News

Self-reported data are no guide to elevated Lp(a)

July 31, 2026

Self-reported data, such as age, sex, family history of MI or stroke, and cardiovascular symptoms, do not accurately predict elevated Lp(a) (≥50 mg/dL) as part of a screening process to prioritise people for Lp(a) testing and lipid lowering treatment (LLT). This is the finding of an analysis of data from 24,994 individuals aged 50-65 years […]

Possible hormonal context for link between raised Lp(a) and VTE

July 20, 2026

Previous research into elevated Lp(a) and venous thromboembolism (VTE) has been inconclusive, but a new study of pre- and post-menopausal women, and men, has suggested a hormonal context for Lp(a)-associated thrombotic risk. In the study of over 240,000 people from UK Biobank, 14% of premenopausal women, 19% of postmenopausal women and 15% of men had […]

Conversion methods for Lp(a) test units compared

July 10, 2026

Expert guidance recommends that Lp(a) test results are reported in the units provided by the laboratory and conversion between units (mg/dL and nmol/L) is avoided, though a conversion factor of 2.5 is commonly used to convert from mg/dL to nmol/L. Now, an analysis of test results in the Lp(a)HORIZON cardiovascular outcomes study has compared the […]

Lp(a) risk variants linked to plaque rupture

May 14, 2026

Two major Lp(a) risk variants (rs10455872 and rs3798220), associated with elevated Lp(a) and increased risk of coronary artery disease, have been linked with plaque rupture, though not with plaque erosion.

Studies support Lp(a) link to high risk plaque

May 8, 2026

Further support for the link between elevated Lp(a) and high risk plaque (HRP) features comes from two recently published studies using coronary computed tomography angiography (CCTA) and validated AI-based algorithms.

Machine learning model could mean fewer Lp(a) tests

April 17, 2026

A machine learning (ML) guided approach to prioritise Lp(a) testing could reduce the number of tests needed to identify individuals with elevated levels. This is the conclusion of researchers who tested an ethnicity-calibrated ML model developed to identify individuals with elevated Lp(a) in UK Biobank.