Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2021 Apr 30;100(17):e25660. doi: 10.1097/MD.0000000000025660

Network pharmacology-based screening of the active ingredients and mechanisms of Huangqi against aging

Siyu Lan a, Jie Duan b, Nan Zeng c, Bin Yu a,∗, Xuping Yang d, Hong Ning a, Yilan Huang d, Youyi Rao a
Editor: Cigdem Sayil
PMCID: PMC8084007  PMID: 33907130

Abstract

Studies have shown that Huangqi (HQ) has anti-aging efficacy. However, its active ingredients and mechanisms for anti-aging are still unclear. In this study, we will systematically screen the active ingredients of HQ and explore the possible mechanism of HQ in prevention from aging through network pharmacology technology.

The main active ingredients of HQ were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The possible targets were predicted by TCMSP. The related targets for aging were obtained from GeneCards (The Human Gene Database) and Online Mendelian Inheritance in Man (OMIM) database. The common targets of HQ and aging were obtained using R 3.6.3 software. The protein–protein interaction (PPI) network and the ingredient-target-disease network were constructed using Cytoscape 3.7.2 software for visualization. In addition, the Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation of potential targets were performed using R 3.6.3 software.

Based on the screening conditions, 16 active ingredients and 28 drug targets were obtained. The PPI network contained 29 proteins, including PTGS2, AR, NOS2, and so on. GO functional enrichment analysis obtained 40 GO items (P < .05). KEGG pathway enrichment analysis obtained 110 aging related pathways (P < .05), including hypoxia inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complication, among others.

Sixteen effective ingredients of HQ and 28 targets against aging were identified through network pharmacology. Multiple pathways were involved in the effect of HQ on preventing aging.

Keywords: active ingredients, aging, huangqi (HQ), network pharmacology

1. Introduction

Aging is defined as the gradual decline of internal physiological functions, which increases the age-specific mortality and age-specific fertility decline.[1] Multiple strategies were explored to prevent aging, including natural molecules,[2] the vitamin C, vitamin E, raspberry leaf cell culture extract,[3] Fillerina (one commercially available dermocosmetic filling treatment),[4] and coenzyme Q10.[5] In traditional Chinese medicine (TCM), some herbals have also been used for anti-aging, such as Lycium barbarum.[6]

HQ, also known as Astragalus membranaceus, is one of the most important qi tonic drugs in TCM.[7] HQ has the functions of protecting liver,[8] diuresis,[9] eliminating fatigue,[10] and enhancing human immunity.[1] Recent studies show that HQ and its ingredients activate telomerase and inhibit the process of replicating aging.[11] It also has antioxidant activity and neuroprotective effect.[11,12] Therefore, HQ has the great potential to prevent aging. However, the active components and of HQ are still unclear. Due to the complexity of TCM ingredients, exploring the possible mechanisms is still difficult.

Network pharmacology is based on the interdisciplinary theory of pharmacology and biology, which employs a variety of cutting-edge technologies such as omics, high-throughput screening, and Network analysis.[13] It can reveal the complex network relationship between “ingredient-gene-target-disease,” helps to understand the molecular basis of disease from multi-dimensional, and predict the potential pharmacological mechanism of drugs.[14] In this study, we will systematically screen the active ingredients of HQ and explore the possible mechanism of HQ in prevention from aging through network pharmacology technology.

2. Materials and methods

Ethical approval was waived or not necessary, all procedures performed in studies do not involve human participants or annimals.

2.1. Screening active ingredients

The flowchart of this study is shown in Figure 1. The active ingredients of HQ were firstly collected from TCM for Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmspw.com/tcmsp.php), a unique system pharmacology platform constructed for Chinese Herbal Medicines.[15]

Figure 1.

Figure 1

Open in a new tab

The flowchart of this study.

