Published online Apr 27, 2022. doi: 10.4254/wjh.v14.i4.719
Peer-review started: May 18, 2021
First decision: July 8, 2021
Revised: July 20, 2021
Accepted: March 25, 2022
Article in press: March 25, 2022
Published online: April 27, 2022
The evaluation of periportal fibrosis (PPF) is essential for a prognostic assessment of patients with Schistosomiasis mansoni. The WHO Niamey Protocol defines patterns of fibrosis from abdominal ultrasonography, 1H-nuclear magnetic resonance (NMR)-based metabonomics has been employed to assess liver fibrosis in some diseases.
To build 1H-NMR-based metabonomics models (MM) to discriminate mild from significant periportal PPF and identify differences in the metabolite profiles.
A prospective cross-sectional study was performed on schistosomiasis patients at a University Hospital in Northeastern Brazil. We evaluated 41 serum samples from 10 patients with mild PPF (C Niamey pattern) and 31 patients with significant PPF (D/E/F Niamey patterns). MM were built using partial least squares-discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) formalisms.
PLS-DA and OPLS-DA resulted in discrimination between mild and significant PPF groups with R2 and Q2 values of 0.80 and 0.38 and 0.72 and 0.42 for each model, respectively. The OPLS-DA model presented accuracy, sensitivity, and specificity values of 92.7%, 90.3%, and 100% to discriminate significant PPF. The metabolites identified as responsible by discrimination were: N-acetylglucosamines, alanine, glycolaldehyde, carbohydrates, and valine.
MMs discriminated mild from significant PPF patterns in patients with Schistosomiasis mansoni through identification of differences in serum metabolites profiles.
Core Tip: In this study, we demonstrated a metabolic signatures and metabolic pathway disturbances that allowed to discriminate mild from significant periportal fibrosis in 41 patients with Schistosomiasis mansoni. Partial least squares-discriminant analysis (PLS-DA) and OPLS metabonomics models provided a clear separation between the groups. PLS-DA model presented accuracy, R2 and Q2 values equal to 0.85, 0.80 and 0.38, respectively, while OPLS model had R2 and Q2 values equal to 0.717 and 0.417, respectively. We also identified some metabolites responsible by discrimination which are associated with changes related to liver function and amino acids metabolism.