Menstrual cycle and detectable human papillomavirus in reproductive-age women: A time series study

Su Hsun Liu*, Rebecca M. Brotman, Jonathan M. Zenilman, Patti E. Gravitt, Derek A.T. Cummings

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

13 Scopus citations

Abstract

Background. Current evidence on the relationship between human papillomavirus (HPV) DNA detection and menstrual cycle has been inconsistent.Methods. We included 21 nonoral contraceptive pill (non-OCP) users who self-collected vaginal samples twice per week for 16 weeks. We explored whether variable detection of HPV DNA exhibited cyclic or other structured temporal patterns. We also evaluated relationships between serial HPV prevalence, sexual behavior, and suspected bacterial vaginosis (BV) as defined by Nugent Gram stain score ≥7.Results. During follow-up, any-type HPV prevalence varied between 61.1% and 85.0%. Although not statistically significant, we observed a maximum autocorrelation in serial HPV prevalence lagging 14 days (correlation coefficient [ρ], -0.24). Any-type HPV detection had a periodic behavior, generally repeating every 28.0 days (bootstrapped interquartile range, 22.4-28.0) and peaking around the ovulation time (adjusted odds ratio, 1.96; 95% confidence interval [CI], 1.06-3.62) as compared to menstruation. We also showed that an increase in any-type HPV prevalence preceded the beginning of a menstrual cycle by 9-12 days. There was no evidence of relationships between HPV prevalence and sexual activity or Nugent score.Conclusions. Serially detected any-type HPV DNA showed a periodic behavior and was likely to peak in the periovulatory phase among non-OCP users.

Original languageEnglish
Pages (from-to)1404-1415
Number of pages12
JournalJournal of Infectious Diseases
Volume208
Issue number9
DOIs
StatePublished - 01 11 2013
Externally publishedYes

Keywords

  • Auto-correlation
  • Bacterial Vaginosis
  • Human Papillomavirus
  • Menstrual Cycle
  • Nugent Score
  • Periodicity
  • Spectral Analysis
  • Time Series Analysis

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