Cohorts using Nightingale Health's data
Collaboration across multiple cohorts ensures convincing scientific results and enables faster translation. Analyze your findings with other cohorts to increase statistical power and publish highly reproducible science.
Numerous cohorts have already used Nightingale Health for metabolic profiling of projects, frequently at the whole cohort scale.
Our technology provides data in absolute concentration units with no batch effects. Therefore, it is straightforward to meta-analyze the data across different cohorts, replicate your findings and increase the statistical power of individual studies. Many scientific publications have proven that biomarkers associations based on Nightingale Health data are highly replicable across cohorts.
Explore the cutting edge research from our UK Biobank data
Metabolomic profiles predict individual multidisease outcomes
Buergel et al., Nat Med., 2022
The ground-breaking study conducted by researchers from universities in Germany, the Netherlands, and the UK on over 100,000 people demonstrates how one blood test can identify early risk signs for many common diseases. The unique snapshot of a person’s health state captured by Nightingale Health’s proprietary metabolomic blood test was able to improve current health screening tools for several costly diseases, such as heart failure, dementia, and diabetes.
A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank
Zhang et al., Nat Com., 2024
A recent study published in Nature Communications analyzed the metabolomic profile of aging in 250,341 participants from the UK Biobank, identifying 54 key aging-related biomarkers linked to all-cause mortality and multiple age-related health outcomes. The study used data from the UK Biobank’s metabolomic data set analyzed by Nightingale Health. Researchers developed a metabolomic aging score that predicts short-term mortality risk more accurately than existing metrics and than chronological age itself. Additionally, they leveraged a subset of the UK Biobank with metabolomic data measured at multiple time points to derive individuals’ aging rates, showing that a faster aging rate is associated with higher mortality risk. This groundbreaking work has huge implications for personalized aging monitoring and early disease intervention.
Genome-wide association study for circulating metabolites in 619,372 individuals
Tambets et al., medRxiv, 2024
Researchers in recently published pre-print have conducted a comprehensive genome-wide association study (GWAS) on 249 circulating metabolite traits in over 619,000 individuals from the Estonian and UK Biobanks. The study identified 89,489 locus-metabolite pairs, of which 4,184 are novel associations. Importantly, 12.4% of lead variants were rare, highlighting the need to study low-frequency variants in metabolic biomarker research. This resource is a major step forward in understanding genetic contributions to disease risk and drug target discovery.
Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: A cohort study and modelling analyses
Scott C. et al., medRxiv, 2024
New pre-print published by the researchers from the University of Cambridge involving around 170,000 UK Biobank participants explored the potential of combining NMR-based metabolomic biomarkers analyzed by Nightingale Health with polygenic risk scores (PRSs) to improve 10-year cardiovascular disease (CVD) risk prediction. Researchers found that adding NMR scores on top of widely used clinical scores (SCORE2) and PGS improves the prediction of CVD events. Additionally, through simulations they show how integrating NMR scores with PGS and SCORE2 could significantly increase the number of prevented CVD events at population scale, up to 170 events prevented per 100,000 screened individuals.
Nightingale Health encourages collaboration across multiple cohorts because such partnerships enable replication and validation of the key results.
This ensures high-quality, reproducible results and is often recommended for publication in the top journals. Eventually, this allows faster translation of the research findings into clinical use.
Uncovering disease mechanisms and translating your findings into a clinical context can help more people live healthier lives.
To make such collaborations easier, we have listed below some of the cohorts using Nightingale Health’s data. To unleash the strengths of replicable biomarker results for your data sets, we recommend contacting the senior study author or study PI for collaboration requests. It is easy to replicate results using summary statistics whenever sharing an individual person's data is challenging.
