A Model Based on Noninvasive Markers Predicts Very Low Hepatocellular Carcinoma Risk After Viral Response in Hepatitis C Virus-Advanced Fibrosis.

BACKGROUND AND AIMS: Patients with hepatitis C virus (HCV) and advanced fibrosis remain at risk of hepatocellular carcinoma (HCC) after sustained viral response (SVR) and need lifelong surveillance. Because HCC risk is not homogenous and may decrease with fibrosis regression, we aimed to identify patients with low HCC risk based on the prediction of noninvasive markers and its changes after SVR. APPROACH AND RESULTS: This is a multicenter cohort study, including patients with HCV and compensated advanced fibrosis that achieved SVR after direct antivirals. Clinical and transient elastography (TE) data were registered at baseline, 1 year, and 3 years after the end of treatment (EOT). All patients underwent liver ultrasound scan every 6 months. Patients with clinical evaluation 1 year after EOT were eligible. Univariate and multivariate Cox regression analysis were performed, and predictive models were constructed. HCC occurrence rates were evaluated by Kaplan-Meier. Nine hundred and ninety-three patients were eligible (56% male; 44% female; median age 62 years), 35 developed HCC (3.9%), and the median follow-up was 45 months (range 13-53). Baseline liver stiffness measurement (LSM) (HR 1.040; 95% CI 1.017-1.064), serum albumin (HR 0.400; 95% CI 0.174-0.923), 1-year DeltaLSM (HR 0.993; 95% CI 0.987-0.998), and 1-year FIB-4 score (HR 1.095; 95% CI 1.046-1.146) were independent factors associated with HCC. The TE-based HCC risk model predicted 0% of HCC occurrence at 3 years in patients with score 0 (baseline LSM ≤ 17.3 kPa, albumin >4.2 g/dL, and 1-year DeltaLSM > 25.5%) versus 5.2% in patients with score 1-3 (Harrell's C 0.779; log-rank 0.002). An alternative model with FIB-4 similarly predicted HCC risk. CONCLUSIONS: A combination of baseline and dynamic changes in noninvasive markers may help to identify patients with a very low risk of HCC development after SVR.

The overlap with metabolic dysfunction-associated steatotic liver disease negatively affects outcomes of primary biliary cholangitis.

BACKGROUND AND AIMS: The relationship between primary biliary cholangitis (PBC) and metabolic dysfunction-associated steatotic liver disease, and its impact on treatment response and prognosis, remains underexplored. METHODS: Patient cohort from two centres comprising long-term follow-up data. All patients had histologically confirmed PBC. Biopsies were classified according to Non-Alcoholic Steatohepatitis Clinical Research Network. Diagnosis of metabolic dysfunction-associated steatotic liver disease was established when steatosis exceeded 5%, along with at least one metabolic risk factor. Patients with specific aetiologies of steatosis, other liver diseases, incomplete results and inadequate treatment with ursodeoxycholic acid were excluded. Data from patients initiating second-line treatment were censored. Treatment response was assessed using the Toronto, Paris II and AST-to-platelet at 12-month criteria. The UK PBC and Globe scores, and liver events were utilized as outcome measures. RESULTS: The study included 129 patients, 36 showing histologically confirmed overlap between PBC and steatosis. Patients with overlap showed worse prognosis according to Paris II (61.1% vs. 33.3%, p = 0.004), Toronto (52.5% vs. 24.7%, p = 0.002), AST-to-platelet 12-month >0.54 (36.1% vs. 17.2%, p = 0.021), Globe >0.30 (49.2% vs. 29.2%, p = 0.033) and UK PBC at 5, 10 and 15 years (p ≤ 0.001). Liver-related mortality and liver transplant were more prevalent in the overlap group (p = 0.001). In the multivariate analysis, steatosis, dyslipidaemia and advanced fibrosis were independently associated to worse outcomes. CONCLUSIONS: Our findings suggest that metabolic dysfunction-associated steatotic liver disease worsens the prognosis of PBC.

Near infrared spectroscopy (NIRS) and machine learning as a promising tandem for fast viral detection in serum microsamples: A preclinical proof of concept.

Fast detection of viral infections is a key factor in the strategy for the prevention of epidemics expansion and follow-up. Hepatitis C is paradigmatic within viral infectious diseases and major challenges to elimination still remain. Near infrared spectroscopy (NIRS) is an inexpensive, clean, safe method for quickly detecting viral infection in transmission vectors, aiding epidemic prevention. Our objective is to evaluate the combined potential of machine learning and NIRS global molecular fingerprint (GMF) from biobank sera as an efficient method for HCV activity discrimination in serum. GMF of 151 serum biobank microsamples from hepatitis C patients were obtained with a FT-NIR spectrophotometer in reflectance mode. Multiple scatter correction, smoothing and Saviztsky-Golay second derivative were applied. Spectral analysis included Principal Component Analysis (PCA), Bootstrap and L1-penalized classification. Microsamples of 70 µl were sufficient for GMF acquisition. Bootstrap evidenced significant difference between HCV PCR positive and negative sera. PCA renders a neat discrimination between HCV PCR-positive and negative samples. PCA loadings together with L1-penalized classification allow the identification of discriminative bands. Active virus positive sera are associated to free molecular water, whereas water in solvation shells is associated to HCV negative samples. Divergences in the water matrix structure and the lipidome between HCV negative and positive sera, as well as the relevance of prooxidants and glucose metabolism are reported as potential biomarkers of viral activity. Our proof of concept demonstrates that NIRS GMF of hepatitis C patients' sera aided by machine learning allows for efficient discrimination of viral presence and simultaneous potential biomarker identification.