Abstract
Background: There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN).
Methods: A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients. Results: The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice. Discussion: DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN. Conclusion: The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.Keywords: Diabetes, diabetes nephropathy, genomics, proteomics, metabolomics, system biology.
[1]
Cho NH, Shaw JE, Karuranga S, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018; 138: 271-81.
[http://dx.doi.org/10.1016/j.diabres.2018.02.023] [PMID: 29496507]
[http://dx.doi.org/10.1016/j.diabres.2018.02.023] [PMID: 29496507]
[2]
Udler MS, McCarthy MI, Florez JC, Mahajan A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev 2019; 40(6): 1500-20.
[http://dx.doi.org/10.1210/er.2019-00088] [PMID: 31322649]
[http://dx.doi.org/10.1210/er.2019-00088] [PMID: 31322649]
[3]
Persson F, Rossing P. Diagnosis of diabetic kidney disease: state of the art and future perspective. Kidney Int Suppl (2011) 2018; 8(1): 2-7.
[http://dx.doi.org/10.1016/j.kisu.2017.10.003] [PMID: 30675433]
[http://dx.doi.org/10.1016/j.kisu.2017.10.003] [PMID: 30675433]
[4]
Brosius FC, Ju W. The promise of systems biology for diabetic kidney disease. Adv Chronic Kidney Dis 2018; 25(2): 202-13.
[http://dx.doi.org/10.1053/j.ackd.2017.10.012] [PMID: 29580584]
[http://dx.doi.org/10.1053/j.ackd.2017.10.012] [PMID: 29580584]
[5]
Tsui NBY, Cheng G, Chung T, et al. Population-wide genetic risk prediction of complex diseases: a pilot feasibility study in Macau population for precision public healthcare planning. Sci Rep 2018; 8(1): 1853.
[http://dx.doi.org/10.1038/s41598-017-19017-y] [PMID: 29382849]
[http://dx.doi.org/10.1038/s41598-017-19017-y] [PMID: 29382849]
[6]
Offit K. Personalized medicine: new genomics, old lessons. Hum Genet 2011; 130(1): 3-14.
[http://dx.doi.org/10.1007/s00439-011-1028-3] [PMID: 21706342]
[http://dx.doi.org/10.1007/s00439-011-1028-3] [PMID: 21706342]
[7]
Doo M, Kim Y. Obesity: interactions of genome and nutrients intake. Prev Nutr Food Sci 2015; 20(1): 1-7.
[http://dx.doi.org/10.3746/pnf.2015.20.1.1] [PMID: 25866743]
[http://dx.doi.org/10.3746/pnf.2015.20.1.1] [PMID: 25866743]
[8]
Duarte TT, Spencer CT. Personalized proteomics: the future of precision medicine. Proteomes 2016; 4(4): 1-18.
[http://dx.doi.org/10.3390/proteomes4040029] [PMID: 27882306]
[http://dx.doi.org/10.3390/proteomes4040029] [PMID: 27882306]
[9]
Barroso I, McCarthy MI. The genetic basis of metabolic disease. Cell 2019; 177(1): 146-61.
[http://dx.doi.org/10.1016/j.cell.2019.02.024] [PMID: 30901536]
[http://dx.doi.org/10.1016/j.cell.2019.02.024] [PMID: 30901536]
[10]
Dubin RF, Rhee EP. Proteomics and metabolomics in kidney disease, including insights into etiology, treatment, and prevention. Clin J Am Soc Nephrol 2020; 15(3): 404-11.
[http://dx.doi.org/10.2215/CJN.07420619] [PMID: 31636087]
[http://dx.doi.org/10.2215/CJN.07420619] [PMID: 31636087]
[11]
Davegårdh C, García-Calzón S, Bacos K, Ling C. DNA methylation in the pathogenesis of type 2 diabetes in humans. Mol Metab 2018; 14: 12-25.
[http://dx.doi.org/10.1016/j.molmet.2018.01.022] [PMID: 29496428]
[http://dx.doi.org/10.1016/j.molmet.2018.01.022] [PMID: 29496428]
[12]
Yu CG, Zhang N, Yuan SS, et al. Endothelial progenitor cells in diabetic microvascular complications: friends or foes? Stem Cells Int 2016; 2016
[http://dx.doi.org/10.1155/2016/1803989] [PMID: 27313624]
[http://dx.doi.org/10.1155/2016/1803989] [PMID: 27313624]
[13]
Dong G, Qu L, Gong X, Pang B, Yan W, Wei J. Effect of social factors and the natural environment on the etiology and pathogenesis of diabetes mellitus. Int J Endocrinol 2019; 2019: 8749291.
