Obesity-dependent metabolic signatures associated with nonalcoholic fatty liver disease progression

Abstract

Our understanding of the mechanisms by which nonalcoholic fatty liver disease (NAFLD) progresses from simple steatosis to steatohepatitis (NASH) is still very limited. Despite the growing number of studies linking the disease with altered serum metabolite levels, an obstacle to the development of metabolome-based NAFLD predictors has been the lack of large cohort data from biopsy-proven patients matched for key metabolic features such as obesity. We studied 467 biopsied individuals with normal liver histology (n=90) or diagnosed with NAFLD (steatosis, n=246; NASH, n=131), randomly divided into estimation (80% of all patients) and validation (20% of all patients) groups. Qualitative determinations of 540 serum metabolite variables were performed using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). The metabolic profile was dependent on patient body-mass index (BMI), suggesting that the NAFLD pathogenesis mechanism may be quite different depending on an individual's level of obesity. A BMI-stratified multivariate model based on the NAFLD serum metabolic profile was used to separate patients with and without NASH. The area under the receiver operating characteristic curve was 0.87 in the estimation and 0.85 in the validation group. The cutoff (0.54) corresponding to maximum average diagnostic accuracy (0.82) predicted NASH with a sensitivity of 0.71 and a specificity of 0.92 (negative/positive predictive values=0.82/0.84). The present data, indicating that a BMI-dependent serum metabolic profile may be able to reliably distinguish NASH from steatosis patients, have significant implications for the development of NASH biomarkers and potential novel targets for therapeutic intervention.

Figures

Figure 1. UPLC-TOF base peak ion intensity chromatograms

Base peak ion intensity chromatograms for the methanol – platform 1 (a), and chloroform/methanol – platform 3 (b) serum extracts. Approximate retention time regions corresponding to identified metabolites are indicated on the plots (see text for abbreviations).

Figure 2. NAFLD serum metabolic profile

Heat map representation of the serum metabolic profile obtained from patients included in the study estimation group. (a), (b), and (c) metabolite ion abundance ratios in BMI cohorts lean/pre-obese (left), obese class I–II (middle), and obese class III (right), comparing histology groups: steatosis/normal liver, NASH/normal liver, and NASH/steatosis respectively. For each comparison, log transformed ion abundance ratios are depicted, as represented by the scales (d), where pronounced colors correspond to significant (p<0.05 – two-tailed Wilcoxon Rank Sum Test) changes, and (e) where light colors correspond to nonsignificant (p>0.05 – two-tailed Wilcoxon Rank Sum Test) changes. Metabolite class specific magnified representations of Figure 2, showing individual metabolite details are provided in Supplementary Figures 3A–K.

Figure 3. Obesity dependent NASH biomarkers

Mean percent ion abundance deviations of acyl carnitines, sphingolipids (upper plots), and oxidized fatty acids (lower plots) found in the sera of patients diagnosed with NASH as compared to isolated steatosis. Data are shown for the lean/pre-obese (a), obese class I–II (b), and obese class III (c) patient cohorts. Positive and negative percentage values indicate higher levels of metabolites in NASH and steatosis patients’ sera respectively. Dark bars denote significant changes (p<0.05, two-tailed Wilcoxon Rank Sum Test).

Figure 4. Obesity dependent metabolic discrimination between steatosis and NASH patients

ROC curves calculated for the estimation (solid line), validation (dotted line), and full cross-validated datasets (dashed line), based for each BMI cohort on all metabolite biomarkers found to be significant (p(a), obese class I–II (b), and obese class III (c) patient cohorts. Optimum cutoff points (solid circles) are provided for each estimation group ROC curve.

Figure 5. BMI-stratified metabolic discrimination between steatosis and NASH patients

(a) Average BMI-stratified accuracy (number of patients correctly classified/total number of patients) as a function of cutoff for the three random forest models combined in the estimation (solid line), validation (dotted line), and full cross-validated datasets (dashed line). Estimation group cutoff points at maximum average accuracy (0.54), 95% probability NASH absence (0.09), and 95% probability NASH presence (0.73) are shown. (b) Associated BMI-stratified ROC curves for the estimation (solid line), validation (dotted line), and full cross-validated datasets (dashed line). The optimum cutoff point for the estimation group (0.54), defined as that at which average diagnostic accuracy was a maximum is indicated (sensitivity 0.71, specificity 0.92) by a solid circle. In addition, the low cutoff point (0.09) to predict the absence of NASH with a probability of 95% and the high cutoff point (0.73) to predict the presence of NASH with a probability of 95% are shown (solid diamonds).