ArticleBMJ open2022
Empowering consumers to PREVENT diet-related diseases through OMICS sciences (PREVENTOMICS): protocol for a parallel double-blinded randomised intervention trial to investigate biomarker-based nutrition plans for weight loss.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04590989 (PREVENTOMICS), which is not on this map. Cited by 10 papers.
What it found
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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.
PREVENTOMICS: Empowering Consumers to 'PREVENT' Diet-related Diseases Through 'OMICS' Sciences - Danish Intervention Study
Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Precision nutrition for the prevention and management of inflammatory bowel disease.Nature reviews. Gastroenterology & hepatology · 2026Review
- Metabolic Phenotypes, Genotypes, and Gut Microbiome Signatures in Obesity: Implications for Precision Nutrition Strategies in Type 2 Diabetes Prevention.Nutrition reviews · 2026Review
- Mitigation of Metabolic Diseases Through Personalized Nutrition: A Critical In-Depth Review.Food science & nutrition · 2026Review
- Precision nutrition: Is tailor‑made dietary intervention a reality yet? (Review).Biomedical reports · 2025Review
- Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives.Biomedicines · 2024Review
- Translational Algorithms for Technological Dietary Quality Assessment Integrating Nutrimetabolic Data with Machine Learning Methods.Nutrients · 2024Article
- A health technology assessment of personalized nutrition interventions using the EUnetHTA HTA Core Model.International journal of technology assessment in health care · 2024Article
- Economic Evaluation of a Personalized Nutrition Plan Based on Omic Sciences Versus a General Nutrition Plan in Adults with Overweight and Obesity: A Modeling Study Based on Trial Data in Denmark.PharmacoEconomics - open · 2024Article
- Role of metabolomics in the delivery of precision nutrition.Redox biology · 2023Article
- Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial.Frontiers in nutrition · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPersonalised nutrition holds immense potential over conventional one-size-fits-all approaches for preventing and treating diet-related diseases, such as obesity. The current study aims to examine whether a personalised nutritional plan produces more favourable health outcomes than a standard approach based on general dietary recommendations in subjects with overweight or obesity and elevated waist circumference. METHODS AND ANALYSIS: This project is a 10-week parallel, double-blinded randomised intervention trial. We plan to include 100 adults aged 18-65 years interested in losing weight, with body mass index ≥27 but<40 kg/m ETHICS AND DISSEMINATION: The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated through peer-reviewed publications, conference presentations and media outlets. TRIAL REGISTRATION NUMBER: NCT04590989.
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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.