[http://dx.doi.org/10.1155/2019/8749291] [PMID: 31341475]
[http://dx.doi.org/10.1155/2019/8749291] [PMID: 31341475]
[14]
Mambiya M, Shang M, Wang Y, et al. The play of genes and non- genetic factors on type 2 diabetes. Front Public Health 2019; 7: 349.
[http://dx.doi.org/10.3389/fpubh.2019.00349] [PMID: 31803711]
[http://dx.doi.org/10.3389/fpubh.2019.00349] [PMID: 31803711]
[15]
Skyler JS, Bakris GL, Bonifacio E, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes 2017; 66(2): 241-55.
[http://dx.doi.org/10.2337/db16-0806] [PMID: 27980006]
[http://dx.doi.org/10.2337/db16-0806] [PMID: 27980006]
[16]
Reddy BM, Pranavchand R, Latheef SAA. Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies. J Biosci 2019; 44(1): 21-8.
[http://dx.doi.org/10.1007/s12038-018-9818-6] [PMID: 30837372]
[http://dx.doi.org/10.1007/s12038-018-9818-6] [PMID: 30837372]
[17]
Durruty P, Sanzana M, Sanhueza L. Pathogenesis of type 2 diabetes mellitus.Type 2 diabetes - from pathophysiology to modern management. Mira Siderova: IntechOpen 2019.
[http://dx.doi.org/10.5772/intechopen.83692]
[http://dx.doi.org/10.5772/intechopen.83692]
[18]
Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 2014; 383(9922): 1068-83.
[http://dx.doi.org/10.1016/S0140-6736(13)62154-6] [PMID: 24315620]
[http://dx.doi.org/10.1016/S0140-6736(13)62154-6] [PMID: 24315620]
[19]
Ren X, Li X. Advances in research on diabetes by human nutriomics. Int J Mol Sci 2019; 20(21): 5375.
[http://dx.doi.org/10.3390/ijms20215375] [PMID: 31671732]
[http://dx.doi.org/10.3390/ijms20215375] [PMID: 31671732]
[20]
Ekoru K, Doumatey A, Bentley AR, et al. Type 2 diabetes complications and comorbidity in Sub-Saharan Africans. E Clinical Medicine 2019; 16: 30-41.
[http://dx.doi.org/10.1016/j.eclinm.2019.09.001] [PMID: 31832618]
[http://dx.doi.org/10.1016/j.eclinm.2019.09.001] [PMID: 31832618]
[21]
Ahluwalia TS, Kilpeläinen TO, Singh S, Rossing P. Editorial: novel biomarkers for type 2 diabetes. Front Endocrinol (Lausanne) 2019; 10: 649.
[http://dx.doi.org/10.3389/fendo.2019.00649] [PMID: 31611845]
[http://dx.doi.org/10.3389/fendo.2019.00649] [PMID: 31611845]
[22]
Lou J, Jing L, Yang H, Qin F, Long W, Shi R. Risk factors for diabetic nephropathy complications in community patients with type 2 diabetes mellitus in Shanghai: Logistic regression and classification tree model analysis. Int J Health Plann Manage 2019; 34(3): 1013-24.
[http://dx.doi.org/10.1002/hpm.2871] [PMID: 31368138]
[http://dx.doi.org/10.1002/hpm.2871] [PMID: 31368138]
[23]
Hou Y, Gao Y, Zhang Y, Lin ST, Yu Y, Yang L. Interaction between ELMO1 gene polymorphisms and environment factors on susceptibility to diabetic nephropathy in Chinese Han population. Diabetol Metab Syndr 2019; 11: 97.
[http://dx.doi.org/10.1186/s13098-019-0492-0] [PMID: 31798690]
[http://dx.doi.org/10.1186/s13098-019-0492-0] [PMID: 31798690]
[24]
Kopel J, Pena-Hernandez C, Nugent K. Evolving spectrum of diabetic nephropathy. World J Diabetes 2019; 10(5): 269-79.
[http://dx.doi.org/10.4239/wjd.v10.i5.269] [PMID: 31139314]
[http://dx.doi.org/10.4239/wjd.v10.i5.269] [PMID: 31139314]
[25]
Al-Kafaji G, Al-Mahroos G, Al-Muhtaresh HA, Skrypnyk C, Sabry MA, Ramadan AR. Decreased expression of circulating microRNA-126 in patients with type 2 diabetic nephropathy: A potential blood-based biomarker. Exp Ther Med 2016; 12(2): 815-22.
