Evidence map›Paper›PMID 28359252›Full record

Observational studyBMC nephrology2017

Early urinary biomarkers of diabetic nephropathy in type 1 diabetes mellitus show involvement of kallikrein-kinin system.

Lenka Vitova, Zdenek Tuma, Jiri Moravec, Milan Kvapil, Martin Matejovic, Jan Mares

Abstract readObservational Study
In one paragraph

Observational study in BMC nephrology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Review
  8. Is there a role for proteomics in diabetic renal disease?Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020
    Article
  9. Article
  10. Innate immunity in diabetic kidney disease.Nature reviews. Nephrology · 2020
    Review
  11. Article
  12. Review
  13. Review
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

6 authors.

Lenka VitovaDepartment of Internal Medicine, Teaching Hospital Motol, V Uvalu 84, Prague, 5, 150 06, Czech Republic. lenka.vitova@fnmotol.cz.ORCID http://orcid.org/0000-0002-8043-2334
Zdenek TumaProteomic Laboratory, Charles University School of Medicine in Pilsen, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic.
Jiri MoravecProteomic Laboratory, Charles University School of Medicine in Pilsen, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic.
Milan KvapilDepartment of Internal Medicine, Teaching Hospital Motol, V Uvalu 84, Prague, 5, 150 06, Czech Republic.
Martin MatejovicDepartment of Internal Medicine I, Charles University School of Medicine in Pilsen, alej Svobody 80, Pilsen, 304 60, Czech Republic.
Jan MaresProteomic Laboratory, Charles University School of Medicine in Pilsen, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdditional urinary biomarkers for diabetic nephropathy (DN) are needed, providing early and reliable diagnosis and new insights into its mechanisms. Rigorous selection criteria and homogeneous study population may improve reproducibility of the proteomic approach.

methodsLong-term type 1 diabetes patients without metabolic comorbidities were included, 11 with sustained microalbuminuria (MA) and 14 without MA (nMA). Morning urine proteins were precipitated and resolved by 2D electrophoresis. Principal component analysis (PCA) and Projection to latent structures discriminatory analysis (PLS-DA) were adopted to assess general data validity, to pick protein fractions for identification with mass spectrometry (MS), and to test predictive value of the resulting model.

resultsProteins (n = 113) detected in more than 90% patients were considered representative. Unsupervised PCA showed excellent natural data clustering without outliers. Protein spots reaching Variable Importance in Projection score above 1 in PLS (n = 42) were subjected to MS, yielding 33 positive identifications. The PLS model rebuilt with these proteins achieved accurate classification of all patients (R2X = 0.553, R2Y = 0.953, Q2 = 0.947). Thus, multiple earlier recognized biomarkers of DN were confirmed and several putative new biomarkers suggested. Among them, the highest significance was met in kininogen-1. Its activation products detected in nMA patients exceeded by an order of magnitude the amount found in MA patients.

conclusionsReducing metabolic complexity of the diseased and control groups by meticulous patients' selection allows to focus the biomarker search in DN. Suggested new biomarkers, particularly kininogen fragments, exhibit the highest degree of correlation with MA and substantiate validation in larger and more varied cohorts.

Indexed as

Kallikrein-Kinin SystemAdultAlbuminuriaBiomarkersDiabetic NephropathiesEarly DiagnosisFemaleHumansMaleNephronsProteinsReproducibility of ResultsSensitivity and SpecificityBiomarkersProteinsDiabetesKallikrein-kinin systemNephropathyProteomics

Identifiers

PMID28359252
PMCPMC5372325

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.