Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?

References

• King H, Aubert RE, Herman WH. Global burden of diabetes, 1995–2025: prevalence, numerical estimates, and projections. Diabetes Care. 1998;21(9):1414–1431. doi:10.2337/diacare.21.9.1414
   PubMed Web of Science ®Google Scholar
• 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;138:271–281. doi:10.1016/j.diabres.2018.02.023
   PubMed Web of Science ®Google Scholar
• Ogurtsova K, da Rocha Fernandes JD, Huang Y, et al. IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50. doi:10.1016/j.diabres.2017.03.024
   PubMed Web of Science ®Google Scholar
• Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813–820. doi:10.1038/414813a
   PubMed Web of Science ®Google Scholar
• Colhoun HM, Marcovecchio ML. Biomarkers of diabetic kidney disease. Diabetologia. 2018;61(5):996–1011. doi:10.1007/s00125-018-4567-5
   PubMed Web of Science ®Google Scholar
• Hathaway CK, Chang AS, Grant R, et al. High Elmo1 expression aggravates and low Elmo1 expression prevents diabetic nephropathy. Proc Natl Acad Sci U S A. 2016;113(8):2218–2222. doi:10.1073/pnas.1600511113
   Web of Science ®Google Scholar
• Tuttle KR, Bakris GL, Bilous RW, et al. Diabetic kidney disease: a report from an ADA consensus conference. Diabetes Care. 2014;37(10):2864–2883. doi:10.2337/dc14-1296
   PubMed Web of Science ®Google Scholar
• Barrios C, Spector TD, Menni C. Blood, urine and faecal metabolite profiles in the study of adult renal disease. Arch Biochem Biophys. 2016;589:81–92. doi:10.1016/j.abb.2015.10.006
   Web of Science ®Google Scholar
• Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032–2045. doi:10.2215/CJN.11491116
   PubMed Web of Science ®Google Scholar
• Nishad R, Tahaseen V, Kavvuri R, et al. Advanced-glycation end-products induce podocyte injury and contribute to proteinuria. Front Med (Lausanne). 2021;8:685447. doi:10.3389/fmed.2021.685447
   Web of Science ®Google Scholar
• Gong P, Chang X, Chen X, et al. Metabolomics study of cadmium-induced diabetic nephropathy and protective effect of caffeic acid phenethyl ester using UPLC-Q-TOF-MS combined with pattern recognition. Environ Toxicol Pharmacol. 2017;54:80–92. doi:10.1016/j.etap.2017.06.021
   PubMed Web of Science ®Google Scholar
• Tang SCW, Yiu WH. Innate immunity in diabetic kidney disease. Nat Rev Nephrol. 2020;16(4):206–222. doi:10.1038/s41581-019-0234-4
   PubMed Web of Science ®Google Scholar
• Tonneijck L, Muskiet MH, Smits MM, et al. Glomerular hyperfiltration in diabetes: mechanisms, clinical significance, and treatment. J Am Soc Nephrol. 2017;28(4):1023–1039. doi:10.1681/ASN.2016060666
   PubMed Web of Science ®Google Scholar
• Wei T, Shu Q, Ning J, et al. The protective effect of basic fibroblast growth factor on diabetic nephropathy through remodeling metabolic phenotype and suppressing oxidative stress in mice. Front Pharmacol. 2020;11:66. doi:10.3389/fphar.2020.00066
   Web of Science ®Google Scholar
• Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–390. doi:10.1038/s41581-020-0278-5
   PubMed Web of Science ®Google Scholar
• Nilavan E, Sundar S, Shenbagamoorthy M, Narayanan H, Nandagopal B, Srinivasan R. Identification of biomarkers for early diagnosis of diabetic nephropathy disease using direct flow through mass spectrometry. Diabetes Metab Syndr. 2020;14(6):2073–2078. doi:10.1016/j.dsx.2020.10.017
   Web of Science ®Google Scholar
• Remuzzi G, Schieppati A, Ruggenenti P. Nephropathy in patients with type 2 diabetes. N Engl J Med. 2002;346(15):1145–1151. doi:10.1056/NEJMcp011773
   PubMed Web of Science ®Google Scholar
• Di Bonito P, Mozzillo E, Rosanio FM, et al. Albuminuric and non-albuminuric reduced eGFR phenotypes in youth with type 1 diabetes: factors associated with cardiometabolic risk. Nutr Metab Cardiovasc Dis. 2021;31(7):2033–2041. doi:10.1016/j.numecd.2021.03.019
   Web of Science ®Google Scholar
• Panagiotopoulos S, Cooper ME, Jerums G. Progressive decline in renal function in diabetic patients with and without albuminuria. Diabetes. 1994;43(5):649–655. doi:10.2337/diab.43.5.649
   Web of Science ®Google Scholar