First, we input the name of the herbal medicine to record the related ingredients. Then, the plasma absorption components were predicted by setting thresholds (oral bioavailability [OB] ≥ 30% and drug similarity [DL] ≥ 0.18) in the comprehensive absorption, distribution, metabolism, and elimination screening system.[16] OB is an important indicator for identifying the nature of classified drugs, representing the percentage of unaltered drugs that reach the systemic circulation, and can be used to determine the dosing regimen.[17,18] DL estimates the “drug-like” degree of a hypothetical ingredient, which helps to optimize its pharmacokinetics and drug properties.[19]

2.2. Screening targets for aging

In GeneCards platform (https://www.genecards.org/) and OMIM platform (https://www.omim.org/), aging, as the key word, was used to get the target of aging. Two copies intersection of data were used to get relevant targets of aging.

2.3. Target prediction and validation

Target prediction can help to elucidate the underlying mechanisms of HQ. We used TCMSP and DrugBank databases (https://www.drugbank.ca/) to search and verify the relevant targets of the ingredients. The UniProt database (https://www.uniprot.org/) was employed to converse the target name to the official name.

2.4. Network construction

To analyze the interaction between targets, we uploaded the relevant targets of HQ against aging to the STRING (https://string-db.org/) platform. Species column was set as Homo sapiens. We performed combined score screening to get protein interaction PPI network. Cytoscape 3.7.2 software was used to construct the HQ active ingredient-target-disease network.

2.5. GO Function and KEGG pathway enrichment analysis

To further understand the function of the core target genes and the main pathways of HQ against aging, The DAVID (https://david.ncifcrf.gov/) database, the Cytoscape 3.7.2 software, and R 3.6.3 software were used to perform GO function and KEGG pathway enrichment analysis on the targets at P < .05.

3. Results

3.1. Active ingredients of GQ

Eighty-seven ingredients of HQ were retrieved through the TCMSP platform. According to screening conditions (OB > 30% and DL > 0.18), 20 main active ingredients were screened, and 4 of them failed to find relevant targets. The targets were further analyzed through DrugBank and UniProt databases to check the correlation with aging. Specific details on these active ingredients and related targets of HQ were shown in Table 1.

Table 1.

The main active components of HQ.

