Evidence map›Paper›PMID 31832131›Full record

ArticleTherapeutic advances in endocrinology and metabolism2019

Evaluation of urinary biomarkers for prediction of diabetic kidney disease: a propensity score matching analysis.

Yongzhang Qin, Shuang Zhang, Xiaofang Shen, Shunming Zhang, Jingyu Wang, Minxia Zuo, Xiao Cui, Zhongai Gao, Juhong Yang, Hong Zhu and 1 more

Abstract read
In one paragraph

Article in Therapeutic advances in endocrinology and metabolism, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–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

18 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
    Review
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  6. Observational
  7. Review
  8. 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

11 authors.

Yongzhang QinNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.ORCID https://orcid.org/0000-0002-8148-0864
Shuang ZhangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Xiaofang ShenNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Shunming ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Jingyu WangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Minxia ZuoNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Xiao CuiNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Zhongai GaoNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Juhong YangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Hong ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Baocheng ChangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to evaluate the diagnostic value of six urinary biomarkers for prediction of diabetic kidney disease (DKD).

methodsThe cross-sectional study recruited 1053 hospitalized patients with type 2 diabetes mellitus (T2DM), who were categorized into the diabetes mellitus (DM) with normoalbuminuria (NA) group (

resultsPatients with DKD had higher levels of all six urinary biomarkers. All indicators demonstrated significantly increased risk of DKD, except for GAL and β2MG. Single RBP yielded the greatest area under the curve (AUC) value of 0.920 compared with the other five markers, followed by TF (0.867) and IgG (0.867). However, GAL, NAG, and β2MG were shown to have a weak prognostic ability. The diagnostic values of the different combinations were not superior to the single RBP.

conclusionsRBP, TF, and IgG could be used as reliable or good predictors of DKD. The combined use of these biomarkers did not improve DKD detection.

Indexed as

biomarkerdiabetesdiabetic kidney diseasepropensity score matching

Identifiers

PMID31832131
PMCPMC6887810

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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.