Neurofilament light chain but not glial fibrillary acidic protein is a potential biomarker of overt hepatic encephalopathy in patients with cirrhosis.

INTRODUCTION AND OBJECTIVES: Hepatic encephalopathy (HE) is a frequent complication of cirrhosis and may cause cerebral damage. Neurodegenerative diseases can induce the release of neuroproteins like neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in body fluids, including blood plasma. We investigated whether NfL and GFAP could serve as potential diagnostic plasma biomarkers for overt HE (oHE). MATERIALS AND METHODS: We included 85 patients from three prospective cohorts with different stages of liver disease and HE severity. The following patients were included: 1) 34 patients with primary sclerosing cholangitis (PSC) with compensated disease; 2) 17 patients with advanced liver disease without oHE before elective transjugular intrahepatic portosystemic shunt (TIPS) placement; 3) 17 intensive care unit (ICU) patients with oHE and 17 ICU patients without cirrhosis or oHE. Plasma NfL and GFAP were measured using single molecule assays. RESULTS: ICU oHE patients had higher NfL concentrations compared to pre-TIPS patients or ICU controls (p < 0.05, each). Median GFAP concentrations were equal in the ICU oHE and pre-TIPS patients or ICU controls. Plasma NfL and GFAP concentrations correlated with Model for End-Stage Liver Disease (MELD) scores (R = 0.58 and R = 0.40, p < 0.001, each). CONCLUSIONS: Plasma NfL deserves further evaluation as potential diagnostic biomarker for oHE and correlates with the MELD score.

Sepsis Subclasses: A Framework for Development and Interpretation.

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.