Publications

Nightingale’s biomarker analysis platform is routinely used in world-class epidemiological and genetic studies. There are over 300 publications that have applied our method. See full list of publications here.

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Metabolic risk factors

Blood biomarker score identifies individuals at high risk for severe COVID-19 - metabolic profiling of 105,000 adults in the UK Biobank

Identification of healthy people at high risk for severe COVID-19 is a global health priority. This study investigated whether blood biomarkers measured by high-throughput metabolomics could be predictive of severe pneumonia and COVID-19 hospitalisation years after the blood sampling. Nightingale metabolomics was used to quantify a comprehensive biomarker profile in 105,146 plasma samples collected in the UK Biobank during 2007-2010 (age range 39-70).

Würtz et al. MedRxiv 2020; preprint

Bioinformatics

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

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

Nutrition

Genome-Wide Association Study for Serum Omega-3 and Omega-6 Polyunsaturated Fatty Acids: Exploratory Analysis of the Sex-Specific Effects and Dietary Modulation in Mediterranean Subjects with Metabolic Syndrome

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

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

Metabolic risk factors

Structure–function relationships of HDL in diabetes and coronary heart disease

Cardner et al. JCI Insight 2019; preprint

Human genetics

Investigating Causality Between Blood Metabolites and Emotional and Behavioral Responses to Traumatic Stress: a Mendelian Randomization Study

Carvalho et al. Mol Neurobiol. 2019; preprint

T2D

Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study

Wang et al. BMC Medicine 2019;17(1):217

T2D

Short-term treatment with high dose liraglutide improves lipid and lipoprotein profile and changes hormonal mediators of lipid metabolism in obese patients with no overt type 2 diabetes mellitus: a randomized, placebo-controlled, cross-over, double-blind clinical trial

Peradze et al. Cardiovasc Diabetol. 2019;18(1):141

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