Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

References

• Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–2251. doi:10.1016/s0140-6736(17)30058-2
   PubMed Web of Science ®Google Scholar
• Wang L, Gao P, Zhang M, et al. Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013. JAMA. 2017;317(24):2515–2523. doi:10.1001/jama.2017.7596
   PubMed Web of Science ®Google Scholar
• Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840–846. doi:10.1038/nature05482
   PubMed Web of Science ®Google Scholar
• Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. doi:10.1038/nrendo.2017.151
   PubMed Web of Science ®Google Scholar
• Meng X, Liu X, Tan J, et al. From Xiaoke to diabetes mellitus: a review of the research progress in traditional Chinese medicine for diabetes mellitus treatment. Chin Med. 2023;18(1):75. doi:10.1186/s13020-023-00783-z
   PubMed Web of Science ®Google Scholar
• Chan KW, Kwong ASK, Tan KCB, et al. Add-on rehmannia-6-based Chinese medicine in type 2 diabetes and CKD: a multicenter randomized controlled trial. Clin J Am Soc Nephrol. 2023;18(9):1163–1174. doi:10.2215/cjn.0000000000000199
   PubMedGoogle Scholar
• Chen YK, Liu TT, Teia FKF, Xie MZ. Exploring the underlying mechanisms of obesity and diabetes and the potential of traditional Chinese medicine: an overview of the literature. Front Endocrinol. 2023;14:1218880. doi:10.3389/fendo.2023.1218880
   PubMed Web of Science ®Google Scholar
• Monteiro MS, Carvalho M, Bastos ML, Guedes de Pinho P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem. 2013;20(2):257–271. doi:10.2174/092986713804806621
   PubMed Web of Science ®Google Scholar
• Wang M, Chen L, Liu D, Chen H, Tang DD, Zhao YY. Metabolomics highlights pharmacological bioactivity and biochemical mechanism of traditional Chinese medicine. Chem Biol Interact. 2017;273:133–141. doi:10.1016/j.cbi.2017.06.011
   PubMed Web of Science ®Google Scholar
• Ren JL, Zhang AH, Kong L, et al. Analytical strategies for the discovery and validation of quality-markers of traditional Chinese medicine. Phytomedicine. 2020;67:153165. doi:10.1016/j.phymed.2019.153165
   PubMed Web of Science ®Google Scholar
• Wang X, Sun H, Zhang A, Sun W, Wang P, Wang Z. Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine. J Pharm Biomed Anal. 2011;55(5):859–868. doi:10.1016/j.jpba.2011.01.042
   PubMed Web of Science ®Google Scholar
• Zhang A, Sun H, Wang Z, Sun W, Wang P, Wang X. Metabolomics: towards understanding traditional Chinese medicine. Planta Med. 2010;76(17):2026–2035. doi:10.1055/s-0030-1250542
   PubMed Web of Science ®Google Scholar
• Tong XL, Dong L, Chen L, Zhen Z. Treatment of diabetes using traditional Chinese medicine: past, present and future. Am J Chin Med. 2012;40(5):877–886. doi:10.1142/s0192415x12500656
   PubMed Web of Science ®Google Scholar
• Wu T, Yang M, Liu T, Yang L, Ji G. A metabolomics approach to stratify patients diagnosed with diabetes mellitus into excess or deficiency syndromes. Evid Based Complement Alternat Med. 2015;2015:350703. doi:10.1155/2015/350703
   PubMed Web of Science ®Google Scholar
• Sun DZ, Li SD, Liu Y, Zhang Y, Mei R, Yang MH. Differences in the origin of philosophy between Chinese medicine and western medicine: exploration of the holistic advantages of Chinese medicine. Chin J Integr Med. 2013;19(9):706–711. doi:10.1007/s11655-013-1435-5
   PubMed Web of Science ®Google Scholar
• Wang J, Ma Q, Li Y, et al. Research progress on traditional Chinese medicine syndromes of diabetes mellitus. Biomed Pharmacother. 2020;121:109565. doi:10.1016/j.biopha.2019.109565
   PubMed Web of Science ®Google Scholar
• Schnyer RN, Citkovitz C. Inter-rater reliability in traditional Chinese Medicine: challenging paradigmatic assumptions. J Altern Complement Med. 2019;25(11):1067–1073. doi:10.1089/acm.2019.0331
   PubMed Web of Science ®Google Scholar
• Schnyer RN, McKnight P, Conboy LA, et al. Can reliability of the Chinese medicine diagnostic process be improved? Results of a prospective randomized controlled trial. J Altern Complement Med. 2019;25(11):1103–1108. doi:10.1089/acm.2019.0260
