What question did this study set out to answer?

To evaluate how lipoprotein(a) and other lipid markers interact with blood pressure control in predicting major cardiovascular events.

June 3, 2026

Blood Pressure Control Unmasks the Cardiovascular Risk Associated With Lipoprotein(a): Insights From the Gaelp(a) Study

Key Points

To evaluate how lipoprotein(a) and other lipid markers interact with blood pressure control in predicting major cardiovascular events.
Analyzed 2,864 patients from the GAELp(a) cohort with data on lipoprotein(a), LDL-C, and blood pressure.
Conducted logistic regression adjusted for demographics and lipid levels to assess risk.
Used various analytical techniques including AUC, NRI, and decision curve analysis.
Lp(a) improved model discrimination metrics over LDL-C and ApoB.
Stronger association between Lp(a) and major adverse cardiovascular events in patients with controlled blood pressure.
Decision curve analysis indicated greater net benefit for models including Lp(a) across various risk thresholds.

Abstract

Objective: To assess the relationship between Lp (a), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (ApoB), and blood pressure control in predicting major adverse cardiovascular events (MACE) in the Argentine multicenter GAELp (a) cohort. Design and method: We analyzed 2, 864 patients with available data on Lp (a), LDL-C, and blood pressure. ApoB measurements, however, were available in 1, 816 out of 3, 652 participants (49. 7%). All analyses including ApoB were therefore restricted to this subset. Among hypertensive patients, 1, 051/2, 052 (51. 2%) had ApoB values available. Lp (a) values were standardized to nmol/L (conversion factor 2. 5 if mg/dL). Controlled blood pressure (BPcontrol) was defined as systolic <130 mmHg and/or diastolic <80 mmHg among hypertensive patients. MACE was defined as ischemic heart disease, peripheral artery disease, or prior ischemic stroke. Logistic regression models were adjusted for age, sex, LDL-C, ApoB, statin use, and blood pressure control. Incremental value was assessed using AUC, NRI, IDI, Brier score, spline modeling, heatmaps, and decision curve analysis. Results: Lp (a) provided incremental predictive value over LDL-C and ApoB (Lp (a) improved model discrimination metrics (AUC, NRI, IDI) ; Brier < 0). The association between Lp (a) and MACE was stronger in patients with controlled blood pressure, suggesting a residual risk independent of hemodynamic load. In contrast, in uncontrolled hypertension, the Lp (a) -MACE association was attenuated. Decision curve analysis demonstrated greater net benefit for the model including Lp (a) across risk thresholds of 5–20%. Conclusions: Elevated Lp (a) is associated with residual cardiovascular risk beyond LDL-C and ApoB, particularly in patients with well-controlled blood pressure. These findings highlight the relevance of Lp (a) screening to identify high-risk individuals despite optimal hypertension management.

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