Investigating the Relationship Between Lipid Markers and LDL Cholesterol: Insights from Robust and Ordinary Regression Analyses

Abstract

Background and Objectives: Cardiovascular disease is the principal cause of death globally while lipid profiles are important in appraising cardiovascular risk. To manage and prevent CVD effectively, it is important to know how different lipid components interact with each other, notably low-density lipoprotein (LDL), which has been tied to higher chances of developing heart conditions. Material and Methods: The OLS and RLM regression methods were both used to examine the connections between Troponin levels and different metabolic as well as lipid markers based on a sample of 220 patients obtained from an extensive health record database. This was done to validate and compare outcomes by comparing the traditional OLS method with a robust regression approach which is particularly effective when it comes to handling outliers. Fasting Blood Sugar (FB.S), High-Density Lipoprotein (HDL), Cholesterol, and oxidized marker labeling one oxidative stress level were some of the key biochemical markers analyzed. These were chosen because they are relevant to cardiovascular health, and could influence Troponin levels, a crucial biomarker for heart disease. However, given potential data anomalies, this showed that using the conventional OLS and Robust Regression approaches in evaluating how these biochemical markers would affect Troponin made the results more reliable. Results: The dataset has been examined in order to have an overview about the elements influencing troponin portion of it. The OLS regression model showed Fasting Blood Sugar (F.B.S) and High-Density Lipoprotein (HDL) as significant predictors. F.B.S was significantly and positively associated with Troponin levels (β = 35, p = 0.001). Other than, the association of HDL with Troponin levels was far more pronounced (β = −80 p < 0.002). This was further validated by the Robust Linear Model (RLM); In this model, F.B.S remained an independent strong positive predictor of Troponin (β = 40, p <.001). Similarly, there was a significant protective effect of HDL with an inverse association (β = -100; p <0.001). The Triglyceride variable also revealed a positive non-significant association with Troponin levels (β = 10, p value 0.212). Conclusion: This two-pronged analytic strategy highlights the relevance of method selection for lipid profile evaluation in research and clinical settings assessing and managing cardiovascular risk.