No. Mol ID Mol name Related targets OB% DL
1 MOL000211 Mairin PGR 55.38 0.78
2 MOL000239 Jaranol NOS2, PTGS1, AR, SCN5A, PTGS2, ESR2, CHEK1, PRSS1, NCOA2 50.83 0.29
3 MOL000296 Hederagenin PGR, NCOA2, CHRM3, CHRM1, CHRM2, ADRA1B, GABRA1, GRIA2, ADH1B, ADH1C, LYZ, PTGS1, SCN5A, PTGS2, RXRA, SLC6A2 36.91 0.75
4 MOL000033 (3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol PGR 36.23 0.78
5 MOL000354 Isorhamnetin NOS2, PTGS1, ESR1, AR, PPARG, PTGS2, ESR2, MAPK14, GSK3B, PRSS1, CCNA2, NCOA2, PYGM, PPARD, CHEK1, AKR1B1, NCOA1, F7, ACHE, GABRA1, MAOB, GRIA2 49.60 0.31
6 MOL000371 3,9-di-O-methylnissolin NOS2, PTGS1, CHRM3, CHRM1, ESR1, ADRB1, SCN5A, PTGS2, HTR3A, ADRA2C, RXRA, ACHE, ADRA1B, ADRB2, ADRA1D, OPRM1, GABRA1, PRSS1, NCOA2 53.74 0.48
7 MOL000378 7-O-methylisomucronulatol NOS2, PTGS1, CHRM3, KCNH2, CHRM1, ESR1, AR, ADRB1, SCN5A, PPARG, CHRM5, PTGS2, ADRA2C, CHRM4, RXRA, OPRD1, ADRA1A, CHRM2, ADRA1B, SLC6A3, ADRB2, ADRA1D, SLC6A4, ESR2, GABRA1, MAPK14, GSK3B, CHEK1, RXRB, PRSS1, CCNA2, NCOA2 74.69 0.30
8 MOL000379 9,10-dimethoxypterocarpan-3-O-beta-D-glucoside PTGS2, NCOA2 36.74 0.92
9 MOL000380 (6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol NOS2, PTGS1, CHRM3, CHRM1, ESR1, SCN5A, PTGS2, HTR3A, RXRA, ACHE, ADRA1B, ADRB2, ADRA1D, GABRA1, PRSS1, NCOA2, NCOA1, CHRM4 64.26 0.42
10 MOL000387 Bifendate PTGS2, KDR, MET, PTGS1 31.10 0.67
11 MOL000392 Formononetin NOS2, PTGS1, CHRM1, ESR1, AR, PPARG, PTGS2, RXRA, ADRA1A, SLC6A3, ADRB2, SLC6A4, ESR2, MAPK14, GSK3B, MAOB, CHEK1, PRSS1, CCNA2, PKIA, ACHE, JUN, ATP5F1B, ND6 69.67 0.21
12 MOL000417 Calycosin NOS2, PTGS1, ESR1, AR, PPARG, PTGS2, RXRA, ESR2, MAPK14, GSK3B, CHEK1, PRSS1, CCNA2, NCOA2, ADRB2 47.75 0.24
13 MOL000422 Kaempferol NOS2, PTGS1, AR, PPARG, PTGS2, NCOA2, PRSS1, PGR, CHRM1, ACHE, SLC6A2, CHRM2ADRA1B, GABRA1, F7, BCL2, JUN, MAPK8, MMP1, HMOX1, CYP3A4, CYP1A2, SELE, VCAM1, ALOX5, GSTP1, AHR, INSR, PPP3CA, GSTM1, GSTM2, AKR1C3 41.88 0.24
14 MOL000433 FA GSK3B 68.96 0.71
15 MOL000442 1,7-Dihydroxy-3,9-dimethoxy pterocarpene PTGS2, RXRA, PRSS1 39.05 0.48
16 MOL000098 Quercetin PTGS1, AR, PPARG, PTGS2, NCOA2, AKR1B1, PRSS1, KCNH2, SCN5A, ADRB2, MMP3, F7, RXRA, ACHE, GABRA1, MAOB, EGFR, VEGFA, BCL2, PLAU, MMP2, MAPK1, EGF, RB1, TNFSF15, JUN, IL6, TP63, POR, ODC1, TOP1, SOD1, MMP1, ACACA, HMOX1, CYP3A4, CYP1A2, F3, GJA1, IL1B, CCL2, SELE, VCAM1, PTGER3, SULT1E1, MGAM, IL2, PLAT, THBD, COL1A1, IFNG, ALOX5, MPO, GSTP1, NQO1, AHR, COL3A1, INSR, ACPP, CTSD, ACPP, CTSD, GSTM1, GSTM2 46.43 0.28
Open in a new tab

3.2. Targets of aging

Through the GeneCards platform (https://www.genecards.org/) and the OMIM platform (https://www.omim.org/), 23,993 targets for aging were identified. According to the relevance score of GeneCards platform, 184 targets with a score of ≥30 points were selected. A total of 623 targets were obtained from OMIM platform. The duplicate targets were eliminated. Finally, 796 targets related to aging were identified. The venn diagram of the drug and disease co-acting target was shown in Figure 2 and we identified 28 targets for the common action of drug and disease.

Figure 2.

Figure 2

Open in a new tab

The vene diagram of HQ therapy and prevention of aging-related targets.

3.3. Network construction and analysis

3.3.1. PPI Network and active ingredient-target-disease network

PPI network is constructed based on protein interaction. There were 29 protein nodes and 182 interaction lines (Fig. 3A). The P value of PPI enrichment is 1.0e-16 (P < .05). We established an active ingredient-target-disease network to facilitate the exploration of the overall regulatory mechanism of HQ in aging prevention. HQ can prevent aging through multiple ingredients and multiple targets, which was shown in (Fig. 3B).

Figure 3.

