Evidence map›Paper›PMID 28714965›Full record

ArticleNature biotechnology2017

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Nathan D Price, Andrew T Magis, John C Earls, Gustavo Glusman, Roie Levy, Christopher Lausted, Daniel T McDonald, Ulrike Kusebauch, Christopher L Moss, Yong Zhou and 6 more

2 registry-linked trialsOpen access · greenAbstract read
In one paragraph

Article in Nature biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 220 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
220citing papers in PubMed, 3 pooled it
43.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04082611 nacompletednot on this mapstarted 2019, after this paper: background citation

The PREVENTION Trial: Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition

TypeinterventionalSponsorSaint John's Cancer InstituteRan2019 to 2025Enrolled97ConditionsAlzheimer Disease, Mild Cognitive ImpairmentArmsData-Driven Clinical Recommendations, Coached Data-Driven Clinical Recommendations
NCT05081700 completednot on this mapstarted 2020, after this paper: background citation

A Systems Approach to Understanding Disease Processes in Multiple Sclerosis

TypeobservationalSponsorProvidence Health & ServicesRan2020 to 2023Enrolled14ConditionsMultiple SclerosisArmsAll patients in the study will be treated with ocrelizumab
3 · Its place in the literature

Who cites it

220 citing papers in PubMed, 3 syntheses or guidelines pooled it, 439 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Trial
  5. Review
  6. Review
  7. Article
  8. Article
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  10. Review
  11. Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  12. Additive and Partially Dominant Effects from Genomic Variation Contribute to Rice Heterosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Longitudinal big biological data in the AI era.Molecular systems biology · 2025
    Review
  20. Article

160 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 3 institutions in 1 country.

Nathan D PriceInstitute for Systems Biology, Seattle, Washington, USA.
Andrew T MagisArivale, Seattle, Washington, USA.
John C EarlsArivale, Seattle, Washington, USA.
Gustavo GlusmanInstitute for Systems Biology, Seattle, Washington, USA.ORCID http://orcid.org/0000-0001-8060-5955
Roie LevyInstitute for Systems Biology, Seattle, Washington, USA.
Christopher LaustedInstitute for Systems Biology, Seattle, Washington, USA.
Daniel T McDonaldInstitute for Systems Biology, Seattle, Washington, USA.
Ulrike KusebauchInstitute for Systems Biology, Seattle, Washington, USA.
Christopher L MossInstitute for Systems Biology, Seattle, Washington, USA.
Yong ZhouInstitute for Systems Biology, Seattle, Washington, USA.
Shizhen QinInstitute for Systems Biology, Seattle, Washington, USA.
Robert L MoritzInstitute for Systems Biology, Seattle, Washington, USA.ORCID http://orcid.org/0000-0002-3216-9447
Kristin BrogaardArivale, Seattle, Washington, USA.
Gilbert S OmennInstitute for Systems Biology, Seattle, Washington, USA.
Jennifer C LovejoyInstitute for Systems Biology, Seattle, Washington, USA.
Leroy HoodInstitute for Systems Biology, Seattle, Washington, USA.
Institute for Systems Biology · USProvidence College · USUniversity of Michigan · US

Funding

Quantitative Systems BiologyP50GM076547 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI DUDLEY, AIMEE M · 2006 to 2016
$30.6M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
Complete Human Peptide- and MRM-AtlasRC2HG005805 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI HOOD, LEROY E, MORITZ, ROBERT L · 2009 to 2010
$4.6M
University of Michigan Proteogenomics Data Analysis CenterU24CA210967 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M, DHANASEKARAN, SARAVANA M · 2016 to 2020
$4.1M
NCI NIH HHS U24 CA210967NHGRI NIH HHS RC2 HG005805NIEHS NIH HHS P30 ES017885NIGMS NIH HHS P50 GM076547
6 · The paper itself

Abstract

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

Indexed as

BiomarkersDatabases, FactualComputational BiologyExerciseGenome-Wide Association StudyHumansLongitudinal StudiesMetabolomeMicrobiotaModels, StatisticalMonitoring, PhysiologicNeoplasmsNutritional StatusProteomeBiomarkersProteome

Identifiers

PMID28714965
PMCPMC5568837
OpenAlexW2734770034

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.