ArticleThe Proceedings of the Nutrition Society2013
Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations.
Article in The Proceedings of the Nutrition Society, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 3 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
- Effect of Personalized Nutrition on Dietary, Physical Activity, and Health Outcomes: A Systematic Review of Randomized Trials.Nutrients · 2022Pooled it
- The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence.Nutrients · 2021Pooled it
- Use of Metabotyping to Identify Individuals With Different Triglyceride Response Curves After Intake of High-Fat Meals.Molecular nutrition & food research · 2026Trial
- 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
- Trial
- Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men.The ISME journal · 2014Trial
- Children's gut microbiota predicts the efficacy of obesity treatment.Gut microbes · 2026Article
- Article
- Sex-Specific Responses to Intermittent Fasting: A Narrative Review Across Physiological, Clinical, and Psychosocial Contexts.Nutrients · 2026Review
- Gene Polymorphisms Associated with Osteoarthritis: Potential Implications for Nutrigenetics and Precision Nutrition.Nutrients · 2026Review
- From cooperation to collapse: the diet-microbiota-host gene triad in disease and aging.Frontiers in microbiomes · 2026Review
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Adherence to the combined Mediterranean-dietary approaches to stop hypertension diet is shaped by neighborhood socio-economics and food environments.Health & place · 2026Article
- Gut Microbiota Associated With Hyperlipidemia: A Systematic Review and Meta-Analysis.Gastro hep advances · 2026Review
- Article
- Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact.Advances in nutrition (Bethesda, Md.) · 2025Review
- Unraveling the nutritional challenges in epilepsy: Risks, deficiencies, and management strategies: A systematic review.World journal of experimental medicine · 2025Article
- The Role of Diet and Nutrition in Cancer Development and Management: From Molecular Mechanisms to Personalized Interventions.Foods (Basel, Switzerland) · 2025Review
- Personalized Nutrition in Chronic Kidney Disease.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Technologic advances now make it possible to collect large amounts of genetic, epigenetic, metabolomic and gut microbiome data. These data have the potential to transform approaches towards nutrition counselling by allowing us to recognise and embrace the metabolic, physiologic and genetic differences among individuals. The ultimate goal is to be able to integrate these multi-dimensional data so as to characterise the health status and disease risk of an individual and to provide personalised recommendations to maximise health. To this end, accurate and predictive systems-based measures of health are needed that incorporate molecular signatures of genes, transcripts, proteins, metabolites and microbes. Although we are making progress within each of these omics arenas, we have yet to integrate effectively multiple sources of biologic data so as to provide comprehensive phenotypic profiles. Observational studies have provided some insights into associative interactions between genetic or phenotypic variation and diet and their impact on health; however, very few human experimental studies have addressed these relationships. Dietary interventions that test prescribed diets in well-characterised study populations and that monitor system-wide responses (ideally using several omics platforms) are needed to make correlation-causation connections and to characterise phenotypes under controlled conditions. Given the growth in our knowledge, there is the potential to develop personalised dietary recommendations. However, developing these recommendations assumes that an improved understanding of the phenotypic complexities of individuals and their responses to the complexities of their diets will lead to a sustainable, effective approach to promote health and prevent disease - therein lies our challenge.
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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.