title: IP-10 predicts viral response and therapeutic outcome in difficult-to-treat patients with HCV genotype 1 infection.
abstract: Plasma from 173 patients with HCV genotype 1 infection was analyzed for IP-10 levels prior to treatment with pegylated interferon-alpha-2a and ribavirin. Significantly lower IP-10 levels were observed in patients achieving a rapid viral response (RVR) (P < .0001), even in those with body mass index (BMI) > or = 25 kg/m2 (P = .004) and with baseline viral load > or = 2 million IU/mL (P = .001). Similarly, significantly lower IP-10 levels were observed in patients obtaining a sustained viral response (SVR) (P = .0002), including those having higher BMI (P < .05), higher viral load (P = .0005), and both higher BMI and viral load (P < .03). In multivariate logistic regression analyses, a low IP-10 value was independently predictive of both RVR and SVR. A baseline cutoff IP-10 value of 600 pg/mL yielded a negative predictive value (NPV) of 79% (19/24) for all genotype 1-infected patients, which was comparable with that observed using a reduction in HCV-RNA by at least 2 logs after 12 weeks of therapy (NPV 86%; 19/22); by combining the two, 30 of 38 patients (NPV 79%) potentially could have been spared unnecessary therapy. In patients having both higher BMI and viral load, cut-off levels of 150 and 600 pg/mL yielded a positive predictive value (PPV) of 71% and NPV of 100%, respectively. In conclusion, pretreatment IP-10 levels predict RVR and SVR in patients infected with HCV genotype 1, even in those with higher BMI and viral load. A substantial proportion of the latter patients may achieve SVR in spite of unfavorable baseline characteristics if their pretreatment IP-10 level is low. Thus, pretreatment IP-10 analysis may prove helpful in decision-making regarding pharmaceutical intervention.
authors.affiliation: Department of Infectious Diseases, University of Göteborg, Sweden. martin.lagging@medfak.gu.se
authors.forename: Martin
authors.initials: M
authors.lastname: Lagging
authors.forename: Ana I
authors.initials: AI
authors.lastname: Romero
authors.forename: Johan
authors.initials: J
authors.lastname: Westin
authors.forename: Gunnar
authors.initials: G
authors.lastname: Norkrans
authors.forename: Amar P
authors.initials: AP
authors.lastname: Dhillon
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Pawlotsky
authors.forename: Stefan
authors.initials: S
authors.lastname: Zeuzem
authors.forename: Michael
authors.initials: M
authors.lastname: von Wagner
authors.forename: Francesco
authors.initials: F
authors.lastname: Negro
authors.forename: Solko W
authors.initials: SW
authors.lastname: Schalm
authors.forename: Bart L
authors.initials: BL
authors.lastname: Haagmans
authors.forename: Carlo
authors.initials: C
authors.lastname: Ferrari
authors.forename: Gabriele
authors.initials: G
authors.lastname: Missale
authors.forename: Avidan U
authors.initials: AU
authors.lastname: Neumann
authors.forename: Elke
authors.initials: E
authors.lastname: Verheij-Hart
authors.forename: Kristoffer
authors.initials: K
authors.lastname: Hellstrand
mesh_terms: D000328:Adult; D015992:Body Mass Index; D054357:Chemokine CXCL10; D019743:Chemokines, CXC / Q000097:blood*; D005260:Female; D016174:Hepacivirus / Q000235:genetics*; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000821:virology; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000627:therapeutic use; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011092:Polyethylene Glycols / Q000627:therapeutic use; D011237:Predictive Value of Tests; D012367:RNA, Viral / Q000032:analysis; D011994:Recombinant Proteins; D012254:Ribavirin / Q000627:therapeutic use; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: C114753:CXCL10 protein, human; D054357:Chemokine CXCL10; D019743:Chemokines, CXC; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D012367:RNA, Viral; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D012254:Ribavirin; C100416:peginterferon alfa-2a