Jan Walter Benjamins

Abstract Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes.

Highlights • Machine learning integration of CTA and clinical data may improve the selection of patients that benefit from advanced perfusion imaging with PET at the individual level. • Although it performs better than calcium score, machine learning analysis of extensive CTA imaging variables may not always outperform expert clinical interpretation. • Machine learning consistently improves performance when integrating data from different sources (clinical and imaging).

Highlights Leveraging data from over 370 000 UK Biobank participants, we found 37 novel genetic variants associated with lipoprotein(a) values. We identified a missense variant that was associated with strong lipoprotein(a)-lowering effects in carriers of the lipoprotein(a) increasing rs10455872 variant.