Association of High-sensitivity C-reactive Protein and Hematologic-inflammatory Indices with Risk of Cardiovascular Diseases: a Population-based Study with Partial Least Squares Structural Equation Modeling Approach

Partial least squares structural equation modeling is a simple approach that may be used to determine the factors associated with diseases. In the current study, we aimed to explore the most associated high-sensitivity C-reactive protein (hs-CRP) as well as hematologic-inflammatory indices for the risk of cardiovascular disease (CVD). A total of 7362 healthy (non-CVD) participants aged 35-65 years old from baseline investigation were evaluated in the Phase 2 follow-up. Of these, 1022 individuals were found to have CVDs in the second phase (10-year follow-up) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. We used partial least squares structural equation modeling to develop a prediction model for association of CVD risk factors and hs-CRP as well as hematologic-inflammatory indices in the study population. According to the study, age had the most significant impact on the presence of CVD. Increasing in age by one unit raises the risk of CVD by 0.166. Also, serum hs-CRP was found to have the second-highest impact on CVD; increasing in age by one unit raises the risk of CVD by 0.042. The study also discovered a strong and significant correlation between red cell distribution width (RDW) and CVD. Moreover, the study found that several factors such as hemoglobin (HGB), neutrophil (NEUT), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and platelet-to-lymphocyte ratio (PLR) have indirect effects on CVD that are mediated by hs-CRP while controlling for age, sex and social-economic factors. Generally, the results showed that age, hs-CRP, and RDW were the most important risk factors on CVD.