Publications
Nightingale Health’s technology is routinely used in world-class epidemiological and genetic studies. There are over 600 publications that have utilized our technology.
Highlighted publications
Metabolomic profiles predict individual multidisease outcomes
Buergel et al., Nat Med., 2022
The ground-breaking study conducted by researchers from universities in Germany, the Netherlands, and the UK on over 100,000 people demonstrates how one blood test can identify early risk signs for many common diseases. The unique snapshot of a person’s health state captured by Nightingale Health’s proprietary metabolomic blood test was able to improve current health screening tools for several costly diseases, such as heart failure, dementia, and diabetes.
Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank
Julkunen et al., Nat Com., 2023
The associations between Nightingale Biomarkers and common chronic diseases like cardiovascular disease or type 2 diabetes have been studied for years. However, in recent studies with data from the UK Biobank, like this oneboth by our internal science team as well as independent study groups, the key findings have been that our biomarkers can predict hundreds of disease endpoints. These findings open countless opportunities to scientists, as they receive not only the wide biomarker panel analyzed from a single blood sample, but also open access to the UK Biobank data which enables them to replicate the study findings in the selection of hundreds of thousands of individuals.
Genome-wide characterization of circulating metabolic biomarkers
Karjalainen et al., Nature, 2024
A genome-wide association study involving 136,000 participants from 33 cohorts (including cohorts like INTERVAL, PROSPER and TUK), identified genetic determinants of 233 circulating metabolic biomarkers analyzed by Nightingale Health. The study found over 8,000 genetic associations across 400 independent loci and identified probable causal genes for two-thirds of these loci through manual curation. The authors emphasize the influence of sample and participant characteristics on the associations, though the inclusion of 33 different cohorts allowed for many robust findings. Comprehensive metabolic profiling was used to explore the impact of known and novel genetic loci on lipoprotein metabolism. Through the public release of their summary statistics, the authors offer new detailed insights into the genetic regulation of metabolism to the whole research community.