{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T02:04:26Z","timestamp":1782525866873,"version":"3.54.5"},"reference-count":87,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"19","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2025,10,7]]},"abstract":"<jats:sec xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Obesity perturbs metabolism, resulting in differential diet\u2013disease associations, but metabolic pathways that differ by obesity remain unclear. We\u00a0investigated whether cross\u2010sectional associations between metabolites and diet quality differ by obesity status in 2832 participants in the CARDIA (Coronary Artery Risk Development in Young Adults) cohort study.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods<\/jats:title>\n            <jats:p xml:lang=\"en\">We used untargeted metabolomics (7255 metabolite peaks) and an a priori diet quality score (APDQS) to (1) compare model accuracy and prediction, stratified by body mass index (BMI); (2) assess modification of APDQS\u2010metabolite associations by BMI in linear regression models; (3) identify pathways with differential metabolic activity by BMI; and (4) assess the strength and direction of associations with identified\/annotated metabolites.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Results<\/jats:title>\n            <jats:p xml:lang=\"en\">\n              In BMI\u2010stratified models, metabolite peaks had higher model accuracy but similar APDQS predictive ability among participants with lower versus higher BMI (BMI&lt;25: Q\n              <jats:sup>2<\/jats:sup>\n              =0.52; 25\u2264BMI&lt;30: Q\n              <jats:sup>2<\/jats:sup>\n              =0.41; BMI\u226530: Q\n              <jats:sup>2<\/jats:sup>\n              =0.21). Of the 7255 metabolite peaks, 180 peaks were associated with APDQS and differed by BMI (false discovery rate&lt;0.1). From pathway enrichment analyses, 6 pathways had differential metabolic activity by BMI (Fisher's exact test &lt;0.05) including primary bile acid biosynthesis; steroid biosynthesis; steroid hormone biosynthesis; caffeine metabolism; valine, leucine, and isoleucine biosynthesis; linoleic acid metabolism. Strength of associations were strongest for those without overweight or obesity.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Diet\u2010metabolite associations were best detected among participants who did not have overweight\/obesity. We identified associated diet\u2013metabolic pathways that differ by obesity, suggesting potential metabolic\u2010related mechanisms perturbed for those with obesity.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.124.037874","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T10:29:00Z","timestamp":1759228140000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Body Mass Index Modifies Associations Between Diet Quality and Circulating Metabolites: The CARDIA Study, 2005 to\u00a02006"],"prefix":"10.1161","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3400-7887","authenticated-orcid":false,"given":"Autumn G.","family":"Hullings","sequence":"first","affiliation":[{"name":"Gillings School of Global Public Health, University of North Carolina at Chapel Hill","place":["Chapel Hill, USA"]},{"name":"University of North Carolina at Chapel Hill","place":["Chapel 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