Not All LDLs Are Created Equal: Discordance Between Calculated LDL-C Estimates and Incident ASCVD in the Multi-Ethnic Study of Atherosclerosis

Elevated levels of low-density lipoprotein cholesterol (LDL-C) can lead to atherosclerosis and increased risk of cardiovascular disease. Lowering cholesterol is a cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention.

The Friedewald equation is a common formula used to estimate LDL-C from a standard lipid panel, but it’s not perfect. In certain patients, this formula can underestimate true LDL-C levels. Newer equations, such as the Martin-Hopkins and Sampson formulas, offer more accurate estimates, but it’s unclear if the differences in LDL-C calculations translate into meaningful differences in ASCVD outcomes for patients.

In our analysis presented at #AHA25, we studied 6,636 participants from the Multi-Ethnic Study of Atherosclerosis. We compared LDL-C values estimated from the Friedewald, Martin-Hopkins, and Sampson equations and tracked ASCVD outcomes, calculating the difference in LDL-C estimations between the three equations and dividing the cohorts into five categories based on the degree of discordance between LDL-C estimations, from least to most discordant values.

Over a median follow-up of nearly two decades, there were 1,275 MIs, strokes, cardiovascular deaths, or revascularizations. We found that greater LDL-C discordance, particularly when the Martin-Hopkins estimate exceeded the Friedewald estimate, was independently associated with higher ASCVD risk, which suggests that underestimation of LDL-C levels by the traditional Friedewald equation may lead to undertreatment in high-risk patients. Incorporating more accurate LDL-C equations, such as the Martin-Hopkins, may help clinicians better identify and treat patients at elevated cardiovascular risk. Ongoing work will explore how discordant LDL-C estimates affect treatment decisions and outcomes at guideline-based LDL-C thresholds.