ReviewThe Proceedings of the Nutrition Society2023
Metabotyping: a tool for identifying subgroups for tailored nutrition advice.
Review in The Proceedings of the Nutrition Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 10 citations in OpenAlex.
- Use of Metabotyping to Identify Individuals With Different Triglyceride Response Curves After Intake of High-Fat Meals.Molecular nutrition & food research · 2026Trial
- Article
- Towards Metabolomics-Guided Healthy and Anti-Aging Nutrition.Metabolites · 2026Article
- Multiomics: the intersection of personalized nutrition in cardiometabolic diseases.Journal of translational medicine · 2026Review
- Metabotype Risk Clustering Based on Metabolic Disease Biomarkers and Its Association with Metabolic Syndrome in Korean Adults: Findings from the 2016-2023 Korea National Health and Nutrition Examination Survey (KNHANES).Diseases (Basel, Switzerland) · 2025Article
- Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact.Advances in nutrition (Bethesda, Md.) · 2025Review
- Perspective on the ethics of AI at the intersection of nutrition and behaviour change.Frontiers in aging · 2025Article
- From Food Supplements to Functional Foods: Emerging Perspectives on Post-Exercise Recovery Nutrition.Nutrients · 2024Review
- Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.
Indexed as
Identifiers
What Socratic 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 Socratic graph.