[http://dx.doi.org/10.3892/etm.2016.3395] [PMID: 27446281]
[http://dx.doi.org/10.3892/etm.2016.3395] [PMID: 27446281]
[26]
Wang G, Ouyang J, Li S, et al. The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases. J Transl Med 2019; 17(1): 264.
[http://dx.doi.org/10.1186/s12967-019-2016-y] [PMID: 31409386]
[http://dx.doi.org/10.1186/s12967-019-2016-y] [PMID: 31409386]
[27]
Sulaiman MK. Diabetic nephropathy: recent advances in pathophysiology and challenges in dietary management. Diabetol Metab Syndr 2019; 11: 7.
[http://dx.doi.org/10.1186/s13098-019-0403-4] [PMID: 30679960]
[http://dx.doi.org/10.1186/s13098-019-0403-4] [PMID: 30679960]
[28]
Patel DN, Kalia K. Characterization of low molecular weight urinary proteins at varying time intervals in type 2 diabetes mellitus and diabetic nephropathy patients. Diabetol Metab Syndr 2019; 11: 39.
[http://dx.doi.org/10.1186/s13098-019-0430-1] [PMID: 31131043]
[http://dx.doi.org/10.1186/s13098-019-0430-1] [PMID: 31131043]
[30]
Mohan V, Unnikrishnan R. Precision diabetes: Where do we stand today? Indian J Med Res 2018; 148(5): 472-5.
[http://dx.doi.org/10.4103/ijmr.IJMR_1628_18] [PMID: 30666973]
[http://dx.doi.org/10.4103/ijmr.IJMR_1628_18] [PMID: 30666973]
[31]
Mohan V, Balasubramanyam M, Radha V. Genomics and proteomics of Type 2 diabetes in Indians. J Assoc Physicians India 2005; 53: 507-9.
[PMID: 16121803]
[PMID: 16121803]
[32]
Elsheikh M, Elhefnawy KA, Emad G, Ismail M, Borai M. Zinc alpha 2 glycoprotein as an early biomarker of diabetic nephropathy in patients with type 2 diabetes mellitus. J Bras Nefrol 2019; 41(4): 509-17.
[http://dx.doi.org/10.1590/2175-8239-jbn-2018-0200] [PMID: 30897192]
[http://dx.doi.org/10.1590/2175-8239-jbn-2018-0200] [PMID: 30897192]
[33]
Dorcely B, Katz K, Jagannathan R, Chiang SS, Oluwadare B, Goldber IJ, et al. Novel biomarkers for prediabetes, diabetes, and associated complications. 2017; 10: 345-61.
[http://dx.doi.org/10.2147/DMSO.S100074]
[http://dx.doi.org/10.2147/DMSO.S100074]
[34]
Bergman M. The early diabetes intervention program-is early actually late? Diabetes Metab Res Rev 2014; 30(8): 654-8.
[http://dx.doi.org/10.1002/dmrr.2563] [PMID: 25400067]
[http://dx.doi.org/10.1002/dmrr.2563] [PMID: 25400067]
[35]
Aghaei Zarch SM, Dehghan Tezerjani M, Talebi M, Vahidi Mehrjardi MY. Molecular biomarkers in diabetes mellitus (DM). Med J Islam Repub Iran 2020; 34: 28.
[PMID: 32617267]
[PMID: 32617267]
[36]
Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab 2017; 25(1): 43-56.
[http://dx.doi.org/10.1016/j.cmet.2016.09.018] [PMID: 28094011]
[http://dx.doi.org/10.1016/j.cmet.2016.09.018] [PMID: 28094011]
[37]
Fu H, Liu S, Bastacky SI, Wang X, Tian XJ, Zhou D. Diabetic kidney diseases revisited: A new perspective for a new era. Mol Metab 2019; 30: 250-63.
[http://dx.doi.org/10.1016/j.molmet.2019.10.005] [PMID: 31767176]
[http://dx.doi.org/10.1016/j.molmet.2019.10.005] [PMID: 31767176]
[38]
Tedla FM, Brar A, Browne R, Brown C. Hypertension in chronic kidney disease: navigating the evidence. Int J Hypertens 2011; 2011: 132405.
