Associations of multiple serum biomarkers and the risk of cardiovascular disease in China

BACKGROUND: Previous studies focus on one or several serum biomarkers and the risk of cardiovascular disease (CVD). This study aims to investigate the association of multiple serum biomarkers and the risk of CVD and evaluate the dose-relationship between a single serum metabolite and CVD. METHODS: Our case-control study included 161 CVD and 160 non-CVD patients who had a physical examination in the same hospital. We used stratified analysis and cubic restricted analysis to investigate the dose-response relationship of individual serum biomarkers and the CVD incident. Moreover, to investigate serum biomarkers and CVD, we used elastic net regression and logistic regression to build a multi-biomarker model. RESULTS: In a single serum biomarker model, we found serum FT4, T4. GLU, CREA, TG and LDL-c were positively associated with CVD. In the male group, serum T4, GLU and LDL-c were positively associated with CVD; and serum TG was positively associated with CVD in the female group. When patients ≤63 years old, serum T4, GLU, CREA and TG were positively associated with CVD, and serum TG and LDL-c were positively associated with CVD when patients > 63 years old. Moreover, serum GLU had nonlinearity relationship with CVD and serum TG and LDL-c had linearity association with CVD. Furthermore, we used elastic regression selecting 5 serum biomarkers (GLU, FT4, TG, HDL-c, LDL-c) which were independently associated with CVD incident and built multi-biomarker model. And the multi-biomarker model had much better sensitivity than single biomarker model. CONCLUSION: The multi-biomarker model had much higher sensitivity than a single biomarker model for the prediction of CVD. Serum FT4, TG and LDL-c were positively associated with the risk of CVD in single and multiple serum biomarkers models, and serum TG and LDL-c had linearity relationship with CVD.