
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-07716
00056
10.1097/MD.0000000000039538
3
4200
Research Article
Systematic Review and Meta-Analysis
Insights into the mechanism of Danggui Liuhuang Tang in treating night sweats: A network pharmacology and molecular docking approach
https://orcid.org/0009-0009-5408-6413
Qiu Jinling MM 20211121626@stu.gzucm.edu.cn
a
Huang Xingran MM 17322058787@163.com
a
Li Hongyang MM 693154897@qq.com
a
Jin Shuying MM 492866835@qq.com
a
Yang Ruo MM 137908705@qq.com
a
https://orcid.org/0009-0001-9211-7892
Gu Wei BD a*
a Huizhou Hospital of Guangzhou University of Chinese Medicine (Huizhou Hospital of Traditional Chinese Medicine), Huizhou, China.
* Correspondence: Wei Gu, Huizhou Hospital of Guangzhou University of Chinese Medicine (Huizhou Hospital of Traditional Chinese Medicine), Huizhou 516000, China (e-mail: gw13927304271@163.com).
06 9 2024
06 9 2024
103 36 e3953809 7 2024
06 8 2024
12 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Background:

Night sweats can occur independently or in association with a number of medical conditions and can significantly disrupt daily life. This study focuses on the treatment of primary night sweats. Despite the considerable interest in Danggui Liuhuang Tang (DGLHT), an effective traditional Chinese medicine formula, its mechanism of action remains unknown. There is also no existing literature on the subject.

Methods:

Network pharmacology and molecular docking techniques.

Results:

Network pharmacology techniques were employed to identify 109 active ingredients and 808 potential targets of DGLHT, as well as 2385 targets associated with night sweating diseases. The screening process yielded 375 common targets shared between DGLHT and night sweating. These included the active ingredients baicalein, quercetin, huarangiin, and tetrahydroafrican antipyrine, and the core targets interleukin 6, serine/threonine protein kinase 1, tumor necrosis factor, GAPDH enzyme, and Src protein kinase were identified. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that DGLHT exerts its therapeutic effects primarily by modulating the PI3K–Akt signaling pathway, neuroactive ligand–receptor interactions, lipid metabolism, and atherosclerosis pathways. Molecular docking revealed strong binding activity between the main active ingredients and their potential targets.

Conclusion:

The research identifies promising active ingredients and targets related to the effectiveness of DGLHT in controlling night sweats, thus contributing to the further exploration of potential therapeutics for this condition. In addition, the results of this experiment provide a basis for future research into night sweats.

Danggui Liuhuang Tang
molecular docking
network pharmacology
Night sweats
traditional Chinese medicine
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

The term “night sweats” is used to describe a phenomenon known as hot flashes, which are accompanied by sweating and occur during the period of sleep.[1] A systematic analysis found that between 10% and 41% of elderly and middle-aged patients in primary care settings report nocturnal sweats.[2] The sole study on nocturnal night sweats in school-age children, conducted in China, indicated a 12% prevalence of weekly episodes in the past year.[3] The occurrence of night sweats can result in a considerable number of nocturnal awakenings, which in turn has a detrimental impact on the overall quality of sleep. Furthermore, night sweats can precipitate emotional lability, anxiety, and irritability, thereby further compromising the overall quality of life for affected individuals. The process of sweating is associated with both thermoregulatory and non-thermoregulatory mechanisms. Additionally, night sweats have been linked to alterations in circadian rhythm, which are not directly related to thermoregulation. It can be concluded that the mechanisms underlying night sweats are of a multifaceted nature. Night sweats are often linked to a range of underlying medical conditions, including malignant tumors, depression, obesity, hyperthyroidism, hypoglycemia, and menopause.[4–6] Furthermore, the misuse of specific pharmaceutical agents has been linked to an increased prevalence of night sweats.[7] Several strategies are used to treat night sweats, including hormone therapy, non-hormonal therapy, and a combination of traditional Chinese and Western medicine. A variety of therapeutic modalities are employed in the management of night sweats, encompassing hormone therapy, non-hormonal therapy, and a combination of traditional Chinese and Western medicine. Nevertheless, a considerable number of women are precluded from undergoing estrogen replacement therapy due to contraindications, and there is a dearth of comprehensive, conclusive studies on the subject. The efficacy of this approach is variable, and different pharmacological agents can cause adverse effects. A trial was conducted to assess the efficacy of mirtazapine, a tetracyclic antidepressant that primarily targets serotonin, in the treatment of hot flashes and night sweats, given its frequent use in addressing sleep disturbances. The results demonstrated a statistically significant reduction in the incidence of both hot flashes and night sweats, with a mean decrease of approximately 53%. Furthermore, the women in the study exhibited a notable degree of control over these symptoms.[8] However, as each patient responds differently to medications in terms of both efficacy and adverse effects, it is essential to conduct a comprehensive assessment of each patient in order to develop a personalized treatment plan.