[http://dx.doi.org/10.4061/2011/132405] [PMID: 21747971]
[http://dx.doi.org/10.4061/2011/132405] [PMID: 21747971]
[39]
Ahlqvist E, van Zuydam NR, Groop LC, McCarthy MI. The genetics of diabetic complications. Nat Rev Nephrol 2015; 11(5): 277-87.
[http://dx.doi.org/10.1038/nrneph.2015.37] [PMID: 25825086]
[http://dx.doi.org/10.1038/nrneph.2015.37] [PMID: 25825086]
[40]
Regine I, Husain RSRA, Aswathi RP, Reddy DR, Ahmed SSSJ, Ramakrishnan V. Association between PPARγrs1801282 polymorphism with diabetic nephropathy and type-2 diabetes mellitus susceptibility in south India and a meta-analysis. Nefrologia 2020; 40(3): 287-98.
[http://dx.doi.org/10.1016/j.nefro.2020.01.005] [PMID: 32417009]
[http://dx.doi.org/10.1016/j.nefro.2020.01.005] [PMID: 32417009]
[41]
Mohammed H, Al-Saegh R, Al-Saadi N. Role of engulfment and cell motility 1 gene polymorphism in type 2 diabetic nephropathy and its association with renal biomarkers: case–control stody. J Egypt Soc Nephrol Transplant 2020; 20: 98-102.
[http://dx.doi.org/10.4103/jesnt.jesnt_22_19]
[http://dx.doi.org/10.4103/jesnt.jesnt_22_19]
[42]
Mehrabzadeh M, Pasalar P, Karimi M, et al. Association between ELMO1 gene polymorphisms and diabetic nephropathy in an Iranian population. J Diabetes Metab Disord 2016; 15: 43.
[http://dx.doi.org/10.1186/s40200-016-0265-3] [PMID: 27761430]
[http://dx.doi.org/10.1186/s40200-016-0265-3] [PMID: 27761430]
[43]
Bodhini D, Chidambaram M, Liju S, et al. Association of rs11643718 SLC12A3 and rs741301 ELMO1 variants with diabetic nephropathy in South Indian population. Ann Hum Genet 2016; 80(6): 336-41.
[http://dx.doi.org/10.1111/ahg.12174] [PMID: 27699784]
[http://dx.doi.org/10.1111/ahg.12174] [PMID: 27699784]
[44]
Gu HF. Genetic and epigenetic studies in diabetic kidney disease. Front Genet 2019; 10: 507.
[http://dx.doi.org/10.3389/fgene.2019.00507] [PMID: 31231424]
[http://dx.doi.org/10.3389/fgene.2019.00507] [PMID: 31231424]
[45]
Conserva F, Gesualdo L, Papale M. A systems biology overview on human diabetic nephropathy: from genetic susceptibility to post-transcriptional and post-translational modifications. J Diabetes Res 2016; 2016: 7934504.
[http://dx.doi.org/10.1155/2016/7934504] [PMID: 26798653]
[http://dx.doi.org/10.1155/2016/7934504] [PMID: 26798653]
[46]
Mooyaart AL, Valk EJ, van Es LA, et al. Genetic associations in diabetic nephropathy: a meta-analysis. Diabetologia 2011; 54(3): 544-53.
[http://dx.doi.org/10.1007/s00125-010-1996-1] [PMID: 21127830]
[http://dx.doi.org/10.1007/s00125-010-1996-1] [PMID: 21127830]
[47]
Kwiendacz H, Nabrdalik K, Adamczyk P, et al. Association of single nucleotide polymorphism (rs741301) of the ELMO1 gene with diabetic kidney disease in Polish patients with type 2 diabetes: a pilot study. Endokrynol Pol 2020; 71(1): 66-72.
[http://dx.doi.org/10.5603/EP.a2019.0066] [PMID: 31909452]
[http://dx.doi.org/10.5603/EP.a2019.0066] [PMID: 31909452]
[48]
Yahya MJ, Ismail PB, Nordin NB, et al. Association of CCL2, CCR5, ELMO1, and IL8 polymorphism with diabetic nephropathy in malaysian type 2 diabetic patients. Int J Chronic Dis 2019; 2019: Article ID 2053015.
[http://dx.doi.org/10.1155/2019/2053015] [PMID: 30713847]
[http://dx.doi.org/10.1155/2019/2053015] [PMID: 30713847]
[49]
Bayoumy NMK, El-Shabrawi MM, Leheta OF, Abo El-Ela AEM, Omar HH. Association of ELMO1 gene polymorphism and diabetic nephropathy among Egyptian patients with type 2 diabetes mellitus. Diabetes Metab Res Rev 2020; 36(5): e3299.
