{"doi":"10.1111/eci.70030","title":"Strengthening the reporting of diet item details in feeding studies measuring the dietary metabolome: The <scp>DID</scp>‐<scp>METAB</scp> core outcome set statement","abstract":"Nutrition research and diet–disease relationships historically rely on self-reported data assessed via dietary assessment instruments such as 24-h dietary recalls, food records, food frequency questionnaires, etc.,1 which are prone to inherent bias and errors.1, 2 While these methods provide detailed information on what, how much, and when individuals eat, involvement from dietitians or nutritionists can help to minimise errors.3 However, misreporting remains inherent and can lead to misinterpretation of diet–disease relationships.2 Controlled human feeding studies provide known amounts of foods/beverages and aim to mitigate inherent biases associated with self-reported dietary assessment while observing individual responses and enhancing adherence; however, they are also highly resource-intensive. The reliability and accuracy of dietary assessment methods have been shown to be increased by substituting or complementing dietary assessment instruments with objective biomarkers of food intake.4-8 Currently, there are few valid dietary biomarkers routinely applied, for example, 24-h urinary sodium for salt,9 plasma carotenoids for fruit and vegetables,10 proline betaine for citrus fruits11; however, their application can be limited to a specific nutrient or food/food group.11 Human feeding studies utilising metabolomics as an adjunct objective dietary assessment method are gaining traction.12-14 However, the methodology of dietary feeding interventions can vary in their approach,15 making cross-comparison between studies and synthesising dietary evidence difficult (see Box 1). Beyond the discovery of metabolites identified from biospecimens for qualifying and quantifying dietary intake of specific foods, nutrients and/or dietary patterns, metabolomics may also reflect the impact of diets on endogenous metabolism, accounting for individual variation driven by factors such as genetics and gut microbiome composition. For example, metabolites derived from the gut microbiome16, 17 or produced through microbial conversion,18, 19 contribute to the diverse metabolic responses to dietary interventions.16 Therefore, metabolomics offers promise for future incorporation within precision and personalised nutrition interventions, ultimately advancing the broader field of nutrition research.16 Consumption of food(s)/beverage(s) may be conducted under the surveillance of researchers and/or consumed away from researchers/research facilities, for example, as part of the participant's usual routine. *While dietary prescription may not be considered a ‘feeding’ intervention, especially in the absence of food provision, these recommendations may still apply to reporting when metabolome data are being collected. While metabolomics is being rapidly integrated as a biological assessment technique in nutrition research,20 it is still in its infancy and therefore improved quality of reporting is required to facilitate consistency, reproducibility of findings, and advancement of the field long-term. We previously demonstrated that there is extensive variability in the reporting of dietary intervention methodologies (e.g. design, delivery, implementation and interpretation) currently used in human feeding studies measuring the metabolome.15 Commonly, insufficient detail is reported, hindering replication, which limits evidence synthesis in the field of metabolomics.15 For example, information about included/restricted foods, the timing of biospecimen collection in relation to dietary assessment instruments used, or methods used to account for the consumption of nonstudy foods. Detailed information on these items is vital for the interpretation of the metabolome data. While reporting guidelines exist for human intervention studies more broadly,21-23 including the developing CONSORT-Nut,24, 25 a nutrition extension of the CONSORT statement, no reporting guidance currently considers the specific nuances in dietary intervention research in which the metabolome ","journal":"European Journal of Clinical Investigation","year":2025,"id":563801,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9526,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1445686,"name":"Erin D. Clarke","orcid":"0000-0001-8250-5990","position":1,"is_corresponding":false},{"id":1031087,"name":"Jordan Stanford","orcid":"0000-0003-3541-8960","position":2,"is_corresponding":false},{"id":1445687,"name":"María Gómez‐Martín","orcid":"0000-0001-7300-7471","position":3,"is_corresponding":false},{"id":471904,"name":"Tammie Jakstas","orcid":"0000-0002-5547-4073","position":4,"is_corresponding":false},{"id":471911,"name":"Clare E. Collins","orcid":"0000-0003-3298-756X","position":5,"is_corresponding":false},{"id":1467010,"name":"DID‐METAB Delphi Working Group Authors","orcid":null,"position":6,"is_corresponding":false},{"id":1445685,"name":"Jessica J. A. Ferguson","orcid":"0000-0002-7962-1840","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:56:17.117043Z","pmid":"40189732","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}