<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Li J</submitter><funding>Novo Nordisk Fonden (Novo Nordisk Foundation)</funding><funding>U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)</funding><funding>U.S. Department of Health &amp; Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes &amp; Digestive &amp; Kidney Diseases)</funding><funding>U.S. Department of Health &amp; Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)</funding><funding>American Heart Association (American Heart Association, Inc.)</funding><pagination>660-670</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12920144</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>32(2)</volume><pubmed_abstract>The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic</pubmed_abstract><journal>Nature medicine</journal><pubmed_title>Circulating metabolites, genetics and lifestyle factors in relation to future risk of type 2 diabetes.</pubmed_title><pmcid>PMC12920144</pmcid><funding_grant_id>R01AG085320</funding_grant_id><funding_grant_id>R01HL060712</funding_grant_id><funding_grant_id>U01DK140761</funding_grant_id><funding_grant_id>R01DK119268; R01DK126698; U01DK140761; R01DK120870</funding_grant_id><funding_grant_id>R01HL136266</funding_grant_id><funding_grant_id>R00DK122128</funding_grant_id><funding_grant_id>R01HL153178</funding_grant_id><funding_grant_id>23POST1020455</funding_grant_id><funding_grant_id>R01DK081572</funding_grant_id><funding_grant_id>NNF24OC0095435</funding_grant_id><funding_grant_id>R01DK134672</funding_grant_id><funding_grant_id>K24HL152440</funding_grant_id><funding_grant_id>R01HL060712; R01HL170904</funding_grant_id><pubmed_authors>Gerszten RE</pubmed_authors><pubmed_authors>Dupuis J</pubmed_authors><pubmed_authors>Florez JC</pubmed_authors><pubmed_authors>Rotter JI</pubmed_authors><pubmed_authors>Guo X</pubmed_authors><pubmed_authors>Merino J</pubmed_authors><pubmed_authors>Boerwinkle E</pubmed_authors><pubmed_authors>Rich SS</pubmed_authors><pubmed_authors>Alkis T</pubmed_authors><pubmed_authors>Truong B</pubmed_authors><pubmed_authors>Wang TJ</pubmed_authors><pubmed_authors>Rebholz CM</pubmed_authors><pubmed_authors>Bhupathiraju SN</pubmed_authors><pubmed_authors>Liang L</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Yao J</pubmed_authors><pubmed_authors>Li J</pubmed_authors><pubmed_authors>Manson JE</pubmed_authors><pubmed_authors>Hu FB</pubmed_authors><pubmed_authors>Wood AC</pubmed_authors><pubmed_authors>Salas-Salvado J</pubmed_authors><pubmed_authors>Rexrode KM</pubmed_authors><pubmed_authors>Porneala BC</pubmed_authors><pubmed_authors>Liu CT</pubmed_authors><pubmed_authors>Hu J</pubmed_authors><pubmed_authors>Brody JA</pubmed_authors><pubmed_authors>Martinez-Gonzalez MA</pubmed_authors><pubmed_authors>Kaplan RC</pubmed_authors><pubmed_authors>Yun H</pubmed_authors><pubmed_authors>Jia C</pubmed_authors><pubmed_authors>Mei Z</pubmed_authors><pubmed_authors>Meigs JB</pubmed_authors><pubmed_authors>Han X</pubmed_authors><pubmed_authors>Clish CB</pubmed_authors><pubmed_authors>Liu S</pubmed_authors><pubmed_authors>Tucker KL</pubmed_authors><pubmed_authors>Sotoodehnia N</pubmed_authors><pubmed_authors>Qi Q</pubmed_authors><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Jung SY</pubmed_authors><pubmed_authors>Selvin E</pubmed_authors><pubmed_authors>Lemaitre RN</pubmed_authors><pubmed_authors>Guasch-Ferre M</pubmed_authors><pubmed_authors>Tinker LF</pubmed_authors><pubmed_authors>Ruiz-Canela M</pubmed_authors><pubmed_authors>Eliassen AH</pubmed_authors><pubmed_authors>Luo K</pubmed_authors><pubmed_authors>Yu B</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Moon EH</pubmed_authors><pubmed_authors>Liu G</pubmed_authors><pubmed_authors>North KE</pubmed_authors></additional><is_claimable>false</is_claimable><name>Circulating metabolites, genetics and lifestyle factors in relation to future risk of type 2 diabetes.</name><description>The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-16T12:59:48.81Z</modification><creation>2026-07-09T10:57:03.675Z</creation></dates><accession>S-EPMC12920144</accession><cross_references><pubmed>41535386</pubmed><doi>10.1038/s41591-025-04105-8</doi></cross_references></HashMap>