Lipid and Bile Acids as NAFLD- Related Biomarkers Puneet Puri, MBBS, MD Division of Gastroenterology, Hepatology and Nutrition Virginia Commonwealth University, Richmond, VA 1st International Workshop on NASH Biomarkers Washington DC, USA Friday 29 th April 2016 No Disclosures
Spectrum of Nonalcoholic Fatty Liver Disease (NAFLD) Who is at risk? BIOMARKERS How can we diagnose? Can we differentiate disease phenotypes? Can we assess risk of progression? How does it change with change in disease condition? How can it guide prognosis? How do these relate to therapeutic intervention?
Overview of Lipid Metabolism in NAFLD Cohen JC et al. Science 332, 1519 (2011)
Overview of Lipid Metabolism in NAFLD 2 1 2 1 3 3 Cohen JC et al. Science 332, 1519 (2011)
A Lipidomic Analysis of Nonalcoholic Fatty Liver Disease Hepatic Lipidome in NASH Low PC High LyPC High Free Cholesterol Puri P et al. Hepatology. 2007 Dec;46(4):1081-90.
The Plasma Lipidomic Signature of Nonalcoholic Steatohepatitis Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
NAFLD is associated with increased de novo lipogenesis A composite fatty acid methyl ester data from all lipid classes reflective of monounsaturated fatty acids metabolism is displayed as pathway maps Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
NAFLD is associated with increased de novo lipogenesis A composite fatty acid methyl ester data from all lipid classes reflective of monounsaturated fatty acids metabolism is displayed as pathway maps Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
NAFLD is associated with peroxisomal dysfunction Docosahexaenoic acid (DHA, 22:6n3) to docosapentaenoic acid (DPA, 22:5n3) ratio Plasmalogen levels Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
NAFLD is Associated with Increased Inflammatory and Oxidative Stress Related Eicosanoid Metabolites Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
Circulating Lipidome Model for NAFLD Pathophysiology INFLAMMATION OXIDATIVE STRESS LIPOGENESIS Puri P et al. Hepatology. 2009 Dec;50(6):1827-38.
Circulating Oxidized Fatty Acids (OxFA) Levels Are Markedly Increased and Relate to Disease Severity in NASH Patients J. Lipid Res. 2010. 51: 3046 3054
Circulating oxnash Score Can Predict NASH 0.83 (95% CI: 0.73, 0.93) 0.74 (95% CI: 0.6, 0.88) oxnash: 13-HODE/LA ratio, age, BMI, and AST J. Lipid Res. 2010. 51: 3046 3054
Higher oxnash Levels Increase the Risk of NASH
Biomarkers of NAFLD progression Common analytes between liver and plasma J. Lipid Res. 2015. 56: 722 736.
Plasma Lipidome Can Distinctly Identify NAFL and NASH NAFL/ Steatosis NASH J. Lipid Res. 2015. 56: 722 736.
Plasma and Hepatic Lipidomic Biomarkers of NAFLD Progression
Disease Progression in NAFLD Training Cohort The Lipidomic Signature of Disease Progression in Non-alcoholic Fatty Liver Disease (NAFLD) AASLD 2015
Metabolites & NAFLD Progression 22 plasma metabolites associated with NAFLD progression The Lipidomic Signature of Disease Progression in Non-alcoholic Fatty Liver Disease (NAFLD) AASLD 2015
Lipid Metabolites Associated With NAFLD Progression INCREASE WITH NAFLD PROGRESSION DECREASE WITH NAFLD PROGRESSION AASLD 2015
Lipid Metabolites & NAFLD progression Previous metabolites associated with NAFLD progression were evaluated in the new cohort. The same trend was obtained for all of them. EASL 2016
Linear regression models Three linear regression models have been built comparing: NAFLD (all samples) vs. controls NAFLD with and without fibrosis NAFLD F3-F4 vs. NAFLD F1-F2 CONTROL NAFLD F1-F2 F3-F4 All the models has been weighted due to the difference in sample size of the groups. Leave-one-out cross validation (LOOCV) of the models has been performed. EASL 2016
NAFLD vs. Controls N=208; Control (N=23) & NAFLD (N=185) 7 variables were included: phospholipids, sphingolipids & acyl carnitines AUROC (se) 0.95 (0.03) Accuracy 0.93 Sensitivity: 0.97 Specificity: 0.61 Pos Pred Value: 0.95 Neg Pred Value: 0.74 Leave One Out Cross Validation (LOOCV): AUROC = 0.91, Accuracy = 0.92 EASL 2016
NAFLD with and without Fibrosis N=185; NAFLD without (N=71) & NAFLD with fibrosis (N=114) 16 variables were included: phospholipids, triacylglycerols & non-esterified fatty acids AUROC (se) 0.92 (0.02) Accuracy 0.85 Sensitivity: 0.90 Specificity: 0.77 Pos Pred Value: 0.86 Neg Pred Value: 0.83 Leave One Out Cross Validation (LOOCV): AUROC = 0.85, Accuracy = 0.78
NAFLD F3-F4 vs. NAFLD F1-F2 N=114; NAFLD F1-F2 (N=80) & NAFLD F3-F4 (N=34) 5 variables were included: phospholipids, triacylglycerols, acyl carnitines, sphingolipids & sterols AUROC (se) 0.89 (0.03) Accuracy 0.83 Sensitivity: 0.62 Specificity: 0.93 Pos Pred Value: 0.78 Neg Pred Value: 0.85 Leave One Out Cross Validation (LOOCV): AUROC = 0.86, accuracy = 0.81
Bile Acids in NAFLD Obeticholic acid: Bile acid (BA) derivative of 6-ethylchenodeoxycholic acid Potent activator of the farnesoid X nuclear receptor (FXR) Improved histological features of NASH Its long-term benefits and safety need further assessment Neuschwander-Tetri BA et al, Lancet 2015 Rinella and Sanyal. Nature Reviews Gastroenterology & Hepatology 12, 65 66 (2015)
Plasma Lipid Metabolites NASH vs. NAFL vs. Control NASH NAFL Control Sterol/Steroid Sphingolipid Lysolipid Carnitine metabolism Glycerolipid metabolism Fatty acid metabolism Fatty acid, Dicarboxylate Fatty acid, Monohydroxy Long chain fatty acids Essential fatty acid Bile Acid Unpublished data
Plasma Lipid Metabolites NASH vs No NASH NASH No NASH Sterol/Steroid Sphingolipid Lysolipid Endocannabanoid Carnitine metabolism Glycerolipid metabolism Inositol metabolism Fatty acid metabolism Fatty acid, Dicarboxylate Fatty acid, Monohydroxy Long chain fatty acid Bile Acid Unpublished data
Fecal Bile Acid Metabolome in NAFLD Training Cohort Validation Cohort Random Forest Classification Features ranked by their respective contribution to classification accuracy EASL 2015
Fecal Glycodeoxycholate is Significantly Higher in NASH Training Cohort Features ranked by their contributions to classification accuracy EASL 2015
Fecal Glycodeoxycholate in Validation Cohort Significantly Higher in NASH Validation Cohort EASL 2015
Conclusion Circulating lipidome and bile acids can differentiate controls from NAFLD, and can separate NAFL from NASH Circulating lipidome is associated with disease progression in NAFLD Three different lipidomic signatures can discriminate between: NAFLD vs. controls NAFLD with and without fibrosis NAFLD F3-F4 vs. NAFLD F1-F2 Fecal glycodeoxycholate is significantly higher in both training and validation cohorts in NASH
Future Directions Diagnosis: Pre-disease to disease Markers for fibrosis and disease progression Prognosis Predictors of response and failure to therapy