Exploration of the Potential and Mechanisms of Diabetic Cognitive Disorder Modulation by Daehwangmokdanpi-tang through a Network Pharmacological Approach

Yebin Lim; Bitna Kweon; Dong-Uk Kim; Do-Eun Lee; Jungtae Leem; Dong-Gu Kim; Hyung Won Kang; Gi-Sang Bae; Lim, Yebin; Kweon, Bitna; Kim, Dong-Uk; Lee, Do-Eun; Leem, Jungtae; Kim, Dong-Gu; Kang, Hyung Won; Bae, Gi-Sang

doi:10.13048/jkm.24022

JKM > Volume 45(2); 2024 > Article

Lim, Kweon, Kim, Lee, Leem, Kim, Kang, and Bae: Exploration of the Potential and Mechanisms of Diabetic Cognitive Disorder Modulation by Daehwangmokdanpi-tang through a Network Pharmacological Approach

Original Article

The Journal of Korean Medicine 2024; 45(2): 23-40.

Published online: June 01, 2024

DOI: https://doi.org/10.13048/jkm.24022

Exploration of the Potential and Mechanisms of Diabetic Cognitive Disorder Modulation by Daehwangmokdanpi-tang through a Network Pharmacological Approach

Yebin Lim¹^{, §}

, Bitna Kweon¹^{, 2}^{, §}

, Dong-Uk Kim¹^{, 2}^{, §}

, Do-Eun Lee³^{, 4}

, Jungtae Leem⁵^{, 6}

, Dong-Gu Kim⁷^{, *}

, Hyung Won Kang³^{, 4}^{, 5}^{, *}

, Gi-Sang Bae¹^{, 2}^{, 5}^{, *}

¹Department of Pharmacology, College of Korean Medicine, Wonkwang University

²Hanbang Cardio-Renal Syndrome Research Center, Wonkwang University

³Department of Korean Neuropsychiatry Medicine, College of Korean Medicine, Wonkwang University

⁴Korean Medicine-Cognitive Disorder Research Center, Wonkwang University

⁵Research center of Traditional Korean medicine, Wonkwang University

⁶Department of Diagnostics, College of Korean Medicine, Wonkwang University

⁷Department of Herbology, College of Korean Medicine, Dong-Eui University

Correspondence to: Dong-Gu Kim, Department of herbology, College of Korean Medicine, Dong-Eui University, 52-57, Yangjeong-ro, Busanjin-gu, Busan, 47227, South Korea, Tel: +82-51-890-3371, E-mail: kdg2409@deu.ac.kr

Correspondence to: Hyung Won Kang, Department of Korean Neuropsychiatry Medicine, College of Korean Medicine, Wonkwang University, 460 Iksandae-ro, Iksan, 54538 Jeonbuk, South Korea, Tel: +82-63-850-6831, E-mail: dskhw@wku.ac.kr

Correspondence to: Gi-Sang Bae, Department of Pharmacology, College of Korean Medicine, Wonkwang University, 460 Iksandae-ro, Iksan, 54538 Jeonbuk, South Korea, Tel: +82-63-850-6842, E-mail: baegs888@wku.ac.kr

§ These authors contributed equally to this work.

Received April 15, 2024 Revised May 7, 2024 Accepted May 7, 2024

Abstract

Objectives

This study utilized a network pharmacology approach to investigate the potential therapeutic effects and underlying mechanisms of Daehwangmokdanpi-tang (DHMDPT) in diabetic cognitive disorder (DCD).

Methods

The compounds of DHMDPT and their target genes were obtained from the OASIS and PubChem databases. These putative target genes were compared with known targets of DCD to identify potential correlations. Using Cytoscape 3.10.2, a network was constructed to highlight key target genes. To further elucidate the underlying mechanisms, functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, CB-DOCK was used to assess binding affinities and confirm the interactions.

Results

The results showed that a total of 27 compounds and 439 related genes were identified from DHMDPT. Among these, 373 genes interacted with the DCD gene set, indicating a close relationship between the effects of DHMDPT and DCD. Through GO enrichment analysis and KEGG pathways, ‘Regulation of Apoptotic Process’, ‘Cytokine-Mediated signaling pathway’, and ‘AGE-RAGE signaling pathway in diabetic complications’ were identified as the functional pathways of the 18 key target genes of DHMDPT on DCD. Additionally, molecular docking was performed to assess the binding affinities of the six most highly associated key target genes of DCD with active compounds.

Conclusions

Using a network pharmacology approach, which included molecular docking, DHMDPT was found to be highly relevant to DCD. This study could serve as a foundation for further research on the cognitive enhancement effects of DHMDPT in DCD.

Keywords: Diabetic cognitive disorder, Daehwangmokdanpi-tang, Network pharmacology, Molecular docking

Fig. 1

Venn diagram illustrating the intersection targets between the target genes of Daehwangmokdanpi-tang (DHMDPT) and the gene sets associated with diabetic cognitive disorder.

Fig. 2

Process of topological screening: (A) Network showing the intersection targets between the target genes of DHMDPT and the gene sets related to diabetic cognitive disorder. (B) and (C) represent the networks of the initial and final screenings of key targets, respectively.

Fig. 3

Biological processes, cellular components, and molecular functions related to the targets of DHMDPT were identified using the GO enrichment analysis database. GO terms were listed in order of significance based on their p-values.

