Objective To establish a new biomarker predictive model for calcific aortic valve disease (CAVD) by analyzing the relationship between new serum markers and CAVD. Methods The peripheral blood and clinical data of 446 in-patients in the department of cardiology were collected. The patients were randomly divided into a screening cohort (n=202) and a verification cohort(n=244) by SPSS20.0. The enzyme-linked immunosorbent assay was used to detect the serum concentrations of glucagon-like peptide 1(GLP-1) and osteopontin (OPN). By analyzing the relationship between multiple factors and clinical variables in the screening cohort and CAVD, and verifying it in the verification cohort, the CAVD predictive model was constructed. Results The binary regression analysis showed that multiple factors in the screening cohort were significantly related to CAVD. Based on it, it was determined that high density lipoprotein cholesterol (HDL-C), GLP-1 and OPN could be included in the predictive model, and a nomogram was drawn. Through Pass C statistics [screening cohort: 0.73(95% CI: 0.66-0.80), verification cohort 0.70 (95% CI: 0.64-0.77)] and Hosmer-Lemeshow test (screening cohort: P=0.14, verification cohort: P=0.23), it was found that the discrimination and consistency of the model in the screening and validation cohorts performed well. Decision curve analysis indicated that the biomarker model had a higher net benefit than that in the clinical factor model. In addition, the model showed nice discriminating ability in different gender and age groups. Conclusions GLP-1, OPN and HDL-C levels are significantly correlated with CAVD. Based on it, a CAVD predictive model which performed well in discriminating ability and accuracy was successfully constructed and verified, and provided the possibility for CAVD prediction.