Evidence map›Paper›PMID 30318341›Full record

ArticleCell metabolism2019

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk.

Elizabeth T Cirulli, Lining Guo, Christine Leon Swisher, Naisha Shah, Lei Huang, Lori A Napier, Ewen F Kirkness, Tim D Spector, C Thomas Caskey, Bernard Thorens and 2 more

Abstract read
In one paragraph

Article in Cell metabolism, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 216 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
216citing papers in PubMed, 4 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

216 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  10. Leptin-Mediated Changes in the Human Metabolome.The Journal of clinical endocrinology and metabolism · 2020
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156 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Elizabeth T CirulliHuman Longevity, Inc., San Diego, CA 92121, USA. Electronic address: liz.cirulli@gmail.com.
Lining GuoMetabolon, Inc., Durham, NC, USA.
Christine Leon SwisherHuman Longevity, Inc., San Diego, CA 92121, USA.
Naisha ShahHuman Longevity, Inc., San Diego, CA 92121, USA.
Lei HuangHuman Longevity, Inc., San Diego, CA 92121, USA.
Lori A NapierHuman Longevity, Inc., San Diego, CA 92121, USA.
Ewen F KirknessHuman Longevity, Inc., San Diego, CA 92121, USA.
Tim D SpectorDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
C Thomas CaskeyMolecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Bernard ThorensCenter for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
J Craig VenterJ. Craig Venter Institute, La Jolla, CA 92037, USA.
Amalio TelentiThe Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: atelenti@scripps.edu.

Funding

Scripps Translational Science InstituteUL1TR002550 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2018 to 2022
$28.9M
Department of HealthMedical Research Council MR/M004422/1NCATS NIH HHS UL1 TR002550Wellcome Trust
6 · The paper itself

Abstract

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Indexed as

AdultAgedAged, 80 and overBody Mass IndexCohort StudiesFemaleHumansMaleMetabolomicsMiddle AgedObesityRisk FactorsTwinsWhole Genome Sequencinghealthhuman geneticsMC4Rmetabolomicsobesitypolygenic risk scoresrare variants

Identifiers

PMID30318341
PMCPMC6370944

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.