Night sweats, originally termed “bedtime sweats” during the Spring and Autumn and Warring States periods, have been documented in the classic traditional Chinese medicine text, Huang Di Nei Jing (The Yellow Emperor Classic of Internal Medicine). In the Han Dynasty, Zhang Zhongjing, in his text “The Essentials of the Golden Chamber,” postulated that individuals with a flat physique and weak pulses often experience night sweats, using the term “night sweats” to describe the phenomenon of sweating during sleep. During the Song Dynasty, Li Dongyuan employed Danggui Liuhuang Tang (DGLHT) to address night sweats, designating it as the “Sacred Remedy for Night Sweats.” Subsequent clinical trials have demonstrated that DGLHT is an effective treatment for night sweats. Gao Jianwei[9] investigated the efficacy of the addition and subtraction of DGLHT in the treatment of night sweats in middle-aged and older individuals. The total effective rate of treatment reached 97.4%, indicating that this formula can effectively improve and relieve night sweats. Zhu Boyu[10] employed the use of DGLHT to address night sweats, resulting in an overall effective rate of 93.21%. Additionally, the average reduction in sweat score was 3.89 points following the course of treatment. It is evident that DGLHT exhibits considerable potential in the management of night sweats; however, its underlying mechanism of action for this condition remains uncertain.

This study employed network pharmacology methods[11,12] and molecular docking techniques.[13–15] The aforementioned computer simulation techniques[16] may elucidate the potential mechanism of DGLHT in the treatment of night sweats. The objective of this study is to establish a theoretical framework for the investigation of the mechanism of DGLHT in the treatment of night sweats, addressing the existing knowledge gap and establishing a foundation for future research.

2. Materials and methods

2.1 . Screening of active ingredients and target genes in DGLHT

A comprehensive search was conducted in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) (https://old.tcmsp-e.com/tcmsp.php) and TCMID (https://www.bidd.group/TCMID/). A chemical composition database for DGLHT was created using the databases TCMSP and TCMID, employing keywords such as Danggui, Huangqi, Shudihuang, Shengdihuang, Huangqin, Huanglian, and Huangbo. The pharmaceutical properties of DGLHT’s active ingredients were evaluated based on oral bioavailability and drug-likeness, with screening criteria set at oral bioavailability ≥ 30% and drug-likeness ≥ 0.18. The effective active ingredients of DGLHT were entered into the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to obtain their chemical structures. The molecular structures in ‘Canonical SMILES’ format were imported into the Swiss Target Prediction database (http://swisstargetprediction.ch/), with Homo sapiens specified as the target species, with the objective of predicting the targets for the active ingredients. A probability threshold exceeding 0 was established to guarantee the dependability of target prediction, and the resulting target data were exported.

In the event that the requisite components were not present in the Swiss database, the corresponding targets were sought in the TCMSP database in order to ascertain their actions. Subsequently, the target data were imported into the UniProt database (https://www.uniprot.org/), with humans designated as the species in question. The nomenclature was normalized and standardized, target proteins lacking corresponding gene names were excluded, and the respective target genes for various active ingredients were retrieved. All drug targets were integrated and categorized, and any that were redundant were eliminated. Furthermore, any active ingredients that lacked specific target interactions were excluded.

2.2. Collection of targets related to night sweats

The Online Mendelian Inheritance in Man (OMIM) database (https://www.omim.org/) and the Gene Comprehensive Database, GeneCards (https://www.genecards.org/), were employed to identify pertinent genes associated with night sweats through the utilization of specific keywords, including “night sweat,” “night-sweat,” “night sweating,” and “night sweating with yin asthenia.” The data were meticulously organized and duplicate entries were eliminated in order to obtain a refined list of night sweats-related target genes.

