ECE2016 Eposter Presentations Obesity (69 abstracts)
The circulating fingerprint revealed by targeted metabolomic as biomarker of metabolic impairment in female overweight/obesity
Marco Mezzullo 1 , Flaminia Fanelli 1 , Alessia Fazzini 1 , Margherita Baccini 1 , Elena Casadio 1 , Daniela Ibarra-Gasparini 1 , Roberta Mazza 1 , Valentina Vicennati 1 , Luca Fontanesi 2 , Renato Pasquali 1 & Uberto Pagotto 1
1Endocrinology Unit and Centro di Ricerca Biomedica Applicata, Department of Medical and Surgical Science, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy; 2Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Bologna, Italy.
The comprehension of the network of metabolic processes may help the understanding of the molecular pathways driving obesity and related complications. However this is a hard task due to the large number of actors and their complex interplay. We aimed at exploring by targeted metabolomic the circulating metabolite profile in lean (NW, n=42; BMI: 18.5–24.9 kg/m2) and age-matched overweight/obese (OB, n=37, BMI≥25.0 kg/m2), drug-free adult overnight-fasted women. Anthropometric, biochemical and hormonal data were collected. One-hundred-eighty molecules among aminoacids, biogenic amines, acylcarnitines, phosphatidyl-choline (PC), lysoposphatidyl-choline (LysoPC) and sphingomyeline (SM) were quantified by the Absolute p180 LC-MS/MS Kit (Biocrates Life Science AG). BMI effect on metabolite profile was investigated by the orthogonal partial least squares-discriminant analysis (OPLS-DA), resulting in the selection of 39 metabolites driving the NW vs OB separation (R2X=0.485, R2Y=0.638, Q2Y=0.505, CV-ANOVA P<0.001). Then, the association between the metabolite model and the individual parameters of metabolic impairment was investigated. The BMI-adjusted stepwise multiple regression analysis revealed the following independent associations: waist circumference was positively associated to Glu (P=0.040) and Val (P<0.001) and negatively to PCaa-C42:2 and LysoPC-C18:2 (P<0.001). Glycaemia positively correlated with Ala (P=0.025) and SM-C18:0 (P=0.008), and negatively with PCae-C34:3 (P=0.004). Insulin positively correlated with Val (P=0.002) and negatively with PCae-C34:2 (P=0.001). HOMA-index was positively associated to Tyr (P<0.001) and negatively to PCae-C34:2 (P=0.001). Triglycerides showed a positive correlation with PCaa-C24:0 (P=0.036) and PCaa-C38:3 (P<0.001) and negative correlation with PCae-C32:1 (P=0.007) and PCae-C36:3 (P=0.012). HDL were positively associated to PCae-C34:3 (P<0.001) and LysoPC-C20:4 (P=0.038) and negatively associated to Val (P<0.001). Finally, total cholesterol positively correlated with SM-C18:0 (P=0.008), SM(OH)-C22:1 (P=0.024) and PCaaC38:3 (P<0.001) and negatively correlated with Tyr (P<0.001). The targeted metabolomic approach allowed the identification of a specific metabolic fingerprint in female non-complicated obesity that should be further explored as early biomarker of dysmetabolism.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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