Machine learning of human plasma lipidomes for obesity estimation in a large population cohort
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Gerl , M J , Klose , C , Surma , M A , Fernandez , C , Melander , O , Männistö , S , Borodulin , K , Havulinna , A S , Salomaa , V , Ikonen , E , Cannistraci , C V & Simons , K 2019 , ' Machine learning of human plasma lipidomes for obesity estimation in a large population cohort ' , PLoS Biology , vol. 17 , no. 10 , e3000443 . https://doi.org/10.1371/journal.pbio.3000443
| Title: | Machine learning of human plasma lipidomes for obesity estimation in a large population cohort |
| Author: | Gerl, Mathias J.; Klose, Christian; Surma, Michal A.; Fernandez, Celine; Melander, Olle; Männistö, Satu; Borodulin, Katja; Havulinna, Aki S.; Salomaa, Veikko; Ikonen, Elina; Cannistraci, Carlo V.; Simons, Kai |
| Contributor: | University of Helsinki, Institute for Molecular Medicine Finland University of Helsinki, Lipid Trafficking Lab |
| Date: | 2019-10-18 |
| Language: | eng |
| Number of pages: | 25 |
| Belongs to series: | PLoS Biology |
| ISSN: | 1544-9173 |
| URI: | http://hdl.handle.net/10138/310640 |
| Abstract: | Obesity is associated with changes in plasma lipids, but while simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. A machine learning study based on lipidomes of a total of 1,311 individuals reveals improved associations of plasma lipids with total body fat and fat distribution compared to routine clinical laboratory variables. |
| Subject: |
BODY-MASS INDEX
COA DESATURASE ACTIVITY FATTY-ACIDS HIGH-THROUGHPUT RISK OVERWEIGHT DIVERSITY MORTALITY EVENTS MODELS 3111 Biomedicine 1182 Biochemistry, cell and molecular biology |
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