Proton density fat-fraction is an accurate biomarker of hepatic steatosis in adolescent girls and young women

Abstract

Objectives: To compare complex quantitative magnetic resonance imaging (MRI) with MR spectroscopy (MRS) for quantification of hepatic steatosis (HS) and determine clinically significant MRI-based thresholds of HS in female youths.

Methods: This prospective, cross-sectional study was conducted in 132 healthy females (11-22 years, mean 13.3 ± 2). Proton density fat-fraction (PDFF) was measured using complex quantitative MRI and MRS. Body mass index (BMI), fasting labs [glucose, insulin, alanine aminotransferase (ALT), and other metabolic markers] were obtained. Outcomes were measured using regression analysis, Spearman-rank correlation, and receiver operator characteristics (ROC) analysis. HS was defined as MRI-PDFF >5.6%.

Results: HS was detected by MRI-PDFF in 15% of all subjects. Linear regression demonstrated excellent correlation and agreement [r(2) = 0.96, slope = 0.97 (95 %CI: 0.94-1.00), intercept = 0.78% (95 %CI: 0.58-0.98%)] between MRI-PDFF and MRS-PDFF. MRI-PDFF had a sensitivity of 100% (95 %CI: 0.79-1.00), specificity of 96.6% (95 %CI: 0.91-0.99), and a kappa index of 87% (95 %CI: 0.75-0.99) for identifying HS. In overweight subjects with HS, MRI-PDFF correlated with ALT (r = 0.84, p < 0.0001) and insulin (r = 0.833, p < 0.001), but not with BMI or WC. ROC analysis ascertained an optimal MRI-PDFF threshold of 3.5% for predicting metabolic syndrome (sensitivity = 76 %, specificity = 83 %).

Conclusion: Complex quantitative MRI demonstrates strong correlation and agreement with MRS to quantify hepatic triglyceride content in adolescent girls and young women. A low PDFF threshold is predictive of metabolic syndrome in this population.

Key points: • Confounder-corrected quantitative MRI (ccqMRI) effectively measures hepatic triglyceride content in adolescent girls. • MRS and ccqMRI strongly correlate in liver proton density fat-fraction (PDFF) detection. • A PDFF threshold of 3.5% may be predictive of paediatric metabolic syndrome.

Fig. 1

Representative examples of MRI-PDFF maps and T2-corrected MRS in three subjects, with low, medium, and high concentrations of fat.

Fig. 2

Scatterplots shown of MRI-PDFF plotted against MRS-PDFF in all 132 subjects; (a) MRI-PDFF measured as the average value of ROIs obtained in the nine Couinaud segments of the liver and (b) MRI-PDFF measured from ROIs that were co-localized with the MR spectroscopy voxel. Linear regression analysis with both plots demonstrated excellent correlation and agreement.

Fig. 3

Scatterplots of MRI-PDFF plotted against MRS-PDFF on a logarithmic scale were performed because clustering was observed at lower PDFF values (Fig. 2). (a) MRI-PDFF measured as the average value of ROIs obtained in the nine Couinaud segments of the liver and (b) MRI-PDFF measured from ROIs that were co-localized with the MR spectroscopy voxel. Although excellent logarithmic correlation was observed, a small positive bias appears to be present at low PDFF values.

Fig. 4

Bland-Altman plot between MRI- and MRS-PDFF measurements. The centre dotted line represents the estimated bias of the MRI-PDFF when compared to MRS-PDFF. The upper and lower dotted lines represent the 95 % confidence limits of the mean difference.

Fig. 5

Linear correlation of MRI-PDFF with common metabolic indicators was analyzed for three groups: all subjects (black linear regression line), overweight subjects (BMI >85th percentile) with hepatic steatosis (HS) (light gray linear regression line), and overweight subjects without HS (medium gray linear regression line). MRI-PDFF correlated with both BMI (a) and waist circumference (b) in all subjects, but neither correlated with MRI-PDFF in a sub-analysis of overweight subjects with and without HS. MRI-PDFF correlated strongly with ALT (c) and fasting insulin (d) in all subjects and in overweight subjects with HS, but not in overweight subjects without HS.