2.3 . Screening of the potential targets of DGLHT for the treatment of night sweats

A comparative analysis was conducted using the Xiantao academic online graphing platform (https://www.xiantaozi.com/products) to examine the overlapping target points between DGLHT’s active ingredients and those associated with night sweats. A Venn diagram was constructed to facilitate a visual representation of the comparison, thereby identifying potential shared target points for the treatment of night sweats using DGLHT.

2.4 . Drug-active ingredient–intersecting target networks

The active ingredients and intersection target data from DGLHT were imported into Cytoscape 3.8.1 software, with the requisite parameters configured accordingly. A network diagram was constructed to illustrate the relationships between drug–active ingredients and intersection targets. This diagram was saved in the .cys file format. The central active components were subjected to effective screening based on their degree values through the utilization of the “CytoNCA” plugin for topological analysis.

2.5 . Protein–protein interaction (PPI) network construction and topology analysis

The intersected targets obtained in Section 2.3 were imported into the STRING database (https://cn.string-db.org/) by selecting “Multiple proteins” and setting the default organism to “Homo sapiens.” The interaction score threshold was set to 0.400, while the remaining parameters were maintained at their default values. In order to generate a PPI network for the intersected targets, the scattered nodes were concealed. The results were saved in the TSV format and imported into Cytoscape 3.8.1 software for visualization and analysis, in accordance with the formatting guidelines of Cytoscape 3.8.1. The CentiScape 2.2 Menu plugin was employed for the topological analysis with the objective of identifying the core target points. This was achieved through the application of a filtering process based on the median values of degree centrality, betweenness centrality, and closeness centrality.

2.6. Gene ontology (GO) functional annotation of intersecting targets and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

The intersecting target genes identified in Section 2.3 were imported into Xiantao Academic for GO functional annotation and KEGG pathway enrichment analysis. This was undertaken in order to elucidate the underlying mechanism of DGLHT in alleviating nocturnal night sweats. The GO enrichment analysis is a tool that can elucidate and annotate genes across 3 dimensions, namely biological process, cellular component, and molecular function. The KEGG pathway enrichment analysis may be employed for the purpose of conducting pathway analysis in the context of the ‘Human’ species, wherein each pathway is required to comprise a minimum of 3 distinct genes. The Benjamin Hochberg method was employed for the purpose of correcting the P-values, and the resulting data were represented visually through the use of bar graphs and bubble charts.

2.7. Component–core target molecular docking validation

The RCSB PDB database (https://www.rcsb.org/), in conjunction with the literature and UniProt databases, was employed to retrieve the PDB structure of the docking protein. Operations such as desolvation and hydrogenation were conducted using AutoDockTools-1.5.7, with the resulting data saved in pdbqt format. The TCMSP database was employed to retrieve the molecular structure files of the core active ingredients. AutoDockTools-1.5.7 was employed to achieve a balance of charges and to identify rotatable bonds in the small molecules, which were then saved in pdbqt format. The obtained pdbqt files were imported into AutoDockTools-1.5.7 software, and the docking box range was defined based on the receptor’s active site. The results were exported as a GPF file. Autogrid4 was then executed with the specified docking parameters and computational methods. Ligand docking was performed using Autodock4, and the structure with the lowest binding energy was selected. The combination energy was quantified as a negative value, with higher magnitudes indicating greater binding affinity and increased complex stability. This suggests that there is a heightened likelihood of interaction between receptor molecules and ligands. The structure exhibiting the lowest binding energy was selected for export as a PDB file. The file was imported into PyMol 2.5.7 software for optimization purposes, with the objective of generating the final visualization. The interaction between receptor proteins and small molecules was represented in a 2-dimensional structural diagram.

3. Results

3.1. Screening of active ingredients of DGLHT

A total of 113 active ingredients were subjected to screening in DGLHT, among which 14 were Huanglian, 2 were Danggui, 37 were Huangbo, 20 were Huangqi, 36 were Huangqin, 2 were Shudihuang, and 2 were Shengdihuang. Following the processing stage, 808 target interactions were identified. After the exclusion of 4 active ingredients lacking corresponding protein targets, a total of 109 efficacious compounds were identified. A total of 13 active ingredients were identified in 2 or more traditional Chinese medicines, including DGLHT. Detailed information is available in Table S1 (Supplemental Digital Content, http://links.lww.com/MD/N493).

3.2. Collection of the various targets related to night sweats

A comprehensive Genecards database screening identified 6029 target genes with a potential association with night sweats. Furthermore, 3 target genes were identified from the OMIM database. Following the standardization of gene nomenclature and the removal of duplicates, a final set of 2385 target genes was established.

