Table 3. Results of multiple linear regression analysis of biochemical and hormonal markers on P53 gene mutation.
Predictor Variable Coefficient Std. Error t-value p-value 95% Confidence Interval
Superoxide Dismutase (U/mL) -0.0233 0.0226 -1.03 0.305 -0.0680 to 0.0215
Testosterone (ng/dL) -0.0008 0.0005 -1.62 0.108 -0.00169 to 0.00017
Follicle Stim. Hormone (FSH, mIU/mL) 0.0087 0.0064 1.35 0.180 -0.0040 to 0.0214
Estradiol (pg/mL) -0.0044 0.0046 -0.95 0.342 -0.0135 to 0.0047
Prostate Specific Antigen (ng/mL) 0.1172 0.0573 2.04 0.043 0.0038 to 0.2306
Interleukin-6 (pg/mL) 0.0346 0.0126 2.75 0.007 0.0097 to 0.0594
Constant 2.3510 0.6927 3.39 0.001 0.9806 to 3.7215

Multivariate linear regression analysis was performed to assess the relationship between biochemical and hormonal predictors and the dependent variable. Coefficients represent the change in the outcome variable for each unit increase in the predictor. Statistically significant associations were observed for prostate-specific antigen (PSA; p=0.043) and interleukin-6 (IL-6; p=0.007), with 95% confidence intervals not crossing zero. Other variables, including superoxide dismutase (SOD), testosterone, follicle-stimulating hormone (FSH), and estradiol, did not show significant predictive value (p>0.05), as shown in Table 3.