   PubMed Web of Science ®Google Scholar
• Wu GS, Li HK, Zhang WD. Metabolomics and its application in the treatment of coronary heart disease with traditional Chinese medicine. Chin J Nat Med. 2019;17(5):321–330. doi:10.1016/s1875-5364(19)30037-8
   PubMed Web of Science ®Google Scholar
• Sun H, Zhang A, Wang X. Potential role of metabolomic approaches for Chinese medicine syndromes and herbal medicine. Phytother Res. 2012;26(10):1466–1471. doi:10.1002/ptr.4613
   PubMed Web of Science ®Google Scholar
• Jiang N, Liu HF, Li SD, et al. An integrated metabonomic and proteomic study on Kidney-Yin deficiency syndrome patients with diabetes mellitus in China. Acta Pharmacol Sin. 2015;36(6):689–698. doi:10.1038/aps.2014.169
   PubMed Web of Science ®Google Scholar
• DeFronzo RA, Abdul-Ghani M. Type 2 diabetes can be prevented with early pharmacological intervention. Diabetes Care. 2011;34 Suppl 2(Suppl 2):S202–S209. doi:10.2337/dc11-s221
   PubMedGoogle Scholar
• Beulens J, Rutters F, Rydén L, et al. Risk and management of pre-diabetes. Eur J Prev Cardiol. 2019;26(2_suppl):47–54. doi:10.1177/2047487319880041
   PubMed Web of Science ®Google Scholar
• Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiological Rev. 2019;99(4):1819–1875. doi:10.1152/physrev.00035.2018
   PubMed Web of Science ®Google Scholar
• Jacob M, Lopata AL, Dasouki M, Abdel Rahman AM. Metabolomics toward personalized medicine. Mass Spectrom Rev. 2019;38(3):221–238. doi:10.1002/mas.21548
   PubMed Web of Science ®Google Scholar
• Jun G, Aguilar D, Evans C, Burant CF, Hanis CL. Metabolomic profiles associated with subtypes of prediabetes among Mexican Americans in Starr County, Texas, USA. Diabetologia. 2020;63(2):287–295. doi:10.1007/s00125-019-05031-4
   PubMed Web of Science ®Google Scholar
• Wang L, Zhang Y, Liu X, et al. Metabolite triplet in serum improves the diagnostic accuracy of prediabetes and diabetes screening. J Proteome Res. 2021;20(1):1005–1014. doi:10.1021/acs.jproteome.0c00786
   PubMed Web of Science ®Google Scholar
• Fikri AM, Smyth R, Kumar V, Al-Abadla Z, Abusnana S, Munday MR. Pre-diagnostic biomarkers of type 2 diabetes identified in the UAE’s obese national population using targeted metabolomics. Sci Rep. 2020;10(1):17616. doi:10.1038/s41598-020-73384-7
   PubMed Web of Science ®Google Scholar
• Bos MM, Noordam R, Bennett K, et al. Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity. Metabolomics. 2020;16(3):35. doi:10.1007/s11306-020-01653-7
   PubMed Web of Science ®Google Scholar
• Yun H, Sun L, Wu Q, et al. Associations among circulating sphingolipids, β-cell function, and risk of developing type 2 diabetes: a population-based cohort study in China. PLoS Med. 2020;17(12):e1003451. doi:10.1371/journal.pmed.1003451
   PubMed Web of Science ®Google Scholar
• Ottosson F, Smith E, Gallo W, Fernandez C, Melander O. Purine metabolites and carnitine biosynthesis intermediates are biomarkers for incident type 2 diabetes. J Clin Endocrinol Metab. 2019;104(10):4921–4930. doi:10.1210/jc.2019-00822
   PubMed Web of Science ®Google Scholar
• Chen GC, Chai JC, Yu B, et al. Serum sphingolipids and incident diabetes in a US population with high diabetes burden: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Am J Clin Nutr. 2020;112(1):57–65. doi:10.1093/ajcn/nqaa114
   PubMed Web of Science ®Google Scholar
• Bell JA, Bull CJ, Gunter MJ, et al. Early metabolic features of genetic liability to type 2 diabetes: cohort study with repeated metabolomics across early life. Diabetes Care. 2020;43(7):1537–1545. doi:10.2337/dc19-2348
   PubMed Web of Science ®Google Scholar
• Vangipurapu J, Stancáková A, Smith U, Kuusisto J, Laakso M. Nine amino acids are associated with decreased insulin secretion and elevated glucose levels in a 7.4-year follow-up study of 5181 Finnish men. Diabetes. 2019;68(6):1353–1358. doi:10.2337/db18-1076
   PubMed Web of Science ®Google Scholar
• Li L, Krznar P, Erban A, et al. Metabolomics identifies a biomarker revealing in vivo loss of functional β-cell mass before diabetes onset. Diabetes. 2019;68(12):2272–2286. doi:10.2337/db19-0131
