To Örebro University

oru.seÖrebro University Publications
Planned maintenance
A system upgrade is planned for 10/12-2024, at 12:00-13:00. During this time DiVA will be unavailable.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes
Örebro University, School of Medical Sciences. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.ORCID iD: 0000-0001-5752-4196
Analytical Resources Core: Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, USA.
Institute of Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA.
Show others and affiliations
2020 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 16474Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2020. Vol. 10, no 1, article id 16474
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:oru:diva-86390DOI: 10.1038/s41598-020-72456-yISI: 000577286700004PubMedID: 33020500Scopus ID: 2-s2.0-85092002062OAI: oai:DiVA.org:oru-86390DiVA, id: diva2:1475488
Funder
The Karolinska Institutet's Research FoundationSwedish Research Council, 2012-2215
Note

Funding Agency:

European Foundation for the Study of Diabetes EFSD/Lilly Young Investigator Research Award

Available from: 2020-10-13 Created: 2020-10-13 Last updated: 2022-09-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Salihovic, Samira

Search in DiVA

By author/editor
Salihovic, Samira
By organisation
School of Medical Sciences
In the same journal
Scientific Reports
Endocrinology and Diabetes

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 136 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf