title: Hepatitis C virus infection diagnosis using metabonomics.
abstract: Metabonomics based on nuclear magnetic resonance (NMR) can reveal the profile of endogenous metabolites of low molecular weight in biofluids related to disease. The profile is identified a 'metabolic fingerprint' like from the pathological process, why this metabonomics has been used as a diagnostic method. The aim of the present study was to apply metabonomics to identify patients infected with the hepatitis C virus (HCV) through an analysis of ¹H NMR spectra of urine samples associated with multivariate statistical methods. A pilot study was carried out for the diagnostic test evaluation, involving two groups: (i) 34 patients positive for anti-HCV and HCV-RNA and negative for anti-HBc (disease group); and (ii) 32 individuals positive for anti-HBc and negative for HBsAg and anti-HCV. The urine samples were analyzed through ¹H NMR, applying principal component analysis and discriminant analysis for classification. The metabonomics model was capable of identifying 32 of the 34 patients in the disease group as positive and 31 of the 32 individuals in the control group as negative, demonstrating 94% sensitivity and specificity of 97% as well as positive and negative predictive values of 97% and 94%, respectively, and 95% accuracy (P < 0.001). In conclusion, the metabonomics model based on ¹H NMR spectra of urine samples in this preliminary study discriminated patients with HCV infection with high sensitivity and specificity, thereby demonstrating this model to be a potential tool for use in medical practice in the near future.
authors.affiliation: Departamento de Medicina Clínica, Hospital das Clínicas, Centro de Ciências da Saúde, Universidade Federal de Pernambuco, Recife, PE, Brazil. michelegodoy@oi.com.br
authors.forename: M M G
authors.initials: MM
authors.lastname: Godoy
authors.forename: E P A
authors.initials: EP
authors.lastname: Lopes
authors.forename: R O
authors.initials: RO
authors.lastname: Silva
authors.forename: F
authors.initials: F
authors.lastname: Hallwass
authors.forename: L C A
authors.initials: LC
authors.lastname: Koury
authors.forename: I M
authors.initials: IM
authors.lastname: Moura
authors.forename: S M C
authors.initials: SM
authors.lastname: Gonçalves
authors.forename: A M
authors.initials: AM
authors.lastname: Simas
mesh_terms: D000328:Adult; D005260:Female; D016174:Hepacivirus / Q000235:genetics / Q000276:immunology / Q000502:physiology; D006510:Hepatitis B Antibodies / Q000097:blood; D006514:Hepatitis B Surface Antigens / Q000097:blood; D006526:Hepatitis C / Q000175:diagnosis* / Q000276:immunology / Q000821:virology; D018937:Hepatitis C Antibodies / Q000097:blood; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012367:RNA, Viral / Q000097:blood; D012680:Sensitivity and Specificity; D014556:Urine / Q000737:chemistry*
chemical_list: D006510:Hepatitis B Antibodies; D006514:Hepatitis B Surface Antigens; D018937:Hepatitis C Antibodies; D012367:RNA, Viral