Evidence map›Paper›PMID 29361466›Full record

ArticleCell systems2018

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.

Brian D Piening, Wenyu Zhou, Kévin Contrepois, Hannes Röst, Gucci Jijuan Gu Urban, Tejaswini Mishra, Blake M Hanson, Eddy J Bautista, Shana Leopold, Christine Y Yeh and 24 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
124citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

124 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Leptin-Mediated Changes in the Human Metabolome.The Journal of clinical endocrinology and metabolism · 2020
    Trial
  5. Trial
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Systemic elevation of gut derivedFrontiers in physiology · 2026
    Article
  14. Article
  15. Article
  16. Longitudinal big biological data in the AI era.Molecular systems biology · 2025
    Review
  17. Review
  18. Article
  19. Article
  20. Review

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

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

34 authors.

Brian D PieningDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Wenyu ZhouDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Kévin ContrepoisDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Hannes RöstDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Gucci Jijuan Gu UrbanDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Tejaswini MishraDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Blake M HansonThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Eddy J BautistaThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Shana LeopoldThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Christine Y YehDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; Canary Center at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA; Biomedical Informatics Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
Daniel SpakowiczThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Imon BanerjeeDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
Cynthia ChenDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
Kimberly KukurbaDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Dalia PerelmanDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Colleen CraigDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Elizabeth ColbertDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Denis SalinsDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Shannon RegoDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Sunjae LeeScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Cheng ZhangScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Jessica WheelerDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
M Reza SailaniDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Liang LiangDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Charles AbbottDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Mark GersteinDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Adil MardinogluScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Ulf SmithDepartment of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.
Daniel L RubinDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
Sharon PitteriDepartment of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; Canary Center at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA.
Erica SodergrenThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Tracey L McLaughlinDivision of Endocrinology, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: tmclaugh@stanford.edu.
George M WeinstockThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA. Electronic address: george.weinstock@jax.org.
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.

Funding

MOLECULAR &CELLULAR IMMUNOBIOLOGYT32AI007290 · NIAID · STANFORD UNIVERSITY · PI Sean Curtis Bendall, Olivia M Martinez · 1985 to 2026
$27.0M
MEDICAL INFORMATICS RESEARCH TRAINING AT YALET15LM007056 · NLM · YALE UNIVERSITY · PI Mark Bender Gerstein, LUCILA OHNO-MACHADO · 1987 to 2026
$22.2M
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
T32 Training Program in Mechanisms and Innovation in Vascular DiseaseT32HL098049 · NHLBI · STANFORD UNIVERSITY · PI Nicholas James Leeper, Philip S Tsao · 2010 to 2026
$6.3M
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
High-throughput sequencer for multi-scale genomic studiesS10OD020141 · OD · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2015 to 2015
$600k
Personal omics profiling of the progression to diabetes mellitus type 2F32DK100072 · NIDDK · STANFORD UNIVERSITY · PI PIENING, BRIAN DONALD · 2014 to 2015
$102k
NHLBI NIH HHS T32 HL098049NIDDK NIH HHS F32 DK100072NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS U54 DK102556NIH HHS S10 OD020141NLM NIH HHS T15 LM007056
6 · The paper itself

Abstract

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Indexed as

AdultBiomarkersGenomicsHumansInsulin ResistanceMaleMetabolomicsObesityPrecision MedicineProteomicsWeight GainWeight LossBiomarkersgenomicsmetabolomicsmicrobiomeobesityproteomicssystems biologytype 2 diabetes

Identifiers

PMID29361466
PMCPMC6021558

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.