Fig. 4

Biological processes related to the targets of DHMDPT were identified using the KEGG pathways database. (A) Pathways were listed in order of significance based on their p-values. (B) SRPLOT was used to illustrate the associations between target genes and KEGG pathways.

Fig. 5

The network illustrating the relationship between the tang-herb-compound-target-pathway of DHMDPT in the treatment of diabetic cognitive disorder. (Abbreviations: PS - Persicae Semen; MRC - Mountan Radicis Cortex; TS - Trichosanthis Semen; RR - Rhei Radix.)

Fig. 6

Results from molecular docking studies between the proteins encoded by the six key target genes and six selected compounds. These compounds were chosen based on their descending degree within the tang-herb -compound-target-pathway network of DHMDPT for the treatment of diabetic cognitive disorder.

Table 1

Physicochemical properties of the compounds in DHMDPT optimized for enhanced oral bioavailability

No.	Compound	Lipinski’s rule				Bioavailability score > 0.1	TPSA < 140 Å²

		MW ≤ 500	HBA ≤ 10	HBD ≤ 5	MLogP ≤ 4.15
1	(+)-Catechin	290.27	6	5	0.24	0.55	110.38
2	Aloe-emodin	270.24	5	3	0.1	0.55	94.83
3	Chrysophanol	254.24	4	2	0.92	0.55	74.6
4	Chrysophanol-8-O-β-D-Glucopyranoside	416.38	9	5	−1.26	0.55	153.75
5	Emodin	270.24	5	3	0.36	0.55	94.83
6	Emodin-1-β-D-glucoside	404.37	9	6	−0.59	0.55	156.91
7	Emodin-glucoside	432.38	10	6	−1.77	0.55	173.98
8	Lindleyin	478.45	11	6	−0.075	0.17	183.21
9	Physcion	284.26	5	2	0.61	0.55	83.83
10	Rhein	284.22	6	3	0.29	0.56	111.9
11	Sennoside A	862.74	20	12	−3.15	0.11	347.96
12	Sennoside B	862.74	20	12	−3.15	0.11	347.96
13	(−)-Epicatechin-3-O-gallate	442.37	10	7	0.05	0.55	177.14
14	Aloe-emodin-8-O-β-D-Glucopyranoside	432.38	10	6	−2.03	0.55	173.98
15	Isolindleyin	478.45	11	6	−0.75	0.17	183.21
16	Rhaponticin	420.41	9	6	−0.65	0.55	149.07
17	1,2,3,4,6-Penta-O-galloyl-beta-D-glucose	940.68	26	15	−2.83	0.17	444.18
18	4-O-Butylpaeoniflorin	536.57	11	4	0.14	0.17	153.37
19	5-Hydroxy-3S-hydroxymethyl-6-methyl-2,3-dihydrobenzofuran	180.2	3	2	0.83	0.55	49.69
20	8-O-Benzoylpaeonidanin	584.61	11	3	1.27	0.17	150.21
21	Methyl 3-hydroxy-4-methoxybenzoate	182.17	4	1	1.06	0.55	55.76
22	Paeonoside	328.31	8	4	−1.57	0.55	125.68
23	Acetovanillone	166.17	3	1	0.83	0.55	46.53
24	Galloyl-oxypaeoniflorin	648.57	16	8	−1.87	0.17	255.27
25	Mudanpioside-H	616.57	14	6	−0.23	0.17	210.9
26	Oxypaeoniflorin	496.46	12	6	−1.17	0.17	184.6
27	Paeoniflorin	480.46	11	5	−0.68	0.55	164.37
28	Paeonol	166.17	3	1	0.83	0.55	46.53
29	Gallic acid	170.12	5	4	−0.16	0.56	97.99
30	Protocatechualdehyde	138.12	3	2	0.18	0.55	57.53
31	Prupersin A	450.44	10	6	−0.93	0.55	166.14
32	Chlorogenic acid	354.31	9	6	−1.05	0.11	164.75
33	Ferulic acid	194.18	4	2	1	0.85	66.76
34	p-Coumaric acid	164.16	3	2	1.28	0.85	57.53
35	Protocatechuic acid	154.12	4	3	0.4	0.56	77.76
36	(-)-Pinoresinol	358.39	6	2	1.17	0.55	77.38
37	23,24-Dihydrocucubitacin B	560.72	8	3	1.85	0.55	138.2
38	3-Epi-isocucubitacin B	558.7	8	3	1.76	0.55	138.2
39	Arvenin I	720.84	13	6	−0.56	0.17	217.35
40	Citrulline	175.19	4	4	−3.21	0.55	118.44
41	Cucumegastigmanes I	240.3	4	3	0.29	0.55	77.76
42	Ehletianol C	556.6	10	5	0.4	0.55	147.3
43	Isocucurbitacin D	516.67	7	4	1.44	0.55	132.13
44	Leucine	265.3	4	2	1.96	0.56	75.63
45	Loliolide	196.24	3	1	1.49	0.55	46.53
46	Opercurins A	720.84	13	6	−0.56	0.17	217.35
47	Pyroglutamic acid	129.11	3	2	−0.93	0.85	66.4
48	Sterol glucoside	410.54	6	4	1.76	0.55	99.38
49	Valine	117.15	3	2	−2.2	0.55	63.32
50	7-Hydroxychromone	162.14	3	1	0.23	0.55	50.44
51	α-Spinasterol	412.69	1	1	6.62	0.55	20.23
52	Arvenins III	678.81	12	7	−0.9	0.17	211.28
53	Cerebroside	714.02	9	7	1.9	0.17	168.94
54	Chrysoeriol 7-O-b-D-Glucopyranoside	608.54	15	8	−3.23	0.17	238.2
55	Cucurbitacin B	558.7	8	3	1.76	0.55	138.2
56	Cucurbitacin D	516.67	7	4	1.44	0.55	132.13
57	Isocucurbitacin B	558.7	8	3	1.76	0.55	138.2
58	Ligballinol	298.33	4	2	1.81	0.55	58.92
59	Luteolin 7-O-b-D-Glucopyranoside	448.38	11	7	−2.1	0.17	190.28
60	Phenyllalanine	165.19	3	2	−1.11	0.55	63.32
61	Stigmasterol	412.69	1	1	6.62	0.55	20.23
62	Tricin	330.29	7	3	−0.07	0.55	109.36

