Testing
ARISE machine-learning tool, validated across four cohorts totalling nearly 480,000 people, cuts the number needed to test for elevated Lp(a) by up to 67.3% (Nat Cardiovasc Res 2024)
Original title: Development and multinational validation of an algorithmic strategy for high Lp(a) screening
With fewer than 0.5% of individuals ever undergoing Lp(a) testing despite its link to premature atherosclerotic cardiovascular disease, the authors developed the Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a machine learning model built using the UK Biobank (n = 456,815), the largest cohort with protocolised Lp(a) testing, to target screening for Lp(a) at or above 150 nmol/L using commonly available clinical features. ARISE was externally validated in three independent cohorts: ARIC (n = 14,484), CARDIA (n = 4,124), and MESA (n = 4,672), showing consistent performance across all three. Depending on the probability threshold used, ARISE reduced the number of people who needed to be tested to find one with elevated Lp(a) by up to 67.3%. This multinational validation supports ARISE as a practical, electronic-health-record-deployable tool to substantially improve the yield of real-world Lp(a) screening as targeted therapies approach approval.
Original abstract
Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.
Summary written by lp-a.org from the published abstract; figures as published. Page updated 18 August 2026. Methods.