title: Identification of prognostic and diagnostic biomarkers of glucose intolerance in ApoE3Leiden mice.
abstract: The prevalence of diabetes mellitus Type 2 could be significantly reduced by early identification of subjects at risk, allowing for better prevention and earlier treatment. Glucose intolerance (GI) is a hallmark of the prediabetic stage. This study aims at identifying 1) prognostic biomarkers predicting the risk of developing GI later in life and 2) diagnostic biomarkers reflecting the degree of already manifest GI. To this end, disease development was followed over time in mice, and biomarkers were identified using lipidomics and transcriptomics. Young adult ApoE3Leiden mice were treated a high-fat diet for 12 wk to induce GI. Blood was collected before and during disease development. The individual extent of GI was determined with a glucose tolerance test and the area under the curve (AUC) was calculated for each animal. Subject-specific AUC values were correlated to the plasma lipidome (t = 0) and the white blood cell (WBC) transcriptome (t = 0, 6, and 12 wk) to identify prognostic and diagnostic biomarkers, respectively. The plasma ratio of specific free fatty acids prior to high-fat feeding (C16:1/C16:0, C18:1/C18:0 and C18:2/C22:6) was significantly correlated with the AUC and predictive for future GI. Subsequently, the expression level of specific WBC genes (Acss2, Arfgap1, Tfrc, Cox6b2, Barhl2, Abcb4, Cyp4b1, Sars2, Fgf16, and Tceal8) reflected the individual degree of GI during disease progression. Specific plasma free fatty acids as well as their ratio can be used to predict future GI. The expression levels of specific WBC genes can serve as easy accessible markers to diagnose and monitor already existing GI.
authors.affiliation: TNO, Research Group Microbiology and Systems Biology, P.O. Box 360, 3700 AJ Zeist, the Netherlands. suzan.wopereis@tno.nl
authors.forename: Suzan
authors.initials: S
authors.lastname: Wopereis
authors.forename: Marijana
authors.initials: M
authors.lastname: Radonjic
authors.forename: Carina
authors.initials: C
authors.lastname: Rubingh
authors.forename: Marjan van
authors.initials: Mv
authors.lastname: Erk
authors.forename: Age
authors.initials: A
authors.lastname: Smilde
authors.forename: Wim van
authors.initials: Wv
authors.lastname: Duyvenvoorde
authors.forename: Nicole
authors.initials: N
authors.lastname: Cnubben
authors.forename: Teake
authors.initials: T
authors.lastname: Kooistra
authors.forename: Ben van
authors.initials: Bv
authors.lastname: Ommen
authors.forename: Robert
authors.initials: R
authors.lastname: Kleemann
mesh_terms: D000818:Animals; D053318:Apolipoprotein E3 / Q000235:genetics*; D015415:Biomarkers / Q000032:analysis* / Q000097:blood / Q000378:metabolism; D020869:Gene Expression Profiling; D018149:Glucose Intolerance / Q000097:blood / Q000175:diagnosis* / Q000235:genetics; D007962:Leukocytes / Q000737:chemistry / Q000378:metabolism; D008055:Lipids / Q000032:analysis / Q000097:blood; D008297:Male; D055442:Metabolome; D051379:Mice; D008822:Mice, Transgenic; D046228:Microarray Analysis; D025202:Molecular Diagnostic Techniques; D011379:Prognosis; D059467:Transcriptome; D054928:Validation Studies as Topic
chemical_list: D053318:Apolipoprotein E3; D015415:Biomarkers; D008055:Lipids