Figure 3

Open in a new tab

PPI network of HQ active ingredients and Ingredient-Target-Disease network between HQ and aging. Blue represents HQ, red represents aging, yellow represents the effective ingredients of HQ, and green represents the common targets of the 2. HQ = Huangqi, PPI = protein–protein interaction.

3.4. GO function enrichment analysis

The Cytoscape 3.7.2 software and R 3.6.3 software were used to analyze the 62 potential targets of HQ against aging using GO annotation analysis. We obtained 40 related biological processes, molecular functions, and cellular components. The GO bar plot and bubble plot of the first 20 entries was shown in Figure 4. GO function enrichment analysis showed that the main biological processes of active ingredients of HQ were cytokine activity, growth factor receptor binding, cytokine receptor binding, receptor ligand activity, phosphatase binding, hormone binding, nuclear receptor activity, transcription factor activity, direct ligand regulated sequence-specific DNA binding, heme-binding, growth factor binding, protein phosphatase binding, tetrapyrrole binding, steroid hormone receptor activity, RNA polymerase II transcription factor binding, growth factor activity, platelet-derived growth factor binding, antioxidant activity, steroid hormone receptor binding, RNA polymerase II basal transcription factor binding, and protease binding.

Figure 4.

Figure 4

Open in a new tab

The bar plot of HQ active ingredients GO function enrichment analysis. GO = Gene Ontology, HQ = Huangqi.

3.5. KEGG pathway enrichment analysis

The Cytoscape 3.7.2 software and R 3.6.3 software were used to analyze the 62 potential targets of HQ against aging using KEGG pathway enrichment analysis. One hundred ten related signaling pathways were obtained. The bar plot and dot plot of the first 20 entries was shown in Figure 5. KEGG pathway enrichment analysis showed that the main signaling pathways and diseases of active ingredients of HQ were hypoxia inducible factor 1 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, relaxin signaling pathway, IL-17 signaling pathway, PI3K-Akt signaling pathway, estrogen signaling pathway, bladder cancer, chagas disease (American trypanosomiasis), fluid shear stress and atherosclerosis, human cytomegalovirus infection, prostate cancer, endocrine resistance, EGFR tyrosine kinase inhibitor resistance, rheumatoid arthritis, amoebiasis, tuberculosis, pancreatic cancer, prion diseases, leishmaniasis, and graft-versus-host disease.

Figure 5.

Figure 5

Open in a new tab

The bar plot of HQ active ingredients KEGG pathway enrichment analysis. HQ = Huangqi, KEGG = Kyoto Encyclopedia of Genes and Genomes.

4. Discussion

Many studies have shown that HQ has significant preventive effects on aging.[20–22] However, the pharmacological mechanism of HQ against aging has not been clearly clarified. In this study, we used network pharmacology strategies to address this problem. After screening and verification, 16 potential active ingredients and 28 corresponding potential targets were retained.

We also established the PPI network and ingredient-target-disease network, and conducted GO and KEGG enrichment analysis to systematically and comprehensively understand potential preventive mechanism of HQ on aging.

PPI network analysis showed that multiple active ingredients interacted tightly with multiple targets. According to degree ranking of main targets, prostaglandin G/H synthase2 (PTGS2), androgen receptor (AR), nitric oxide synthase2 (NOS2), peroxisome proliferator-activated receptor gamma (PPARG), estrogen receptor 1 (ESR1), and muscarinic acetylcholine receptor M3 (CHRM3) are the important targets involved in HQ against aging. PTGS2, encoding for prostaglandin-endoperoxide synthase2, cyclooxygenase-2, or COX-2, plays indispensable roles in premature aging.[23,24] Low et al also identified the age-related changes of testosterone and androgen receptors (ARs) in male rats, and the serum content of androgen receptor immunoreactivity is decreased with the age of rats.[25] Moxibustion can inhibit reproductive aging by increasing the expression of AR.[26] Therefore, we speculate that AR has a greater impact on male aging than females, and may be negatively correlated with growth of age. NOS2 has also been shown to be closely related to human skin aging.[27] Interestingly, we reported that the 3 human genes (NOS1, -2, -3) coding for the 3 isoforms of the NADPH-dependent enzymes had an effect on common age related phenotypes and longevity in humans.[28] PPARG is an important metabolic control factor that can extend lifespan. In mouse models, decreased expression of PPARG reduces lifespan.[29] Studies showed that ESR1 regulated female premature ovarian failure and provided a new entry point for revealing the genetic mechanisms involved in ovarian reserve and oocyte aging.[30,31] Hardy et al found that CHRM3 seemed to decline with maturation and aging and to be less expressed in males.[32] There are few studies on the prevention of aging by CHMR3. So, the role of this target is still unclear. In total, HQ may prevent aging through targeting PTGS2, AR, NOS2, and ESR1 PPARG and CHRM3.