MW. Molecular Weight; HBA. Hydrogen Bond Acceptor; HBD. Hydrogen Bond Donor; MlogP. Partition Coefficient

Table 2

List of active compounds from DHMDPT with their PubChem IDs.

Compounds	Pubchem IDs	origin
(−)-Pinoresinol	12309636	Trichosanthis Semen
23,24-Dihydrocucurbitacin B	267250	Trichosanthis Semen
7-Hydroxychromone	5409279	Trichosanthis Semen
α-Spinasterol	5281331	Trichosanthis Semen
Citrulline	9750	Trichosanthis Semen
Cucurbitacin B	5281316	Trichosanthis Semen
Cucurbitacin D	5281318	Trichosanthis Semen
Leucine	74840	Trichosanthis Semen
Loliolide	100332	Trichosanthis Semen
Phenylalanine	994	Trichosanthis Semen
Pyroglutamic acid	7405	Trichosanthis Semen
Stigmasterol	5280794	Trichosanthis Semen
Tricin	5281702	Trichosanthis Semen
Valine	6287	Trichosanthis Semen
(+)-Catechin	9064	Rhei Radix
Aloe-emodin	10207	Rhei Radix
Chrysophanol	10208	Rhei Radix
Emodin	3220	Rhei Radix
Physcion	10639	Rhei Radix
Rhein	10168	Rhei Radix
Ferulic acid	445858	Persicae Semen
Gallic acid	370	Persicae Semen
p-Coumaric acid	637542	Persicae Semen
Protocatechualdehyde	8768	Persicae Semen
Protocatechuic acid	72	Persicae Semen
Acetovanillone	2214	Moutan Radicis Cortex
Paeonol	11092	Moutan Radicis Cortex

Table 3

Detailed information on the 18 key targets.

Target	Degree Centrality	Betweenness Centrality	Closeness Centrality
AKT1	120	0.053737	0.562205
ALB	67	0.048454	0.52346
BCL2	84	0.025613	0.525773
CASP3	78	0.020443	0.517391
CD4	72	0.022304	0.503526
CTNNB1	82	0.026072	0.516643
EGFR	95	0.035504	0.544207
HSP90AA1	73	0.025037	0.500701
IL1B	107	0.033345	0.550077
IL6	116	0.039494	0.563981
INS	85	0.080247	0.538462
MMP9	72	0.020374	0.512931
MYC	84	0.026169	0.517391
SRC	80	0.026384	0.515896
STAT3	103	0.027504	0.536036
TLR4	78	0.026528	0.506383
TNF	115	0.052121	0.557812
TP53	126	0.100412	0.566667

sTable 1

List of genes common to both the DHMDPT and diabetic cognitive disorder gene sets.

