posted on 2023-03-31, 13:25authored bySalvatore Vaccarella, Silvia Franceschi, Peter J.F. Snijders, Rolando Herrero, Chris J.L.M. Meijer, Martyn Plummer
Supplementary Table 1 from Concurrent Infection with Multiple Human Papillomavirus Types: Pooled Analysis of the IARC HPV Prevalence Surveys
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ARTICLE ABSTRACT
To understand viral interactions and the cross-reactivity of natural or vaccine-induced responses, we investigated whether multiple human papillomavirus (HPV) infections, particularly certain combinations of types, have the tendency to cluster together. Cervical cell samples were collected from women in the framework of the IARC HPV Prevalence Surveys. Women with a cytology diagnosis of high-grade squamous intraepithelial lesion or worse were excluded, leaving 13,961 women for this analysis. HPV DNA was assessed using a general GP5+/6+ primer–mediated PCR. HPV genotyping was done using enzyme immunoassay or reverse line blot analysis. Logistic regression with type-specific HPV positivity as an outcome was used, adjusted for age, study area, and lifetime number of sexual partners. Woman-level random effects were added to represent unobservable risk factors common to all HPV types. The observed-to-expected ratio was 1.20 (95% credible interval, 1.14-1.26) for infection with two HPV types and 1.02 (95% credible interval, 0.91-1.12) for three for more types, with the best possible adjustment. Among combinations of specific HPV types, the tendency to cluster increased with the genetic similarity of the L1 region. High observed-to-expected ratios were found for closely homologous types, including HPV33/58, 18/45, 33/35, and 31/35. The excess of multiple infections, however, was clearly evident only when enzyme immunoassay, and not reverse line blot, was used as the genotyping method. The different results by genotyping method suggest that the apparent clustering of HPV infections was an artifact of the measurement process. Further investigation is required to evaluate other widely used HPV detection methods. Cancer Epidemiol Biomarkers Prev; 19(2); 503–10