Evidence map›Paper›PMID 25713200›Full record

ReviewDiabetes2015

Metabolomics and diabetes: analytical and computational approaches.

Kelli M Sas, Alla Karnovsky, George Michailidis, Subramaniam Pennathur

Open access · bronzeAbstract readReview
In one paragraph

Review in Diabetes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed
8.0field-weighted citation impact, top 2% 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

84 citing papers in PubMed, 166 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Review
  6. Use of technology in prediabetes and precision prevention.Journal of diabetes investigation · 2025
    Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Metabolomics Insights into the Benefits of SGLT2 Inhibitors in Type 2 Diabetes.Clinical pharmacology : advances and applications · 2025
    Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Large-Scale Population-Based Studies of Blood Metabolome and Brain Health.Current topics in behavioral neurosciences · 2024
    Review
  19. Article
  20. Article

24 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

4 authors at 1 institution in 1 country.

Kelli M SasDivision of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
Alla KarnovskyDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI.
George MichailidisDepartment of Statistics, University of Michigan, Ann Arbor, MI.
Subramaniam PennathurDivision of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI spennath@umich.edu.
University of Michigan–Ann Arbor · US

Funding

Michigan Institute for Clinical and Health Research (MCHR)UL1TR000433 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$49.9M
Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mehboob A Hussain · 2013 to 2026
$24.3M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Joyce Lee · 2010 to 2026
$20.3M
Integrated Systems Biology Approach to Diabetic Microvascular ComplicationsR24DK082841 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABCOUWER, STEVEN F, BROSIUS, FRANK C · 2008 to 2020
$13.1M
University of Michigan O'Brien Kidney Translational Core CenterP30DK081943 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PENNATHUR, SUBRAMANIAM · 2008 to 2022
$12.9M
Statistics and Bioinformatics Core - WangU24DK097153 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BURANT, CHARLES F · 2012 to 2017
$10.1M
Tissue-Specific Metabolic Reprogramming in Diabetic ComplicationsDP3DK094292 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BROSIUS, FRANK C, BURANT, CHARLES F · 2011 to 2011
$5.5M
NCATS NIH HHS 2UL1-TR-000433NCATS NIH HHS UL1 TR000433NIDDK NIH HHS DK-081943NIDDK NIH HHS DK-082841NIDDK NIH HHS DK-089503NIDDK NIH HHS DK-094292NIDDK NIH HHS DK-097153NIDDK NIH HHS DP3 DK094292NIDDK NIH HHS P30 DK020572NIDDK NIH HHS P30 DK081943NIDDK NIH HHS P30 DK089503NIDDK NIH HHS R24 DK082841NIDDK NIH HHS U24 DK097153
6 · The paper itself

Abstract

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

Indexed as

AnimalsDiabetes MellitusHumansMagnetic Resonance SpectroscopyMass SpectrometryMetabolomics

Identifiers

PMID25713200
PMCPMC4338589
OpenAlexW2011444679

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.