Evidence map›Paper›PMID 31068711›Full record

ArticleNature medicine2019

A longitudinal big data approach for precision health.

Sophia Miryam Schüssler-Fiorenza Rose, Kévin Contrepois, Kegan J Moneghetti, Wenyu Zhou, Tejaswini Mishra, Samson Mataraso, Orit Dagan-Rosenfeld, Ariel B Ganz, Jessilyn Dunn, Daniel Hornburg and 20 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 250 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
250citing papers in PubMed, 7 pooled it
27.2field-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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

250 citing papers in PubMed, 7 syntheses or guidelines pooled it, 495 citations in OpenAlex.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

30 authors at 8 institutions in 2 countries.

Sophia Miryam Schüssler-Fiorenza RoseDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6311-6671
Kévin ContrepoisDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9678-5161
Kegan J MoneghettiStanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
Wenyu ZhouDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Tejaswini MishraDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9931-1260
Samson MatarasoDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.ORCID http://orcid.org/0000-0003-3146-2243
Orit Dagan-RosenfeldDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Ariel B GanzDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Jessilyn DunnDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Daniel HornburgDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Shannon RegoDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Dalia PerelmanDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Sara AhadiDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
M Reza SailaniDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Yanjiao ZhouThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Shana R LeopoldThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Jieming ChenBakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA.
Melanie AshlandDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Jeffrey W ChristleStanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
Monika AvinaDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Patricia LimcaocoDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Camilo RuizDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Marilyn TanDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA.
Atul J ButteBakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-7433-2740
George M WeinstockThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.ORCID http://orcid.org/0000-0002-2997-4592
George M SlavichCousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-5710-3818
Erica SodergrenThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Tracey L McLaughlinDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA.
Francois HaddadStanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. fhaddad@stanford.edu.ORCID http://orcid.org/0000-0002-9648-7671
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID http://orcid.org/0000-0003-0784-7987
Stanford University · USJackson Laboratory · USCardiovascular Institute of the South · USUniversity of California, San Francisco · USThe University of Melbourne · AUUniversity of California, Berkeley · USUniversity of California, Los Angeles · USVA Palo Alto Health Care System · US

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Melinda L. Telli · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Longitudinal Multiomics Microbial Profiling in Healthy and Disease IndividualsU54DK102556 · NIDDK · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., WEINSTOCK, GEORGE M · 2014 to 2015
$7.7M
Longitudinal Multi-Omic Profiles to Reveal Mechanisms of Obesity-Mediated Insulin ResistanceR01DK110186 · NIDDK · STANFORD UNIVERSITY · PI MCLAUGHLIN, TRACEY, SNYDER, MICHAEL P. · 2017 to 2021
$3.2M
Longitudinal Multiomics Microbial Profiling in Healthy and Disease IndividualsU54DE023789 · NIDCR · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., WEINSTOCK, GEORGE M · 2013 to 2014
$3.0M
Integrating the Exposome into Longitudinal Multiomics ProfilingK08ES028825 · NIEHS · STANFORD UNIVERSITY · PI ROSE, SOPHIA MIRYAM SCHÜSSLER-FIORENZA · 2018 to 2022
$1.0M
Neural, Inflammatory, and Genomic Mechanisms Underlying Risk for Depression in AdolescenceK08MH103443 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SLAVICH, GEORGE MICHAEL · 2015 to 2018
$772k
Computational appliance: a supercomputer for modern biomedical researchS10OD023452 · OD · STANFORD UNIVERSITY · PI DATTA, SOMALEE · 2017 to 2017
$600k
High-throughput sequencer for multi-scale genomic studiesS10OD020141 · OD · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2015 to 2015
$600k
NCATS NIH HHS UL1 TR001085NCATS NIH HHS UL1 TR003142NCI NIH HHS P30 CA124435NIDCR NIH HHS U54 DE023789NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS U54 DK102556NIEHS NIH HHS K08 ES028825NIH HHS S10 OD020141NIMH NIH HHS K08 MH103443
6 · The paper itself

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.

Indexed as

Big DataAdultAgedCardiovascular DiseasesCohort StudiesDiabetes Mellitus, Type 2ExomeFemaleGastrointestinal MicrobiomeHumansInsulin ResistanceLongitudinal StudiesMaleMetabolomeMiddle AgedModels, Biological

Identifiers

PMID31068711
PMCPMC6713274
OpenAlexW2944477354

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.