Evidence mapPaperPMID 38217017Full record

ArticleSystematic reviews2024

Renal-protective effects of Chinese medicinal herbs and compounds for diabetic kidney disease in animal models: protocol for systematic review and meta-analysis.

Meifang Liu, Yuan Ming Di, Anthony Lin Zhang, Junhui Chen, Ruobing Wang, Juan Huang, Lei Zhang, Charlie Changli Xue, Xusheng Liu

Open access · goldAbstract read
In one paragraph

Article in Systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 2 institutions in 2 countries.

Meifang Liu *The China-Australia International Research Centre for Chinese Medicine, School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Yuan Ming Di *The China-Australia International Research Centre for Chinese Medicine, School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Anthony Lin ZhangThe China-Australia International Research Centre for Chinese Medicine, School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Junhui Chen *The Second Clinical College of Guangzhou University of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Ruobing Wang *The Second Clinical College of Guangzhou University of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Juan HuangPharmaceutical Research Department for New Drug Development and Authentication of Chinese Medicines, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Lei ZhangState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Charlie Changli XueThe China-Australia International Research Centre for Chinese Medicine, School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia. charlie.xue@rmit.edu.au.
Xusheng LiuState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. liuxusheng@gzucm.edu.cn.
Guangzhou University of Chinese Medicine · CNRMIT University · AU

Funding

Guangzhou Municipal Science and Technology Project 202206010076The State Key Laboratory of Dampness Syndrome of Chinese Medicine in Guangdong Provincial Hospital of Chinese Medicine SZ2021ZZ43
6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) is a common and severe complication of diabetes that can lead to end-stage renal disease with no cure. The first-line drugs recommended by clinical guidelines fail to achieve satisfactory effects for people with DKD. A Chinese herbal medicine Tangshen Qushi Formula (TQF) shows preliminary efficacy and safety in preserving renal function for people with DKD, but the effects on comprehensive renal outcomes remain unclear. We will conduct a systematic review and meta-analysis to evaluate the effects of TQF herbs and their compounds identified from ultra-high performance liquid chromatography-MS/MS in diabetic animal models with renal outcomes.

methodsThis protocol complies with the guideline Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. We will include studies investigating the effects of TQF herbs and compounds on diabetic rats or mice with renal outcomes. Six electronic databases will be searched from their inception to February 2023. Quality assessment will be conducted using SYRCLE's risk of bias tool. Standardized or weighted mean differences will be estimated for renal outcomes (creatinine, urea, proteinuria, histological changes, oxidative stress, inflammation, and kidney fibrosis). Data will be pooled using random-effects models. Heterogeneity across studies will be expressed as I DISCUSSION: The results of this review will provide valuable insights into the potential effects of TQF in managing DKD. The limitation is that the included studies will be animal studies from specific databases, and the interpretation of the findings must be cautious. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023432895. Registered on 19 July 2023 ( https://www.crd.york.ac.uk/PROSPERO/#recordDetails ).

Indexed as

Diabetes Mellitus, ExperimentalDiabetic NephropathiesPlants, MedicinalAnimalsHumansKidneyMeta-Analysis as TopicMiceRatsSystematic Reviews as TopicTandem Mass Spectrometry

Identifiers

PMID38217017
PMCPMC10785383
OpenAlexW4390790961

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.