According to degree ranking in ingredient-target-disease network, the top 5 active ingredients are quercetin, formononetin, calycosin, kaempferol, and isorhamnetin. Quercetin is a natural ingredient with antioxidant potential, and aging is a deteriorating process that reduces physical and functional potential due to oxidative stress.[33,34] Quercetin supplements are considered as promising natural protective ingredients that can be used to delay the aging process and maintain human health.[35] Formononetin has protective effect on the skin against UV-induced oxidation.[36] Formononetin can penetrate into the epidermis and dermis and has the potential to prevent skin aging in cosmetics.[37] In Hsu and Chiang's research,[38] they found that calycosin had skincare functions, but the mechanism remains unknown. Zhang et al showed that calycosin alone does not promote the proliferation of aging hematopoietic stem cells, but it can significantly promote the proliferation of aging hematopoietic stem cell in combination with calycosin-7-glucoside. In addition, calycosin, formononetin, astragalus glycosides, and ferulic acid have a synergistic effect in promoting cell proliferation and inhibiting cell senescence.[39] Therefore, we speculate that calycosin alone has no obvious effect on aging, but it may enhance the anti-aging effect of other substances. Kaempferol is a natural flavonoid and present in different plant species. It has the therapeutic properties of anti-inflammatory, antioxidant and anti-cancer. Kaempferol can alleviate aging by reducing oxidative stress and improving mitochondrial function.[40] Hussin et al[41] found that isorhamnetin had anti-aging properties using proton nuclear magnetic resonance methods. Totally, these 5 substances are the main ingredients of HQ, and they all have certain anti-aging effects.

The enrichment of GO biological processes in prevention of aging by HQ mainly involves cytokine activity, cytokine receptor binding, receptor ligand activity, and so on. Cytokines are proteins secreted by cells and play important roles in maintaining normal cell function.[42] Cytokines play important roles in inflammation. Inflammaging is also a key to understand aging. So, the cytokines may be the potential target of HQ against aging.[43]

The KEGG pathway enrichment analysis showed that the prevention of HQ on aging mainly involves hypoxia inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, and AGE-RAGE signaling pathway. Studies have shown that hypoxia potentially contributes to functional decline during the aging process.[44] PI3K and AKT are novel targets for chemical modulation of telomere protection against aging.[45] AGE/RAGE also mediate cellular dysfunction and affect the aging process.[46] So, these signal pathways may also be the targets of HQ against aging.

5. Conclusion

In this study, we used network pharmacology to analyze the effective ingredients and potential targets of HQ in preventing aging. Sixteen effective ingredients of HQ and 28 targets were identified. Multiple ingredients, multiple targets, and multiple pathways were involved in the effect of HQ on preventing aging.

Author contributions

Conceptualization: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Data curation: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Formal analysis: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Funding acquisition: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Investigation: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Methodology: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Project administration: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Resources: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Software: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Supervision: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Validation: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Visualization: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Writing – original draft: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Writing – review & editing: Siyu Lan, Jie Duan, Nan Zeng, Bin Yu, Xuping Yang, Hong Ning, Yilan Huang, Youyi Rao.

