ArticlePLoS computational biology2021
Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Computational modeling enables individual assessment of postprandial glucose and insulin responses after bariatric surgery.Communications medicine · 2025Article
- Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans.iScience · 2024Article
- Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models.iScience · 2024Article
- Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism.Scientific reports · 2024Article
- Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.PLoS computational biology · 2023Article
- Article
- Article
- Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model.iScience · 2022Article
- Oral glucose tolerance test: Ηow to maximize its diagnostic value in children and adolescents.Acta bio-medica : Atenei Parmensis · 2022Article
- 'Obesities': Position statement on a complex disease entity with multifaceted drivers.European journal of clinical investigation · 2022Article
- The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study: Rationale, Design and Preliminary Screening Results.Frontiers in nutrition · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.