   PubMed Web of Science ®Google Scholar
• Lu J, Lam SM, Wan Q, et al. High-coverage targeted lipidomics reveals novel serum lipid predictors and lipid pathway dysregulation antecedent to type 2 diabetes onset in normoglycemic Chinese adults. Diabetes Care. 2019;42(11):2117–2126. doi:10.2337/dc19-0100
   PubMed Web of Science ®Google Scholar
• Gu X, Al Dubayee M, Alshahrani A, et al. Distinctive metabolomics patterns associated with insulin resistance and type 2 diabetes mellitus. Front Mol Biosci. 2020;7:609806. doi:10.3389/fmolb.2020.609806
   PubMed Web of Science ®Google Scholar
• Diamanti K, Cavalli M, Pan G, et al. Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes. Sci Rep. 2019;9(1):9653. doi:10.1038/s41598-019-45906-5
   PubMedGoogle Scholar
• Mack CI, Ferrario PG, Weinert CH, et al. Exploring the diversity of sugar compounds in healthy, prediabetic, and diabetic volunteers. Mol Nutr Food Res. 2020;64(9):e1901190. doi:10.1002/mnfr.201901190
   PubMed Web of Science ®Google Scholar
• Calvani R, Rodriguez-Mañas L, Picca A, et al. Identification of a circulating amino acid signature in frail older persons with type 2 diabetes mellitus: results from the Metabofrail Study. Nutrients. 2020;12(1):199. doi:10.3390/nu12010199
   PubMed Web of Science ®Google Scholar
• Mora-Ortiz M, Nuñez Ramos P, Oregioni A, Claus SP. NMR metabolomics identifies over 60 biomarkers associated with Type II diabetes impairment in db/db mice. Metabolomics. 2019;15(6):89. doi:10.1007/s11306-019-1548-8
   PubMed Web of Science ®Google Scholar
• Godzien J, Kalaska B, Adamska-Patruno E, et al. Oxidized glycerophosphatidylcholines in diabetes through non-targeted metabolomics: their annotation and biological meaning. J Chromatogr B Analyt Technol Biomed Life Sci. 2019;1120:62–70. doi:10.1016/j.jchromb.2019.04.053
   PubMed Web of Science ®Google Scholar
• Al-Sulaiti H, Diboun I, Agha MV, et al. Metabolic signature of obesity-associated insulin resistance and type 2 diabetes. J Transl Med. 2019;17(1):348. doi:10.1186/s12967-019-2096-8
   PubMed Web of Science ®Google Scholar
• Ji Y, Yao Y, Duan Y, et al. Association between urinary organophosphate flame retardant diesters and steroid hormones: a metabolomic study on type 2 diabetes mellitus cases and controls. Sci Total Environ. 2021;756:143836. doi:10.1016/j.scitotenv.2020.143836
   PubMed Web of Science ®Google Scholar
• Ottosson F, Smith E, Fernandez C, Melander O. Plasma metabolites associate with all-cause mortality in individuals with type 2 diabetes. Metabolites. 2020;10(8):315. doi:10.3390/metabo10080315
   PubMed Web of Science ®Google Scholar
• Salihovic S, Broeckling CD, Ganna A, et al. Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes. Sci Rep. 2020;10(1):16474. doi:10.1038/s41598-020-72456-y
   PubMed Web of Science ®Google Scholar
• Ahola-Olli AV, Mustelin L, Kalimeri M, et al. Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia. 2019;62(12):2298–2309. doi:10.1007/s00125-019-05001-w
   PubMed Web of Science ®Google Scholar
• Zeng Y, Mtintsilana A, Goedecke JH, Micklesfield LK, Olsson T, Chorell E. Alterations in the metabolism of phospholipids, bile acids and branched-chain amino acids predicts development of type 2 diabetes in black South African women: a prospective cohort study. Metabolism. 2019;95:57–64. doi:10.1016/j.metabol.2019.04.001
   PubMed Web of Science ®Google Scholar
• Gar C, Rottenkolber M, Prehn C, Adamski J, Seissler J, Lechner A. Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes. Crit Rev Clin Lab Sci. 2018;55(1):21–32. doi:10.1080/10408363.2017.1414143
   PubMed Web of Science ®Google Scholar
• Guasch-Ferré M, Hruby A, Toledo E, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016;39(5):833–846. doi:10.2337/dc15-2251
   PubMed Web of Science ®Google Scholar
• Klein MS, Shearer J. Metabolomics and type 2 diabetes: translating basic research into clinical application. J Diabetes Res. 2016;2016:3898502. doi:10.1155/2016/3898502
   PubMed Web of Science ®Google Scholar