373 Common Genes of DHMDPT and Diabetic cognitive disorder

ABCA1, ABCB1, ABCB11, ABCB4, ABCC1, ABCC2, ABCG1, ACACA, ACACB, ACE, ACE2, ACHE, ACKR3, ACOX1, ADH1A, ADIPOQ, AGER, AGTR1, AGTR2, AKT1, AKT1S1, ALB, ALOX15, ALOX5, AMY2A, ANG, ANK1, APOE, ARG1, ARG2, ATF6, ATG5, ATP2B1, BACE1, BCHE, BCL2, BCL2L1, BDNF, BECN1, BIRC5, BMP2, BRCA1, BRCA2, BSG, CAMK4, CAPN2, CASP1, CASP3, CASP7, CASP8, CASP9, CAT, CAV1, CCK, CCL11, CCL2, CCL5, CCN2, CCNB1, CCND1, CCR1, CCR2, CD14, CD274, CD36, CD38, CD4, CD40, CD68, CD86, CD8A, CDC25C, CDH1, CDH2, CDK1, CDK2, CDK4, CDK9, CDKN1A, CDKN1B, CEBPA, CHEK1, CHGA, CIP2A, CKM, CLEC7A, COL18A1, COMT, CPT1A, CRAT, CSNK2A1, CSNK2A2, CSNK2B, CTNNB1, CTSB, CTSK, CXCL10, CXCL11, CXCL12, CXCL8, CXCR3, CXCR4, CYBB, CYP1A1, CYP1A2, CYP1B1, CYP2E1, CYP3A4, DCT, DGAT1, DHFR, DLL1, DLL3, DNAH8, DNMT1, DNTT, DRD2, DYNC1H1, DYRK1B, EDN1, EGF, EGFR, EIF2AK2, EIF2AK3, ELANE, ENDOG, ENO2, ERBB2, ERBB3, ERCC1, ERVW-1, ESR1, F2, F9, FABP4, FAM20C, FAS, FASN, FGF7, FGFR1, FOXM1, GAA, GAST, GCG, GCH1, GFAP, GGT1, GLB1, GLUL, GNRH1, GOLPH3, GORASP1, GOT1, GPT, GPX4, GPX6, GSDMD, GSK3A, GSR, GSTM1, GSTP1, GUSB, HBA2, HBB, HDAC2, HEXD, HEY1, HIBCH, HIF1A, HLA-B, HLA-DRB1, HMGB1, HMGCR, HMOX1, HPRT1, HSP90AA1, HSP90B1, HSPA5, HSPA9, ICAM1, IFNAR1, IFNLR1, IL10, IL13, IL17A, IL18, IL1B, IL2, IL22, IL4, IL5, IL6, INS, INSR, IRS1, IRS2, ITGAM, ITM2B, JAK2, JUN, JUND, KCNA6, KCNB1, KDR, KEAP1, KIT, KNG1, KRT20, LDHA, LDLR, LEP, LTB, LYZ, MAP1LC3A, MAP3K5, MAPK1, MAPK14, MAPK3, MAPK8, MAPK9, MBP, MBTPS1, MCL1, MDM2, METAP2, MGAM, MKI67, MME, MMP1, MMP2, MMP3, MMP9, MPO, MT-ND2, MT-ND6, MTOR, MYB, MYC, MYD88, MYLK, NAT1, NCF1, NF2, NFATC1, NFATC2, NFE2L2, NFKB1, NGF, NLRP3, NOS1, NOS2, NOS3, NOTCH1, NOX1, NOX4, NQO1, NQO2, NR1H2, NR1H4, NR3C2, NTS, OLR1, OPRM1, P2RX7, PARK7, PARP1, PCNA, PCSK9, PDX1, PECAM1, PIK3C2A, PIK3C3, PIK3CA, PIN1, PLAT, PLAU, PLG, PML, PNLIP, POLB, PPA1, PPARA, PPARG, PPARGC1A, PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PRKCD, PRKCQ, PRNP, PROKR2, PRSS1, PTEN, PTGS1, PTGS2, PTK2, PTPN1, PVALB, PXDN, PYCARD, RAC1, RAF1, RB1, RBFOX3, RELA, REN, RHOA, RRM1, RRM2, RUNX2, SCD, SCN2A, SIRT1, SIRT6, SLC15A1, SLC16A1, SLC22A6, SLC29A4, SLC2A1, SLC2A2, SLC2A4, SLC3A2, SLC6A3, SLC6A4, SLC7A7, SMAD2, SMAD3, SNCA, SOD1, SOD2, SOX2, SP1, SQSTM1, SRC, SREBF1, SREBF2, STAT1, STAT3, SYK, SYP, TBC1D4, TBXT, TERT, TGFB1, TGM2, TLR2, TLR4, TNF, TNFRSF1A, TP53, TRADD, TRAF1, TRAF2, TRAF6, TREM2, TRH, TRPA1, TRPV1, TRPV4, TUBB4B, TYR, UGT1A1, UGT1A9, VCAM1, VIM, WNT5A, XDH, YBX1, ZEB2

sTable 2

Analysis of GO biological processes for the 18 key targets.

