Serum metabolic profiling of targeted bile acids reveals potentially novel biomarkers for primary biliary cholangitis and autoimmune hepatitis

Figure 1 Multivariate statistical analysis on serum profiling data in positive ions between primary biliary cholangitis and control. A: Plots of principal component analysis (PCA) in positive ion mode. (1) Primary biliary cholangitis (PBC); (2) Control; and (3) Quality control (QC); B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a positive model of serum from patients with PBC, autoimmune hepatitis and healthy controls. (1) PBC; (2) Control; and (3) QC; C: Validation plot of the original PLS-DA with a positive model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 2 Multivariate statistical analysis on serum profiling data in positive ions between autoimmune hepatitis and control. A: Plots of principal component analysis in positive ion mode. (1) Autoimmune hepatitis (AIH); (2) Control; and (3) Quality control (QC); B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a positive model of serum from patients with primary biliary cholangitis (PBC), AIH and healthy controls. (1) PBC; (2) Control; and (3) QC; C: Validation plot of the original PLS-DA with a positive model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 3 Multivariate statistical analysis on serum profiling data in positive ions between primary biliary cholangitis and autoimmune hepatitis. A: Plots of principal component analysis in positive ion mode. (1) Autoimmune hepatitis (AIH); (2) Primary biliary cholangitis (PBC); and (3) Quality control; B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a positive model of serum from patients with PBC, AIH and healthy controls. (1) PBC; and (2) AIH; C: Validation plot of the original PLS-DA with a positive model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 4 Multivariate statistical analysis on serum profiling data in negative ions between primary biliary cholangitis and control. A: Plots of principal component analysis in negative ion mode. (1) Primary biliary cholangitis (PBC); (2) Control; and (3) Quality control (QC); B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a negative model of serum from patients with PBC, autoimmune hepatitis and healthy controls. (1) PBC; (2) Control; and (3) QC; C: Validation plot of the original PLS-DA with a negative model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 5 Multivariate statistical analysis on serum profiling data in negative ions between autoimmune hepatitis and control. A: Plots of principal component analysis in negative ion mode. (1) Autoimmune hepatitis (AIH); (2) Control; and (3) Quality control (QC); B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a negative model of serum from patients with Primary biliary cholangitis, AIH and healthy controls. (1) Autoimmune hepatitis (AIH); (2) Control; and (3) QC; C: Validation plot of the original PLS-DA with a negative model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 6 Multivariate statistical analysis on serum profiling data in negative ions between primary biliary cholangitis and autoimmune hepatitis. A: Plots of principal component analysis in negative ion mode. (1) Autoimmune hepatitis (AIH); (2) Primary biliary cholangitis (PBC); and (3) Quality control; B: Scatter plots of partial least squares-discriminant analysis (PLS-DA) with a negative model of serum from patients with PBC, AIH and healthy controls. (1) AIH; and (2) PBC; C: Validation plot of the original PLS-DA with a negative model, strongly indicating that the original model is valid and shows signs of overfitting. The permutation test was repeated 200 times in the cross-validation plot.

Figure 7 Comparative analysis of alterations in serum bile acid levels in patients in the mild and severe injury groups, and in healthy controls. A: Chenodeoxycholic acid; B: Lithocholic acid (LCA); C: Taurolithocholic acid (TLCA); D: LCA + TLCA; ^aP < 0.05; ^bP < 0.0001; NS: Not significant.

Figure 8 Receiver operating characteristic curve analysis. A: Lithocholic acid (LCA), sum of LCA and taurolithocholic acid, chenodeoxycholic acid; B: Common clinical biochemical indicators.

Figure 9 Cholesterol host cell metabolism. CA: Cholic acid; CDCA: Chenodeoxycholic acid; DCA: Deoxycholic acid; TCA: Taurocholic acid; GCA: Glycocholic acid; TDCA: Taurodeoxycholic acid; GDCA: Glycodeoxycholic acid; TCDCA: Taurochenodeoxycholic acid; TLCA: Taurolithocholic acid; TUDCA: Tauroursodeoxycholic acid.

Figure 10  Bile acid levels are expressed in log10 concentrations. Statistically significant differences in bile acid concentrations between controls and patients were determined by the rank sums Mann-Whitney test. Primary biliary cholangitis (PBC)-A vs control, ^aP < 0.05; PBC-B vs control, ^bP < 0.05; PBC-C vs control, ^cP < 0.05; autoimmune hepatitis (AIH)-A vs control, ^dP < 0.05; AIH-B vs control, ^eP < 0.05; PBC-A vs PBC-B, ^fP < 0.05; PBC-A vs PBC-C, ^gP < 0.05; AIH-A vs AIH-B, ^hP < 0.05; PBC-A vs AIH-A. A: Glycocholic acid; B: Taurocholic acid; C: Glycochenodeoxycholic acid; D: Glycochenodeoxycholic sulfate; E: Taurodeoxycholic acid; F: Tauroconjugted bile acid. GCDCA: Glycochenodeoxycholic acid; GCDCS: Glycochenodeoxycholic sulfate; TDCA: Taurodeoxycholic acid; GCA: Glycocholic acid; TCA: Taurocholic acid.