Upcoming webinars
Biomarker science and clinical translation in atherosclerotic cardiovascular disease with Nightingale Health metabolomics
June 1, 2026
3 p.m. CEST
In this webinar, Dr. Mattia Cordioli and Dr. Luke Jostins-Dean will show how new developments in metabolomic profiling can be used to advance research into biomarkers for cardiovascular disease. They will discuss breakthroughs in NMR metabolomics that allow researchers to measure new fatty acid subtypes, to detect additional polar metabolites, and to predict high levels of Lp(a).
They will demonstrate the value of these new methods by discovering new genetic associations with newly added metabolites in UK Biobank (n=500k), and providing answers to the question “Are all omega‑3s equal for CVD prevention?"
Dr. Mattia Cordioli
Dr. Mattia Cordioli is a Senior Data Scientist at Nightingale Health. He completed his PhD in Population Health in 2024 at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland. In his doctoral work, which was selected as one of the four best thesis of the year in Finland, he worked with large-scale biobank data, contributing to the largest global study on the genetics of COVID-19, exploring participation bias in biobank-based studies and how genetic and socio-demographic factors jointly influence medication adherence.
Dr. Luke Jostins-Dean
Dr. Luke Jostins-Dean is the Principal Scientist at Nightingale Health, and an Associate Professor and group leader at the Kennedy Institute of Rheumatology at the University of Oxford. His research focuses on developing statistical approaches to leverage genetic, metagenomic and metabolomic profiling to understand and predict the onset and progression of common diseases. His research into the genetics inflammatory bowel disease and other inflammatory conditions has been highly influential and widely cited, with recent work focusing on predicting response to treatment in inflammatory disease from host and microbiome genetics, understanding the osteoarthritis pathogenesis using proteomics, and predicting the onset of common diseases in healthy individuals from metabolomics.