Process	GO term	p-value	Genes
Membrane Raft	GO:0045121	1.37252 * 10⁻⁵	CD4;SRC;TNF;EGFR
Intracellular Organelle Lumen	GO:0070013	7.20093 * 10⁻⁵	HSP90AA1;IL6;ALB;MMP9;EGFR;INS
Clathrin-Coated Endocytic Vesicle Membrane	GO:0030669	0.001682481	CD4;EGFR
Endoplasmic Reticulum Lumen	GO:0005788	0.001973874	IL6;ALB;INS
Nucleus	GO:0005634	0.002306348	HSP90AA1;MYC;CASP3;STAT3;ALB;BCL2 ;AKT1;CTNNB1;TP53;EGFR
Clathrin-Coated Endocytic Vesicle	GO:0045334	0.002612975	CD4;EGFR
Secretory Granule Lumen	GO:0034774	0.00267344	HSP90AA1;ALB;INS
Clathrin-Coated Vesicle Membrane	GO:0030665	0.002923575	CD4;EGFR
Multivesicular Body, Internal Vesicle	GO:0097487	0.004492293	EGFR
Cytoplasmic Vesicle Lumen	GO:0060205	0.004721828	HSP90AA1;INS
Focal Adhesion	GO:0005925	0.004726569	SRC;CTNNB1;EGFR
Cell-Substrate Junction	GO:0030055	0.005004	SRC;CTNNB1;EGFR
Ficolin-1-Rich Granule Lumen	GO:1904813	0.005381775	HSP90AA1;MMP9
Intracellular Membrane-Bounded Organelle	GO:0043231	0.007031367	HSP90AA1;MYC;CASP3;STAT3;ALB;BCL2 ;AKT1;CTNNB1;TP53;EGFR
Endocytic Vesicle Membrane	GO:0030666	0.008839126	CD4;EGFR
Protein Phosphatase Binding	GO:0019903	2.98237 * 10⁻⁶	STAT3;CTNNB1;TP53;EGFR
Ubiquitin Protein Ligase Binding	GO:0031625	3.26446 * 10⁻⁶	HSP90AA1;BCL2;CTNNB1;TP53;EGFR
DNA-binding Transcription Factor Binding	GO:0140297	3.96492 * 10⁻⁶	MYC;STAT3;BCL2;CTNNB1;TP53
Protease Binding	GO:0002020	4.1574 * 10⁻⁶	BCL2;TNF;TP53;INS
Ubiquitin-Like Protein Ligase Binding	GO:0044389	4.46885 * 10⁻⁶	HSP90AA1;BCL2;CTNNB1;TP53;EGFR
Protein Tyrosine Kinase Binding	GO:1990782	5.7727 * 10⁻⁵	HSP90AA1;CD4;TP53
DNA Binding	GO:0003677	6.74489 * 10⁻⁵	MYC;STAT3;ALB;BCL2;TP53;EGFR
Growth Factor Receptor Binding	GO:0070851	8.29924 * 10⁻⁵	IL6;SRC;IL1B
Phosphatase Binding	GO:0019902	0.000131202	STAT3;CTNNB1;EGFR
Receptor Ligand Activity	GO:0048018	0.000162861	IL6;IL1B;TNF;INS
Disordered Domain Specific Binding	GO:0097718	0.000191392	HSP90AA1;TP53
MHC Class II Protein Complex Binding	GO:0023026	0.000226706	HSP90AA1;CD4
Protein Homodimerization Activity	GO:0042803	0.000234228	HSP90AA1;CD4;STAT3;BCL2;AKT1
Core Promoter Sequence-Specific DNA Binding	GO:0001046	0.000447244	MYC;TP53
Cytokine Activity	GO:0005125	0.000512604	IL6;IL1B;TNF
Regulation Of Apoptotic Process	GO:0042981	8.47452 * 10⁻¹⁶	HSP90AA1;SRC;TNF;MMP9;EGFR;IL6;MY C;CASP3;ALB;BCL2;AKT1;CTNNB1;TP53
Positive Regulation Of Protein Phosphorylation	GO:0001934	2.61059 * 10⁻¹⁵	HSP90AA1;CD4;IL6;SRC;IL1B;AKT1;TNF; MMP9;TP53;EGFR;INS
Negative Regulation Of Apoptotic Process	GO:0043066	3.88738 * 10⁻¹⁴	IL6;SRC;MYC;ALB;BCL2;AKT1;CTNNB1; TNF;MMP9;TP53;EGFR
Negative Regulation Of Programmed Cell Death	GO:0043069	2.13787 * 10⁻¹³	IL6;SRC;MYC;ALB;BCL2;AKT1;CTNNB1; MMP9;TP53;EGFR
Positive Regulation Of Cellular Biosynthetic Process	GO:0031328	1.13392 * 10⁻¹²	HSP90AA1;IL6;IL1B;AKT1;CTNNB1;TNF;T LR4;INS
Positive Regulation Of Intracellular Signal Transduction	GO:1902533	5.1694 * 10⁻¹²	HSP90AA1;CD4;IL6;SRC;IL1B;TNF;TLR4;T P53;EGFR;INS
Positive Regulation Of Macromolecule Metabolic Process	GO:0010604	8.35353 * 10⁻¹²	IL6;MYC;IL1B;STAT3;AKT1;TNF;TLR4;TP 53;INS
Positive Regulation Of Nucleic Acid-Templated Transcription	GO:1903508	9.27673 * 10⁻¹²	CD4;IL6;MYC;IL1B;STAT3;AKT1;CTNNB1 ;TNF;TP53;EGFR
Positive Regulation Of Cellular Process	GO:0048522	1.74957 * 10⁻¹¹	IL6;MYC;IL1B;BCL2;AKT1;CTNNB1;TNF; TLR4;EGFR;INS
Positive Regulation Of DNA-binding Transcription Factor Activity	GO:0051091	1.83931 * 10⁻¹¹	IL6;IL1B;STAT3;AKT1;CTNNB1;TNF;TLR4 ;INS
Positive Regulation Of Nitric Oxide Biosynthetic Process	GO:0045429	2.56366 * 10⁻¹¹	HSP90AA1;IL1B;AKT1;TNF;TLR4
Cytokine-Mediated Signaling Pathway	GO:0019221	2.61009 * 10⁻¹¹	CD4;IL6;SRC;IL1B;STAT3;AKT1;TNF;TP53
Regulation Of Nitric-Oxide Synthase Activity	GO:0050999	3.1193 * 10⁻¹¹	IL1B;AKT1;TNF;EGFR;INS
Positive Regulation Of Nitric Oxide Metabolic Process	GO:1904407	3.1193 * 10⁻¹¹	HSP90AA1;IL1B;AKT1;TNF;TLR4
Transmembrane Receptor Protein Tyrosine Kinase Signaling Pathway	GO:0007169	5.79508 * 10⁻¹¹	CD4;SRC;CASP3;STAT3;AKT1;MMP9;EGF R;INS

sTable 3

Analysis of KEGG pathways for the 18 key targets.

