Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

Abstract

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals’ challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals’ responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals’ metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

“…The relationship between the parameter k 5 and independent insulin-resistant measures such as the hyperinsulinemic-euglycaemic clamp and HOMA-IR is in-line with the findings of a recent study by Edrős et al. who report a statistically significant correlation of 0.68 between k 5 and the Matsuda index ( Matsuda and DeFronzo, 1999 ) when applying a concordant model of glucose and insulin dynamics to OGTT responses from a large population of overweight individuals (n = 738) ( Erdős et al., 2021 ). Although we see consistent trends in estimated model parameters when comparing insulin-sensitive and insulin-resistant groups within the MetFlex and Nutritech studies, the numerical values of the parameter estimates do differ between the study populations.…”

“…describes the dynamics between glucose and insulin under fasting and fed conditions and has been used as an alternative to animal testing when training control algorithms for insulin pump devices ( Dalla Man et al., 2007 , 2014 ; Kovatchev et al., 2009 ). More recently, the application of a physiology-based computational model of the glucose-insulin system to a large population of overweight and obese individuals was shown to capture features of insulin sensitivity and rate of insulin secretion from OGTT responses ( Erdős et al., 2021 ). Although these models have been shown to capture responses to complex meals, their focus on the glucose-insulin system means that, like the simple summary measures applied to OGTTs, they fail to capture deviations in postprandial plasma triglyceride trajectories that may provide insight into processes that drive the development of cardio-metabolic diseases.…”

The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study: Rationale, Design and Preliminary Screening Results

“…In addition, the large amount of collected data will allow for the application of computational techniques to elucidate the inter-individual differences in glucose homeostasis and derive new functional insights. Both mechanistic and data-driven computational modeling approaches have been employed to expand on the physiological properties underlying meal responses ( 6 , 7 , 86 ). The frequently-sampled time series of metabolites (e.g., glucose, insulin) from the OGTT and continuous glucose monitoring will be used to construct models of short-term postprandial dynamics, facilitating the assessment of individuals' capacity to regulate glucose levels in response to a meal.…”

‘Obesities’: Position statement on a complex disease entity with multifaceted drivers

“…In addition, dynamic molecular endophenotypes focusing on postprandrial immunometabolic responses can further characterize a personalized, patient‐centric approach aimed at identifying early risk. Extensive anthropometric variables as well as beta cell and glucose‐insulin axis phenotypes capturing pivotal metabolic features also provide extremely useful information 16 …”

“…Extensive anthropometric variables as well as beta cell and glucose-insulin axis phenotypes capturing pivotal metabolic features also provide extremely useful information. 16 Sarcopenic obesity (SO) requires particular attention given the demographic characteristics of an ageing population amidst an obesogenic environment. 17 The so-called dynapenic abdominal obesity, characterized by visceral obesity, sarcopenia and muscle weakness is frequently observed in older patients.…”