Footnotes

Abbreviations: ADME = absorption, distribution, metabolism and elimination, AR = androgen receptor, AR = androgen receptors, CHRM3 = muscarinic acetylcholine receptor M3, DL = drug similarity, ESR1 = estrogen receptor 1, GO = Gene Ontology, HQ = Huangqi, KEGG = Kyoto Encyclopedia of Genes and Genomes, NOS2 = nitric oxide synthase2, OB = oral bioavailability, OMIM = Online Mendelian Inheritance in Man, PPARG = peroxisome proliferator-activated receptor gamma, PPI = protein-protein interaction, PTGS2 = prostaglandin G/H synthase2, TCM = traditional Chinese medicine, TCMSP = Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.

How to cite this article: Lan S, Duan J, Zeng N, Yu B, Yang X, Ning H, Huang Y, Rao Y. Network pharmacology-based screening of the active ingredients and mechanisms of Huangqi against aging. Medicine. 2021;100:17(e25660).

Funding: Scientific Research Subject of Health Commission of Sichuan Province (18PJ540).

The work was supported by Scientific Research Subject of Health Commission of Sichuan Province (18PJ540).

The authors report no conflicts of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

AR = androgen receptor, DL = drug similarity, HQ = Huangqi, NOS2 = nitric oxide synthase2, OB = oral bioavailability, PPARG = peroxisome proliferator-activated receptor gamma.