Pathway	p-value	Genes
AGE-RAGE signaling pathway in diabetic complications	1.92 * 10⁻¹²	IL6;CASP3;IL1B;STAT3;BCL2;AKT1;TNF
HIF-1 signaling pathway	3.56 * 10⁻¹²	IL6;STAT3;BCL2;AKT1;TLR4;EGFR;INS
PI3K-Akt signaling pathway	6.51 * 10⁻¹²	HSP90AA1;IL6;MYC;BCL2;AKT1;TLR4;TP53;EGFR;INS
MAPK signaling pathway	7.64 * 10⁻¹¹	MYC;CASP3;IL1B;AKT1;TNF;TP53;EGFR;INS
IL-17 signaling pathway	1.63 * 10⁻¹⁰	HSP90AA1;IL6;CASP3;IL1B;TNF;MMP9
TNF signaling pathway	4.73 * 10⁻¹⁰	IL6;CASP3;IL1B;AKT1;TNF;MMP9
Estrogen signaling pathway	1.61 * 10⁻⁹	HSP90AA1;SRC;BCL2;AKT1;MMP9;EGFR

참고문헌

1. Pranata S, Wu S-FV, Alizargar J, Liu J-H, Liang S-Y, Lu Y-Y. 2021; Precision health care elements, definitions, and strategies for patients with diabetes: A literature review. International Journal of Environmental Research and Public Health. 18:12. 6535https://doi.org/10.3390/ijerph181265351

2. Kim J, Son C, Cho C, Kim C. 2010; Review of Randomized Controlled Clinical Trials Targeting Treatment of Diabetic Peripheral Neuropathy. Journal of Korean Medicine. 31:4. 164–170.

3. Kang S-B. 1998; The Comparative Study between the Transformations of Sogal and the Complications of Diabetes Mellitus. Journal of Korean Oriental Medicine. 19:2. 137–152.

4. Li H, Ren J, Li Y, Wu Q, Wei J. 2023; Oxidative stress: The nexus of obesity and cognitive dysfunction in diabetes. 14https://doi.org/10.3389/fendo.2023.1134025

5. Kodl CT, Seaquist ER. 2008; Cognitive Dysfunction and Diabetes Mellitus. Endocrine Reviews. 29:4. 494–511. https://doi.org/10.1210/er.2007-0034

6. Ninomiya T. 2019; Epidemiological evidence of the relationship between diabetes and dementia. Diabetes Mellitus: A risk factor for Alzheimer’s Disease. 13–25.

7. Kawamura T, Umemura T, Hotta N. 2012; Cognitive impairment in diabetic patients: Can diabetic control prevent cognitive decline? Journal of Diabetes Investigation. 3:5. 413–423. https://doi.org/10.1111/j.2040-1124.2012.00234.x

8. Sadeghi A, Hami J, Razavi S, Esfandiary E, Hejazi Z. 2016; The Effect of Diabetes Mellitus on Apoptosis in Hippocampus: Cellular and Molecular Aspects. International journal of preventive medicine. 7:57https://doi.org/10.4103/2008-7802.178531

9. Guan D, Lewis MO, Li P, Zhang Y, Zhang P, Tang S, et al. 2024; Incremental burden on health-related quality of life, health service utilization and direct medical expenditures associated with cognitive impairment among non-institutionalized people with diabetes aged 65 years and older. Diabetes, Obesity and Metabolism. 26:1. 275–282. https://doi.org/10.1111/dom.15313

10. Chan-hee J, Ji-oh M. 2022; Cognitive Dysfuntion and Diabetes. The Journal of Korean Diabetes. 23:3. 165–177. https://doi.org/10.4093/jkd.2022.23.3.165

11. Umegaki H. 2016; Therapeutic potential of antidiabetic medications in the treatment of cognitive dysfunction and dementia. Drugs & aging. 33:399–409. https://doi.org/10.1007/s40266-016-0375-0

12. Han SY, Kim YK. 2016; New approach for herbal formula research: Network pharmacology. The Physiological Society of Korean Medicine and The Society of Pathology in Korean Medicine. 30:6. 385–396. https://doi.org/10.15188/kjopp.2016.12.30.6.385

13. Hopkins AL. 2008; Network pharmacology: the next paradigm in drug discovery. J Nature chemical biology. 4:11. 682–690.

14. Nabirotchkin S, Peluffo AE, Rinaudo P, Yu J, Hajj R, Cohen D. 2020; Next-generation drug repurposing using human genetics and network biology. J Current Opinion in Pharmacology. 51:78–92. https://doi.org/10.1016/j.coph.2019.12.004

15. Rao RV, Subramaniam KG, Gregory J, Bredesen AL, Coward C, Okada S, et al. 2023; Rationale for a multi-factorial approach for the reversal of cognitive decline in Alzheimer’s disease and MCI: a review. J International Journal of Molecular Sciences. 24:2. 1659https://doi.org/10.3390/ijms24021659

16. Kumar B, Thakur A, Dwivedi AR, Kumar R, Kumar V. 2022; Multi-target-directed ligands as an effective strategy for the treatment of Alzheimer’s disease. J Current Medicinal Chemistry. 29:10. 1757–1803. https://doi.org/10.2174/0929867328666210512005508

17. Young-Gab Y. 1998. 東醫方劑와 處方解說. 1:Republic of Korea: Kim Jun-Su.

18. Lee S-E. 2023; Antioxidative and Anti-inflammatory Effects of Daehwangmokdanphee-tang. Journal of the Korean Society of Cosmetology. 29:1190–1198. https://doi.org/10.52660/JKSC.2023.29.5.1190

19. Sung-Joo Park J-GJ, Seo Sang-Wan, Hwang Sang-Wook, Kim Yong-Woo, Song Dal-Soo, Chae Young-Seok, Shin Min-Kyo, Song Ho-Joon. 2005; Effects of Gami-Daehwangmokdanpi-Tang against CCK-induced acute pancreatitis. The Korea Journal of Herbology. 20:03. 59–65.

