All analysis was done in R, version 3.2.1 (The R Foundation for Statistical Computing, Auckland, New Zealand). The weighted least square regression was fitted in R using the “lm” function as follows: lm(y ∼ b0 + b1 ∙ x1 + b2 ∙ x2 + b3 ∙ x1 ∙ x2 + b4 ∙ x12 + b5 ∙ x12, weight = N), where b0 is the intercept and b1 to b5 are the coefficients for each term. The P value and standard errors of parameters for each group (gender and biomarker) were used to evaluate the significance of each coefficient. R squared, Cross validation, AIC and BIC were used to determine the best model for each biomarker.