Evidence map›Paper›PMID 20195502›Full record

ArticlePLoS computational biology2010

Interpreting metabolomic profiles using unbiased pathway models.

Rahul C Deo, Luke Hunter, Gregory D Lewis, Guillaume Pare, Ramachandran S Vasan, Daniel Chasman, Thomas J Wang, Robert E Gerszten, Frederick P Roth

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

29 citing papers in PubMed, 55 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms.Mammalian genome : official journal of the International Mammalian Genome Society · 2019
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Blood transcriptomics and metabolomics for personalized medicine.Computational and structural biotechnology journal · 2016
    Review
  9. Lipid and Metabolic Changes in Rheumatoid Arthritis.Current rheumatology reports · 2015
    Review
  10. Review
  11. Article
  12. Article
  13. Integration of untargeted metabolomics with transcriptomics reveals active metabolic pathways.Metabolomics : Official journal of the Metabolomic Society · 2014
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Top-down network analysis to drive bottom-up modeling of physiological processes.Journal of computational biology : a journal of computational molecular cell biology · 2013
    Article
  19. Article
  20. Article
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

9 authors at 4 institutions in 1 country.

Rahul C DeoDepartment of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America.
Luke Hunter
Gregory D Lewis
Guillaume Pare
Ramachandran S Vasan
Daniel Chasman
Thomas J Wang
Robert E Gerszten
Frederick P Roth
Massachusetts General Hospital · USBrigham and Women's Hospital · USFramingham Heart Study · USHarvard University · US

Funding

Genomic Analysis of Network Perturbations in Human DiseaseP50HG004233 · NHGRI · DANA-FARBER CANCER INST · PI VIDAL, MARC · 2007 to 2017
$31.6M
MULTIDISCIPLINARY RESEARCH TRAINING IN CARDIOLOGYT32HL007208 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Patrick Thomas Ellinor, David E Sosnovik · 1985 to 2026
$16.1M
TRIGEMINAL NERVE--CONTROL OF THE BRAIN VASCULATUREP01NS035611 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI MOSKOWITZ, MICHAEL A. · 1996 to 2011
$15.4M
Metabolomic predictors of insulin resistance and diabetesR01DK081572 · NIDDK · VANDERBILT UNIVERSITY · PI CLISH, CLARY B, GERSZTEN, ROBERT E · 2008 to 2025
$9.3M
Patients undergoing alcohol septal ablation for hypertrophic cardiomyopathyU01HL081341 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI GERSZTEN, ROBERT E · 2005 to 2008
$5.3M
Discovery & Application of Proteomics-Based Biomarkers of Early Myocardial InjuryR01HL096738 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI CARR, STEVEN A, GERSZTEN, ROBERT E · 2009 to 2012
$2.9M
Computational Methods for Directed Functional GenomicsR01HG003224 · NHGRI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI ROTH, FREDERICK P · 2005 to 2010
$2.2M
NHGRI NIH HHS HG0017115NHGRI NIH HHS HG003224NHGRI NIH HHS HG004233NHGRI NIH HHS P50 HG004233NHGRI NIH HHS R01 HG003224NHLBI NIH HHS HL081341NHLBI NIH HHS HL096738NHLBI NIH HHS R01 HL096738NHLBI NIH HHS T32 HL007208NHLBI NIH HHS U01 HL081341NIDDK NIH HHS DK081572-02NIDDK NIH HHS R01 DK081572NINDS NIH HHS NS035611NINDS NIH HHS P01 NS035611
6 · The paper itself

Abstract

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

Indexed as

MetabolomeModels, BiologicalAmino Acid Transport SystemsCarrier ProteinsCluster AnalysisComputational BiologyExtracellular SpaceGABA Plasma Membrane Transport ProteinsGlucoseGlucose Metabolism DisordersGlucose Tolerance TestHumansIntracellular SpaceMetabolic Networks and PathwaysPrincipal Component AnalysisAmino Acid Transport Systemsbetaine plasma membrane transport proteinsCarrier ProteinsGABA Plasma Membrane Transport ProteinsGlucose

Identifiers

PMID20195502
PMCPMC2829050
OpenAlexW2130147841

What OpenQuestion holds

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LicenceCC BY
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.