Predictors of lipoprotein(a) variability in clinical practice and their impact on cardiovascular risk.

backgroundLipoprotein(a) (Lp[a]) is an established cardiovascular risk marker; however, its intraindividual variability and implications for risk stratification remain poorly understood. This study investigated the clinical and biochemical predictors of high Lp(a) levels and evaluated their potential roles in cardiovascular risk assessment to inform evidence-based public health strategies for cardiovascular disease prevention.

methodsThis retrospective multicenter observational study was conducted using data from three tertiary university hospitals in Korea. Patients with at least two Lp(a) measurements taken ≥ 90 days apart were included (n = 5,305). High Lp(a)-level variability was defined as an absolute change of > 10 mg/dL and a relative change of > 25%. Predictors of high-variability were identified through regression analyses, and risk reclassification across Lp(a) risk categories was performed.

resultsBaseline and follow-up Lp(a) levels were strongly correlated (r = 0.89, P < 0.01); however, substantial individual variability was observed, with a median absolute change of 3.9 mg/dL and a median percentage change of 26.3%. Approximately 19.9% of the patients exhibited high Lp(a) level variability, which was associated with lower baseline Lp(a) levels and higher follow-up Lp(a) levels, lower body mass indices, higher hemoglobin levels, elevated white blood cell and platelet counts, increased serum glucose levels, lower high-density lipoprotein cholesterol levels, and use of antihypertensive medications. Notably, risk reclassification analysis revealed marked variability among patients in the intermediate "gray-zone."

conclusionsThe findings of this study indicate that Lp(a) level variability is associated with adverse cardiovascular risk profiles and dynamic risk reclassification. These results highlight the potential of serial Lp(a) measurements to refine cardiovascular risk stratification, particularly in intermediate-risk patients. Integrating these findings into clinical practice guidelines has the potential to improve cardiovascular risk management at the population level, reduce healthcare disparities, and inform targeted public health interventions aimed at cardiovascular prevention.