• Tao S, Zheng W, Liu Y, et al. Analysis of serum metabolomics among biopsy-proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls. RSC Adv. 2019;9(33):18713–18719. doi:10.1039/C9RA01561B
   Web of Science ®Google Scholar
• Luciano RL, Moeckel GW. Update on the native kidney biopsy: core curriculum 2019. Am J Kidney Dis. 2019;73(3):404–415. doi:10.1053/j.ajkd.2018.10.011
   PubMed Web of Science ®Google Scholar
• Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451–459. doi:10.1038/nrm.2016.25
   PubMed Web of Science ®Google Scholar
• Chen X, Shi BL, Qi RZ, Chang X, Zheng HG. Ultra-performance liquid chromatography/mass spectrometry-based metabolomics for discovering potential biomarkers and metabolic pathways of colorectal cancer in mouse model (ApcMin/+) and revealing the effect of honokiol. Front Oncol. 2021;11:671014. doi:10.3389/fonc.2021.671014
   Web of Science ®Google Scholar
• Khodadadi M, Pourfarzam M. A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry. Metabolomics. 2020;16(6):66. doi:10.1007/s11306-020-01687-x
   Web of Science ®Google Scholar
• Liu C, Gu C, Huang W, Sheng X, Du J, Li Y. Targeted UPLC-MS/MS high-throughput metabolomics approach to assess the purine and pyrimidine metabolism. J Chromatogr B Analyt Technol Biomed Life Sci. 2019;1113:98–106. doi:10.1016/j.jchromb.2019.03.008
   Web of Science ®Google Scholar
• Zhou J, Liu H, Liu Y, Liu J, Zhao X, Yin Y. Development and evaluation of a parallel reaction monitoring strategy for large-scale targeted metabolomics quantification. Anal Chem. 2016;88(8):4478–4486. doi:10.1021/acs.analchem.6b00355
   PubMed Web of Science ®Google Scholar
• Zhou J, Yin Y. Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry. Analyst. 2016;141(23):6362–6373. doi:10.1039/C6AN01753C
   PubMed Web of Science ®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
• Jiang Z, Liang Q, Luo G, Hu P, Li P, Wang Y. HPLC-electrospray tandem mass spectrometry for simultaneous quantitation of eight plasma aminothiols: application to studies of diabetic nephropathy. Talanta. 2009;77(4):1279–1284. doi:10.1016/j.talanta.2008.08.031
   PubMed Web of Science ®Google Scholar
• Ibarra-Gonzalez I, Cruz-Bautista I, Bello-Chavolla OY, et al. Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics. Acta Diabetol. 2018;55(11):1151–1161. doi:10.1007/s00592-018-1213-0
   Web of Science ®Google Scholar
• Fan Y, Li Y, Chen Y, et al. Comprehensive metabolomic characterization of coronary artery diseases. J Am Coll Cardiol. 2016;68(12):1281–1293. doi:10.1016/j.jacc.2016.06.044
   PubMed Web of Science ®Google Scholar
• Benito S, Sanchez-Ortega A, Unceta N, et al. Untargeted metabolomics for plasma biomarker discovery for early chronic kidney disease diagnosis in pediatric patients using LC-QTOF-MS. Analyst. 2018;143(18):4448–4458. doi:10.1039/C8AN00864G
   Web of Science ®Google Scholar
• Liu JJ, Ghosh S, Kovalik JP, et al. Profiling of plasma metabolites suggests altered mitochondrial fuel usage and remodeling of sphingolipid metabolism in individuals with type 2 diabetes and kidney disease. Kidney Int Rep. 2017;2(3):470–480. doi:10.1016/j.ekir.2016.12.003
   PubMed Web of Science ®Google Scholar
• Nishi H, Yamanaka D, Kamei H, et al. Importance of serum amino acid profile for induction of hepatic steatosis under protein malnutrition. Sci Rep. 2018;8(1):5461. doi:10.1038/s41598-018-23640-8
   Web of Science ®Google Scholar
• Rotbain Curovic V, Hansen TW, Eickhoff MK, et al. Urinary tubular biomarkers as predictors of kidney function decline, cardiovascular events and mortality in microalbuminuric type 2 diabetic patients. Acta Diabetol. 2018;55(11):1143–1150. doi:10.1007/s00592-018-1205-0
   Web of Science ®Google Scholar
• Tan SM, Ziemann M, Thallas-Bonke V, et al. Complement C5a induces renal injury in diabetic kidney disease by disrupting mitochondrial metabolic agility. Diabetes. 2020;69(1):83–98. doi:10.2337/db19-0043
   PubMed Web of Science ®Google Scholar
• Xiao L, Xu X, Zhang F, et al. The mitochondria-targeted antioxidant MitoQ ameliorated tubular injury mediated by mitophagy in diabetic kidney disease via Nrf2/PINK1. Redox Biol. 2017;11:297–311. doi:10.1016/j.redox.2016.12.022