20. Bitna K, Dong-uk K, Gabsik Y, Il-joo J. 2023; Prediction the efficacy and mechanism of action of Dawhwangmokdanpitang to treat psoriasis based on network pharmacology. The Korea Journal of Herbology. 38:6. 73–91. http://dx.doi.org/10.6116/kjh.2023.38.6.73

21. APA. 2015. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Korea: Hakjisa.

22. Yang W, Kim JK, Park KW, Suh S, Lee H-J, Park M-K. 2020; Correlation between Peripheral Neuropathy and Cognitive Factors in Type 2 Diabetic Patients. Journal of Life Science. 30:3. 250–259. https://doi.org/10.5352/JLS.2020.30.3.250

23. Sims-Robinson C, Kim B, Feldman EL. Diabetes and cognitive dysfunction. Neurobiology of Brain Disorders. Elsevier;2015. p. 189–201.

24. Varghese SM, Joy N, John AM, George G, Chandy GM, Benjamin AI. 2022; Sweet memories or not? A comparative study on cognitive impairment in diabetes mellitus. Frontiers in Public Health. 10:822062https://doi.org/10.3389/fpubh.2022.822062

25. Han E, Lee J-Y, Han K-d, Cho H, Kim KJ, Lee B-W, et al. 2020; Gamma glutamyltransferase and risk of dementia in prediabetes and diabetes. Scientific reports. 10:1. 6800https://doi.org/10.1038/s41598-020-63803-0

26. Lee YJ, Kang HM, Kim NK, Yang JY, Noh JH, Ko KS, et al. 2014; Factors associated for mild cognitive impairment in older Korean adults with type 2 diabetes mellitus. Diabetes & Metabolism Journal. 38:2. 150https://doi.org/10.4093/dmj.2014.38.2.150

27. Biessels GJ, Whitmer RA. 2020; Cognitive dysfunction in diabetes: how to implement emerging guidelines. Diabetologia. 63:1. 3–9. https://doi.org/10.1007/s00125-019-04977-9

28. Yin Q, Gao Y, Wang X, Li S, Hou X, Bi W. 2023; China should emphasize understanding and standardized management in diabetic cognitive dysfunction. Frontiers in Endocrinology. 14https://doi.org/10.3389/fendo.2023.1195962

29. Pan G, Chai L, Chen R, Yuan Q, Song Z, Feng W, et al. 2024; Potential mechanism of Qinggong Shoutao pill alleviating age-associated memory decline based on integration strategy. Pharm Biol. 62:1. 105–119. http://doi.org/10.1080/13880209.2023.2291689

30. Chen J, Zhang T, Luo Q, Wang R, Dai Y, Chen Z, et al. 2024; Network pharmacology combined with experimental validation to investigate the effect of Rongjin Niantong Fang on chondrocyte apoptosis in knee osteoarthritis. Mol Med Rep. 29:6. https://doi.org/10.3892/mmr.2024.13226

31. Wu T, Zhang H, Jin Y, Zhang M, Zhao Q, Li H, et al. 2024; The active components and potential mechanisms of Wuji Wan in the treatment of ethanol-induced gastric ulcer: An integrated metabolomics, network pharmacology and experimental validation. Journal of ethnopharmacology. 326:117901https://doi.org/10.1016/j.jep.2024.117901

32. Sung-Ha Myung Y-SK. 2005; Anti-Oxidative and Neuroprotective Effects of Rhei Rhizoma on BV-2 Microglia Cells and Hippocampal Neurons. The Korean Journal of Oriental Physiology & Pathology. 19:03. 647–655.

33. Won-Chul L. 2003; Effect of Rhei Rhizoma on HSP70 Expression and Ischemic Damaged Hippocampus of the Aged BCAO Rats. J of Oriental Chr Dis. 91:21–29.

34. Moon S, Seon K, Lim J, Song B. 2009; The Effect of the Moutan Radicis Cortex on Expression of CD81 and GFAP in Injured Astrocyte. the Journal of Internal Korean Medicine. 30:1. 24–35.

35. Chen W, Liu Q, Gao X, Geng Y, Kan H. 2024; Observational study on the potential mechanism of Sanao decoction in the treatment of asthma based on network pharmacology and molecular docking. Medicine. 103:12. e37592https://doi.org/10.1097/MD.0000000000037592

36. Piperi C, Goumenos A, Adamopoulos C, Papavassiliou AG. 2015; AGE/RAGE signalling regulation by miRNAs: associations with diabetic complications and therapeutic potential. J The international journal of biochemistry cell biology. 60:197–201. https://doi.org/10.1016/j.biocel.2015.01.009

37. Derk J, MacLean M, Juranek J, Schmidt AM. 2018; The receptor for advanced glycation endproducts (RAGE) and mediation of inflammatory neurodegeneration. J Journal of Alzheimer’s disease Parkinsonism. 8:1. https://doi.org/10.4172/2161-0460.1000421

38. Erekat NS. 2022; Apoptosis and its therapeutic implications in neurodegenerative diseases. J Clinical Anatomy. 35:1. 65–78. https://doi.org/10.1002/ca.23792