• Drogan D, Dunn WB, Lin W, et al. Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin Chem. 2015;61(3):487–497. doi:10.1373/clinchem.2014.228965
   PubMed Web of Science ®Google Scholar
• Nikiforova VJ, Giesbertz P, Wiemer J, et al. Glyoxylate, a new marker metabolite of type 2 diabetes. J Diabetes Res. 2014;2014:685204. doi:10.1155/2014/685204
   PubMed Web of Science ®Google Scholar
• Sas KM, Karnovsky A, Michailidis G, Pennathur S. Metabolomics and diabetes: analytical and computational approaches. Diabetes. 2015;64(3):718–732. doi:10.2337/db14-0509
   PubMed Web of Science ®Google Scholar
• Sun Y, Gao HY, Fan ZY, He Y, Yan YX. Metabolomics signatures in type 2 diabetes: a systematic review and integrative analysis. J Clin Endocrinol Metab. 2020;105(4):1000–1008. doi:10.1210/clinem/dgz240
   Web of Science ®Google Scholar
• Jia W. Diabetes research in China: making progress. Lancet Diabetes Endocrinol. 2017;5(1):9–10. doi:10.1016/s2213-8587(16)30094-8
   PubMed Web of Science ®Google Scholar
• Tian J, Jin D, Bao Q, et al. Evidence and potential mechanisms of traditional Chinese medicine for the treatment of type 2 diabetes: a systematic review and meta-analysis. Diabetes Obesity Metab. 2019;21(8):1801–1816. doi:10.1111/dom.13760
   PubMed Web of Science ®Google Scholar
• Luo M, Zhang Z, Lu Y, et al. Urine metabolomics reveals biomarkers and the underlying pathogenesis of diabetic kidney disease. Int Urol Nephrol. 2023;55(4):1001–1013. doi:10.1007/s11255-022-03326-x
   PubMed Web of Science ®Google Scholar
• Zou J, Xiang Q, Tan D, et al. Zuogui-Jiangtang-Qinggan-Fang alleviates high-fat diet-induced type 2 diabetes mellitus with non-alcoholic fatty liver disease by modulating gut microbiome-metabolites-short chain fatty acid composition. Biomed Pharmacother. 2023;157:114002. doi:10.1016/j.biopha.2022.114002
   PubMed Web of Science ®Google Scholar
• Guo S, Qiu S, Cai Y, et al. Mass spectrometry-based metabolomics for discovering active ingredients and exploring action mechanism of herbal medicine. Front Chem. 2023;11:1142287. doi:10.3389/fchem.2023.1142287
   PubMed Web of Science ®Google Scholar
• Lin L, Zhang S, Lin Y, et al. Untargeted metabolomics analysis on Cicer arietinium L.-induced amelioration in T2D rats by UPLC-Q-TOF-MS/MS. J Ethnopharmacol. 2020;261:113013. doi:10.1016/j.jep.2020.113013
   PubMed Web of Science ®Google Scholar
• Yan Z, Wu H, Zhou H, et al. Integrated metabolomics and gut microbiome to the effects and mechanisms of naoxintong capsule on type 2 diabetes in rats. Sci Rep. 2020;10(1):10829. doi:10.1038/s41598-020-67362-2
   PubMed Web of Science ®Google Scholar
• Pan L, Li Z, Wang Y, Zhang B, Liu G, Liu J. Network pharmacology and metabolomics study on the intervention of traditional Chinese medicine Huanglian Decoction in rats with type 2 diabetes mellitus. J Ethnopharmacol. 2020;258:112842. doi:10.1016/j.jep.2020.112842
   PubMed Web of Science ®Google Scholar
• Guo Q, Niu W, Li X, et al. Study on hypoglycemic effect of the drug pair of astragalus radix and dioscoreae rhizoma in T2DM rats by network pharmacology and metabonomics. Molecules. 2019;24(22):4050. doi:10.3390/molecules24224050
   PubMed Web of Science ®Google Scholar
• Weng J, Zhou J, Liang L, Li L. UHPLC/QTOF-MS-based metabolomics reveal the effect of melastoma dodecandrum extract in type 2 diabetic rats. Pharm Biol. 2019;57(1):807–815. doi:10.1080/13880209.2019.1693605
   PubMed Web of Science ®Google Scholar
• Wang L, Yang C, Song F, Liu Z, Liu S. Therapeutic effectiveness of gardenia jasminoides on type 2 diabetic rats: mass spectrometry-based metabolomics approach. Journal of Agricultural and Food Chemistry. 2020;68(36):9673–9682. doi:10.1021/acs.jafc.0c02873
   PubMed Web of Science ®Google Scholar
• Wang H, Huang R, Li H, Jiao L, Liu S, Wu W. Serum metabolomic analysis of the anti-diabetic effect of Ginseng berry in type II diabetic rats based on ultra high-performance liquid chromatography-high resolution mass spectrometry. J Pharm Biomed Anal. 2021;196:113897. doi:10.1016/j.jpba.2021.113897
   PubMed Web of Science ®Google Scholar
