ArticleThe American journal of clinical nutrition2019
Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals.
Article in The American journal of clinical nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 3 registered trials, which are not on this map. Cited by 48 papers, 2 of them syntheses that pooled it.
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.
Validation of the Measurement and Prediction of the Postprandial Glycemic Response to Food and Providing Personally Tailored Dietary Recommendations
Personalized Nutrition Based on the Glycemic Response: Effect of Diet and Intestinal
Postprandial Glucose Levels, Gut Microbiota and Supplementation With Functional Foods in Adults
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision nutrition-based interventions for the management of obesity in children and adolescents up to the age of 19 years.The Cochrane database of systematic reviews · 2025Pooled it
- The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence.Nutrients · 2021Pooled it
- A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c.The American journal of clinical nutrition · 2023Trial
- Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch.Nutrients · 2022Trial
- Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals.Nature medicine · 2021Trial
- Hemoglobin A1c Levels Modify Associations between Dietary Acid Load and Breast Cancer Recurrence.Nutrients · 2020Trial
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- Quantifying the Contributions of Food, Glucose, Sleep, and Microbiome Data to Personalized Glycemic Response Prediction.Current developments in nutrition · 2026Article
- Review
- Review
- Postprandial Glycemic Impact of Meal Timing and Staple Type in Outpatients with Dysglycemia: A Pilot Study Under a Streamlined and Real-World Framework.International journal of general medicine · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.Molecular nutrition & food research · 2025Review
- Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses: Development of a Clustering Algorithm.JMIR diabetes · 2025Article
- Obesity Adversely Affects Dietary Metabolism: Time to Inform Guidelines?Journal of the American Heart Association · 2025Article
- CGMacros: a pilot scientific dataset for personalized nutrition and diet monitoring.Scientific data · 2025Article
- Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management.Scientific reports · 2025Article
- Assessing the Links Between Artificial Intelligence and Precision Nutrition.Current nutrition reports · 2025Review
- Guidance and Position of RINN22 regarding Precision Nutrition and Nutriomics.Lifestyle genomics · 2025Review
- One-year outcomes of a digital twin intervention for type 2 diabetes: a retrospective real-world study.Scientific reports · 2024Observational
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
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.
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.