Prediction of HCV load using genotype, liver biomarkers, and clinical symptoms by a mathematical model in patients with HCV infection.

Hepatitis C virus (HCV) infection is a major public health problem with about 1.75 million new HCV cases and 71 million chronic HCV infections worldwide. The study aimed to evaluate clinical, serological, molecular, and liver markers to develop a mathematical predictive model for the quantification of the HCV viral load in chronic HCV infected patients. In this cross-sectional study, blood samples were taken from 249 recently diagnosed HCV-infected subjects and were tested for liver condition, viral genotype, and HCV RNA load. Receiver operating characteristics (ROC) curves and multiple linear regression analysis were used to predict the HCV-RNA load. Genotype 3 followed by genotype 1 were the most prevalent genotypes in Mashhad, Northeastern Iran. The maximum levels of viral load were detected in the mixed genotype group, and the lowest levels in the undetectable genotype group. The log of the HCV viral load was significantly associated with thrombocytopenia and higher serum levels of alanine transaminase (ALT). In addition, the log HCV RNA was significantly higher in patients with arthralgia, fatigue, fever, vomiting, or dizziness. Moreover, genotype 3 was significantly associated with icterus. A ROC curve analysis revealed that the best cut-off points for serum levels of aspartate aminotransferase (AST), ALT, and alkaline phosphatase (ALP) were >31, >34, and ≤246 IU/L, respectively. Sensitivity, specificity, and positive predictive values for AST were 87.7%, 84.36%, and 44.6%, for ALT they were 83.51%, 81.11%, and 36%, and for ALP were 72.06%, 42.81%, and 8.3%, respectively. A mathematical regression model was developed that could estimate the HCV-RNA load. Regression model: log viral load = 7.69 - 1.01 × G3 - 0.7 × G1 + 0.002 × ALT - 0.86 × fatigue.

[To consider negative viral loads below the limit of quantification can lead to errors in the diagnosis and treatment of hepatitis C virus infection]. / Considerar negativa una carga viral expresada como menor del límite inferior de cuantificación puede inducir a error en el diagnóstico y manejo terapéutico de la hepatitis C.

INTRODUCTION: For years many clinical laboratories have routinely classified undetectable and unquantifiable levels of hepatitis C virus RNA (HCV-RNA) determined by RT-PCR as below limit of quantification (BLOQ). This practice might result in erroneous clinical decisions. AIM: To assess the frequency and clinical relevance of assuming that samples that are BLOQ are negative. MATERIAL AND METHOD: We performed a retrospective analysis of RNA determinations performed between 2009 and 2011 (Cobas/Taqman, lower LOQ: 15 IU/ml). We distinguished between samples classified as «undetectable¼ and those classified as «<1.50E+01IU/mL¼ (BLOQ). RESULTS: We analyzed 2.432 HCV-RNA measurements in 1.371 patients. RNA was BLOQ in 26 samples (1.07%) from 23 patients (1.68%). BLOQ results were highly prevalent among patients receiving Peg-Riba: 23 of 216 samples (10.6%) from 20 of 88 patients receiving treatment (22.7%). The clinical impact of BLOQ RNA samples was as follows: a) 2 patients initially considered to have negative results subsequently showed quantifiable RNA; b) 8 of 9 patients (88.9%) with BLOQ RNA at week 4 of treatment later showed sustained viral response; c) 3 patients with BLOQ RNA at weeks 12 and 48 of treatment relapsed; d) 4 patients with BLOQ RNA at week 24 and/or later had partial or breakthrough treatment responses, and e) in 5 patients the impact were null or could not be ascertained. CONCLUSIONS: This study suggests that BLOQ HCV-RNA indicates viremia and that equating a BLOQ result with a negative result can lead to treatment errors. BLOQ results are highly prevalent in on-treatment patients. The results of HCV-RNA quantification should be classified clearly, distinguishing between undetectable levels and levels that are BLOQ.

Viral load is associated with abnormal serum levels of micronutrients and glutathione and glutathione-dependent enzymes in genotype 3 HCV patients.

BACKGROUND: Oxidative stress in hepatitis C patients has been linked to hepatitis C virus. We verified this assumption in HCV genotype 3 patients by detecting the relationship between viral load and certain specific oxidative stress markers like Cu, Mn, Fe, Se, Zn and glutathione and glutathione-dependent enzymes. METHOD: Subjects (n = 200, average age 24 years) with quantitative HCV RNA polymerase chain reaction-proven genotype 3 hepatitis C were simultaneously evaluated. Cu, Mn, Fe, Se and Zn serum levels were by using atomic absorption spectrophotometer. Internationally accepted methods were used for viral load quantification of glutathione, GR and Gpx serum levels. RESULT: There was a significant correlation between HCV viral load and studied parameters. With the increase of viral load from mild group (200,000-1,000,000 copies/ml) to severe group (5,000,000-25,000,000 copies/ml) the serum levels of Cu, Mn, Zn, and Fe and glutathione reductase were found to be abnormally high. However, in severe viral load group serum concentration of Se and glutathione was less than the healthy controls. CONCLUSION: As a significant correlation was detected between the study parameters in genotype 3 HCV patients, it is concluded that the studied micronutrients and glutathione and glutathione-dependent enzymes are the biomolecular targets of HCV to induce oxidative stress. GENERAL SIGNIFICANCE: Constant monitoring and regulation of the recommended biomolecular targets of HCV can improve the plight of more than 170 million patients suffering from hepatitis C virus around the globe.