Siegert, Sabine, Yu, Zhonghao, Wang-Sattler, Rui ORCID: 0000-0002-8794-8229, Illig, Thomas, Adamski, Jerzy ORCID: 0000-0001-9259-0199, Hampe, Jochen ORCID: 0000-0002-2421-6127, Nikolaus, Susanna, Schreiber, Stefan, Krawczak, Michael ORCID: 0000-0003-2603-1502, Nothnagel, Michael ORCID: 0000-0001-8305-7114 and Noethlings, Ute (2013). Diagnosing Fatty Liver Disease: A Comparative Evaluation of Metabolic Markers, Phenotypes, Genotypes and Established Biomarkers. PLoS One, 8 (10). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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Abstract

Background: To date, liver biopsy is the only means of reliable diagnosis for fatty liver disease (FLD). Owing to the inevitable biopsy-associated health risks, however, the development of valid noninvasive diagnostic tools for FLD is well warranted. Aim: We evaluated a particular metabolic profile with regard to its ability to diagnose FLD and compared its performance to that of established phenotypes, conventional biomarkers and disease-associated genotypes. Methods: The study population comprised 115 patients with ultrasound-diagnosed FLD and 115 sex-and age-matched controls for whom the serum concentration was measured of 138 different metabolites, including acylcarnitines, amino acids, biogenic amines, hexose, phosphatidylcholines (PCs), lyso-PCs and sphingomyelins. Established phenotypes, biomarkers, disease-associated genotypes and metabolite data were included in diagnostic models for FLD using logistic regression and partial least-squares discriminant analysis. The discriminative power of the ensuing models was compared with respect to area under curve (AUC), integrated discrimination improvement (IDI) and by way of cross-validation (CV). Results: Use of metabolic markers for predicting FLD showed the best performance among all considered types of markers, yielding an AUC of 0.8993. Additional information on phenotypes, conventional biomarkers or genotypes did not significantly improve this performance. Phospholipids and branched-chain amino acids were most informative for predicting FLD. Conclusion: We show that the inclusion of metabolite data may substantially increase the power to diagnose FLD over that of models based solely upon phenotypes and conventional biomarkers.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Siegert, SabineUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Yu, ZhonghaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wang-Sattler, RuiUNSPECIFIEDorcid.org/0000-0002-8794-8229UNSPECIFIED
Illig, ThomasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Adamski, JerzyUNSPECIFIEDorcid.org/0000-0001-9259-0199UNSPECIFIED
Hampe, JochenUNSPECIFIEDorcid.org/0000-0002-2421-6127UNSPECIFIED
Nikolaus, SusannaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schreiber, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Krawczak, MichaelUNSPECIFIEDorcid.org/0000-0003-2603-1502UNSPECIFIED
Nothnagel, MichaelUNSPECIFIEDorcid.org/0000-0001-8305-7114UNSPECIFIED
Noethlings, UteUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-474082
DOI: 10.1371/journal.pone.0076813
Journal or Publication Title: PLoS One
Volume: 8
Number: 10
Date: 2013
Publisher: PUBLIC LIBRARY SCIENCE
Place of Publication: SAN FRANCISCO
ISSN: 1932-6203
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
WHOLE-GENOME ASSOCIATION; INSULIN-RESISTANCE; NONALCOHOLIC STEATOHEPATITIS; LIPID-METABOLISM; INDEX; EPIDEMIOLOGY; POPULATION; GENETICS; RISK; PREDICTIONMultiple languages
Multidisciplinary SciencesMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/47408

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