List of cohorts
Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation trial (ADVANCE trial)
Welsh et al. Diabetologia 2018
https://link.springer.com/article/1…Almonds Trial Targeting Dietary Intervention with Snacks study (ATTIS)
Dikariyanto et al. The Americal Journal of Clinical Nutrition 2020
https://www.ncbi.nlm.nih.gov/pmc/ar…Alzheimer's Disease Neuroimaging Initiative (ADNI)
https://sites.duke.edu/adnimetab/te…Assessing the safety of lipid-modifying medications among Chinese adolescents: a drug-target Mendelian randomization study
Luo et al. BMC Med 2023
https://pubmed.ncbi.nlm.nih.gov/379…Avon Longitudinal Study of Parents and Children (ALSPAC)
Bell et al. Journal of the American College of Cardiology 2018
https://www.jacc.org/doi/full/10.10…Biobanking Netherlands BBMRI-NL
Including Rotterdam Study, the LifeLines-DEEP cohort, Growing Old Together Study, Netherlands Epidemiology of Obesity
https://www.bbmri.nl/Omics-metabolo…Breastfeeding and ApoB in late adolescence: a Hong Kong birth cohort study
Schooling et al. Eur J Pediatr 2023
https://pubmed.ncbi.nlm.nih.gov/372…British Regional Heart Study
Joshi et al. European Journal of Preventive Cardiology 2020
https://journals.sagepub.com/doi/10…British Women’s Heart and Health Study (BWHHS)
Borges et al. Circulation: Cardiovascular Genetics. 2017
https://www.ahajournals.org/doi/ful…Cardiovascular Risk in Young Finns Study
Sliz et al. Human Molecular Genetics 2018
https://academic.oup.com/hmg/articl…Carotid Atherosclerosis: Metformin for Insulin Resistance study (CAMERA)
Preiss et al. Diabetic Medicine 2016
https://onlinelibrary.wiley.com/doi…Cooperative Health Research in the Region of Augsburg (KORA)
Wahl et al. BMC Med 2015
https://www.ncbi.nlm.nih.gov/pmc/ar…Estonian Biobank
https://genomics.ut.ee/en…FinnDiane Study
Mäkinen et al. Journal of Internal Medicine 2013
https://onlinelibrary.wiley.com/doi…Finnish National THL Biobank
https://thl.fi/en/web/thl-biobank/f…London Life Sciences Prospective Population Study (LOLIPOP)
Chambers et al. Lancet Diabetes Endocrinol 2015
https://www.ncbi.nlm.nih.gov/pmc/ar…Longitudinal Study of Australian Children
Wang et al. International Journal of Epidemiology 2019
https://academic.oup.com/ije/articl…Metabolic Syndrome In Men study (METSIM)
Gallois et al. Nature Communications 2019
https://www.nature.com/articles/s41…Mexico City Prospective Study
https://www.ctsu.ox.ac.uk/research/…Northern Finland Birth Cohort 1966 and 1986
Wang et al. BMC Medicine 2016
https://www.ncbi.nlm.nih.gov/pmc/ar…Prevención con Dieta Mediterránea PREDIMED+
Coltell et al. Nutrients 2020
https://www.mdpi.com/2072-6643/12/2…Prevention of Renal and Vascular End-stage Disease Intervention Trial (PREVEND IT)
Kofink et al. Circulation: Genomic and Precision Medicine 2017
https://www.ahajournals.org/doi/ful…PROspective Study of Pravastatin in the Elderly at Risk trial (PROSPER)
Delles et al. European journal of heart failure 2018
https://www.ncbi.nlm.nih.gov/pmc/ar…Prostate Testing for Cancer and Treatment (ProtecT)
Beynon et al. International Journal of Cancer 2018
https://onlinelibrary.wiley.com/doi…Singapore Multiethnic Cohort (MEC)
https://blog.nus.edu.sg/sphs/multie…South Asia Biobank
https://www.sabiobank.org/about/…Southall And Brent Revisited Study (SABRE)
Tillin et al. Diabetologia 2015
https://link.springer.com/article/1…Special Turku Coronary Risk Factor Intervention Project study (STRIP)
Lehtovirta et al. the Journal of Pediatrics 2018
https://www.jpeds.com/article/S0022…UK Biobank
https://biobank.ndph.ox.ac.uk/showc…UK Pregnancies Better Eating and Activity Trial (UPBEAT)
Mills et al. BMC Medicine 2019
https://bmcmedicine.biomedcentral.c…United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS)
Borges et al. Circulation: Cardiovascular Genetics. 2017
https://www.ahajournals.org/doi/ful…
If you have profiled your cohort with Nightingale Health and would like to be added to this list to enhance your collaboration with this large research community, please contact us at: research[at]nightingalehealth.com or with the form below.