3.3 . Targets of action of DGLHT in the treatment of night sweating

A comprehensive analysis identified 808 potential target proteins associated with the drug’s active ingredients and 2385 target proteins specifically related to night sweats. The potential targets were imported into the Xiantao Academic Website, and a Venn diagram was constructed to illustrate the extent of their overlap. The intersection revealed 375 common targets for both the active ingredients and the disease, indicating the potential of DGLHT as a therapeutic agent for the treatment of night sweats (Figure S1, Supplemental Digital Content, http://links.lww.com/MD/N494).

3.4. Drug–active ingredient–target network construction

The active ingredients and their corresponding intersecting targets were subsequently imported into Cytoscape 3.8.1 software for the purpose of designing a network diagram that would illustrate the potential interactions between the various active ingredients and core targets. This is depicted in Figure S2 (Supplemental Digital Content, http://links.lww.com/MD/N494). The total number of nodes was 452, with 1376 edges involved. In the network diagram, the rectangular nodes represented the drugs and active ingredients, while the diamond-shaped nodes indicated the diseases. The hexagonal nodes represented the shared targets, while the edges illustrated potential associations between drugs, active ingredients, and shared targets. In Figure S2 (Supplemental Digital Content, http://links.lww.com/MD/N494), the color blue represents Huanglian and its active constituents, brown indicates Danggui and its active constituents, purple represents Huangbo and its active constituents, pink denotes Huangqi and its active constituents, green indicates Shengdihuang and its active constituents, orange represents Huangqin and its active constituents, dark yellow indicates diseases, and the light yellow denotes common targets.

The “CytoNCA” plugin was employed for the purpose of topological analysis, with a view to computing the degree centrality values of the chemical components. The highest degree centrality value was 57, while the lowest was 2, with an average of 14.3. Table S2 (Supplemental Digital Content, http://links.lww.com/MD/N493) presents a ranking of the top 10 active components based on their degree of centrality. Wogonin (degree = 57), quercetin (degree = 54), and Jaranol (degree = 54) exhibited the highest degrees, indicating their potential as key active ingredients for the treatment of night sweats with DGLHT.

3.5 . PPI network construction and topology analysis

A PPI network was constructed by importing the 375 overlapping target points from 2.3 into the STRING database, as illustrated in Figure S3 (Supplemental Digital Content, http://links.lww.com/MD/N494). The network diagram comprised 373 nodes and 8512 edges, with an average degree value of 45.6 per node and an average local clustering coefficient of 0.501. The PPI enrichment P-value was found to be below the threshold of 1.0e-16, which indicates a significant role for the proteins with a higher number of adjacent nodes in the network. The TSV file data of the PPI network were imported into Cytoscape 3.8.1 for visualization and subsequent topological analysis, which was conducted using the CentiScape 2.2 Menu. The network was filtered based on 3 threshold values, namely “Betweenness > 385.419354838707,” “Closeness > 0.00134057555281868,” and ‘Degree > 45.763440860215’. Subsequently, the filtered results were optimized to represent the higher degree values with larger nodes and darker colors, in order to facilitate their visual interpretation. Consequently, 75 significant targets were identified, as demonstrated in Figure S4 (Supplemental Digital Content, http://links.lww.com/MD/N494). The key target points for the treatment of night sweats with DGLHT were identified through the application of a degree value ranking system, as detailed in Table S3 (Supplemental Digital Content, http://links.lww.com/MD/N493).

3.6. GO and KEGG pathway enrichment analysis

Following the application of a GO functional annotation analysis to 375 overlapping targets, a total of 3842 entries were obtained, with a P-value of <.05. As illustrated in Figure S5 (Supplemental Digital Content, http://links.lww.com/MD/N494), the top 10 items with the smallest P-values were selected and plotted based on their significance. Among them, 3320 biological process were primarily associated with the following processes: response to xenobiotic stimulus, positive regulation of the MAPK pathway, calcium ion homeostasis, and positive regulation of kinase activity. There were a total of 198 different cellular components, the majority of which were found to be integral components of the membrane raft, membrane microdomain, synaptic membrane, neuronal cell body, and an integral component of the presynaptic membrane. There were a total of 324 primary activities associated with the protein serine/threonine/tyrosine kinase activity, protein tyrosine kinase activity, transmembrane receptor protein tyrosine kinase activity, transmembrane receptor protein kinase activity and protein serine/threonine kinase activity.