[http://dx.doi.org/10.1002/dmrr.3299] [PMID: 32043290]
[http://dx.doi.org/10.1002/dmrr.3299] [PMID: 32043290]
[50]
Bülow RD, Boor P. Extracellular matrix in kidney fibrosis: more than just a scaffold. J Histochem Cytochem 2019; 67(9): 643-61.
[http://dx.doi.org/10.1369/0022155419849388] [PMID: 31116062]
[http://dx.doi.org/10.1369/0022155419849388] [PMID: 31116062]
[51]
Zhang B, Kuster B. Proteomics is not an island: multi-omics integration is the key to understanding biological systems. Mol Cell Proteomics 2019; 18(8)(Suppl. 1): S1-4.
[http://dx.doi.org/10.1074/mcp.E119.001693] [PMID: 31399542]
[http://dx.doi.org/10.1074/mcp.E119.001693] [PMID: 31399542]
[52]
Kim SW, Choi JW, Yun JW, et al. Proteomics approach to identify serum biomarkers associated with the progression of diabetes in Korean patients with abdominal obesity. PLoS One 2019; 14(9): e0222032.
[http://dx.doi.org/10.1371/journal.pone.0222032] [PMID: 31504048]
[http://dx.doi.org/10.1371/journal.pone.0222032] [PMID: 31504048]
[53]
Sims EK, Evans-Molina C. Urinary biomarkers for the early diagnosis of retinopathy and nephropathy in type 1 diabetes mellitus: a “steady stream” of information using proteomics. Transl Res 2014; 163(3): 183-7.
[http://dx.doi.org/10.1016/j.trsl.2013.11.013] [PMID: 24355258]
[http://dx.doi.org/10.1016/j.trsl.2013.11.013] [PMID: 24355258]
[54]
Glazyrin YE, Veprintsev DV, Ler IA, et al. Proteomics-based machine learning approach as an alternative to conventional biomarkers for differential diagnosis of chronic kidney diseases. Int J Mol Sci 2020; 21(13): 4802.
[http://dx.doi.org/10.3390/ijms21134802] [PMID: 32645927]
[http://dx.doi.org/10.3390/ijms21134802] [PMID: 32645927]
[55]
Virzì GM, Clementi A, Battaglia GG, Ronco C. Multi-omics approach: new potential key mechanisms implicated in cardiorenal syndromes. Cardiorenal Med 2019; 9(4): 201-11.
[http://dx.doi.org/10.1159/000497748] [PMID: 30939477]
[http://dx.doi.org/10.1159/000497748] [PMID: 30939477]
[56]
Pena MJ, Mischak H, Heerspink HJ. Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease. Diabetologia 2016; 59(9): 1819-31.
[http://dx.doi.org/10.1007/s00125-016-4001-9] [PMID: 27344310]
[http://dx.doi.org/10.1007/s00125-016-4001-9] [PMID: 27344310]
[57]
Zürbig P, Jerums G, Hovind P, et al. Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes 2012; 61(12): 3304-13.
[http://dx.doi.org/10.2337/db12-0348] [PMID: 22872235]
[http://dx.doi.org/10.2337/db12-0348] [PMID: 22872235]
[58]
Abdulwahab RA, Alaiya A, Shinwari Z, Allaith AAA, Giha HA. LC-MS/MS proteomic analysis revealed novel associations of 37 proteins with T2DM and notable upregulation of immunoglobulins. Int J Mol Med 2019; 43(5): 2118-32.
[http://dx.doi.org/10.3892/ijmm.2019.4127] [PMID: 30864687]
[http://dx.doi.org/10.3892/ijmm.2019.4127] [PMID: 30864687]
[59]
Fiorentino L, Cavalera M, Menini S, et al. Loss of TIMP3 underlies diabetic nephropathy via FoxO1/STAT1 interplay. EMBO Mol Med 2013; 5(3): 441-55.
[http://dx.doi.org/10.1002/emmm.201201475] [PMID: 23401241]
[http://dx.doi.org/10.1002/emmm.201201475] [PMID: 23401241]
[60]
Rossi C, Marzano V, Consalvo A, et al. Proteomic and metabolomic characterization of streptozotocin-induced diabetic nephropathy in TIMP3-deficient mice. Acta Diabetol 2018; 55(2): 121-9.
[http://dx.doi.org/10.1007/s00592-017-1074-y] [PMID: 29134286]
[http://dx.doi.org/10.1007/s00592-017-1074-y] [PMID: 29134286]
[61]
Kang HM, Ahn SH, Choi P, et al. Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med 2015; 21(1): 37-46.
[http://dx.doi.org/10.1038/nm.3762] [PMID: 25419705]
[http://dx.doi.org/10.1038/nm.3762] [PMID: 25419705]
[62]
Barallobre-Barreiro J, Chung YL, Mayr M. Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease. Rev Esp Cardiol (Engl Ed) 2013; 66(8): 657-61.
[http://dx.doi.org/10.1016/j.rec.2013.04.009] [PMID: 24776335]
[http://dx.doi.org/10.1016/j.rec.2013.04.009] [PMID: 24776335]
[63]
Wanichthanarak K, Fahrmann JF, Grapov D. Genomic, proteomic, and metabolomic data integration strategies. Biomark Insights 2015; 10(Suppl. 4): 1-6.
[http://dx.doi.org/10.4137/BMI.S29511] [PMID: 26396492]
[http://dx.doi.org/10.4137/BMI.S29511] [PMID: 26396492]
[64]
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.
[http://dx.doi.org/10.1155/2020/3049098] [PMID: 32190695]
[http://dx.doi.org/10.1155/2020/3049098] [PMID: 32190695]
[65]
Makarova E, Makrecka-Kuka M, Vilks K, et al. Decreases in circulating concentrations of long-chain acylcarnitines and free fatty acids during the glucose tolerance test represent tissue-specific insulin sensitivity. Front Endocrinol (Lausanne) 2019; 10: 870.
[http://dx.doi.org/10.3389/fendo.2019.00870] [PMID: 31920980]
[http://dx.doi.org/10.3389/fendo.2019.00870] [PMID: 31920980]
[66]
Li L, Wang C, Yang H, et al. Metabolomics reveal mitochondrial and fatty acid metabolism disorders that contribute to the development of DKD in T2DM patients. Mol Biosyst 2017; 13(11): 2392-400.
[http://dx.doi.org/10.1039/C7MB00167C] [PMID: 28956034]
[http://dx.doi.org/10.1039/C7MB00167C] [PMID: 28956034]
[67]
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.
[http://dx.doi.org/10.21037/atm.2020.01.42] [PMID: 32309346]
[http://dx.doi.org/10.21037/atm.2020.01.42] [PMID: 32309346]
[68]
Bergman HM, Lindfors L, Palm F, Kihlberg J, Lanekoff I. Metabolite aberrations in early diabetes detected in rat kidney using mass spectrometry imaging. Anal Bioanal Chem 2019; 411(13): 2809-16.
[http://dx.doi.org/10.1007/s00216-019-01721-5] [PMID: 30895347]
[http://dx.doi.org/10.1007/s00216-019-01721-5] [PMID: 30895347]
[69]
Heinzel A, Mühlberger I, Stelzer G, et al. Molecular disease presentation in diabetic nephropathy. Nephrol Dial Transplant 2015; 30(Suppl. 4): iv17-25.
[http://dx.doi.org/10.1093/ndt/gfv267] [PMID: 26209734]
[http://dx.doi.org/10.1093/ndt/gfv267] [PMID: 26209734]
[70]
Long NP, Nghi TD, Kang YP, et al. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020; 10(2): 1-28.
[http://dx.doi.org/10.3390/metabo10020051] [PMID: 32013105]
[http://dx.doi.org/10.3390/metabo10020051] [PMID: 32013105]
[71]
Fang H, Hong H, Liu Z, Perkins R, Kelly R, Beresney J, et al. Omics biomarkers in risk assessment. Computational Toxicology 2013; 2013: 195-213.
[http://dx.doi.org/10.1016/B978-0-12-396461-8.00013-0]
[http://dx.doi.org/10.1016/B978-0-12-396461-8.00013-0]
[72]
Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019; 20(19): 1-13.
[http://dx.doi.org/10.3390/ijms20194781] [PMID: 31561483]
[http://dx.doi.org/10.3390/ijms20194781] [PMID: 31561483]
[73]
Chen ZZ, Gerszten RE. Metabolomics and proteomics in type 2 diabetes. Circ Res 2020; 126(11): 1613-27.
[http://dx.doi.org/10.1161/CIRCRESAHA.120.315898] [PMID: 32437301]
[http://dx.doi.org/10.1161/CIRCRESAHA.120.315898] [PMID: 32437301]
Article Metrics
49
1