Evidence map›Paper›PMID 28577020›Full record

ArticleScientific reports2017

Assessment of the diagnostic value of different biomarkers in relation to various stages of diabetic nephropathy in type 2 diabetic patients.

Khalid Al-Rubeaan, Khalid Siddiqui, Mohammed A Al-Ghonaim, Amira M Youssef, Ahmed H Al-Sharqawi, Dhekra AlNaqeb

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
6.7field-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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
    Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Pathomechanisms of Diabetic Kidney Disease.Journal of clinical medicine · 2023
    Review
  16. Article
  17. Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023
    Review
  18. Article
  19. Review
  20. 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

6 authors at 1 institution in 1 country.

Khalid Al-RubeaanUniversity Diabetes Center, College of medicine, King Saud University, Riyadh, PO Box 18397, Saudi Arabia. krubeaan@dsrcenter.org.
Khalid SiddiquiStrategic Center for Diabetes Research, King Saud University, Riyadh, PO Box 245, Saudi Arabia.ORCID 0000-0003-4860-1262
Mohammed A Al-GhonaimCollege of medicine, King Khalid University Hospital, King Saud University, Riyadh, PO Box 45299, Saudi Arabia.
Amira M YoussefRegistry Department, University Diabetes Center, King Saud University, Riyadh, PO Box 245, Saudi Arabia.
Ahmed H Al-SharqawiUniversity Diabetes Center, College of medicine, King Saud University, Riyadh, PO Box 18397, Saudi Arabia.
Dhekra AlNaqebResearch Department, University Diabetes Center, King Saud University, PO Box 245, Riyadh, Saudi Arabia.
King Saud University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Albuminuria is widely used to indicate early phases of diabetic nephropathy although it is limited by the fact that structural damage might precede albumin excretion. This necessitates identifying better biomarkers that diagnose or predict diabetic nephropathy. This is a cross-sectional hospital based study recruiting type 2 diabetic patients cohort aged 35-75 years with diabetes duration of ≥10 years. Out of total eligible 467 patients, 200 patients were with normal albumin excretion, 184 patients with microalbuminuria and 83 patients with macroalbuminuria. All the patients were tested for the 22 selected biomarkers including serum, plasma and urinary markers. Sensitivity, specificity, and area under the curve (AUC) were calculated as measures of diagnostic accuracy. Out of the tested biomarkers, urinary transferrin, urinary Retinol binding protein (RBP) and serum osteopontin had the best diagnostic value for diabetic nephropathy presence based on the AUC value. The rest of the biomarkers had comparatively less or even no discriminative power. The urinary transferrin and RBP and serum osteopontin, had the best diagnostic value in type 2 diabetic patients at different stages of diabetic nephropathy. Further longitudinal prospective studies are needed to evaluate the predictive power of those markers for detecting diabetic nephropathy before any structural damage occurs.

Indexed as

BiomarkersArea Under CurveCohort StudiesDiabetes Mellitus, Type 2Diabetic NephropathiesHumansMiddle AgedReproducibility of ResultsSensitivity and SpecificitySeverity of Illness IndexBiomarkers

Identifiers

PMID28577020
PMCPMC5457399
OpenAlexW2619559855

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.