Evidence map›Paper›PMID 31409386›Full record

ArticleJournal of translational medicine2019

The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases.

Gang Wang, Jian Ouyang, Shen Li, Hui Wang, Baofeng Lian, Zhihong Liu, Lu Xie

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 4 pooled it
8.7field-weighted citation impact, top 1% 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

45 citing papers in PubMed, 4 syntheses or guidelines pooled it, 94 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Gang WangDivision of Nephrology, Jinling Hospital, Southern Medical University, Nanjing, 210016, China.
Jian OuyangShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China.
Shen LiNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210016, China.
Hui WangNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210016, China.
Baofeng LianShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China.
Zhihong LiuDivision of Nephrology, Jinling Hospital, Southern Medical University, Nanjing, 210016, China. liuzhihong@nju.edu.cn.
Lu XieShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China. luxiex2017@outlook.com.ORCID 0000-0001-7541-2243
Nanjing General Hospital of Nanjing Military Command · CNShanghai Academy of Science & Technology · CN

Funding

National Key Research and Development Program of China 2016YFC0904101
6 · The paper itself

Abstract

backgroundDiabetic nephropathy (DN) affects about 40% of diabetes mellitus (DM) patients and is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) all over the world, especially in high- and middle-income countries. Most DN has been present for years before it is diagnosed. Currently, the treatment of DN is mainly to prevent or delay disease progression. Although many important molecules have been discovered in hypothesis-driven research over the past two decades, advances in DN management and new drug development have been very limited. Moreover, current animal/cell models could not replicate all the features of human DN, while the development of Epigenetics further demonstrates the complexity of the mechanism of DN progression. To capture the key pathways and molecules that actually affect DN progression from numerous published studies, we collected and analyzed human DN prognostic markers (independent risk factors for DN progression).

methodsOne hundred and fifty-one DN prognostic markers were collected manually by reading 2365 papers published between 01/01/2002 and 12/15/2018. One hundred and fifteen prognostic markers of other four common CKDs were also collected. GO and KEGG enrichment analysis was done using g:Profiler, and a relationship network was built based on the KEGG database. Tissue origin distribution was derived mainly from The Human Protein Atlas (HPA), and a database of these prognostic markers was constructed using PHP Version 5.5.15 and HTML5.

resultsSeveral pathways were significantly enriched corresponding to different end point events. It is shown that the TNF signaling pathway plays a role through the process of DN progression and adipocytokine signaling pathway is uniquely enriched in ESRD. Molecules, such as TNF, IL6, SOD2, etc. are very important for DN progression, among which, it seems that "AGER" plays a pivotal role in the mechanism. A database, dbPKD, was constructed containing all the collected prognostic markers.

conclusionsThis study developed a database for all prognostic markers of five common CKDs, offering some bioinformatics analyses of DN prognostic markers, and providing useful insights towards understanding the fundamental mechanism of human DN progression and for identifying new therapeutic targets.

Indexed as

Databases as TopicDisease ProgressionBiomarkersDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Diabetic NephropathiesEndpoint DeterminationHumansPrognosisRenal Insufficiency, ChronicRisk FactorsUser-Computer InterfaceBiomarkersBioinformatics analysisDatabaseDiabetic nephropathyPrognostic markerProgressionRisk factor

Identifiers

PMID31409386
PMCPMC6693179
OpenAlexW2968804032

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LicenceCC BY
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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.