Background: LDLC equations have varying levels of underestimation for the calculated LDLC. Therefore, underestimating LDLC should be avoided as much as possible. We need to establish LDLC equations that underestimate LDLC as little as possible.
Methods: We established the equations with a healthy cohort from Shuyang Hospital and validated the equations with an unselected patient cohort from The Second People’s Hospital of Lianyungang. We established the novel LDLC equations by using the regression equation. The relationship between two markers was analysed using Pearson’s approach. The 95% limits of measuring agreement within ± 2 SD for the LDLC equations was performed using Bland‒Altman analysis. ROC curve analysis was used to predict LDLC levels and the accuracy of the LDLC equation for determining the direct LDLC levels at LDLC cut-offs was assessed.
Results: We obtained two novel LDLC equations (LDL_nonHDLC equation=− 0.899+1.195*nonHDLC-0.00347*nonHDLC² and LDL_TC(total cholesterol) equation=− 2.775+1.29*TC − 0.00990* TC ²). The correlation coefficient between the novel LDLC equation and the direct LDLC measurements is not lower than that between the LDL_NIH equation and the direct LDLC measurements. The AUCs of our novel LDLC equations were greater than those of the LDL_NIH equation and the LDL_F equation at the LDLC cut-offs for clinical decision-making. The measuring agreement in the methods of the LDL_nonHDL equation is superior to that of the LDL_NIH equation.
Conclusion: LDLC calculated by the novel LDL_nonHDL equation exhibited superiority over the LDL_NIH equation. Combining the LDL_NIH equation and our novel LDLC equation may improve accuracy and avoid undertreatment of high LDLC levels.

Keywords: low-density lipoprotein cholesterol, NIH LDL-C equation, friedewald equation, novel LDLC equation