Evidence map›Paper›PMID 35655297›Full record

SynthesisBMC endocrine disorders2022

Diagnostic value of serum cystatin C for diabetic nephropathy: a meta-analysis.

Xueling Liao, Yan Zhu, Chao Xue

Abstract readMeta-Analysis
In one paragraph

Synthesis in BMC endocrine disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
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  10. [High serum cystatin C is an independent risk factor for poor renal prognosis in IgA nephropathy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025
    Article
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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.

Xueling LiaoDepartment of Nephrology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, China.
Yan ZhuDepartment of Nephrology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, China.
Chao XueDepartment of Nephrology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, China. xuechao@stu.gxmu.edu.cn.ORCID http://orcid.org/0000-0002-4850-1029

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough dozens of studies have investigated the relationship between the content of serum cystatin C (Cys-C) and diabetic nephropathy (DN), the results are still controversial. Hence, This study aims to explore the accuracy of serum Cys-C for diagnosing DN by meta-analysis.

methodsThe studies about serum Cys-C diagnosing DN were searched from six online databases from inception to September 22, 2020. The data were processed by Stata 15.0 statistic software. The corresponding diagnostic effect sizes, such as sensitivity and specificity, were obtained. We drew a summary receiver operating characteristic (SROC) curve. We assess the risk of literature bias was following the QUADAS-2 guidelines.

resultsTwenty-six published studies were identified. The results showed a pooled sensitivity of 0.86 (95% confidence interval (CI): 0.82-0.90), specificity of 0.89 (95%CI: 0.85-0.92), positive likelihood ratio of 7.59 (95%CI: 5.66-10.19), negative likelihood ratio of 0.16 (95%CI: 0.12-0.21), and diagnostic odds ratio of 48.03 (95%CI: 30.64-75.29). The area under the SROC curve was given a value of 0.94 (95%CI: 0.91-0.96).

conclusionSerum cystatin C has an excellent diagnostic value with good sensitivity and specificity for diabetic nephropathy.

Indexed as

Diabetes MellitusDiabetic NephropathiesCystatin CHumansOdds RatioROC CurveSensitivity and SpecificityCystatin CCystatin CDiabetic nephropathyDiagnostic valueMeta-analysis

Identifiers

PMID35655297
PMCPMC9164876

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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.