Plasma and stool metabolomic biomarkers of non-alcoholic fatty liver disease in Argentina
Authors: Mazzini Flavia Noelia , Cook Frank , Gounarides John , Marciano Sebastián , Haddad Leila , Tamaroff Ana Jesica , Casciato Paola , Narvaez Adrián , Mascardi María Florencia , Anders Margarita , Orozco Federico , Quiróz Nicolás , Risk Marcelo , Gutt Susana , Gadano Adrián , Méndez García Celia , Marro Martin , Penas-Steinhardt Alberto , Trinks Julieta 
Published: August 2020

ABSTRACTBackground and AimsNon-invasive biomarkers are urgently needed to identify patients with non-alcoholic fatty liver disease (NAFLD) especially those at risk of disease progression. This is particularly true in high prevalence areas such as Latin America. The gut microbiome and intestinal permeability may play a role in the risk of developing NAFLD and NASH, but the mechanism by which microbiota composition disruption (or dysbiosis) may affect NAFLD progression is still unknown. Targeted metabolomics is a powerful technology for discovering new associations between gut microbiome-derived metabolites and disease. Thus, we aimed to identify potential metabolomic biomarkers related to the NAFLD stage in Argentina, and to assess their relationship with clinical and host genetic factors.Materials and methodsAdult healthy volunteers (HV) and biopsy-proven simple steatosis (SS) or non-alcoholic steatohepatitis (NASH) patients were recruited. Demographic, clinical and food frequency consumption data, as well as plasma and stool samples were collected. SNP rs738409 (PNPLA3 gene) was determined in all volunteers. HPLC and flow injection analysis with MS/MS in tandem was applied for metabolomic studies using the MxP Quant 500 Kit (Biocrates Life Sciences AG, Austria). Significantly different metabolites among groups were identified with MetaboAnalyst v4.0. Bivariate and multivariate analyses were used to identify variables that were independently related to NAFLD stage. Forward stepwise logistic regression models were constructed to design the best feature combination that could distinguish between study groups. Receiver Operating Characteristic (ROC) curves were used to evaluate models’ accuracy.ResultsA total of 53 volunteers were recruited: 19 HV, 12 SS and 22 NASH. Diet was similar between groups. The concentration of 33 out of 424 detected metabolites (25 in plasma and 8 in stool) was significantly different among study groups. Levels of triglycerides (TG) were higher among NAFLD patients, whereas levels of phosphatidylcholines (PC) and lysoPC were depleted relative to HV. The PNPLA3 risk genotype for NAFLD and NASH (GG) was related to higher plasma levels of eicosenoic acid FA(20:1) (p<0.001). Plasma metabolites showed a higher accuracy for diagnosis of NAFLD and NASH when compared to stool metabolites. Body mass index (BMI) and plasma levels of PC aa C24:0, FA(20:1) and TG(16:1_34:1) showed high accuracy for diagnosis of NAFLD; whereas the best AUROC for discriminating NASH from SS was that of plasma levels of PC aa C24:0 and PC ae C40:1.ConclusionA panel of plasma and stool biomarkers could distinguish between NAFLD and NASH in a cohort of patients from Argentina. Plasma biomarkers may be diagnostic in these patients and could be used to assess disease progression. Further validation studies including a larger number of patients are needed.