Serum N-glycan fingerprint nomogram predicts liver fibrosis: a multicenter study

Abstract

Abstract Objectives Liver cirrhosis (LC) is the end-stage of fibrosis in chronic liver diseases, non-invasive early detection of liver fibrosis (LF) is particularly essential for therapeutic decision. Aberrant glycosylation of glycoproteins has been demonstrated to be closely related to liver abnormalities. Methods This study was designed to enroll a total of 1,565 participants with LC/LF, chronic hepatitis virus (CHB) and healthy controls. Fibrosis was confirmed by liver biopsy. Using capillary electrophoresis N-glycan fingerprint (NGFP) analysis, we developed a nomogram algorithm (FIB-G) to discriminate LC from non-cirrhotic subjects. Results The FIB-G demonstrated good diagnostic performances in identifying LC with the area under the curve (AUC) 0.895 (95%CI: 0.857–0.915). Furthermore, the diagnostic efficiencies of FIB-G were superior to that of log (P2/P8), procollagen III N-terminal (PIIINP), type IV collage (IV-C), laminin (LN), hyaluronic acid (HA), aspartate transaminase to platelets ratio index (APRI), and FIB-4 when detecting significant fibrosis (S0–1 vs. S2–4, AUC: 0.787, 95%CI: 0.701–0.873), severe fibrosis (S0–2 vs. S3–4, AUC: 0.844, 95%CI: 0.763–0.924), and LC (S0–3 vs. S4, AUC: 0.773, 95%CI: 0.667–0.880). Besides, changes of FIB-G were associated well with the regression of fibrosis and liver function Child–Pugh classification. Conclusions FIB-G is an accurate multivariant N-glycomic algorithm for LC prediction and fibrosis progression/regression monitoring. The high throughput feasible NGFP using only 2 μL of serum could help physicians make the more precise non-invasive staging of LF or cirrhosis and reduce the need for invasive liver biopsy.