Your browser does not support the NLM PubReader view.

Go to this page to see a list of supporting browsers.

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

Article information

J Korean Med. 2024;45(2):23-40
Publication date (electronic) : 2024 June 01
doi : https://doi.org/10.13048/jkm.24022
Yebin Lim1,§orcid_icon, Bitna Kweon1,2,§orcid_icon, Dong-Uk Kim1,2,§orcid_icon, Do-Eun Lee3,4orcid_icon, Jungtae Leem5,6orcid_icon, Dong-Gu Kim7,orcid_icon, Hyung Won Kang3,4,5,orcid_icon, Gi-Sang Bae1,2,5,orcid_icon
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 2024 April 15; Revised 2024 May 7; Accepted 2024 May 7.

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.

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

List of active compounds from DHMDPT with their PubChem IDs.

Detailed information on the 18 key targets.

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

Analysis of GO biological processes for the 18 key targets.

Analysis of KEGG pathways for the 18 key targets.

References

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):6535. https://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 :14. https://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 7:57. https://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):1659. https://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 1Republic 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 edth 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 10:822062. https://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):6800. https://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):150. https://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 :14. https://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 326:117901. https://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):e37592. https://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 14:1128872. https://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 10:129. https://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 322:117583. https://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 :2008. https://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):71. https://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.

Article information Continued

Fig. 1

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

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

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

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

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

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