Non-invasive detection of non-alcoholic Steatohepatitis using clinical markers and circulating levels of lipids and metabolites

Zhou, You ORCID: https://orcid.org/0000-0002-1743-1291, Oresic, Matej, Leivonen, Marja, Gopalacharyulu, Peddinti, Hyysalo, Jenni, Arola, Johanna, Verrijken, An, Francque, Sven, Van Gaal, Luc, Hyötyläinen, Tuulia and Yki-Järvinen, Hannele 2016. Non-invasive detection of non-alcoholic Steatohepatitis using clinical markers and circulating levels of lipids and metabolites. Clinical Gastroenterology and Hepatology 14 (10) , pp. 1463-1472. 10.1016/j.cgh.2016.05.046

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Abstract

Background & Aims Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409. Methods We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling. Results Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.792 (95% confidence interval, 0.726–0.859). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820–0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone. Conclusions A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Systems Immunity Research Institute (SIURI)
Uncontrolled Keywords:	Liver; Nonalcoholic Fatty Liver Disease; Diagnosis; Prediction; Triglycerides
Publisher:	Elsevier
ISSN:	1542-3565
Date of First Compliant Deposit:	23 July 2016
Date of Acceptance:	16 May 2016
Last Modified:	29 Nov 2024 01:00
URI:	https://orca.cardiff.ac.uk/id/eprint/93205

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