Evidence map›Paper›PMID 23894188›Full record

ArticleDiabetes2013

A new strategy for early diagnosis of type 2 diabetes by standard-free, label-free LC-MS/MS quantification of glycated peptides.

Mei Zhang, Wei Xu, Yulin Deng

Open access · hybridAbstract read
In one paragraph

Article in Diabetes, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
  2. The chemical language of protein glycation.Nature chemical biology · 2025
    Review
  3. Review
  4. Review
  5. Article
  6. Maillard Proteomics: Opening New Pages.International journal of molecular sciences · 2017
    Review
  7. Article
  8. Article
  9. Article
  10. 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

3 authors at 1 institution in 1 country.

Mei ZhangSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Wei Xu
Yulin Deng
Beijing Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The early diagnosis of diabetes, one of the top three chronic incurable diseases, is becoming increasingly important. Here, we investigated the applicability of an (18)O-labeling technique for the development of a standard-free, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the early diagnosis of type 2 diabetes mellitus (T2DM). Rather than attempting to identify quantitative differences in proteins as biomarkers, glycation of the highest abundance protein in human plasma, human serum albumin (HSA), was monitored through quantitative analysis of HSA characteristic peptides using the (18)O-labeling technique. Eight glucose-sensitive peptides and one glucose-insensitive peptide were discovered. The glucose-insensitive peptide served as the internal standard, and a standard-free, label-free LC-MS/MS method was developed. This method was then used to select putative biomarkers for T2DM in a clinical trial with 389 human plasma samples. As a result, three of the eight glucose-sensitive peptides (FKDLGEENFK, LDELRDEGK, and KVPQVSTPTLVEVSR) were selected and could be used as potential biomarkers for the early diagnosis of T2DM.

Indexed as

Oxygen IsotopesAdultAgedAmino Acid SequenceBiomarkersChromatography, LiquidDiabetes Mellitus, Type 2Early DiagnosisFemaleGlucose IntoleranceGlycated Serum AlbuminGlycation End Products, AdvancedGlycosylationHumansMaleMiddle AgedBiomarkersGlycated Serum AlbuminGlycation End Products, AdvancedOxygen IsotopesPeptide FragmentsSerum Albumin

Identifiers

PMID23894188
PMCPMC3806625
OpenAlexW2117873514

What OpenQuestion holds

Textmetadata
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.