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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.

SUBMITTER: Erdos B

PROVIDER: S-EPMC8011733 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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

Erdős Balázs B van Sloun Bart B Adriaens Michiel E ME O'Donovan Shauna D SD Langin Dominique D Astrup Arne A Blaak Ellen E EE Arts Ilja C W ICW van Riel Natal A W NAW

PLoS computational biology 20210331 3

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 ...[more]

PMID: 33788828

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Project description:Several studies have demonstrated food texture manipulation on oral processing behaviour (OPB). We explored the effect of texture-differences of equivalent carbohydrate load on OPB, bolus properties and postprandial glycaemic responses (PPG). In a randomised cross-over, within-subjects, non-blinded design, healthy male participants (N = 39) consumed fixed portions of white rice (WR) and rice cake (RC) while being video recorded to measure microstructural eating behaviours. PPG was compared between test foods over a period of 120-min, and the bolus properties and saliva uptake at swallow were measured for both test foods. RC displayed higher instrumental hardness, chewiness and Young's modulus than WR (p = 0.01), and participants perceived RC as more springy and sticky than WR (p < 0.001). The RC meal was chewed more per bite (p < 0.001) and consumed at a faster eating rate (p = 0.033) than WR. WR bolus particles were smaller at swallow (p < 0.001) with a larger total surface area (p < 0.001), compared to RC. The glucose response for RC was significantly higher during the first 30-min postprandial period (p = 0.010), and lower in the later (30-120 min) postprandial period (p = 0.031) compared to WR. Total blood glucose iAUC did not differ significantly between WR and RC meals despite their large differences in texture, OPB and bolus properties. Oro-sensory exposure time was a significant predictor of glucose iAUC30min for both test meals (RC, p = 0.003; WR, p = 0.029). Saliva uptake in the bolus was significantly positively associated with blood glucose during the first 30-min postprandial period for the RC meal (p = 0.008), but not for WR. We conclude that food texture modifications can influence OPB and bolus properties which are key contributors to the dynamic evolution of the glycaemic response. Total blood glucose responses were the same for both test foods, though differences in oral processing and bolus properties influenced temporal changes in PPG.

Project description:IntroductionAn elevated postprandial glucose response is associated with an increased risk of cardiometabolic diseases. Existing research suggests large heterogeneity in the postprandial glucose responses to identical meals and food products between individuals, but the effect of other consumed meals during the day and the order of meals during the day on the heterogeneity in postprandial glucose responses still needs to be investigated. In addition, the robustness of the glucose responses to meals or foods is still unknown.ObjectivesThe overall aim of the project is to assess whether the glucose response to a meal is sufficiently person-specific to use in personalized dietary advice. We aim to answer the question: "How replicable are glucose responses to meals within individuals and how consistent is the variation in glucose responses between individuals?"MethodsThe question will be assessed under standardized conditions of a 9-week fully controlled dietary intervention in which all meals are the same between individuals and consumed in a fixed order at a fixed time. 63 apparently healthy men and women with a BMI of 25-40 kg/m2 and aged 45-75 years were enrolled in the RepEAT study (NCT05456815), of whom 53 participants completed the study. The RepEAT study comprised a fully controlled dietary intervention of nine weeks, consisting of three repetitive periods of three weeks. Within each three-week period, a variety of meals and food products were offered during breakfast, lunch, dinner and in between meal snacks. Throughout the dietary intervention, glucose was continuously monitored using Freestyle Libre Pro IQ monitors. Physical activity was monitored using the ActiGraph and ActivPAL. To measure the association between glucose responses and an individual's phenotype, various measurements were performed before the start of the dietary intervention including an oral glucose tolerance test, a high-fat mixed meal challenge, assessment of body fat distribution including liver fat (MRI/MRS), and cardiometabolic markers.DiscussionThe repetitive and fully controlled nature of the dietary study allows detailed assessment of the replicability of the glucose responses to meals and food products within individuals. Furthermore, the consistency of the variation between individuals independent of insulin resistance will be determined.

Project description:BackgroundControlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches.ObjectiveWe tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US.DesignWe recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts.ResultsWhen trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome.ConclusionsWe show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.

