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HIV co-infection is associated with increased transmission risk in patients with chronic hepatitis C virus.


ABSTRACT: Molecular epidemiological analysis of viral pathogens can identify factors associated with increased transmission risk. We investigated the frequency of genetic clustering in a large data set of NS34A, NS5A, and NS5B viral sequences from patients with chronic hepatitis C virus (HCV). Within a subset of patients with longitudinal samples, Receiver Operator Characteristic (ROC) analysis was applied which identified a threshold of 0.02 substitutions/site as most appropriate for clustering. From the 7457 patients with chronic HCV infection included in this analysis, we inferred 256 clusters comprising 541 patients (7.3%). We found that HCV/HIV co-infection, young age, and high HCV viral load were all associated with increased clustering frequency, an indicator of increased transmission risk. In light of previous work on HCV/HIV co-infection in acute HCV cohorts, our results suggest that patients with HCV/HIV co-infection may disproportionately be the source of new HCV infections and treatment efforts should be geared towards viral elimination in this vulnerable population.

SUBMITTER: Ragonnet-Cronin M 

PROVIDER: S-EPMC6800583 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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HIV co-infection is associated with increased transmission risk in patients with chronic hepatitis C virus.

Ragonnet-Cronin Manon M   Hostager Reilly R   Hedskog Charlotte C   Osinusi Ana A   Svarovskaia Eugenia E   Wertheim Joel O JO  

Journal of viral hepatitis 20190704 11


Molecular epidemiological analysis of viral pathogens can identify factors associated with increased transmission risk. We investigated the frequency of genetic clustering in a large data set of NS34A, NS5A, and NS5B viral sequences from patients with chronic hepatitis C virus (HCV). Within a subset of patients with longitudinal samples, Receiver Operator Characteristic (ROC) analysis was applied which identified a threshold of 0.02 substitutions/site as most appropriate for clustering. From the  ...[more]

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