Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Show full item record



Permalink

http://hdl.handle.net/10138/310640

Citation

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
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
journal.pbio.3000443.pdf 1.718Mb PDF View/Open
journal.pbio.3000443.pdf 1.851Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record