Substantial Change Between Repeated Lipoprotein(a) Measurements Is Associated with Residual Cardiovascular Risk in a Real-World Multicenter Cohort - Mi-Na Kim; Dongkuk Kim; Soon Jun Hong; Cheol Woong Yu; Seung Yong Shin; Eung Ju Kim; Hyung Joon Joo (Journal of Clinical Medicine, 2026)
Background/Objectives: Lipoprotein(a) [Lp(a)] is a genetically determined, largely stable risk factor for atherosclerotic cardiovascular disease, and guidelines recommend at least one measurement during adulthood. Whether intra-individual change in Lp(a) between repeated measurements carries prognostic information beyond static risk categories in routine practice is uncertain. Methods: In a multicenter retrospective cohort from three South Korean tertiary hospitals, we studied adults with two Lp(a) measurements at least 90 days apart (2019–2024; n = 13,914). High variability was defined as an absolute change > 10 mg/dL combined with a relative change > 25%. The primary outcome was major adverse cardiovascular events (MACE), analyzed with Cox proportional hazards models adjusted for clinical covariates including baseline and follow-up Lp(a) risk categories. Results: Despite a strong rank correlation between measurements (Spearman’s ρ = 0.92), 2156 patients (15.5%) showed high variability. MACE occurred more frequently in the high-variability group (6.8% vs. 4.2%, p < 0.001), and high variability remained independently associated with MACE (adjusted hazard ratio 1.46, 95% CI 1.18–1.80). The association was consistent across multiple sensitivity analyses, persisted after excluding heart-failure hospitalization from MACE (HR 1.41), and was present for both increases and decreases in Lp(a). Incremental discrimination over a base model was modest, though statistically significant (ΔC-statistic 0.006, 95% CI 0.001–0.012; continuous net reclassification index 0.090, p < 0.001). Conclusions: Intra-individual change in Lp(a) between two measurements is independently associated with cardiovascular outcomes but provides only modest incremental discrimination. These hypothesis-generating findings require prospective validation before they can inform repeat-testing strategies.