, il Modello Minimo Orale del C-peptide (OCMM), attualmente considerato lo stato del l’arte per la descrizione della cinetica e la secrezione del C-peptide, non descrive con accuratezza la realtà fisiologica dei pazienti sottoposti a bypass gastrico. In particolare, non riesce a predire adeguatamente la complessa dinamica della secrezione di insulina dopo l’intervento chirurgico, influenzata da profonde alterazioni metaboliche che possono indurre ipoglicemia iperinsulinemica postprandiale, determinando un rapido calo della glicemia al di sotto dei livelli basali. Questo fenomeno compromette l’affidabilità del modello, formulato per soggetti che mantengono livelli glicemici prevalentemente superiori al basale. Di conseguenza, l’OCMM tende a predire in modo non accurato sia i picchi iniziali che la fase finale della curva di concentrazione del C-peptide misurata in seguito a un carico orale di glucosio. L’obiettivo di questo studio è adattare il modello esistente a questa specifica popolazione attraverso due strategie principali. La prima prevede una ridefinizione matematica del parametro β, utilizzato per descrivere la sensibilità della secrezione insulinica alla concentrazione di glucosio. La seconda consiste in una revisione del modello di secrezione, introducendo una relazione non lineare tra secrezione e glucosio, così da modellare direttamente un comportamento più fisiologico ed evitare le approssimazioni imposte dal modello originale. L’applicazione di queste strategie porta allo sviluppo di tre nuovi modelli, che mirano a migliorare la predizione della secrezione insulinica e a offrire una rappresentazione più realistica della funzione β cellulare nei pazienti sottoposti a bypass gastrico. I risultati ottenuti dai diversi modelli sviluppati sono stati confrontati per valutare la capacità di descrivere accuratamente la responsività delle cellule β in questa popolazione e identificare il modello più efficace.
Modelli matematici della responsività beta-cellulare al glucosio in pazienti sottoposti a bypass gastrico
GRISI, GIORGIA
2024/2025
Abstract
The assessment of insulin secretion is a key aspect in analyzing the functionality of pancreatic β cells and determining the amount of insulin produced in response to glucose levels. However, the Oral Minimal Model of C-peptide (OCMM), currently considered the state of the art to describe C-peptide kinetics and secretion, does not accurately represent the physiological reality of patients undergoing gastric bypass surgery. In particular, it fails to adequately predict the complex dynamics of insulin secretion after surgery, which are influenced by profound metabolic alterations that can lead to postprandial hyperinsulinemic hypoglycemia, causing a rapid decline in blood glucose levels below basal. This phenomenon compromises the reliability of the model, which was originally designed for individuals with glucose levels mostly above basal. Consequently, the OCMM tends to inaccurately predict both the initial peaks and the tail phase of the C-peptide concentration curve measured after an oral glucose challenge. The aim of this study is to adapt the existing model to this specific population through two main strategies. The first involves a mathematical redefinition of the β parameter, which is used to describe the sensitivity of insulin secretion to glucose concentration. The second consists of revising the secretion model itself by introducing a nonlinear dynamic between secretion and glucose, allowing for a more physiologically accurate representation while avoiding the approximations imposed by the original model. The implementation of these strategies leads to the development of three new models, designed to improve the prediction of insulin secretion and to provide a more realistic representation of β-cell function in patients who have undergone gastric bypass surgery. The results obtained from the different models will be compared to evaluate the ability to accurately describe β-cell responsiveness in this population and identify the most effective model.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/85220