KEGG analysis demonstrated enrichment in 49 distinct pathways, ranked in descending order based on the Gene Ratio (the ratio of differentially expressed genes enriched in a specific pathway to the total number of differentially expressed genes that can be enriched in KEGG). Figure S6 (Supplemental Digital Content, http://links.lww.com/MD/N494) illustrates the significantly enriched pathways, including neuroactive ligand–receptor interaction, PI3K–Akt signaling pathway, calcium signaling pathway, lipid and atherosclerosis, and pathways associated with neurodegeneration-multiple diseases.

3.7. Molecular docking

In the drug–target network of DGLHT, wogonin, quercetin, jaranol, and other constituents exhibited high degree values, indicating their potential as crucial active ingredients. The top 5 core targets, IL6, AKT1, TNF, GAPDH, and SRC, which were identified from the PPI network analysis, were subjected to molecular docking with the main potential active ingredients. The findings demonstrated that the active ingredients exhibited binding affinities to the core targets, with minimum binding energies ranging from −7.47 to −4.84 kcal/mol, as shown in Table S4 (Supplemental Digital Content, http://links.lww.com/MD/N493). The existence of a negative combining energy value suggests the possibility of a drug molecule forming a bond with its intended target. A combining energy value ≤−5 kcal/mol indicates a stronger affinity between the molecule and the target, with lower combining energies denoting potentially more favorable docking interactions. The results demonstrated that quercetin demonstrated the highest binding affinity towards AKT1, thereby corroborating the predictive outcomes obtained through network pharmacology analysis. Molecular docking visualization was conducted using PyMol software, as illustrated in Figure S7 (Supplemental Digital Content, http://links.lww.com/MD/N494).

4. Discussion

Night sweats are characterized by an abnormal excretion of sweat, which presents as nocturnal perspiration and is often accompanied by a waking sensation of moisture on the skin.[17] Abnormal sweating has been demonstrated to impact a number of metabolic pathways, thereby leading to the development of metabolic abnormalities that are themselves implicated in disease pathology.[18] This study employed a combination of network pharmacology and molecular docking to investigate the potential active ingredients, targets, and mechanism of action of DGLHT for the treatment of night sweats.

A chemical analysis of DGLHT revealed that its primary active ingredients are flavonoids, alkaloids, glycosides, and sterols. The most potent of these are wogonin, quercetin, jaranol, isocorypalmine, and palmatine. The topological analysis of the Angelica DGLHT–night sweats intersection targets revealed that the 5 core targets: IL6, AKT1, TNF, GAPDH, and SRC were primarily responsible for the majority of the DGLHT-induced therapy of night sweats. The GO and KEGG pathway enrichment analysis indicated that DGLHT exerts its anti-night sweats effect through the PI3K–Akt signaling pathway, neuroactive ligand–receptor interactions, lipid and atherosclerosis pathways, calcium signaling route, and MAPK signaling pathway. In light of the active chemical components, intersecting targets, and enrichment pathway results, it can be postulated that night sweats may be associated with cell growth and apoptosis, oxidative responses, inflammation, lipid and atherosclerosis, and neuroactive ligand–receptor interactions.

The PI3K–Akt signaling pathway[19] plays a pivotal role in regulating glucose metabolism. Phosphatidylinositol 3-kinases (PI3Ks) function as pivotal downstream molecules of tyrosine kinases and G-protein-coupled receptors, facilitating the production of the second messenger 3,4,5-trisphosphatidylinositol (PIP3). This pathway activates downstream molecules including Akt, glycogen synthase kinase-3 (GSK-3), Forkhead transcription factor (FoxO1), and mammalian target of rapamycin (mTOR), thereby transmitting signals from various growth factors and cytokines to cells and regulating a range of biological processes, including cell proliferation, differentiation, apoptosis, and glucose transport. Akt1, a principal downstream molecule of the PI3K signaling pathway, is subjected to phosphorylation through multiple pathways, thereby promoting cell growth and inhibiting apoptosis.[20] It is noteworthy that previous research has demonstrated that wogonin can impede the proliferation and migration of hepatocellular carcinoma cells, as well as induce apoptosis by downregulating the expression of CDK1 and SRC proteins and attenuating the activation of the PI3K/AKT pathway.[21] TNF is a pleiotropic cytokine with immunomodulatory properties that can regulate the apoptosis of inflammatory cells and cause tumorigenesis to be inhibited.[22] Patients with cancer are susceptible to nocturnal hyperhidrosis as a consequence of the adverse effects of therapeutic modalities such as radiotherapy and chemotherapy. Clinical studies[23] have demonstrated that DGLHT can be employed to treat malignant tumors. The total effective rate of treatment in the experimental group was 95.56%, which was higher than that of 77.78% in the control group, and the difference was statistically significant. A recent animal experiment demonstrated that DGLHT-containing serum could effectively inhibit the proliferation and promote the apoptosis of PC-3 cells in prostate cancer. These findings suggest that the anti-PC-3 cell effects of DGLHT may be exerted by inhibiting the EGFR/PI3K/AKT signaling pathway.[24]