39. Waghela BN, Vaidya FU, Ranjan K, Chhipa AS, Tiwari BS, Pathak C. 2021; AGE-RAGE synergy influences programmed cell death signaling to promote cancer. J Molecular & Cellular Biochemistry. 476:585–598. https://doi.org/10.1007/s11010-020-03928-y

40. Piras S, Furfaro A, Domenicotti C, Traverso N, Marinari U, Pronzato M, Nitti M. 2016; RAGE expression and ROS generation in neurons: differentiation versus damage. J Oxidative medicine cellular longevity. 2016; https://doi.org/10.1155/2016/9348651

41. Taguchi K, Fukami K. 2023; RAGE signaling regulates the progression of diabetic complications. J Frontiers in Pharmacology. 14:1128872https://doi.org/10.3389/fphar.2023.1128872

42. Marioni RE, Strachan MW, Reynolds RM, Lowe GD, Mitchell RJ, Fowkes FGR, et al. 2010; Association between raised inflammatory markers and cognitive decline in elderly people with type 2 diabetes: the Edinburgh Type 2 Diabetes Study. J Diabetes. 59:3. 710–713. https://doi.org/10.2337/db09-1163

43. Geng J, Wang L, Zhang L, Qin C, Song Y, Ma Y, et al. 2018; Blood-brain barrier disruption induced cognitive impairment is associated with increase of inflammatory cytokine. J Frontiers in aging neuroscience. 10:129https://doi.org/10.3389/fnagi.2018.00129

44. Liu H, Guo D, Wang J, Zhang W, Zhu Z, Zhu K, et al. 2024; Aloe-emodin from Sanhua Decoction inhibits neuroinflammation by regulating microglia polarization after subarachnoid hemorrhage. Journal of ethnopharmacology. 322:117583https://doi.org/10.1016/j.jep.2023.117583

45. Hu B, Zhang H, Meng X, Wang F, Wang P. 2014; Aloe-emodin from rhubarb (Rheum rhabarbarum) inhibits lipopolysaccharide-induced inflammatory responses in RAW264. 7 macrophages. Journal of ethnopharmacology. 153:3. 846–853. https://doi.org/10.1016/j.jep.2014.03.059

46. Kuhad A, Bishnoi M, Tiwari V, Chopra K. 2009; Suppression of NF-κβ signaling pathway by tocotrienol can prevent diabetes associated cognitive deficits. Pharmacology Biochemistry and Behavior. 92:2. 251–259. https://doi.org/10.1016/j.pbb.2008.12.012

47. Ghasemi R, Zarifkar A, Rastegar K, Moosavi M. 2014; Insulin protects against Aβ-induced spatial memory impairment, hippocampal apoptosis and MAPKs signaling disruption. Neuropharmacology. 85:113–120. https://doi.org/10.1016/j.neuropharm.2014.01.036

48. Maiti P, Singh SB, Mallick B, Muthuraju S, Ilavazhagan G. 2008; High altitude memory impairment is due to neuronal apoptosis in hippocampus, cortex and striatum. J Journal of chemical neuroanatomy. 36:3–4. 227–238. https://doi.org/10.1016/j.jchemneu.2008.07.003

49. Jafari Anarkooli I, Sankian M, Ahmadpour S, Varasteh A-R, Haghir H. 2008; Evaluation of Bcl-2 family gene expression and Caspase-3 activity in hippocampus STZ-induced diabetic rats. Journal of Diabetes Research. 2008https://doi.org/10.1155/2008/638467

50. Liu Z, Kumar M, Kabra A. 2022; Cucurbitacin B exerts neuroprotection in a murine Alzheimer’s disease model by modulating oxidative stress, inflammation, and neurotransmitter levels. J Frontiers in Bioscience-Landmark. 27:2. 71https://doi.org/10.31083/j.fbl2702071

51. Zhao W-Q, Chen H, Quon MJ, Alkon DL. 2004; Insulin and the insulin receptor in experimental models of learning and memory. European journal of pharmacology. 490:1–3. 71–81. https://doi.org/10.1016/j.ejphar.2004.02.045

52. Arnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang H-Y, Ahima RS, et al. 2018; Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nature Reviews Neurology. 14:3. 168–181. https://doi.org/10.1038/nrneurol.2017.185

53. Kim B, Feldman EL. 2015; Insulin resistance as a key link for the increased risk of cognitive impairment in the metabolic syndrome. J Experimental molecular medicine. 47:3. e149–e149. https://doi.org/10.1038/emm.2015.3

54. Banu GS. 2017; Cucurbitacin augments insulin sensitivity and glucose uptake through translocation and activation of GLUT-4 in PI3K/ALT signalling pathway. J World Journal of Pharmaceutical Research. 6:1078–1096.

55. Cao Y, Chang S, Dong J, Zhu S, Zheng X, Li J, et al. 2016; Emodin ameliorates high-fat-diet induced insulin resistance in rats by reducing lipid accumulation in skeletal muscle. J European Journal of Pharmacology. 780:194–201. https://doi.org/10.1016/j.ejphar.2016.03.049

56. Naowaboot J, Piyabhan P, Tingpej P, Munkong N, Parklak W, Pannangpetch P. 2018; Anti-insulin resistant effect of ferulic acid on high fat diet-induced obese mice. J Asian Pacific Journal of Tropical Biomedicine. 8:12. 604–608. http://doi.org/10.4103/2221-1691.248098