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A longitudinal big data approach for precision health.

Publication ,  Journal Article
Schüssler-Fiorenza Rose, SM; Contrepois, K; Moneghetti, KJ; Zhou, W; Mishra, T; Mataraso, S; Dagan-Rosenfeld, O; Ganz, AB; Dunn, J; Hornburg, D ...
Published in: Nature medicine
May 2019

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, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

Duke Scholars

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Published In

Nature medicine

DOI

EISSN

1546-170X

ISSN

1078-8956

Publication Date

May 2019

Volume

25

Issue

5

Start / End Page

792 / 804

Related Subject Headings

  • Transcriptome
  • Risk Factors
  • Precision Medicine
  • Models, Biological
  • Middle Aged
  • Metabolome
  • Male
  • Longitudinal Studies
  • Insulin Resistance
  • Immunology
 

Citation

APA
Chicago
ICMJE
MLA
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Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., … Snyder, M. P. (2019). A longitudinal big data approach for precision health. Nature Medicine, 25(5), 792–804. https://doi.org/10.1038/s41591-019-0414-6
Schüssler-Fiorenza Rose, Sophia Miryam, Kévin Contrepois, Kegan J. Moneghetti, Wenyu Zhou, Tejaswini Mishra, Samson Mataraso, Orit Dagan-Rosenfeld, et al. “A longitudinal big data approach for precision health.Nature Medicine 25, no. 5 (May 2019): 792–804. https://doi.org/10.1038/s41591-019-0414-6.
Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, et al. A longitudinal big data approach for precision health. Nature medicine. 2019 May;25(5):792–804.
Schüssler-Fiorenza Rose, Sophia Miryam, et al. “A longitudinal big data approach for precision health.Nature Medicine, vol. 25, no. 5, May 2019, pp. 792–804. Epmc, doi:10.1038/s41591-019-0414-6.
Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, Dagan-Rosenfeld O, Ganz AB, Dunn J, Hornburg D, Rego S, Perelman D, Ahadi S, Sailani MR, Zhou Y, Leopold SR, Chen J, Ashland M, Christle JW, Avina M, Limcaoco P, Ruiz C, Tan M, Butte AJ, Weinstock GM, Slavich GM, Sodergren E, McLaughlin TL, Haddad F, Snyder MP. A longitudinal big data approach for precision health. Nature medicine. 2019 May;25(5):792–804.

Published In

Nature medicine

DOI

EISSN

1546-170X

ISSN

1078-8956

Publication Date

May 2019

Volume

25

Issue

5

Start / End Page

792 / 804

Related Subject Headings

  • Transcriptome
  • Risk Factors
  • Precision Medicine
  • Models, Biological
  • Middle Aged
  • Metabolome
  • Male
  • Longitudinal Studies
  • Insulin Resistance
  • Immunology