Weighted multivariable logistic regression.

BackgroundAlthough prior research has indicated that nutritional and inflammatory markers may play a role in prostate cancer development, the exact interplay and underlying mechanisms are not yet fully understood.Materials and MethodsThe study population comprised 16,481 males from the NHANES database, after excluding participants with missing covariates. The Neutrophil Percentage-to-Albumin Ratio (NPAR) was used to assess the inflammation and nutritional status. Statistical methods such as multivariable logistic regression, eXtreme Gradient Boosting model, subgroup analysis, and Generalized Additive Model were used to analyze the relationship between NPAR and prostate cancer prevalence.ResultsThe restricted cubic splines of the independent variable NPAR and the dependent variable (prostate cancer prevalence) were statistically significant based on the logistic regression analysis. The eXtreme Gradient Boosting machine learning model identified the NPAR and age as the most influential variables for prostate cancer. Subgroup analysis revealed significant correlations between the NPAR and age, race, and smoking status. Clinical validation has confirmed the diagnostic significance of NPAR in prostate cancer.ConclusionA positive correlation was observed between NPAR and prostate cancer prevalence, indicating the potential mechanism of developing the disease. However, due to the cross-sectional design and self-reported cancer diagnoses in the NHANES database, causality cannot be established.