The findings of studies conducted by Yali Hou and Wen Zhou[25] indicate that wogonin has the capacity to effectively regulate the expression of a range of rate-limiting enzymes involved in glucose metabolism, thereby reducing oxidative stress and inflammatory responses. The wound healing properties of quercetin have been demonstrated in numerous studies, with its primary mechanism of action being antioxidant activity through the scavenging of free radicals.[26,27] Quercetin and its metabolites have been shown to exert substantial antioxidant effects by inhibiting nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity in vascular smooth muscle cells[28] and also suppress oxidative stress-related inflammatory biomarkers such as malondialdehyde and superoxide dismutase.[29]

IL6 displays both pro-inflammatory and anti-inflammatory characteristics and is capable of regulating metabolic processes, bone formation, and hematopoiesis, which is vital for both innate and adaptive immunity.[30] It has been demonstrated that Garanol can inhibit the activation of extracellular signal-regulated kinase (ERKs) and nuclear factor kappa B (NF-κB), which results in the production of significant anti-inflammatory effects.[31] It has been demonstrated that in individuals with diabetes and glucose intolerance, the function of the sympathetic nerves in the skin is impaired, resulting in poor function of the sweat glands innervated by these nerves. Furthermore, prolonged hyperglycemia can lead to microvascular damage, abnormal responses to environmental changes, and, ultimately, night sweats.[32] A study[33] demonstrated that DGLHT markedly suppresses the secretion of serum inflammatory factors (IL-1β, TNF-α, and IL6) in diabetic mice, effectively regulating the inflammatory response and blood glucose levels. The available evidence indicates that DGLHT exerts a beneficial influence on glycolipid metabolism, suppresses the secretion of pro-inflammatory cytokines, enhances immune and metabolic homeostasis, and alleviates inflammatory responses by effectively regulating the PI3K–Akt signaling pathway in mice.[34]

Chinese physician Wang Zengbao[35] used DGLHT to treat patients with diabetic night sweats. His research found statistically significant differences between the observation and control groups in triacylglycerol and LDL cholesterol levels and night sweat scores. This suggests that DGLHT may regulate sweat secretion through lipid-modulating metabolic pathways. However, in certain studies, DGLHT was observed to suppress serum cholinesterase secretion and reduce sweat gland cell secretion in rats. These observations suggest that DGLHT may have antiperspirant effects by reducing serum cholinesterase.[36]

Molecular docking results showed that quercetin had the highest binding affinity to AKT1. In night sweats caused by metabolic abnormalities, DGLHT, through its active ingredient quercetin and other compounds that activate AKT1 via the PI3K–Akt pathway, may ameliorate endocrine metabolic abnormalities and potentially treat night sweats. This hypothesis requires further experimental testing. The active ingredients in DGLHT, such as wogonin, quercetin, jaranol, isocorypalmine and palmatin, can regulate multiple signaling pathways, including PI3K–Akt, neuroactive ligand–receptor interactions, lipids, and atherosclerosis, through their actions on key targets such as IL6, AKT1, TNF, GAPDH, and SRC. In addition, molecular docking technology has demonstrated that DGLHT’s lead compounds can form a stable docking model with key target proteins. This further supports the potential of DGLHT as a viable treatment for night sweats.

5. Conclusions

This study is the first to investigate the mechanism of DGLHT in the treatment of night sweats. The research provides a theoretical framework for clinical application and paves the way for further research. The findings suggest that DGLHT could effectively treat night sweats through multiple components, targets, and pathways. Due to experimental limitations, this study could not experimentally validate the signaling pathway, leading to certain limitations. Future research should improve experimental procedures and explore the specific mechanism underlying the efficacy of DGLHT in treating night sweats.

