Integrated analysis of the transcriptome and its interaction with the metabolome in metabolic associated fatty liver disease: Gut microbiome signatures, correlation networks, and effect of PNPLA3 genotype.

Interactions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH.

Chronic kidney disease (CKD) and chronic kidney insufficiency (CKI) diagnosing equation in cirrhotic patients.

Cirrhotic patients can develop acute kidney injury (AKI), and chronic kidney disease (CKD). Therefore, renal functional evaluation is crucial in cirrhotic patients. However, serum creatinine and urea levels, as well as measured or estimated glomerular filtration rate is not reliable renal functional markers in these patients compared to other patient groups. In the present study, four original equations are designed and tested for screening chronic kidney disease (CKD) and chronic kidney insufficiency (CKI) in stable cirrhotic patients. MATERIAL & METHOD: estimated GFR (CKD-EPI creatinine and cystatin equations) were recorded in 175 adult stable patients suffering from cirrhosis, and these patients were classified as presenting or not CKD and CKI after evaluation by two independent nephrologists. Based on these data, the variables with the significant discriminating capability to identify CKD and CKI (based on creatinine and cystatin) were detected by applying the Student's t-test for two independent groups, later confirmed by the lambda test of Wilks, in order to obtain the renal function equations. RESULTS: CKD equation (creatinine) = 7.094238-0.043104 × CKD-EPI creatinine - 0.057537 × haematocrit. CKD equation (cystatin) = 8.375074-0.117218 × CKD-EPI cystatin. CKI equation (creatinine) = 0.428389-0.043214 × CKD-EPI creatinine +0.183051 × Child-Pugh score + 0.050162 × age (in years). CKI equation (cystatin) = 9.169579-0.139319 × CKD-EPI cystatin. CONCLUSION: Simple and reliable equations have been obtained for screening chronic kidney disease and chronic kidney insufficiency in cirrhotic patients.

Plasma and stool metabolomics to identify microbiota derived-biomarkers of metabolic dysfunction-associated fatty liver disease: effect of PNPLA3 genotype.

INTRODUCTION: Non-invasive biomarkers are needed for metabolic dysfunction-associated fatty liver disease (MAFLD), especially for patients at risk of disease progression in high-prevalence areas. The microbiota and its metabolites represent a niche for MAFLD biomarker discovery. However, studies are not reproducible as the microbiota is variable. OBJECTIVES: We aimed to identify microbiota-derived metabolomic biomarkers that may contribute to the higher MAFLD prevalence and different disease severity in Latin America, where data is scarce. METHODS: We compared the plasma and stool metabolomes, gene patatin-like phospholipase domain-containing 3 (PNPLA3) rs738409 single nucleotide polymorphism (SNP), diet, demographic and clinical data of 33 patients (12 simple steatosis and 21 steatohepatitis) and 19 healthy volunteers (HV). The potential predictive utility of the identified biomarkers for MAFLD diagnosis and progression was evaluated by logistic regression modelling and ROC curves. RESULTS: Twenty-four (22 in plasma and 2 in stool) out of 424 metabolites differed among groups. Plasma triglyceride (TG) levels were higher among MAFLD patients, whereas plasma phosphatidylcholine (PC) and lysoPC levels were lower among HV. The PNPLA3 risk genotype was related to higher plasma levels of eicosenoic acid or fatty acid 20:1 (FA(20:1)). Body mass index and plasma levels of PCaaC24:0, FA(20:1) and TG (16:1_34:1) showed the best AUROC for MAFLD diagnosis, whereas steatosis and steatohepatitis could be discriminated with plasma levels of PCaaC24:0 and PCaeC40:1. CONCLUSION: This study identified for the first time MAFLD potential non-invasive biomarkers in a Latin American population. The association of PNPLA3 genotype with FA(20:1) suggests a novel metabolic pathway influencing MAFLD pathogenesis.

Cystatin C and Sarcopenia Predict Acute on Chronic Liver Failure Development and Mortality in Patients on the Liver Transplant Waiting List.

BACKGROUND: Cystatin C (CysC) is an early biomarker of renal dysfunction scarcely studied in patients awaiting liver transplantation (LT). Sarcopenia is frequent in cirrhosis and impacts prognosis. We aimed to assess the capability of these factors to predict survival and acute-on-chronic liver failure (ACLF) in patients awaiting LT, as well as early post-LT outcomes. METHODS: Single-center study that included all cirrhotic patients listed for LT between 2014 and 2017. Competing risk regression analysis was used to evaluate the capability of liver-, kidney-, and global status-related variables at waitlist (WL) inclusion to predict WL mortality and ACLF. Variables associated with post-LT outcomes were evaluated with logistic regression analysis. RESULTS: One-hundred-and-eighty patients were included. Fifty-six (31%) patients developed ACLF, 54 (30%) underwent LT and 35 (19%) died. In the adjusted competing risk regression analysis, CysC ≥ 1.5 mg/L, sarcopenia and MELD-Na were independent predictors of ACLF in the WL, while CysC ≥ 1.5 mg/L, sarcopenia and albumin were independent predictors of mortality. The cumulative incidence of ACLF and mortality at 12 months were 50% and 34% in patients with sarcopenia and CysC ≥1.5 mg/L. An estimated glomerular filtration rate by chronic kidney disease (CKD)-EPI-CysC-creatinine <60 mL/min/1.73 m at WL inclusion was an independent predictor of the need for renal replacement therapy (RRT) in the first month post-LT. CONCLUSIONS: Higher levels of CysC and sarcopenia are strongly associated with the ACLF and mortality in WL. The assessment of both risk factors may improve the prognostic evaluation and allow identifying a group of patients with a very high risk of poor outcomes while awaiting LT.

Impact of acute-on-chronic liver failure on post-transplant survival and on kidney outcomes.

OBJECTIVES: We aimed to evaluate the effect of acute-on-chronic liver failure (ACLF) on patients' 1-year post-liver transplant (LT) survival. In addition, we evaluated the effect of ACLF on the development of post-LT chronic kidney disease (CKD) and early allograft dysfunction (EAD). PATIENTS AND METHODS: A retrospective cohort of patients who underwent transplantation from 2010 to 2016 was studied. EASL-CLIF's definition of ACLF was used. The risk of post-LT death, CKD, and EAD was estimated with regression models weighted by inverse probability weighting considering the recipients' characteristics. Donor's BMI and donor risk index were included in the models as well. RESULTS: A total of 185 patients were included: 125 (67.6%) without ACLF and 60 (32.4%) with ACLF. The 1-year post-LT survival rate was 91.2% [95% confidence interval (CI): 84.6-95.1%] in patients without ACLF versus 84.9% (95% CI: 73.1-91.9%) in patients with ACLF. Post-LT CKD occurred in 43 (38.7%) patients without ACLF versus 26 (52.0%) patients with ACLF. EAD occurred in 40 (32.3%) patients without ACLF versus 15 (28.8%) patients with ACLF. No effect of ACLF was found on survival (hazard ratio 1.75; 95% CI: 0.64-4.75, P = 0.272), CKD (odds ratio: 1.31; 95% CI: 0.60-2.86; P = 0.491), or EAD (odds ratio: 0.74; 95% CI: 0.38-1.66, P = 0.473). CONCLUSION: In this study, which included mainly patients with grade 1 ACLF at the time of LT, its presence had no impact on post-LT survival or on the occurrence of CKD or EAD.