Project description:Introduction: Fatty acids are a major nutrient in dietary fat, some of which are ligands of long-chain fatty acid receptors, including G-protein-coupled receptor (GPR) 40 and GPR120. Pretreatment with GPR40 agonists enhanced the secretion of insulin in response to elevating blood glucose levels after glucose load in a diabetes model, but pretreatment with GPR120 agonist did not ameliorate postprandial hyperglycemia. This study examined whether oral administration of linoleic acid (LA), a GPR40 and GPR120 agonist, immediately before glucose load would affect the elevation of postprandial blood glucose levels in rats. Methods: Male rats and rats with type 1 diabetes administered streptozocin were orally administered LA, trilinolein, α-linolenic acid (α-LA), oleic acid, TAK-875, or TUG-891 immediately before glucose load. Blood glucose levels were measured before, then 15, 30, 60 and 120 min after glucose load. CACO-2 cells were used to measure the uptake of [14C] α-MDG for 30 min with or without LA. Gastric content from rats administered LA was collected 15 and 30 min after glucose load, and blood samples were collected for measurement of glucagon-like peptide 1 (GLP-1) and cholecystokinin concentrations. Results: The elevation of postprandial blood glucose levels was slowed by LA but not by trilinolein in rats without promotion of insulin secretion, and this effect was also observed in rats with type 1 diabetes. The uptake of α-MDG, an SGLT-specific substrate, was, however, not inhibited by LA. Gastric emptying was slowed by LA 15 min after glucose load, and GLP-1, but not cholecystokinin, level was elevated by LA 15 min after glucose load. TUG-891, a GPR120 agonist, ameliorated postprandial hyperglycemia but TAK-875, a GPR40 agonist, did not. Pretreatment with AH7614, a GPR120 antagonist, partially canceled the improvement of postprandial hyperglycemia induced by LA. α-LA, which has high affinity with GPR120 as well as LA, slowed the elevation of postprandial blood glucose levels, but oleic acid, which has lower affinity with GPR120 than LA, did not. Conclusion: Oral administration of LA immediately after glucose load ameliorated postprandial hyperglycemia due to slowing of gastric emptying via promotion of GLP-1 secretion. The mechanisms may be associated with GPR120 pathway.

Project description:AIM:The oral glucose tolerance test (OGTT), widely used as a gold standard for gestational diabetes mellitus (GDM) diagnosis, provides a broad view of glucose pathophysiology in response to a glucose challenge. We conducted the present study to evaluate metabolite changes before and after an oral glucose challenge in pregnancy; and to examine the extent to which metabolites may serve to predict GDM diagnosis in pregnant women. METHODS:Peruvian pregnant women (n=100) attending prenatal clinics (mean gestation 25 weeks) participated in the study with 23% of them having GDM diagnosis. Serum samples were collected immediately prior to and 2-hours after administration of a 75-g OGTT. Targeted metabolic profiling was performed using a LC-MS based metabolomics platform. Changes in metabolite levels were evaluated using paired Student's t-tests and the change patterns were examined at the level of pathways. Multivariate regression procedures were used to examine metabolite pairwise differences associated with subsequent GDM diagnosis. RESULTS:Of the 306 metabolites detected, the relative concentration of 127 metabolites were statistically significantly increased or decreased 2-hours after the oral glucose load (false discovery rate [FDR] corrected P-value<0.001). We identified relative decreases in metabolites in acylcarnitines, fatty acids, and diacylglycerols while relative increases were noted among bile acids. In addition, we found that C58:10 triacylglycerol (?=-0.08, SE=0.04), C58:9 triacylglycerol (?=-0.07, SE=0.03), adenosine (?=0.70, SE=0.32), methionine sulfoxide (?=0.36, SE=0.13) were significantly associated with GDM diagnosis even after adjusting for age and body mass index. CONCLUSIONS:We identified alterations in maternal serum metabolites, representing distinct cellular and metabolic pathways including fatty acid metabolism, in response to an oral glucose challenge. These findings offer novel perspectives on the pathophysiological mechanisms underlying GDM.

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

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

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