Journal article
A longitudinal big data approach for precision health
SM Schüssler-Fiorenza Rose, K Contrepois, KJ Moneghetti, W Zhou, T Mishra, S Mataraso, O Dagan-Rosenfeld, AB Ganz, J Dunn, D Hornburg, S Rego, D Perelman, S Ahadi, MR Sailani, Y Zhou, SR Leopold, J Chen, M Ashland, JW Christle, M Avina Show all
Nature Medicine | NATURE PORTFOLIO | Published : 2019
Abstract
Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, ..
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Awarded by National Institutes of Health
Funding Acknowledgements
Our work was supported by grants from the National Institutes of Health (NIH) Human Microbiome Project (HMP) 1U54DE02378901 (G.M.W. and M.P.S.), an NIH grant no. R01 DK110186-03 (T.L.M.), a NIH National Center for Advancing Translational Science Clinical and Translational Science Award (no. UL1TR001085). This work used the Genome Sequencing Service Center by the Stanford Center for Genomics and Personalized Medicine Sequencing Center (supported by NIH grant no. S10OD020141), the Diabetes Genomics Analysis Core and the Clinical and Translational Core of the Stanford Diabetes Research Center (NIH grant no. P30DK116074). S.M.S.-F.R. was supported by a Department of Veteran Affairs Office of Academic Affiliations Advanced Fellowship in Spinal Cord Injury Medicine and a NIH Career Development Award no. K08 ES028825. G.M.S. was supported by NIH grant no. K08 MH103443. D.H. was supported by a Stanford School of Medicine Dean's Postdoctoral Fellowship and a Stanford Center for Computational, Evolutionary and Human Genomics Fellowship. M.R.S. was supported by grant nos. P300PA_161005 and P2GEP3_151825 from the Swiss National Science Foundation (SNSF). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the Department of Veteran Affairs, or the SNSF. We thank S. Chen and B. Lee for their work in metabolomics data production. A. Breschi generously shared her code for the ISR calculations. Finally, we thank the iPOP participants who generously gave their time and biological samples.