<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Oluwagbemigun K</submitter><funding>Bundesministerium für Bildung und Forschung</funding><funding>Ministry of Science and Research of North Rhine Westphalia</funding><funding>Science Foundation Ireland</funding><pagination>647-656</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7948843</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>113(3)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Gut microbiota composition as influenced by long-term diet may be associated with the risk of adult chronic diseases. Thus, establishing the relation of long-term diet, particularly starting from early life, with adult microbiota composition would be an important research advance.&lt;h4>Objective&lt;/h4>We aimed to investigate the association of long-term intake of energy, carbohydrate, fiber, protein, and fat from infancy to late adolescence with microbiota composition in adulthood.&lt;h4>Methods&lt;/h4>Within the prospective DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study, we sampled stool 1 or 2 times within 1 y from 128 adults (median age: 29 y). Microbiota composition was profiled by 16S ribosomal RNA sequencing. Annual dietary records from age 1 to 18 y were retrieved. We estimated trajectories of energy, energy-adjusted carbohydrate, fiber, protein, and fat intake with multilevel models, producing predicted intake at age 1 y and rates of change in intake. A multivariate, zero-inflated, logistic-normal model was used to model the association between intake trajectories and the composition of 158 genera in single-sampled individuals. Associations found in this model were confirmed in double-sampled individuals using a zero-inflated Beta regression model.&lt;h4>Results&lt;/h4>Adjusting for covariates and temporal differences in microbiota composition, long-term carbohydrate intake was associated with 3 genera. Specifically, carbohydrate intake at age 1 y was negatively associated with Phascolarctobacterium [coefficient = -4.31; false discovery rate (FDR)-adjusted P = 0.006] and positively associated with Dialister (coefficient = 3.06; FDR-adjusted P = 0.003), and the rate of change in carbohydrate intake was positively associated with Desulfovibrio (coefficient = 13.16; FDR-adjusted P = 0.00039). Energy and other macronutrients were not associated with any genus.&lt;h4>Conclusions&lt;/h4>This work links long-term carbohydrate intake to microbiota composition. Considering the associations of high carbohydrate intake and microbiota composition with some diseases, these findings could inform the development of gut microbiota-targeted dietary recommendations for disease prevention.</pubmed_abstract><journal>The American journal of clinical nutrition</journal><pubmed_title>Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood.</pubmed_title><pmcid>PMC7948843</pmcid><funding_grant_id>01EA1809A</funding_grant_id><funding_grant_id>16/ERA-HDHL/3362</funding_grant_id><pubmed_authors>Lyons K</pubmed_authors><pubmed_authors>Cryan J</pubmed_authors><pubmed_authors>Nothlings U</pubmed_authors><pubmed_authors>Clarke G</pubmed_authors><pubmed_authors>Oluwagbemigun K</pubmed_authors><pubmed_authors>Alexy U</pubmed_authors><pubmed_authors>Stanton C</pubmed_authors><pubmed_authors>Berding K</pubmed_authors><pubmed_authors>Schmid M</pubmed_authors><pubmed_authors>O'Donovan AN</pubmed_authors></additional><is_claimable>false</is_claimable><name>Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood.</name><description>&lt;h4>Background&lt;/h4>Gut microbiota composition as influenced by long-term diet may be associated with the risk of adult chronic diseases. Thus, establishing the relation of long-term diet, particularly starting from early life, with adult microbiota composition would be an important research advance.&lt;h4>Objective&lt;/h4>We aimed to investigate the association of long-term intake of energy, carbohydrate, fiber, protein, and fat from infancy to late adolescence with microbiota composition in adulthood.&lt;h4>Methods&lt;/h4>Within the prospective DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study, we sampled stool 1 or 2 times within 1 y from 128 adults (median age: 29 y). Microbiota composition was profiled by 16S ribosomal RNA sequencing. Annual dietary records from age 1 to 18 y were retrieved. We estimated trajectories of energy, energy-adjusted carbohydrate, fiber, protein, and fat intake with multilevel models, producing predicted intake at age 1 y and rates of change in intake. A multivariate, zero-inflated, logistic-normal model was used to model the association between intake trajectories and the composition of 158 genera in single-sampled individuals. Associations found in this model were confirmed in double-sampled individuals using a zero-inflated Beta regression model.&lt;h4>Results&lt;/h4>Adjusting for covariates and temporal differences in microbiota composition, long-term carbohydrate intake was associated with 3 genera. Specifically, carbohydrate intake at age 1 y was negatively associated with Phascolarctobacterium [coefficient = -4.31; false discovery rate (FDR)-adjusted P = 0.006] and positively associated with Dialister (coefficient = 3.06; FDR-adjusted P = 0.003), and the rate of change in carbohydrate intake was positively associated with Desulfovibrio (coefficient = 13.16; FDR-adjusted P = 0.00039). Energy and other macronutrients were not associated with any genus.&lt;h4>Conclusions&lt;/h4>This work links long-term carbohydrate intake to microbiota composition. Considering the associations of high carbohydrate intake and microbiota composition with some diseases, these findings could inform the development of gut microbiota-targeted dietary recommendations for disease prevention.</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Mar</publication><modification>2025-04-26T11:54:10.587Z</modification><creation>2025-04-06T13:47:25.747Z</creation></dates><accession>S-EPMC7948843</accession><cross_references><pubmed>33471048</pubmed><doi>10.1093/ajcn/nqaa340</doi></cross_references></HashMap>