Unmasking residual cardiovascular risk: The paradoxical interaction between remnant cholesterol and calculated LDL-C in a high-risk cohort

Background Residual atherosclerotic cardiovascular disease (ASCVD) risk often remains even after low-density lipoprotein cholesterol (LDL-C) levels have been brought down to target levels. Remnant cholesterol (RC) and inflammation have been increasingly linked to the residual risk. We aimed at investigating whether the ability of RC to discriminate and its claimed interactions with LDL-C are due to a real clinical phenotype or are affected by formula-dependent biases between the Friedewald and Sampson-NIH equations. Methods We performed a cross-sectional analysis of consecutively tested adults (n = 3,342) using residual serum samples from routine clinical monitoring. To reduce analytical variability, all lipid profiles were analyzed using a single, dedicated reagent lot. We contrasted risk models with Friedewald-calculated versus Sampson-NIH-calculated LDL-C to uncover equation-dependent biases. Lipid parameters, hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and C-reactive protein (CRP) were measured. ASCVD was defined using ICD-10 codes. Missing biochemical data were handled via multiple imputation by chained equations (m = 50) with age and sex; all model covariates were included in the imputation model. The discriminative performance of nested logistic regression models was assessed through the pooled area under the receiver operating characteristic curve (AUC) and pooled DeLong p-values. Results The primary outcome, prevalent ASCVD, was found in 11.4% of the cohort, while atherogenic dyslipidemia (AD) was diagnosed in 9.4% of the participants. In the primary analysis with Friedewald LDL-C, we detected a statistically significant (p < 0.001) negative interaction between LDL-C and RC. Interestingly, when we verified this using the more accurate Sampson-NIH equation to minimize the possibility that the result would be solely due to calculation bias, the paradoxical interaction was still statistically significant (p = 0.003) along with a strong model performance (AUC: 0.729). The fact that this interaction was still observed indicates that the 'interaction' cannot be entirely explained by the mathematical artifact of the Friedewald formula, but rather, it may be a continuum of a clinical phenomenon in this cohort. Conclusion RC adds statistically significant value to risk discrimination. The continuous inverse relationship of LDL-C with high RC may identify a 'treated high-risk phenotype' that could reflect intensified statin therapy which lowers LDL-C but leaves remnant lipoproteins at an elevated level. Being aware of this potential suppressor effect is of paramount importance for achieving the best risk stratification in tertiary-care settings.