Predictive Factors for Cervical Intraepithelial Neoplasia in Women with Abnormal Cytology According to Human Papillomavirus Genotype: An Observational Study

根据人乳头瘤病毒基因型分析宫颈细胞学异常女性宫颈上皮内瘤变的预测因素:一项观察性研究

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Abstract

Cervical cancer remains a leading cause of mortality among women, particularly in regions with limited resources. Persistent high-risk human papillomavirus (HR-HPV) infection is the main etiological factor for CIN and cervical cancer. This study aimed to evaluate the association between HPV genotypes, age, and cytological findings and the presence of CIN2-3 in women presenting with abnormal cervical cytology. This cross-sectional study included 189 women with abnormal cytology who attended a tertiary center in Peru. All participants underwent partial HPV genotyping using the Cobas 4800 assay, colposcopic evaluation, and colposcopically directed biopsies, which served as the diagnostic reference. Sociodemographic characteristics and reproductive histories were also collected. Multiple logistic regression was performed to assess the associations among specific HPV genotypes, age, cytological results, and CIN2-3 outcomes. Most participants were between 30 and 59 years of age (76.7%), and multiparity was common (77.8%). The most frequent cytological abnormalities were ASC-US (36.0%) and LSIL (28.0%), followed by HSIL (20.1%). HPV16 was detected in 24.3% of cases, HPV18 in 2.1%, and other HR-HPV types in 73.6%. HSIL cytology showed high concordance with histological CIN2-3 (>95%). Logistic regression demonstrated that age ≥ 30 years (aOR 4.50, 95% CI 1.90-10.65) and HPV16 infection (aOR 4.19, 95% CI 1.95-9.00) were the strongest independent predictors of high-grade disease. HPV18 was rare and not significantly associated, whereas other HR-HPV types showed an inverse association with CIN2-3. HPV16 and age ≥ 30 years were the most significant predictors of CIN2-3 in women with abnormal cytology, underscoring the dominant oncogenic role of HPV16. Integrating HPV genotyping, cytological findings, and age into risk-stratified algorithms could optimize cervical cancer prevention, ensuring timely detection of high-grade lesions while minimizing overtreatment in low-risk populations.

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