Publications
Nightingale’s technology is routinely used in world-class epidemiological and genetic studies. There are over 450 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
Metabolic risk factors
Association of the functional ovarian reserve with serum metabolomic profiling by nuclear magnetic resonance spectroscopy: a cross-sectional study of ~ 400 women
Al Rashid et al. BMC Med. 2020;18(1)
Huhtala et al. BMC Pregnancy Childbirth 2020;20(1):401
Human genetics
Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data
Pool et al. Twin Res Hum Genet. 2020 Jun;23(3):145-155
Metabolic risk factors
GlycA, a novel marker for low grade inflammation, reflects gut microbiome diversity and is more accurate than high sensitive CRP in reflecting metabolomic profile
Mokkala et al. Metabolomics 2020;16(7):76
Cardiovascular diseases
Sphingomyelin and progression of renal and coronary heart disease in individuals with type 1 diabetes
Pongrac Barlovic et al. Diabetologia 2020;63(9):1847-1856
Cardiovascular diseases
Cardiovascular health and retinal microvascular geometry in Australian 11-12 year-olds
Liu et al. Microvascular Research 2020;129
Dikariyanto et al. The Americal Journal of Clinical Nutrition 2020;111(6):1178-1189
Harris et al. Diabetologia 2020;63(8):1637-1647
Robinson et al. Aging Cell. 2021; 19:e13149
Method description
Reply To: "Methodological Issues Regarding: "A Third of Nonfasting Plasma Cholesterol Is in Remnant Lipoproteins: Lipoprotein Subclass Profiling in 9293 Individuals""
Würtz and Soininen Atherosclerosis 2020;S0021-9150(20)30193-3.
Tofte et al. Journal of Clinical Endocrinology and Metabolism 2020;105(7):dgaa173
Human genetics
Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models
Mefford et al. J Comput Biol. 2020 Apr;27(4):599-612
Metabolic risk factors
Serum GlycA Level is Elevated in Active Systemic Lupus Erythematosus and Correlates to Disease Activity and Lupus Nephritis Severity.
Dierckx et al. J Clin Med. 2020
Metabolic risk factors
Inflammation mediates the relationship between obesity and retinal vascular calibre in 11-12 year-olds children and mid-life adults
Liu et al. Scientific Reports 2020;10:5006
Cardiovascular diseases
Circulating Fatty Acids and Risk of Coronary Heart Disease and Stroke: Individual Participant Data Meta‐Analysis in Up to 16 126 Participants
Borges et al. Journal of the American Heart Association 2020;9(5):e013131.