Evidence map›Paper›PMID 41323135›Full record

ArticleFood & nutrition research2025

Integrating network pharmacology, transcriptomics, molecular docking and in vitro experiments to investigate the material basis and mechanism of Liuwei Dihuang Pills for treating diabetic nephropathy.

Chaoqun Liu, Xiaopeng Zhao, Anlei Yuan, Jiaye Tian, Bin Yu, Zewen Wang, Zhenzhen Xu, Yanxia Liu, Shijie Bi, Liansheng Qiao and 2 more

Abstract read
In one paragraph

Article in Food & nutrition research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Chaoqun Liu *Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Xiaopeng Zhao *Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Anlei YuanKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Jiaye TianKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Bin YuKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Zewen WangKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Zhenzhen XuKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Yanxia LiuKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Shijie BiKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Liansheng QiaoKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Zhaozhou LinKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Yanling ZhangKey Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetic nephropathy (DN) is a common microvascular complication of diabetes. Objective: This study aimed to explore the substance basis and mechanism of action of LW in treating DN. Design: The potential mechanism of LW was investigated through UPLC/LTQ-Orbitrap-MS and network pharmacology. The activity of LW and its constituent herbs was evaluated in human renal glomerular endothelial cells (HRGEC) and HK2 cells. Key pathways and targets were identified by transcriptomics and validated by qRT-PCR. Molecular docking was employed to screen for active ingredients of LW. Results: The results showed that LW, Cornus Officinalis (CO), Moutan Cortex (MC), Rhizoma Dioscoreae (RD), and Alismatis Rhizoma (AR) mitigated oxidative damage in HRGEC cells. LW primarily targeted VEGFA and EGR1, thereby modulating the AGE-RAGE signaling pathway in diabetic complications. LW also reduced fibrosis in HK2 cells by up-regulating BMP4 and modulating the TGF-beta signaling pathway. Rehmanniae Radix Praeparata (RRP), CO, MC, RD, and Poria Cocos (PC) were identified as key contributors to improving renal fibrosis. Additionally, 43 potential active ingredients were identified in LW, 13 of which exhibited favorable ADMET properties. Six key ingredients, including taxifolin, cianidanol, gallic acid, caffeic acid, 5-hydroxymethylfurfural, and paeonol were found to be primarily responsible for the effects of LW on microvascular endothelial injury and renal fibrosis. Conclusion: In summary, these findings reveal the material basis and mechanism of LW against DN, providing a foundation for its clinical application.

Indexed as

diabetic nephropathyLiuwei Dihuang Pillsmaterial basismechanism

Identifiers

PMID41323135
PMCPMC12664296

What Socratic holds

Textmetadata
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 Socratic graph.