• Li YY, Stewart DA, Ye XM, et al. A metabolomics approach to investigate kukoamine B-A potent natural product with anti-diabetic properties. Front Pharmacol. 2018;9:1575. doi:10.3389/fphar.2018.01575
   PubMed Web of Science ®Google Scholar
• Qiu F, Zhang YQ. Metabolic effects of mulberry branch bark powder on diabetic mice based on GC-MS metabolomics approach. Nutr Metab. 2019;16:10. doi:10.1186/s12986-019-0335-x
   Google Scholar
• Zheng J, Guo Y, Hu B, et al. Serum metabolomic profiles reveal the impact of BuZangTongLuo formula on metabolic pathways in diabetic mice with hindlimb ischemia. J Ethnopharmacol. 2020;258:112928. doi:10.1016/j.jep.2020.112928
   PubMed Web of Science ®Google Scholar
• Pan LL, Sun QH, Liu GR, Guo JY. Urinary Metabolomics Study of the intervention effect of hypoglycemic decoction on type 2 diabetes mellitus rats model. Evid Based Complement Alternat Med. 2019;2019:1394641. doi:10.1155/2019/1394641
   PubMed Web of Science ®Google Scholar
• He WJ, Cao DM, Chen YB, et al. Explore of the beneficial effects of Huang-Lian-Jie-Du Decoction on diabetic encephalopathy in db/db mice by UPLC-Q-Orbitrap HRMS/MS based untargeted metabolomics analysis. J Pharm Biomed Anal. 2021;192:113652. doi:10.1016/j.jpba.2020.113652
   PubMed Web of Science ®Google Scholar
• Xiang Z, Xie H, Tong Q, et al. Revealing hypoglycemic and hypolipidemic mechanism of Xiaokeyinshui extract combination on streptozotocin-induced diabetic mice in high sucrose/high fat diet by metabolomics and lipidomics. Biomed Pharmacother. 2021;135:111219. doi:10.1016/j.biopha.2021.111219
   PubMed Web of Science ®Google Scholar
• Yang J, Chen H, Nie Q, Huang X, Nie S. Dendrobium officinale polysaccharide ameliorates the liver metabolism disorders of type II diabetic rats. Int J Biol Macromol. 2020;164:1939–1948. doi:10.1016/j.ijbiomac.2020.08.007
   PubMed Web of Science ®Google Scholar
• Man S, Ma J, Yao J, et al. Systemic perturbations of key metabolites in type 2 diabetic rats treated by polyphenol extracts from Litchi chinensis seeds. Journal of Agricultural and Food Chemistry. 2017;65(35):7698–7704. doi:10.1021/acs.jafc.7b02206
   PubMed Web of Science ®Google Scholar
• Avogaro A, Fadini GP. Microvascular complications in diabetes: a growing concern for cardiologists. Int J Cardiol. 2019;291:29–35. doi:10.1016/j.ijcard.2019.02.030
   PubMed Web of Science ®Google Scholar
• Eid S, Sas KM, Abcouwer SF, et al. New insights into the mechanisms of diabetic complications: role of lipids and lipid metabolism. Diabetologia. 2019;62(9):1539–1549. doi:10.1007/s00125-019-4959-1
   PubMed Web of Science ®Google Scholar
• Wang W, Lo ACY. Diabetic retinopathy: pathophysiology and treatments. Int J Mol Sci. 2018;19(6). doi:10.3390/ijms19061816
   Web of Science ®Google Scholar
• Matuszewski W, Baranowska-Jurkun A, Stefanowicz-Rutkowska MM, Gontarz-Nowak K, Gątarska E, Bandurska-Stankiewicz E. The safety of pharmacological and surgical treatment of diabetes in patients with diabetic retinopathy-a review. J Clin Med. 2021;10(4):705. doi:10.3390/jcm10040705
   PubMed Web of Science ®Google Scholar
• Yamazaki T, Mimura I, Tanaka T, Nangaku M. Treatment of diabetic kidney disease: current and future. Diabet Metabol J. 2021;45(1):11–26. doi:10.4093/dmj.2020.0217
   PubMed Web of Science ®Google Scholar
• Raghu ALB, Parker T, Aziz TZ, et al. Invasive electrical neuromodulation for the treatment of painful diabetic neuropathy: systematic review and meta-analysis. Neuromodulation. 2021;24(1):13–21. doi:10.1111/ner.13216
   PubMed Web of Science ®Google Scholar
• Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376(9735):124–136. doi:10.1016/s0140-6736(09)62124-3
   PubMed Web of Science ®Google Scholar
• Tomita Y, Cagnone G, Fu Z, et al. Vitreous metabolomics profiling of proliferative diabetic retinopathy. Diabetologia. 2021;64(1):70–82. doi:10.1007/s00125-020-05309-y
   PubMed Web of Science ®Google Scholar
• Yun JH, Kim JM, Jeon HJ, Oh T, Choi HJ, Kim BJ. Metabolomics profiles associated with diabetic retinopathy in type 2 diabetes patients. PLoS One. 2020;15(10):e0241365. doi:10.1371/journal.pone.0241365
