Publications
Nightingale’s technology is routinely used in world-class epidemiological and genetic studies. There are over 400 publications that have utilized our technology. If you are interested in using our technology for medical research, visit our website for researchers here.
All
Ageing
Bioinformatics
Cancer
Cardiovascular diseases
Drug development
Fatty liver disease
Gut microbiota
Human genetics
Kidney disease
Maternal health
Metabolic risk factors
Method description
Neurological diseases
Nutrition
T1D
T2D
Zuber et al. MedRxiv 2020; preprint
Human genetics
Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes
Lotta et al. BioRxiv 2020; preprint
Cardiovascular diseases
Triglyceride-containing lipoprotein sub-fractions and risk of coronary heart disease and stroke: A prospective analysis in 11,560 adults
Joshi et al. Eur J Prev Cardiol. 2020; preprint
Coltell et al. Nutrients 2020;12(2):E310
Metabolic risk factors
Sex differences in infant blood metabolite profile in association with weight and adiposity measures
Ellul et al. Pediatr Res. 2020; preprint
Bioinformatics
EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics
Ekholm et al. Int J Epidemiol. 2020; preprint
Cardiovascular diseases
Sex differences in cardiometabolic traits at four life stages: cohort study with repeated metabolomics
Bell et al. MedRxiv 2020; preprint
Drug development
Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug–metabolite atlas
Liu et al. Nat Med. 2020;26;110–117
Human genetics
Heritability estimates for 361 blood metabolites across 40 genome-wide association studies
Hagenbeek et al. Nat Commun.2020;11(1):39
Bioinformatics
Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization
Zuber et al.Nat Commun. 2020;11(1):29
Cardiovascular diseases
Lipoprotein Signatures of Cholesteryl Ester Transfer Protein and HMG-CoA Reductase Inhibition
Kettunen et al. PLoS Biology 2019;17(12):e3000572
Metabolic risk factors
Structure–function relationships of HDL in diabetes and coronary heart disease
Cardner et al. JCI Insight 2019; preprint
Cardiovascular diseases
Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease
Ohukainen et al. Atherosclerosis 2020; preprint
Carvalho et al. Mol Neurobiol. 2019; preprint
Wang et al. BMC Medicine 2019;17(1):217