References

  • [1].Liu P, Zhao H, Luo Y. Anti-aging implications of Astragalus membranaceus (Huangqi): a well-known Chinese tonic. Aging Dis 2017;8:868–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Zaid AN, Al Ramahi R. Depigmentation and anti-aging treatment by natural molecules. Curr Pharm Des 2019;25:2292–312. [DOI] [PubMed] [Google Scholar]
  • [3].Rattanawiwatpong P, Wanitphakdeedecha R, Bumrungpert A, et al. Anti-aging and brightening effects of a topical treatment containing vitamin C, vitamin E, and raspberry leaf cell culture extract: a split-face, randomized controlled trial. J Cosmet Dermatol 2020;19:671–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Nobile V, Buonocore D, Michelotti A, et al. Anti-aging and filling efficacy of six types hyaluronic acid based dermo-cosmetic treatment: double blind, randomized clinical trial of efficacy and safety. J Cosmet Dermatol 2014;13:277–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Yadav NK, Nanda S, Sharma G, et al. Systematically optimized coenzyme q10-loaded novel proniosomal formulation for treatment of photo-induced aging in mice: characterization, biocompatibility studies, biochemical estimations and anti-aging evaluation. J Drug Target 2016;24:257–71. [DOI] [PubMed] [Google Scholar]
  • [6].Gao Y, Wei Y, Wang Y, et al. Lycium barbarum: a traditional Chinese herb and a promising anti-aging agent. Aging Dis 2017;8:778–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Auyeung KK, Han QB, Ko JK. Astragalus membranaceus: a review of its protection against inflammation and gastrointestinal cancers. Am J Chin Med 2016;44:01–22. [DOI] [PubMed] [Google Scholar]
  • [8].Liu YT, Lv WL. Research progress in Astragalus membranaceus and its active components on immune responses in liver fibrosis. Chin J Integr Med 2020;26:794–800. [DOI] [PubMed] [Google Scholar]
  • [9].Ai P, Yong G, Dingkun G, et al. Aqueous extract of Astragali Radix induces human natriuresis through enhancement of renal response to atrial natriuretic peptide. J Ethnopharmacol 2008;116:413–21. [DOI] [PubMed] [Google Scholar]
  • [10].Yeh TS, Chuang HL, Huang WC, et al. Astragalus membranaceus improves exercise performance and ameliorates exercise-induced fatigue in trained mice. Molecules 2014;19:2793–807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Berezutsky MA, Durnova NA, Vlasova IA. [Experimental and clinical studies of mechanisms of the anti-aging effects of chemical compounds in Astragalus membranaceus (review)]. Adv Gerontol 2019;32:702–10. [PubMed] [Google Scholar]
  • [12].Tsoukalas D, Fragkiadaki P, Docea AO, et al. Discovery of potent telomerase activators: Unfolding new therapeutic and anti-aging perspectives. Mol Med Rep 2019;20:3701–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Wu CW, Lu L, Liang SW, et al. [Application of drug-target prediction technology in network pharmacology of traditional Chinese medicine]. Zhongguo Zhong Yao Za Zhi 2016;41:377–82. Chinese. [DOI] [PubMed] [Google Scholar]
  • [14].Zhang R, Zhu X, Bai H, et al. Network pharmacology databases for traditional chinese medicine: review and assessment. Front Pharmacol 2019;10:123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014;6:13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Wang Y, Zheng C, Huang C, et al. Systems pharmacology dissecting holistic medicine for treatment of complex diseases: an example using cardiocerebrovascular diseases treated by TCM. Evid Based Complement Alternat Med 2015;2015:980190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Varma MV, Obach RS, Rotter C, et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem 2010;53:1098–108. [DOI] [PubMed] [Google Scholar]
  • [18].Xu X, Zhang W, Huang C, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci 2012;13:6964–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Tao W, Xu X, Wang X, et al. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol 2013;145:01–10. [DOI] [PubMed] [Google Scholar]
  • [20].Bai L, Shi GY, Yang YJ, et al. Rehmannia glutinosa exhibits anti-aging effect through maintaining the quiescence and decreasing the senescence of hematopoietic stem cells. Animal Model Exp Med 2018;1:194–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].M’kacher R, Breton L, Colicchio B, et al. Benefit of an association of an antioxidative substrate and a traditional Chinese medicine on telomere elongation. Cell Mol Biol (Noisy-le-grand) 2019;65:54–8. [PubMed] [Google Scholar]
  • [22].Selim AM, Nooh MM, El-Sawalhi MM, et al. Amelioration of age-related alterations in rat liver: effects of curcumin C3 complex, Astragalus membranaceus and blueberry. Exp Gerontol 2020;137:110982. [DOI] [PubMed] [Google Scholar]
  • [23].Elkahloun AG, Saavedra JM. Candesartan neuroprotection in rat primary neurons negatively correlates with aging and senescence: a transcriptomic analysis. Mol Neurobiol 2020;57:1656–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Li J, Zhou Y, Du G, et al. Bioinformatic analysis reveals key genes and pathways in aging brain of senescence-accelerated mouse P8 (SAMP8). CNS Neurol Disord Drug Targets 2018;17:712–22. [DOI] [PubMed] [Google Scholar]