   PubMed Web of Science ®Google Scholar
• Haines NR, Manoharan N, Olson JL, D’Alessandro A, Reisz JA. Metabolomics analysis of human vitreous in diabetic retinopathy and rhegmatogenous retinal detachment. J Proteome Res. 2018;17(7):2421–2427. doi:10.1021/acs.jproteome.8b00169
   PubMed Web of Science ®Google Scholar
• Xuan Q, Ouyang Y, Wang Y, et al. Multiplatform metabolomics reveals novel serum metabolite biomarkers in diabetic retinopathy subjects. Adv Sci. 2020;7(22):2001714. doi:10.1002/advs.202001714
   Web of Science ®Google Scholar
• Zhu XR, Yang FY, Lu J, et al. Plasma metabolomic profiling of proliferative diabetic retinopathy. Nutr Metab. 2019;16:37. doi:10.1186/s12986-019-0358-3
   Google Scholar
• Sumarriva K, Uppal K, Ma C, et al. Arginine and carnitine metabolites are altered in diabetic retinopathy. Invest Ophthalmol Vis Sci. 2019;60(8):3119–3126. doi:10.1167/iovs.19-27321
   PubMed Web of Science ®Google Scholar
• Jin H, Zhu B, Liu X, Jin J, Zou H. Metabolic characterization of diabetic retinopathy: an (1)H-NMR-based metabolomic approach using human aqueous humor. J Pharm Biomed Anal. 2019;174:414–421. doi:10.1016/j.jpba.2019.06.013
   PubMed Web of Science ®Google Scholar
• Wang H, Fang J, Chen F, et al. Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor. Acta Diabetol. 2020;57(1):41–51. doi:10.1007/s00592-019-01363-0
   PubMed Web of Science ®Google Scholar
• Rhee SY, Jung ES, Park HM, et al. Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy. Metabolomics. 2018;14(7):89. doi:10.1007/s11306-018-1383-3
   PubMed Web of Science ®Google Scholar
• Xuan Q, Zheng F, Yu D, et al. Rapid lipidomic profiling based on ultra-high performance liquid chromatography-mass spectrometry and its application in diabetic retinopathy. Anal Bioanal Chem. 2020;412(15):3585–3594. doi:10.1007/s00216-020-02632-6
   PubMed Web of Science ®Google Scholar
• Wang X, Li Y, Xie M, Deng L, Zhang M, Xie X. Urine metabolomics study of Bushen Huoxue prescription on diabetic retinopathy rats by UPLC-Q-exactive orbitrap-MS. Biomed Chromatogr. 2020;34(4):e4792. doi:10.1002/bmc.4792
   PubMed Web of Science ®Google Scholar
• Warren AM, Knudsen ST, Cooper ME. Diabetic nephropathy: an insight into molecular mechanisms and emerging therapies. Expert Opin Ther Targets. 2019;23(7):579–591. doi:10.1080/14728222.2019.1624721
   PubMed Web of Science ®Google Scholar
• Maqbool M, Cooper ME, Jandeleit-Dahm KAM. Cardiovascular disease and diabetic kidney disease. Semin Nephrol. 2018;38(3):217–232. doi:10.1016/j.semnephrol.2018.02.003
   PubMed Web of Science ®Google Scholar
• Shao M, Lu H, Yang M, et al. Serum and urine metabolomics reveal potential biomarkers of T2DM patients with nephropathy. Ann Transl Med. 2020;8(5):199. doi:10.21037/atm.2020.01.42
   PubMed Web of Science ®Google Scholar
• Zhang S, Li X, Luo H, Fang ZZ, Ai H. Role of aromatic amino acids in pathogeneses of diabetic nephropathy in Chinese patients with type 2 diabetes. J diabet complicat. 2020;34(10):107667. doi:10.1016/j.jdiacomp.2020.107667
   Google Scholar
• Zhang H, Zuo JJ, Dong SS, et al. Identification of potential serum metabolic biomarkers of diabetic kidney disease: a Widely Targeted Metabolomics Study. J Diabetes Res. 2020;2020:3049098. doi:10.1155/2020/3049098
   PubMed Web of Science ®Google Scholar
• Tofte N, Vogelzangs N, Mook-Kanamori D, et al. Plasma metabolomics identifies markers of impaired renal function: a meta-analysis of 3089 persons with type 2 diabetes. J Clin Endocrinol Metab. 2020;105(7):2275–2287. doi:10.1210/clinem/dgaa173
   Web of Science ®Google Scholar
• Kwan B, Fuhrer T, Zhang J, et al. Metabolomic markers of kidney function decline in patients with diabetes: evidence from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2020;76(4):511–520. doi:10.1053/j.ajkd.2020.01.019
   PubMed Web of Science ®Google Scholar
• Ma T, Liu T, Xie P, et al. UPLC-MS-based urine nontargeted metabolic profiling identifies dysregulation of pantothenate and CoA biosynthesis pathway in diabetic kidney disease. Life Sci. 2020;258:118160. doi:10.1016/j.lfs.2020.118160
