Evidence map›Paper›PMID 22872235›Full record

ArticleDiabetes2012

Urinary proteomics for early diagnosis in diabetic nephropathy.

Petra Zürbig, George Jerums, Peter Hovind, Richard J Macisaac, Harald Mischak, Stine E Nielsen, Sianna Panagiotopoulos, Frederik Persson, Peter Rossing

Open access · greenAbstract read
In one paragraph

Article in Diabetes, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 3 pooled it
15.2field-weighted citation impact, top 1% 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

112 citing papers in PubMed, 3 syntheses or guidelines pooled it, 269 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.Journal of the American Society of Nephrology : JASN · 2015
    Trial
  6. Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2014
    Trial
  7. Trial
  8. Article
  9. A Five-Plasma Protein-Based Algorithm for Predicting Incident CKD in Type 2 Diabetes.Journal of the American Society of Nephrology : JASN · 2026
    Article
  10. Diabetic kidney disease, biomarkers, and finerenone.Diabetes, obesity & metabolism · 2026
    Review
  11. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
    Review
  12. Article
  13. Article
  14. Review
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  16. Article
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  19. Article
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52 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 4 countries.

Petra Zürbigmosaiques diagnostics GmbH, Hannover, Germany. zuerbig@mosaiques-diagnostics.com
George Jerums
Peter Hovind
Richard J Macisaac
Harald Mischak
Stine E Nielsen
Sianna Panagiotopoulos
Frederik Persson
Peter Rossing
Steno Diabetes Center · DKAustin Health · AUMosaiques Diagnostics and Therapeutics (Germany) · DESt Vincent's Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis-coupled mass spectrometry was used to profile the low-molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3-5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.

Indexed as

AlbuminuriaDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Diabetic NephropathiesElectrophoresis, CapillaryHumansMass SpectrometryProteomics

Identifiers

PMID22872235
PMCPMC3501878
OpenAlexW2099000521

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.