ArticleNature medicine2020
Human postprandial responses to food and potential for precision nutrition.
Article in Nature medicine, 2020. 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 7 registered trials, which are not on this map. Cited by 473 papers, 4 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.
Predicting Inter-individual Differences in Biochemical and Behavioral Response to Meals With Different Nutritional Compositions Using Metabolomic and Microbiome Profiling.
Exploring Individual Determinants of postpranDIAl Glucose Response in Type 2 diaBEteS to opTimize Therapeutic Strategies With a Personalized Approach (DIABEST)
New Participatory Research Strategy and Multidimensional Phenotyping Towards Personalized Preventive Nutritional Support for Elderly People
Effect of Changes in Carbohydrate Intake Patterns on Glucose Control in Patients With Type 1 Diabetes
Role of the Fruit Matrix on the Absorption of Glucose From Orange Juice in Humans
Enabling Microbiomics- Driven Personalized Nutrition (EMPOWER)
Exploration Des Typologies Alimentaires et caractérisation Par un phénotypage Multidimensionnel d'Une Population de Personnes âgées de la région Auvergne-Rhône Alpes
Who cites it
473 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Proteomic profiling in personalized nutrition: a systematic review and methodological frameworks of randomized controlled trials.Frontiers in nutrition · 2026Pooled it
- Effects of Single Low-Carbohydrate, High-Fat Meal Consumption on Postprandial Lipemia and Markers of Endothelial Dysfunction: A Systematic Review of Current Evidence.Nutrition reviews · 2025Pooled it
- Associations Between TCF7L2, PPARγ, and KCNJ11 Genotypes and Insulin Response to an Oral Glucose Tolerance Test: A Systematic Review.Molecular nutrition & food research · 2025Pooled it
- A scoping review of the health effects of fermented foods in specific human populations and their potential role in precision nutrition: current knowledge and gaps.Frontiers in nutrition · 2025Pooled it
- Calorie-restricted oat diet is associated with zonulin and short-chain fatty acid response in metabolic syndrome: a randomized controlled trial.Gut microbes · 2026Trial
- Blood Metabolomic Signatures of 1-Hour Glucose Predict Cardiometabolic Risk.Circulation research · 2026Trial
- Machine learning identifies microbiome and clinical predictors of sustained weight loss following prolonged fasting.Genome medicine · 2026Trial
- Impact of Heated Versus Unheated Cooking Oil on Postprandial Vascular Function and Metabolism.Lipids · 2026Trial
- Baseline gut microbiome and metabolome profiles predict weight loss after a structured lifestyle intervention.Microbiome · 2026Trial
- Oral microbiome modulation mitigates hyperglycemia exacerbation in gestational diabetes mellitus.Nature communications · 2026Trial
- Trial
- Gut microbiota responses to isocaloric macronutrient modulation in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial.The American journal of clinical nutrition · 2026Trial
- Machine Learning-Based Prediction of Comprehensive Lipid Response to Dietary Interventions in Overweight and Obese Women.Nutrients · 2026Trial
- Use of Machine Learning to Identify Determinants of Habitual-Preformed Water Intake.The Journal of nutrition · 2026Trial
- Use of Metabotyping to Identify Individuals With Different Triglyceride Response Curves After Intake of High-Fat Meals.Molecular nutrition & food research · 2026Trial
- Functional gut microbiome signatures underlying interindividual variability in metabolic responses to red raspberry consumption.Scientific reports · 2026Trial
- Cholesterol-lowering effects of oats induced by microbially produced phenolic metabolites in metabolic syndrome: a randomized controlled trial.Nature communications · 2026Trial
- Differential effects of lifestyle interventions on continuous glucose monitoring metrics in persons with type 2 diabetes: Potential for personalised treatment.Diabetes, obesity & metabolism · 2025Trial
- GLP-1 Responses to a Single Meal Fortified With Oyster Mushroom Powder in Adults With Impaired Glucose Tolerance Depend on the Gut Microbiota Composition Before the Meal.Molecular nutrition & food research · 2025Trial
- Continuous glucose monitor overestimates glycemia, with the magnitude of bias varying by postprandial test and individual - a randomized crossover trial.The American journal of clinical nutrition · 2025Trial
413 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Commented on by
- Erratum issued
Authors and funding
25 authors.
Funding
Abstract
Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.
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.