   PubMed Web of Science ®Google Scholar
• Cordero-Pérez P, Sánchez-Martínez C, García-Hernández PA, Saucedo AL. [Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators] Metabolómica de la nefropatía diabética: tras la huella de indicadores de desarrollo y progresión. Nefrologia. 2020;40(6):585–596. Spanish. doi:10.1016/j.nefro.2020.07.002
   PubMed Web of Science ®Google Scholar
• Meng X, Ma J, Kang AN, Kang SY, Jung HW, Park YK. A novel approach based on metabolomics coupled with intestinal flora analysis and network pharmacology to explain the mechanisms of action of bekhogainsam decoction in the improvement of symptoms of streptozotocin-induced diabetic nephropathy in mice. Front Pharmacol. 2020;11:633. doi:10.3389/fphar.2020.00633
   PubMed Web of Science ®Google Scholar
• Wang X, He Q, Chen Q, et al. Network pharmacology combined with metabolomics to study the mechanism of Shenyan Kangfu tablets in the treatment of diabetic nephropathy. J Ethnopharmacol. 2021;270:113817. doi:10.1016/j.jep.2021.113817
   PubMed Web of Science ®Google Scholar
• Du Y, Xu BJ, Deng X, et al. Predictive metabolic signatures for the occurrence and development of diabetic nephropathy and the intervention of Ginkgo biloba leaves extract based on gas or liquid chromatography with mass spectrometry. J Pharm Biomed Anal. 2019;166:30–39. doi:10.1016/j.jpba.2018.12.017
   PubMed Web of Science ®Google Scholar
• Guo JC, Pan HC, Yeh BY, et al. Associations between using Chinese herbal medicine and long-term outcome among pre-dialysis diabetic nephropathy patients: a Retrospective Population-Based Cohort Study. Front Pharmacol. 2021;12:616522. doi:10.3389/fphar.2021.616522
   PubMed Web of Science ®Google Scholar
• Li CL, Liu B, Wang ZY, et al. Salvianolic acid B improves myocardial function in diabetic cardiomyopathy by suppressing IGFBP3. J Mol Cell Cardiol. 2020;139:98–112. doi:10.1016/j.yjmcc.2020.01.009
   PubMed Web of Science ®Google Scholar
• Yin Z, Wang X, Yang X, Chen Y, Duan Y, Han J. Salvia miltiorrhiza in anti-diabetic angiopathy. Curr Mol Pharmacol. 2021;14(6):960–974. doi:10.2174/1874467214999210111222918
   PubMed Web of Science ®Google Scholar
• Xiang X, Cai HD, Su SL, et al. Salvia miltiorrhiza protects against diabetic nephropathy through metabolome regulation and wnt/β-catenin and TGF-β signaling inhibition. Pharmacol Res. 2019;139:26–40. doi:10.1016/j.phrs.2018.10.030
   PubMed Web of Science ®Google Scholar
• Selvarajah D, Kar D, Khunti K, et al. Diabetic peripheral neuropathy: advances in diagnosis and strategies for screening and early intervention. Lancet Diabetes Endocrinol. 2019;7(12):938–948. doi:10.1016/s2213-8587(19)30081-6
   PubMed Web of Science ®Google Scholar
• Mizukami H, Osonoi S, Takaku S, et al. Role of glucosamine in development of diabetic neuropathy independent of the aldose reductase pathway. Brain Comm. 2020;2(2):fcaa168. doi:10.1093/braincomms/fcaa168
   PubMedGoogle Scholar
• Fridman V, Zarini S, Sillau S, et al. Altered plasma serine and 1-deoxydihydroceramide profiles are associated with diabetic neuropathy in type 2 diabetes and obesity. J diabet complicat. 2021;35(4):107852. doi:10.1016/j.jdiacomp.2021.107852
   Google Scholar
• Durán AM, Salto LM, Câmara J, et al. Effects of omega-3 polyunsaturated fatty-acid supplementation on neuropathic pain symptoms and sphingosine levels in Mexican-Americans with type 2 diabetes. Diabetes Metab Syndr Obes. 2019;12:109–120. doi:10.2147/dmso.S187268
   PubMed Web of Science ®Google Scholar
• Lin HT, Cheng ML, Lo CJ, et al. (1)H Nuclear Magnetic Resonance (NMR)-based cerebrospinal fluid and plasma metabolomic analysis in type 2 diabetic patients and risk prediction for diabetic microangiopathy. J Clin Med. 2019;8(6). doi:10.3390/jcm8060874
   Web of Science ®Google Scholar
• Liang X, Cui L, Guo S. [Clinical study on jinmaitong composita on diabetic peripheral neuropathy]. Zhongguo Zhong xi yi jie he za zhi Zhongguo Zhongxiyi jiehe zazhi. 1999;19(9):517–519. Chinese.
