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.
All
Ageing & dementia
Cancer
Cardiovascular disease
Diabetes
Exposome & nutrition
Genetics
Inflammation & immunity
Kidney disease
Liver disease
Lung disease
Maternal & child health
Medicines & trials
Methods
Mortality & multi-morbidity
Obesity & bodyweight
Psychiatric disorders
Risk prediction
J. Ren et. al, Human Molecular Genetics, 2023
Z. Lin et. al, Human Genetics and Genomics Advances, 2022
Genetics, Diabetes
Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes
M. O’Connor et. al, Diabetes, 2021
X. Yu et. al, Frontiers in Public Health, 2022
Ageing & dementia
APOE4poses opposite effects of plasma LDL on white matter integrity in older adults
Z. Ye et. al, 2023
Cardiovascular disease, Risk prediction
Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank
D. Jin et. al, Journal of Epidemiology and Community Health, 2023
N. Loh et. al, Obesity, 2023
Exposome & nutrition, Risk prediction, Cardiovascular disease
Association of Fish Oil Supplementation with Risk of Coronary Heart Disease in Individuals with Diabetes and Prediabetes: A Prospective Study in the UK Biobank
X. Liu et. al, Nutrients, 2023
Maternal & child health
Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight
C. Barry et. al, Metabolites, 2022
Psychiatric disorders
Cognition, psychosis risk and metabolic measures in two adolescent birth cohorts
H. Ramsay et. al, Psychological Medicine, 2018
Kidney disease
Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function
E. Valo et. al, 2023
Exposome & nutrition
High intake of vegetables is linked to lower white blood cell profile and the effect is mediated by the gut microbiome
C. Menni et. al, BMC Medicine, 2021
Risk prediction
Machine learning to determine relative contribution of modifiable and non-modifiable risk factors of major eye diseases
S. Nusinovici et. al, British Journal of Ophthalmology, 2020
Ageing & dementia
Relationships between Lipid-Related Metabolites and Age-Related Macular Degeneration Vary with Complement Genotype
R. Sim et. al, Ophthalmology Science, 2022
S. Nusinovici et. al, Ophthalmology, 2021