{"entity": "publication", "iuid": "34511e43e9ea45bda7bbb6db940e2c8f", "timestamp": "2026-08-20T21:08:12.373Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/34511e43e9ea45bda7bbb6db940e2c8f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/34511e43e9ea45bda7bbb6db940e2c8f"}}, "title": "Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes.", "authors": [{"family": "Salihovic", "given": "Samira", "initials": "S"}, {"family": "Broeckling", "given": "Corey D", "initials": "CD"}, {"family": "Ganna", "given": "Andrea", "initials": "A"}, {"family": "Prenni", "given": "Jessica E", "initials": "JE"}, {"family": "Sundstr\u00f6m", "given": "Johan", "initials": "J"}, {"family": "Berne", "given": "Christian", "initials": "C"}, {"family": "Lind", "given": "Lars", "initials": "L"}, {"family": "Ingelsson", "given": "Erik", "initials": "E"}, {"family": "Fall", "given": "Tove", "initials": "T"}, {"family": "\u00c4rnl\u00f6v", "given": "Johan", "initials": "J"}, {"family": "Nowak", "given": "Christoph", "initials": "C", "orcid": "0000-0001-8435-3978", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/60b9c212e2234dae88d48ac2ce701915.json"}}], "type": "journal article", "published": "2020-10-05", "journal": {"title": "Sci Rep", "issn": "2045-2322", "volume": "10", "issue": "1", "pages": "16474", "issn-l": "2045-2322"}, "abstract": "Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.", "doi": "10.1038/s41598-020-72456-y", "pmid": "33020500", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7536211"}, {"db": "pii", "key": "10.1038/s41598-020-72456-y"}], "notes": [], "created": "2026-08-20T09:05:03.459Z", "modified": "2026-08-20T09:05:03.537Z"}