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 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 data are highly replicable across cohorts.
Nightingale 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.
Ultimately, uncovering disease mechanisms and translating your findings to clinical context can help more people live healthier lives. To make such collaborations easier, we have listed below some of the cohorts that have Nightingale’s data available. To unleash the strengths of replicable biomarker results also for your data sets, we recommend you to reach out to the senior study author or study PI for collaboration requests. It is easy to replicate results using summary statistics whenever sharing individual person's data is challenging.
Welsh et al. Diabetologia 2018. https://link.springer.com/article/10.1007%2Fs00125-018-4619-x
Bell et al. Journal of the American College of Cardiology 2018. https://www.jacc.org/doi/full/10.1016/j.jacc.2018.09.066
Dikariyanto et al. The Americal Journal of Clinical Nutrition 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266688/
Ellul et al. Pediatric Research 2020. https://www.nature.com/articles/s41390-020-0762-4
Including Rotterdam Study, the LifeLines-DEEP cohort, Growing Old Together Study, Netherlands Epidemiology of Obesity. https://www.bbmri.nl/Omics-metabolomics
Joshi et al. European Journal of Preventive Cardiology 2020. https://journals.sagepub.com/doi/10.1177/2047487319899621
Borges et al. Circulation: Cardiovascular Genetics. 2017. https://www.ahajournals.org/doi/full/10.1161/CIRCGENETICS.117.001837
Taylor et al. Metabolites 2019. https://www.mdpi.com/2218-1989/9/9/190/htm
Preiss et al. Diabetic Medicine 2016. https://onlinelibrary.wiley.com/doi/abs/10.1111/dme.13097
Sliz et al. Human Molecular Genetics 2018. https://academic.oup.com/hmg/article/27/12/2214/4966855
Holmes et al. JACC 2018. https://www.jacc.org/doi/full/10.1016/j.jacc.2017.12.006
Wahl et al. BMC Med 2015. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367822/
Balling et al. JACC 2020. https://www.sciencedirect.com/science/article/abs/pii/S0735109720374660
Mäkinen et al. Journal of Internal Medicine 2013. https://onlinelibrary.wiley.com/doi/full/10.1111/joim.12026
Locke et al. Nature 2019. https://www.nature.com/articles/s41586-019-1457-z
Fest et al. Endocrinology 2019. https://academic.oup.com/endo/article/160/7/1731/5497119
Ference et al. JAMA 2017. https://pubmed.ncbi.nlm.nih.gov/28846118/
Chambers et al. Lancet Diabetes Endocrinol 2015. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4724884/
Wang et al. International Journal of Epidemiology 2019. https://academic.oup.com/ije/article/48/5/1556/5366222?login=true
Gallois et al. Nature Communications 2019. https://www.nature.com/articles/s41467-019-12703-7
Wang et al. BMC Medicine 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153817/
Coltell et al. Nutrients 2020. https://www.mdpi.com/2072-6643/12/2/310/htm
Kofink et al. Circulation: Genomic and Precision Medicine 2017. https://www.ahajournals.org/doi/full/10.1161/circgenetics.117.001759
Delles et al. European journal of heart failure 2018. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5947152/
Beynon et al. International Journal of Cancer 2018. https://onlinelibrary.wiley.com/doi/full/10.1002/ijc.31929
Tillin et al. Diabetologia 2015. https://link.springer.com/article/10.1007/s00125-015-3517-8
Lehtovirta et al. the Journal of Pediatrics 2018. https://www.jpeds.com/article/S0022-3476(17)31613-X/fulltext
Asnicaret al. Nature Medicine 2021. https://www.nature.com/articles/s41591-020-01183-8
Borges et al. Circulation: Cardiovascular Genetics. 2017. https://www.ahajournals.org/doi/full/10.1161/CIRCGENETICS.117.001837
Mills et al. BMC Medicine 2019. https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1248-7
Akbaraly et al. Scientific Reports 2018. https://www.nature.com/articles/s41598-018-26441-1