   PubMedGoogle Scholar
• Song W, Jiang W, Wang C, et al. Jinmaitong, a traditional Chinese compound prescription, ameliorates the streptozocin-induced diabetic peripheral neuropathy rats by increasing sciatic nerve IGF-1 and IGF-1R expression. Front Pharmacol. 2019;10:255. doi:10.3389/fphar.2019.00255
   PubMed Web of Science ®Google Scholar
• Zhang Q, Song W, Liang X, et al. A metabolic insight into the neuroprotective effect of Jin-Mai-Tong (JMT) decoction on diabetic rats with peripheral neuropathy using untargeted metabolomics strategy. Front Pharmacol. 2020;11:221. doi:10.3389/fphar.2020.00221
   PubMed Web of Science ®Google Scholar
• Zheng X, Chen T, Jiang R, et al. Hyocholic acid species improve glucose homeostasis through a distinct TGR5 and FXR signaling mechanism. Cell Metab. 2021;33(4):791–803.e7. doi:10.1016/j.cmet.2020.11.017
   PubMed Web of Science ®Google Scholar
• Guo R, Luo X, Liu J, Liu L, Wang X, Lu H. Omics strategies decipher therapeutic discoveries of traditional Chinese medicine against different diseases at multiple layers molecular-level. Pharmacol Res. 2020;152:104627. doi:10.1016/j.phrs.2020.104627
   PubMed Web of Science ®Google Scholar
• Sha Q, Lyu J, Zhao M, Li H, Guo M, Sun Q. Multi-omics analysis of diabetic nephropathy reveals potential new mechanisms and drug targets. Front Genetics. 2020;11:616435. doi:10.3389/fgene.2020.616435
   PubMed Web of Science ®Google Scholar
• Darmayanti S, Lesmana R, Meiliana A, Abdulah R. Genomics, proteomics and metabolomics approaches for predicting diabetic nephropathy in type 2 diabetes mellitus patients. Curr Diabetes Rev. 2020. doi:10.2174/1573399817666210101105253
   Web of Science ®Google Scholar
• Chen D, Zhao X, Sui Z, et al. A multi-omics investigation of the molecular characteristics and classification of six metabolic syndrome relevant diseases. Theranostics. 2020;10(5):2029–2046. doi:10.7150/thno.41106
   PubMed Web of Science ®Google Scholar
• Chen ZZ, Gerszten RE. Metabolomics and proteomics in type 2 diabetes. Circ Res. 2020;126(11):1613–1627. doi:10.1161/circresaha.120.315898
   PubMed Web of Science ®Google Scholar
• Yang M, Lao L. Emerging applications of metabolomics in Traditional chinese medicine treating hypertension: biomarkers, pathways and more. Front Pharmacol. 2019;10:158. doi:10.3389/fphar.2019.00158
   PubMed Web of Science ®Google Scholar
• Kikuchi K, Saigusa D, Kanemitsu Y, et al. Gut microbiome-derived phenyl sulfate contributes to albuminuria in diabetic kidney disease. Nat Commun. 2019;10(1):1835. doi:10.1038/s41467-019-09735-4
   PubMedGoogle Scholar
• Vangipurapu J, Fernandes Silva L, Kuulasmaa T, Smith U, Laakso M. Microbiota-related metabolites and the risk of type 2 diabetes. Diabetes Care. 2020;43(6):1319–1325. doi:10.2337/dc19-2533
   PubMed Web of Science ®Google Scholar
• Hameed A, Mojsak P, Buczynska A, Suleria HAR, Kretowski A, Ciborowski M. Altered metabolome of lipids and amino acids species: a source of early signature biomarkers of T2DM. J Clin Med. 2020;9(7):2257. doi:10.3390/jcm9072257
   PubMed Web of Science ®Google Scholar
• Li J, Li R, Li N, et al. Mechanism of antidiabetic and synergistic effects of ginseng polysaccharide and ginsenoside Rb1 on diabetic rat model. J Pharm Biomed Anal. 2018;158:451–460. doi:10.1016/j.jpba.2018.06.024
   PubMed Web of Science ®Google Scholar
• Thingholm LB, Rühlemann MC, Koch M, et al. Obese individuals with and without type 2 diabetes show different gut microbial functional capacity and composition. Cell Host Microbe. 2019;26(2):252–264.e10. doi:10.1016/j.chom.2019.07.004
   PubMed Web of Science ®Google Scholar
• Cani PD. Microbiota and metabolites in metabolic diseases. Nat Rev Endocrinol. 2019;15(2):69–70. doi:10.1038/s41574-018-0143-9
   PubMed Web of Science ®Google Scholar
• Zhao L, Lou H, Peng Y, Chen S, Fan L, Li X. Elevated levels of circulating short-chain fatty acids and bile acids in type 2 diabetes are linked to gut barrier disruption and disordered gut microbiota. Diabetes Res Clin Pract. 2020;169:108418. doi:10.1016/j.diabres.2020.108418
   PubMed Web of Science ®Google Scholar
• Yang G, Wei J, Liu P, et al. Role of the gut microbiota in type 2 diabetes and related diseases. Metabolism. 2021;117:154712. doi:10.1016/j.metabol.2021.154712
   PubMed Web of Science ®Google Scholar
• Goodrich JK, Davenport ER, Beaumont M, et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe. 2016;19(5):731–743. doi:10.1016/j.chom.2016.04.017
   PubMed Web of Science ®Google Scholar
• Lin H, He QY, Shi L, Sleeman M, Baker MS, Nice EC. Proteomics and the microbiome: pitfalls and potential. Expert Rev Proteomics. 2019;16(6):501–511. doi:10.1080/14789450.2018.1523724
   PubMed Web of Science ®Google Scholar
• Peng W, Huang J, Yang J, et al. Integrated 16S rRNA sequencing, metagenomics, and metabolomics to characterize gut microbial composition, function, and fecal metabolic phenotype in non-obese type 2 diabetic Goto-Kakizaki rats. Front Microbiol. 2019;10:3141. doi:10.3389/fmicb.2019.03141
   PubMed Web of Science ®Google Scholar
• Zhou W, Sailani MR, Contrepois K, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569(7758):663–671. doi:10.1038/s41586-019-1236-x
   PubMed Web of Science ®Google Scholar