Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids

Abstract

Aims/hypothesis: We examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data.

Methods: We analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy ((1)H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively.

Results: Individuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids ('lipid triplet') was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study.

Conclusions/interpretation: The liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease.

Fig. 1

Mean lipid levels within each cluster, shown separately for patients with NAFLD (NAFLD+, black bars) and without NAFLD (NAFLD−, white bars) in the biomarker-discovery cohort. The data for each lipid are scaled to zero mean and unit variance. Statistical comparison was performed using the two-sided t test. The cluster summaries are shown in Table 2. Error marks show standard error of the mean. *p < 0.05 vs NAFLD−; ***p < 0.001 vs NAFLD−. For LC7 the two groups are different at the marginal significance level (p = 0.097)

Fig. 2

The relationship between liver-fat content and the selected representative lipids from the clusters that are significantly altered in NAFLD: (a) TG(16:0/16:0/18:1) from cluster LC9 (Spearman rank correlation r, 0.45, p < 0.001); (b) lysoPC from LC2 (r, −0.32, p < 0.001); and (c) PC(O-24:1/20:4) from LC4 (r −0.31, p < 0.001). The regression lines are drawn as guides

Fig. 3

Prediction of liver-fat content from the model including three lipids, TG(48:0), PC(O-24:1/20:4) and PC(18:1/22:6). (a) The relationship between measured and predicted liver fat (log₁₀ scale on both axes). Pearson correlation coefficient r 0.54 (p < 0.001). Black circles, men; white circles, women. (b) ROC curve for NAFLD diagnosis (biomarker-discovery and validation series combined), based on predicted liver fat, and the ROC curves of the reference model [10]. Test lipid combination AUC 0.79 (95% CI 0.75, 0.82), reference AUC 0.78 (95% CI 0.74, 0.82). Optimal cut-off point corresponding to the maximum sum of sensitivity and specificity: test lipid combination 0.648 (95% CI 0.745, 0.693); reference 0.409 (95% CI 0.648, 0.746). Cut-off point for 95% sensitivity: test lipid combination 0.353 (95% CI 0.354, 0.938); reference 0.241 (95% CI 0.286, 0.954). Cut-off point for 95% specificity: test lipid combination 0.918 (95% CI 0.936, 0.343); reference 0.723 (95% CI 0.924, 0.431). Black curve, biomarker-discovery and validation series combined; red curve, reference

Fig. 4

Comparison of measured and estimated liver fat using the serum molecular signature in participants before and after intervention with rimonabant or placebo (log₁₀ scale on both axes). Pearson correlation coefficient r, 0.53 (p < 0.001). White circles, placebo at baseline; white squares, rimonabant at baseline; black circles, placebo at 48 weeks; black squares, rimonabant at 48 weeks