Intraindividual lipoprotein(a) variability exceeds 20% in 51% of pediatric patients, with risk reclassification concentrated near decision thresholds (J Clin Lipidol 2026)

Background: Current guidelines recommend 1 measurement of lipoprotein(a) [Lp(a)] in lifetime based on presumed genetically-determined stability. Emerging evidence suggests intraindividual variability during childhood, which can affect risk stratification and clinical decision-making.

Objective: Quantify intraindividual Lp(a) variability in childhood and adolescence and evaluate its impact on risk categorization and different risk stratification approaches.

Methods: We performed a retrospective analysis of serial Lp(a) measurements in 224 children and adolescents from a pediatric outpatient lipid clinic between 2014 and 2024. 736 measurements were analyzed, with 2 to 12 measurements per patient (median 4) over a median follow-up of 1.7 years. Intraindividual variability was assessed using maximum percent change; significant variation was defined as >20%. Four approaches were compared: first measured, peak, mean, and last measured values.

Results: Intraindividual variability exceeding 20% was observed in 51% of patients. Risk category transitions occurred in 20% of patients, with particular instability in borderline (50 to <75 nmol/L) and elevated (75 to <120 nmol/L) categories, where upward reclassification dominated. Patients with low (<50 nmol/L) or highly elevated (≥120 nmol/L) values showed higher stability. Lipid-lowering therapy was associated with higher variability (47.6% vs 20.9% with >40% change). The peak-value strategy demonstrated the highest concordance with alternative approaches (87%-92% agreement).

Conclusion: Clinically relevant Lp(a) variability in children was observed, concentrated near decision thresholds. While a proportion of the observed variability may reflect analytical noise, the persistence of variability among untreated patients and the differential patterns across risk categories support a biological contribution. Risk-stratified monitoring with a peak-value approach may optimize pediatric risk evaluation.