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ARTICLE INFO ______________________________________________________________ ______________________Purpose: The effects of serum testosterone in the lower urinary tract symptoms (LUTS) in patients with benign prostatic hyperplasia (BPH) are not well established. The objective of the study is to evaluate the association of sex hormones with LUTS and control the results by patient weight. Materials and Methods:The study comprised a cross-sectional analysis of 725 men included in a prostate cancer screening program at University of Sao Paulo Medical School. The serum concentrations of total testosterone (TT), free testosterone (FT) and sex hormone binding globulin (SHBG) were measured. Variables analyzed were age, American Urological Association (AUA) symptom score, storage symptoms, voiding symptoms, quality of life score, prostate specific antigen levels and prostate volume. Obesity was measured through the calculation of body mass index (BMI). A regression analysis model was performed.Results: Median patient age was 65 years (48 to 94). A higher TT level was significantly associated with a severe AUA symptom score only among patients with a BMI ≥ 25. Median TT was 371, 370 and 427ng/dL (p = 0.017) in patients with mild, moderate and severe LUTS respectively. The multivariate regression analysis in patients with BMI ≥ 25 showed that only age, TT and sex score were related to LUTS. Conclusions: A higher TT is associated with a severe AUA score symptom index only in obese patients. Further analysis are necessary to evaluate the mechanisms through which testosterone may influence LUTS in these patients.

Risk factor interactions determined different severities of lower urinary tract symptoms and erectile dysfunction. The effect of lower urinary tract symptoms and cardiovascular risk outweighed that of age. While in the youngest patients lower urinary tract symptoms acted as a single risk factor for erectile dysfunction, the contribution of vascular disease resulted in significantly more severe dysfunction. Applying a risk factor interaction model to prospective trials could reveal distinct classes of drug responses and help define optimal treatment strategies for specific groups.

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