Publications
Nightingale’s technology is routinely used in world-class epidemiological and genetic studies. There are over 350 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
Würtz et al. PLoS Medicine 2014;11(12):e1001765
Fatty liver disease
Lipoprotein subclass metabolism in nonalcoholic steatohepatitis
Männistö et al. Journal of Lipid Research 2014;55(12):2676-84
Cardiovascular diseases
Interactions between genetic variants and dietary lipid composition: effects on circulating LDL cholesterol in children
Ahola-Olli et al. The American Journal of Clinical Nutrition 2014;100(6):1569-77
Metabolic risk factors
Blood microRNA profile associates with the levels of serum lipids and metabolites associated with glucose metabolism and insulin resistance and pinpoints pathways underlying metabolic syndrome
Raitoharju et al. Molecular and Cellular Endocrinology 2014;391(1-2):41-9
Lassenius et al. Nutrition & Metabolism 2014;11:28
Cardiovascular diseases
Effect of fatty and lean fish intake on lipoprotein subclasses in subjects with coronary heart disease: a controlled trial
Erkkilä et al. Journal of Clinical Lipidology 2014;8(1):126-33
Metabolic risk factors
Cross-sectional and longitudinal associations of circulating omega-3 and omega-6 fatty acids with lipoprotein particle concentrations and sizes: population-based cohort study with 6-year follow-up
Mäntyselkä et al. Lipids in Health and Disease 2014;13:28
Metabolic risk factors
Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons
Fischer et al. PLoS Medicine 2014:11(2):e1001606
Lankinen et al. PLoS One 2014;9(2):e90352
Human genetics
Association between serum fatty acids and lipoprotein subclass profile in healthy young adults
Jelenkovic et al. Atherosclerosis 2014;233(2):394-402
Badeau et al. Annals of Medicine 2014;46(1):18-23
Auro et al. Nature Communications 2014;5:4708
Mahendran et al. Diabetes Care 2013;36(11):3732-38
Mahendran et al. Diabetes 2013;62(10):3618-26
Larmo et al. The American Journal of Clinical Nutrition 2013:98(4);941-51