  • [25].Low KL, Tomm RJ, Ma C, et al. Effects of aging on testosterone and androgen receptors in the mesocorticolimbic system of male rats. Horm Behav 2020;120:104689. [DOI] [PubMed] [Google Scholar]
  • [26].Yao C, Zhao C, Zhang S, et al. Effect of moxibustion on testosterone secretion and apoptosis of spermatogenic cells in aging rats. Evid Based Complement Alternat Med 2019;2019:5186408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Jiang QY, Zheng MS, Yang XJ, et al. Analysis of molecular networks and targets mining of Chinese herbal medicines on anti-aging. BMC Complement Altern Med 2016;16:520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Montesanto A, Crocco P, Tallaro F, et al. Common polymorphisms in nitric oxide synthase (NOS) genes influence quality of aging and longevity in humans. Biogerontology 2013;14:177–86. [DOI] [PubMed] [Google Scholar]
  • [29].Argmann C, Dobrin R, Heikkinen S, et al. Ppargamma2 is a key driver of longevity in the mouse. PLoS Genet 2009;5:e1000752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].M’Rabet N, Moffat R, Helbling S, et al. The CC-allele of the PvuII polymorphic variant in intron 1 of the (-estrogen receptor gene is significantly more prevalent among infertile women at risk of premature ovarian aging. Fertil Steril 2012;98:965–72. e1-5. [DOI] [PubMed] [Google Scholar]
  • [31].Qin Y, Sun M, You L, et al. ESR1, HK3 and BRSK1 gene variants are associated with both age at natural menopause and premature ovarian failure. Orphanet J Rare Dis 2012;7:05. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Hardy CC, Keilich SR, Harrison AG, et al. The aging bladder phenotype is not the direct consequence of bladder aging. Neurourol Urodyn 2019;38:2121–9. [DOI] [PubMed] [Google Scholar]
  • [33].Chen WK, Tsai YL, Shibu MA, et al. Exercise training augments Sirt1-signaling and attenuates cardiac inflammation in D-galactose induced-aging rats. Aging (Albany NY) 2018;10:4166–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Ma Y, Ma B, Shang Y, et al. Flavonoid-rich ethanol extract from the leaves of diospyros kaki attenuates D-galactose-induced oxidative stress and neuroinflammation-mediated brain aging in mice. Oxid Med Cell Longev 2018;2018:8938207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].El-Far AH, Lebda MA, Noreldin AE, et al. Quercetin attenuates pancreatic and renal d-galactose-induced aging-related oxidative alterations in rats. Int J Mol Sci 2020;21:4348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Kitagawa S, Inoue K, Teraoka R, et al. Enhanced skin delivery of genistein and other two isoflavones by microemulsion and prevention against UV irradiation-induced erythema formation. Chem Pharm Bull (Tokyo) 2010;58:398–401. [DOI] [PubMed] [Google Scholar]
  • [37].Dias PH, Scopel M, Martiny S, et al. Hydroxypropyl-(-cyclodextrin-containing hydrogel enhances skin formononetin permeation/retention. J Pharm Pharmacol 2018;70:865–73. [DOI] [PubMed] [Google Scholar]
  • [38].Hsu MF, Chiang BH. Stimulating effects of Bacillus subtilis natto-fermented Radix astragali on hyaluronic acid production in human skin cells. J Ethnopharmacol 2009;125:474–81. [DOI] [PubMed] [Google Scholar]
  • [39].Zhang W, Zhu JH, Xu H, et al. Five active components compatibility of astragali radix and angelicae sinensis radix protect hematopoietic function against cyclophosphamide-induced injury in mice and t-BHP-induced injury in HSCs. Front Pharmacol 2019;10:936. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  • [40].Yao X, Jiang H, Li YH, et al. Kaempferol alleviates the reduction of developmental competence during aging of porcine oocytes. Anim Sci J 2019;90:1417–25. [DOI] [PubMed] [Google Scholar]
  • [41].Hussin M, Abdul Hamid A, Abas F, et al. NMR-based metabolomics profiling for radical scavenging and anti-aging properties of selected herbs. Molecules 2019;24:3208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Duitman EH, Orinska Z, Bulfone-Paus S. Mechanisms of cytokine secretion: a portfolio of distinct pathways allows flexibility in cytokine activity. Eur J Cell Biol 2011;90:476–83. [DOI] [PubMed] [Google Scholar]
  • [43].Minciullo PL, Catalano A, Mandraffino G, et al. Inflammaging and anti-inflammaging: the role of cytokines in extreme longevity. Arch Immunol Ther Exp (Warsz) 2016;64:111–26. [DOI] [PubMed] [Google Scholar]
  • [44].Yeo EJ. Hypoxia and aging. Exp Mol Med 2019;51:01–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Méndez-Pertuz M, Martínez P, Blanco-Aparicio C, et al. Modulation of telomere protection by the PI3K/AKT pathway. Nat Commun 2017;8:1278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Fleming TH, Humpert PM, Nawroth PP, et al. Reactive metabolites and AGE/RAGE-mediated cellular dysfunction affect the aging process: a mini-review. Gerontology 2011;57:435–43. [DOI] [PubMed] [Google Scholar]

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES