{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:31:40Z","timestamp":1780511500593,"version":"3.54.1"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["647783"],"award-info":[{"award-number":["647783"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (<jats:italic>p<\/jats:italic>\u2009\u2264\u20090.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health.<\/jats:p><\/jats:sec><jats:sec><jats:title>Clinical trial registration<\/jats:title><jats:p><jats:ext-link>isrctn.com<\/jats:ext-link>, identifier ISRCTN15305840.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnut.2023.1282741","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T08:26:43Z","timestamp":1700036803000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["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"],"prefix":"10.3389","volume":"10","author":[{"given":"Elaine","family":"Hillesheim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorraine","family":"Brennan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1016\/S0140-6736(19)30041-8","article-title":"Health effects of dietary risks in 195 countries, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017","volume":"393","year":"2019","journal-title":"Lancet"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"bmj.k2173","DOI":"10.1136\/bmj.k2173","article-title":"Personalised nutrition and health","volume":"361","author":"Ordovas","year":"2018","journal-title":"BMJ"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1093\/advances\/nmz086","article-title":"Perspective: guiding principles for the implementation of personalized nutrition approaches that benefit health and function","volume":"11","author":"Adams","year":"2020","journal-title":"Adv Nutr"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1038\/s41591-020-0934-0","article-title":"Human postprandial responses to food and potential for precision nutrition","volume":"26","author":"Berry","year":"2020","journal-title":"Nat Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","article-title":"Personalized nutrition by prediction of glycemic responses","volume":"163","author":"Zeevi","year":"2015","journal-title":"Cells"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1093\/advances\/nmaa144","article-title":"Does personalized nutrition advice improve dietary intake in healthy adults? 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