Acknowledgments

The authors are thankful to Huizhou Hospital of Guangzhou University of Chinese Medicine for funding this work.

Author contributions

Conceptualization: Jinling Qiu.

Data curation: Hongyang Li.

Funding acquisition: Wei Gu.

Investigation: Jinling Qiu.

Methodology: Jinling Qiu, Xingran Huang.

Project administration: Wei Gu.

Resources: Jinling Qiu, Xingran Huang.

Software: Xingran Huang.

Supervision: Wei Gu.

Validation: Jinling Qiu, Shuying Jin, Ruo Yang.

Visualization: Jinling Qiu, Xingran Huang.

Writing – original draft: Jinling Qiu.

Writing – review & editing: Jinling Qiu, Xingran Huang, Hongyang Li.

Supplementary Material

Abbreviations:

DGLHT Danggui Liuhuang Tang

GO gene ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

OMIM Online Mendelian Inheritance in Man

PPI protein–protein interaction

TCMSP Traditional Chinese Medicine Systems Pharmacology

The study was conducted on data from public databases, so there were no ethical implications.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Qiu J, Huang X, Li H, Jin S, Yang R, Gu W. Insights into the mechanism of Danggui Liuhuang Tang in treating night sweats: A network pharmacology and molecular docking approach. Medicine 2024;103:36(e39538).

