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PLoS One. 2018 Apr 12;13(4):e0195690. doi: 10.1371/journal.pone.0195690. eCollection 2018.

A case-control study of lower urinary-tract infections, associated antibiotics and the risk of developing prostate cancer using PCBaSe 3.0.

Author information

1
King's College London, Division of Cancer Studies, Translational Oncology & Urology Research (TOUR), London, United Kingdom.
2
Regional Cancer Centre, Uppsala, Sweden.
3
University of South Australia, Centre for Population Health Research, Adelaide, Australia.
4
Department Surgical Sciences, Uppsala University, Uppsala, Sweden.
5
CLINTEC department, Karolinska Institutet, Stockholm, Sweden.

Abstract

OBJECTIVES:

To investigate the association between lower urinary-tract infections, their associated antibiotics and the subsequent risk of developing PCa.

SUBJECTS/PATIENTS (OR MATERIALS) AND METHODS:

Using data from the Swedish PCBaSe 3.0, we performed a matched case-control study (8762 cases and 43806 controls). Conditional logistic regression analysis was used to assess the association between lower urinary-tract infections, related antibiotics and PCa, whilst adjusting for civil status, education, Charlson Comorbidity Index and time between lower urinary-tract infection and PCa diagnosis.

RESULTS:

It was found that lower urinary-tract infections did not affect PCa risk, however, having a lower urinary-tract infection or a first antibiotic prescription 6-12 months before PCa were both associated with an increased risk of PCa (OR: 1.50, 95% CI: 1.23-1.82 and 1.96, 1.71-2.25, respectively), as compared to men without lower urinary-tract infections. Compared to men with no prescriptions for antibiotics, men who were prescribed ≥10 antibiotics, were 15% less likely to develop PCa (OR: 0.85, 95% CI: 0.78-0.91).

CONCLUSION:

PCa was not found to be associated with diagnosis of a urinary-tract infection or frequency, but was positively associated with short time since diagnoses of lower urinary-tract infection or receiving prescriptions for antibiotics. These observations can likely be explained by detection bias, which highlights the importance of data on the diagnostic work-up when studying potential risk factors for PCa.

PMID:
29649268
PMCID:
PMC5896993
DOI:
10.1371/journal.pone.0195690
[Indexed for MEDLINE]
Free PMC Article

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