Diagnosing Fatty Liver Disease: A Comparative Evaluation of Metabolic Markers, Phenotypes, Genotypes and Established Biomarkers

Abstract

BackgroundTo 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.AimWe 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.MethodsThe 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).ResultsUse 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.ConclusionWe 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.

Association between the Fatty Liver Index and Risk of Type 2 Diabetes in the EPIC-Potsdam Study

“…The FLI provided a good diagnostic accuracy for fatty liver in the original study population (AUROC = 0.85; 95% CI 0.81–0.88). External validation studies in German and Korean individuals found comparable AUROC values by using ultrasound [ 52 – 55 ]. However, especially in validation studies against MRS [ 9 ] or liver biopsy [ 56 , 57 ] lower AUROC values were obtained.…”

Associations of Erythrocyte Fatty Acids in the De Novo Lipogenesis Pathway with Proxies of Liver Fat Accumulation in the EPIC-Potsdam Study

“…However, serum fetuin-A concentrations, ALT and GGT activity have been associated with liver fat accumulation, even independently of BMI [ 49 – 51 ]. The FLI showed a good predictive accuracy of fatty liver disease (AUC ROC 0.85 (0.81–0.88)) [ 28 ] and was externally validated [ 29 – 35 ]. However, the FLI was created for the purpose to determine occurrence of fatty liver disease and its suitability for estimating percentage of liver fat is unclear.…”

“…[ 28 ], have been used to reflect liver fat accumulation. The FLI showed a good accuracy in detecting fatty liver disease [ 28 ] and was externally validated [ 29 – 35 ]. In addition, the FLI was shown to predict fatty liver better than liver enzymes [ 31 ].…”

Clinical Risk Scoring Models for Prediction of Acute Kidney Injury after Living Donor Liver Transplantation: A Retrospective Observational Study

“…Risk scoring models used the odds ratio of variables in the multivariate analysis. Our risk models were validated by way of cross-validation [ 33 , 34 ]. Our risk scoring models were applied to randomly generated validation samples by selecting mutually exclusive ten subgroups of our cohort to assess the accuracy of the score’s AKI prediction.…”