JQ and XH contributed equally to this work.
==== Refs
References

[1] Mold JW Lawler F . The prognostic implications of night sweats in two cohorts of older patients. J Am Board Fam Med. 2010;23 :97–103.20051548
[2] Mold JW Mathew MK Belgore S DeHaven M . Prevalence of night sweats in primary care patients: an OKPRN and TAFP-Net collaborative study. J Fam Pract. 2002;51 :452–6.12019054
[3] So HK Li AM Au CT . Night sweats in children: prevalence and associated factors. Arch Dis Child. 2012;97 :470–3.21427123
[4] Herber-Gast GC Mishra GD van der Schouw YT Brown WJ Dobson AJ . Risk factors for night sweats and hot flushes in midlife: results from a prospective cohort study. Menopause. 2013;20 :953–9.23531688
[5] Mold JW Roberts M Aboshady HM . Prevalence and predictors of night sweats, day sweats, and hot flashes in older primary care patients: an OKPRN study. Ann Fam Med. 2004;2 :391–7.15506569
[6] Morales J Schneider D . Hypoglycemia. Am J Med. 2014;127 (10 Suppl ):S17–24.
[7] Mold JW Holtzclaw BJ . Selective serotonin reuptake inhibitors and night sweats in a primary care population. Drugs Real World Outcomes. 2015;2 :29–33.27747615
[8] Tremblay A Sheeran L Aranda SK . Psychoeducational interventions to alleviate hot flashes: a systematic review. Menopause. 2008;15 :193–202.17589375
[9] Jianwei G . Analysis of the efficacy of Danggui Liuhuang Tang in treating yin deficiency and fire-excess type sweating and the effect of spontaneous sweating and night sweats in middle-aged and old-age people. Clin J Chin Med. 2018;10 :100–1.
[10] Boyu Z Haiyan L . Clinical observation on the application of DGLHT in the treatment of sweating syndrome by Liu Haiyan, Director of Traditional Chinese Medicine. China Tradit Chin Med Modern Distance Educ. 2020;18 :73–5.
[11] Yanqin Y Yu Z . Clinical study on the treatment of Night-sweat with acupuncture and medicine. Modern J Integr Med. 2020;29 :4024–8.
[12] Barabási AL Gulbahce N Loscalzo J . Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12 :56–68.21164525
[13] Naqvi AAT Mohammad T Hasan GM Hassan MI . Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structurefunction relationships. Curr Top Med Chem. 2018;18 :1755–68.30360721
[14] Mesquita KDSM Feitosa BS Cruz JN . Chemical Composition and Preliminary Toxicity Evaluation of the Essential Oil from Peperomia circinnata Link var. circinnata. (Piperaceae) in Artemia salina Leach. Molecules. 2021;26 :7359.34885940
[15] Silva LB Ferreira EFB Maryam , . Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach. Molecules. 2023;28 :1035.36770702
[16] Alves FS Rodrigues Do Rego JA Da Costa ML . Spectroscopic methods and in silico analyses using density functional theory to characterize and identify piperine alkaloid crystals isolated from pepper (Piper Nigrum L.). J Biomol Struct Dyn. 2020;38 :2792–9.31282297
[17] Tinghuai W . Physiology, 9th ed. Beijing:People’s Health Press: Beijing, China, 2018; pp. 221–222.35.
[18] Dangsheng X Jiezhong Y Hui F . Heat-powered water excretion is the physiological basis of the solar meridian. China J Basic Chin Med. 2020;26 :25–9.
[19] Chi YJ Li J Guan YF . Regulation of glucose metabolism by PI3K-Akt signalling pathway. Chin J Biochem Mol Biol. 2010;26 :879–85.
[20] Meng L Cai-Ping D . Akt1K64/276 binds to SUMO1 to activate the ERK1/2-EIk1-BDNF signalling pathway. Chin J Biochem Mol Biol. 2022;38 :1661–70.
[21] Anyin Y Hongli L Miaoyang C Yufeng Z Zhiyuan X Yongfeng Y . Exploring the mechanism of action and in vitro experimental study of wogonin in the treatment of hepatocellular carcinoma based on network pharmacology approach. Chin Family Med. 2024;27 :4040–9.
[22] Tong Z Pengtao W Yuting K . Effects of short-chain fatty acids on THP-1 cells and inflammatory responses in COPD mice. J Xi’an Jiaotong Univ (Medical Edition). 2022;43 :361–7.
[23] Chao N . Study on the effect of addition and subtraction of DGLHT in the treatment of Night-sweat in malignant tumour. Chin Pract Med. 2023;18 :144–6.
[24] Hongquan C Zixue S Shijie P . Effects of Danggui Liuhuang Tang-containing serum on GFR/PI3K/AKT signalling pathway in denuded resistant prostate cancer PC-3 cells. Lishizhen Medicine Materia Medica Res. 2024;35 :840–4.
[25] Yali H Wen Z . Effects of wogonin on fasting blood glucose in mice with type 2 diabetes induced by high-fat feeding plus low-dose STZ. Guangdong Med. 2016;37 :2569–72.
[26] Shahane K Kshirsagar M Tambe S . An Updated Review on the Multifaceted Therapeutic Potential of Calendula officinalis L. Pharmaceuticals (Basel). 2023;16 :611.37111369
[27] Muzammil S Neves Cruz J Mumtaz R . Effects of Drying Temperature and Solvents on In Vitro Diabetic Wound Healing Potential of Moringa oleifera Leaf Extracts. Molecules. 2023;28 :710.36677768
[28] Jimenez R Lopez-Sepulveda R Romero M . Quercetin and its metabolites inhibit the membrane NADPH oxidase activity in vascular smooth muscle cells from normotensive and spontaneously hypertensive rats. Food Funct. 2015;6 :409–14.25562607
[29] Shaukat B Mehmood MH Shah S Anwar H . Ziziphus Oxyphylla hydro-methanolic extract ameliorates hypertension in L-NAME induced hypertensive rats through NO/cGMP pathway and suppression of oxidative stress related inflammatory biomarkers. J Ethnopharmacol. 2022;285 :114825.34774683
[30] McElvaney OJ Curley GF Rose-John S McElvaney NG . Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med. 2021;9 :643–54.33872590
[31] Thapa R Afzal O Alfawaz Altamimi AS . Galangin as an inflammatory response modulator: An updated overview and therapeutic potential. Chem Biol Interact. 2023;378 :110482.37044286
[32] Vinik AI Maser RE Mitchell BD Freeman R . Diabetic autonomic neuropathy. Diabetes Care. 2003;26 :1553–79.12716821
[33] Rui Z , Protective effect of DGLHT on pancreatic β-cells in mice with type 1 diabetes and its granule preparation, Diploma Thesis, Qingdao University of Science and Technology, Qingdao, China, 2023.
[34] Rong F . Research on the effect and mechanism of DGLHT on non-alcoholic fatty liver disease, Diploma Thesis, Wuhan: Huazhong University of Science and Technology, Wuhan, China, 2017.
[35] Zengbao W . Clinical efficacy observation of diabetic hyperhidrosis treated with addition and subtraction of DGLHT. Diabetes Mellitus New World. 2023;26 :170–3.
[36] Xiaodan Y . Effects of DGLHT on yin deficiency sweating and true cholinesterase in SD rats, Diploma Thesis, Hunan University of Traditional Chinese Medicine, Hunan, China, 2021.
