Evidence map›Paper›PMID 36117980›Full record

SynthesisFrontiers in medicine2022

Performance of urinary liver-type fatty acid-binding protein in diabetic nephropathy: A meta-analysis.

Li Zhang, Shuai Xue, Meiyan Wu, Dan Dong

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2022. 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
1.0field-weighted citation impact, top 23% 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, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
    Review
  7. Review
  8. Urinary L-FABP: A Novel Biomarker for Evaluating Diabetic Nephropathy Onset and Progression. A Narrative Review.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025
    Review
  9. Article
  10. 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

4 authors at 2 institutions in 1 country.

Li ZhangDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Shuai XueThyroid Surgery Department, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Meiyan WuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Dan DongDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Jilin University · CNFirst Hospital of Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Diabetic nephropathy (DN) is one of the main causes of chronic kidney disease (CKD), which increases the risk of cardiovascular diseases and progresses to end-stage renal failure. Thus, early diagnostic markers for diabetic patients are urgently needed to improve the prognosis of DN and predict DN progression. Materials and methods: PubMed, MEDLINE, EMBASE, and Scopus were searched for publications until February 24, 2021. Review Manager 5.4 software was used for meta-analysis. We performed the heterogeneity test using the I Results: We included 13 studies. The urinary liver-type fatty acid-binding protein (uL-FABP) concentrations in the normal albuminuria group were significantly higher than those in the normal control group without diabetes mellitus (DM) [ Conclusion: Urinary L-FABP may be a potential marker for the detection of all stages of DN and for the prediction of the progression and severity of DN in patients with type 1 and 2 DM.

Indexed as

biomarkerschronic kidney disease (CKD)diabetic kidney diseasemeta-analysisurinary liver-type fatty acid binding protein

Identifiers

PMID36117980
PMCPMC9479543
OpenAlexW4294238419

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.