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Nat Med. 2019 May;25(5):792-804. doi: 10.1038/s41591-019-0414-6. Epub 2019 May 8.

A longitudinal big data approach for precision health.

Author information

1
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
2
Spinal Cord Injury Service, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
3
Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
4
Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
5
Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
6
Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Australia.
7
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
8
Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA.
9
Mobilize Center, Stanford University, Stanford, CA, USA.
10
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
11
Department of Medicine, University of Connecticut Health, Farmington, CT, USA.
12
Bakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA.
13
Department of Bioengineering, Stanford University, Stanford, CA, USA.
14
Division of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA.
15
Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
16
Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. fhaddad@stanford.edu.
17
Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. fhaddad@stanford.edu.
18
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.
19
Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. mpsnyder@stanford.edu.

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, 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.

PMID:
31068711
PMCID:
PMC6713274
DOI:
10.1038/s41591-019-0414-6
[Indexed for MEDLINE]
Free PMC Article

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