Proton nuclear magnetic resonance-based metabonomic models for non-invasive diagnosis of liver fibrosis in chronic hepatitis C: Optimizing the classification of intermediate fibrosis

Abstract

AIMTo develop metabonomic models (MMs), using 1H nuclear magnetic resonance (NMR) spectra of serum, to predict significant liver fibrosis (SF: Metavir ≥ F2), advanced liver fibrosis (AF: METAVIR ≥ F3) and cirrhosis (C: METAVIR = F4 or clinical cirrhosis) in chronic hepatitis C (CHC) patients. Additionally, to compare the accuracy of the MMs with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4).METHODSSixty-nine patients who had undergone biopsy in the previous 12 mo or had clinical cirrhosis were included. The presence of any other liver disease was a criterion for exclusion. The MMs, constructed using partial least squares discriminant analysis and linear discriminant analysis formalisms, were tested by cross-validation, considering SF, AF and C.RESULTSResults showed that forty-two patients (61%) presented SF, 28 (40%) AF and 18 (26%) C. The MMs showed sensitivity and specificity of 97.6% and 92.6% to predict SF; 96.4% and 95.1% to predict AF; and 100% and 98.0% to predict C. Besides that, the MMs correctly classified all 27 (39.7%) and 25 (38.8%) patients with intermediate values of APRI and FIB-4, respectively.CONCLUSIONThe metabonomic strategy performed excellently in predicting significant and advanced liver fibrosis in CHC patients, including those in the gray zone of APRI and FIB-4, which may contribute to reducing the need for these patients to undergo liver biopsy.

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics

“…PCA loading plot shows that the control group samples signals were more intense at δ 3.16–3.24 ppm, indicating that healthy volunteers had choline and taurine serum levels higher than the osteopenia/osteoporosis patients. This was also reported by Long et al[26] and Lv et al[20], who observed a decrease in choline concentration in patients diagnosed with osteoporosis. Taurine is one of the most abundant non-essential amino acids found in bones.…”

“…These findings already have been reported by Chen et al[31], who observed an increase in allantoin serum level after osteoporosis prophylaxis. Maritz et al [25] and Lv et al[20] reported an association between cholesterol serum level and osteoporosis diagnosis. Xue et al[3] and Dixon and Sims[32] associated lactate serum level with osteoblasts inhibition and osteoclasts formation.…”

“…Xue et al[3] and Dixon and Sims[32] associated lactate serum level with osteoblasts inhibition and osteoclasts formation. While Xue et al[3], Lv et al[20] and Parhami et al[33] all reported that the products of lipids oxidation are associated to osteoblast differentiation inhibition. Table 3 summarizes the metabolites that are associated to observed discriminations in this study, as well as indicates data from the literature that show the relation among these metabolites and the diagnosis of osteoporosis.…”

“…Gouveia et al[19] developed metabonomics models to investigate periportal liver fibrosis caused by mansonic schistosomiasis among patients diagnosed with viral hepatitis. Batista et al [20] discriminated intermediate from advanced liver fibrosis in patients using 1 H NMR-based metabonomics. Godoy et al[21] used LDA metabonomics model to made hepatitis C virus diagnosis from urine 1 H NMR spectra.…”

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

“…1 H NMR was used to analyze serum, but not to identify metabolites. The PLS-DA 3-D scores plots showed clustering and separation for F0-F1 vs. F2-F4, F0-F2 vs. F3-F4 with partial separation of F0-F3 vs. F4, leading the authors to hypothesize that their metabolomic strategy could distinguish between significant fibrosis, advanced fibrosis, and cirrhosis [121]. Without knowledge of the altered metabolites central to the metabolomic model used, it is not possible to delineate whether the discriminatory signals arise as biomarkers for the disease process or due to confounding factors such as comorbidities or drug treatment, as commented in another similar case (see below) [122].…”