Evidence map›Paper›PMID 21487016›Full record

ArticleThe Journal of biological chemistry2011

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus.

Andrew D Patterson, Jessica A Bonzo, Fei Li, Kristopher W Krausz, Gabriel S Eichler, Sadaf Aslam, Xenia Tigno, John N Weinstein, Barbara C Hansen, Jeffrey R Idle and 1 more

Open access · hybridAbstract read
In one paragraph

Article in The Journal of biological chemistry, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.3field-weighted citation impact, top 5% 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

38 citing papers in PubMed, 78 citations in OpenAlex.

  1. Article
  2. The Functions of SARS-CoV-2 Receptors in Diabetes-Related Severe COVID-19.International journal of molecular sciences · 2024
    Review
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  10. Vitamin B6 and Diabetes: Relationship and Molecular Mechanisms.International journal of molecular sciences · 2020 · on this map
    Review
  11. Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification.Journal of the American Society for Mass Spectrometry · 2019
    Article
  12. Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites.Metabolomics : Official journal of the Metabolomic Society · 2018
    Article
  13. Article
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  15. Article
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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

11 authors at 4 institutions in 2 countries.

Andrew D PattersonLaboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Jessica A Bonzo
Fei Li
Kristopher W Krausz
Gabriel S Eichler
Sadaf Aslam
Xenia Tigno
John N Weinstein
Barbara C Hansen
Jeffrey R Idle
Frank J Gonzalez
National Institutes of Health · USUniversity of South Florida · USCenter for Cancer Research · USUniversity of Bern · CH

Funding

Xenobiotic receptorsZIABC005562 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI GONZALEZ, FRANK J · 2009 to 2025
$26.9M
OBESITY, DIABETES, &AGING ANIMAL RESOURCESN01AG042100 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI HANSEN, BARBARA C · 1994 to 1997
–
Intramural NIH HHSNIA NIH HHS AG-42100NIA NIH HHS N01-AG-3-1012PHS HHS HHSN26300800022C
6 · The paper itself

Abstract

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Indexed as

Amino Acid Transport Systems, NeutralAnimalsBetaineCitric AcidDiabetes Mellitus, ExperimentalDiabetes Mellitus, Type 2GlucoseGlycosuriaHumansKidney Tubules, ProximalKynurenic AcidMacaca mulattaMaleMetabolomicsMicePipecolic AcidsAmino Acid Transport Systems, NeutralBetaineCitric AcidGlucoseKynurenic Acidpipecolic acidPipecolic AcidsRNA, Messenger

Identifiers

PMID21487016
PMCPMC3103330
OpenAlexW2048914567

What OpenQuestion holds

Textmetadata
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.