   PubMed Web of Science ®Google Scholar
• Chou CA, Lin CN, Chiu DT, Chen IW, Chen ST. Tryptophan as a surrogate prognostic marker for diabetic nephropathy. J Diabetes Investig. 2018;9(2):366–374. doi:10.1111/jdi.12707
   PubMed Web of Science ®Google Scholar
• Barrios C, Zierer J, Wurtz P, et al. Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations. Sci Rep. 2018;8(1):15249. doi:10.1038/s41598-018-33507-7
   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 Diabetes Complications. 2020;34(10):107667. doi:10.1016/j.jdiacomp.2020.107667
   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
• Woo CY, Baek JY, Kim AR, et al. Inhibition of ceramide accumulation in podocytes by myriocin prevents diabetic nephropathy. Diabetes Metab J. 2020;44(4):581–591. doi:10.4093/dmj.2019.0063
   Web of Science ®Google Scholar
• Tofte N, Suvitaival T, Ahonen L, et al. Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes. Sci Rep. 2019;9(1):16398. doi:10.1038/s41598-019-52916-w
   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 Genet. 2020;11:616435. doi:10.3389/fgene.2020.616435
   Web of Science ®Google Scholar
• Niewczas MA, Sirich TL, Mathew AV, et al. Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study. Kidney Int. 2014;85(5):1214–1224. doi:10.1038/ki.2013.497
   PubMed Web of Science ®Google Scholar
• Chen CJ, Liao WL, Chang CT, Lin YN, Tsai FJ. Identification of urinary metabolite biomarkers of type 2 diabetes nephropathy using an untargeted metabolomic approach. J Proteome Res. 2018;17(11):3997–4007. doi:10.1021/acs.jproteome.8b00644
   Web of Science ®Google Scholar
• Peng L, Sun B, Liu Y, et al. Increased lipoxygenase and decreased cytochrome P450s metabolites correlated with the incidence of diabetic nephropathy: potential role of eicosanoids from metabolomics in type 2 diabetic patients. Clin Exp Pharmacol Physiol. 2021;48(5):679–685. doi:10.1111/1440-1681.13471
   Web of Science ®Google Scholar
• Niewczas MA, Mathew AV, Croall S, et al. Circulating modified metabolites and a risk of ESRD in patients with type 1 diabetes and chronic kidney disease. Diabetes Care. 2017;40(3):383–390. doi:10.2337/dc16-0173
   PubMed Web of Science ®Google Scholar
• Coughlan MT, Nguyen TV, Penfold SA, et al. Mapping time-course mitochondrial adaptations in the kidney in experimental diabetes. Clin Sci (Lond). 2016;130(9):711–720. doi:10.1042/CS20150838
   Web of Science ®Google Scholar
• Forbes JM, Thorburn DR. Mitochondrial dysfunction in diabetic kidney disease. Nat Rev Nephrol. 2018;14(5):291–312. doi:10.1038/nrneph.2018.9
   PubMed Web of Science ®Google Scholar
• Kimura T, Isaka Y, Yoshimori T. Autophagy and kidney inflammation. Autophagy. 2017;13(6):997–1003. doi:10.1080/15548627.2017.1309485
   PubMed Web of Science ®Google Scholar
• Ducasa GM, Mitrofanova A, Fornoni A. Crosstalk between lipids and mitochondria in diabetic kidney disease. Curr Diab Rep. 2019;19(12):144. doi:10.1007/s11892-019-1263-x
   Web of Science ®Google Scholar
• Hallan S, Afkarian M, Zelnick LR, et al. Metabolomics and gene expression analysis reveal down-regulation of the citric acid (TCA) cycle in non-diabetic CKD patients. EBioMedicine. 2017;26:68–77. doi:10.1016/j.ebiom.2017.10.027
   PubMed Web of Science ®Google Scholar
• Liu JJ, Liu S, Gurung RL, et al. Urine tricarboxylic acid cycle metabolites predict progressive chronic kidney disease in type 2 diabetes. J Clin Endocrinol Metab. 2018;103(12):4357–4364. doi:10.1210/jc.2018-00947
   PubMed Web of Science ®Google Scholar
• Afshinnia F, Nair V, Lin J, et al. Increased lipogenesis and impaired beta-oxidation predict type 2 diabetic kidney disease progression in American Indians. JCI Insight. 2019;4(21):e31573977. doi:10.1172/jci.insight.130317
   Web of Science ®Google Scholar
• Davies EG, Petty RG, Kohner EM. Long term effectiveness of photocoagulation for diabetic maculopathy. Eye (Lond). 1989;3(6):764–767. doi:10.1038/eye.1989.119
   Google Scholar
• Fouque D, Holt S, Guebre-Egziabher F, et al. Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. J Ren Nutr. 2006;16(2):125–131. doi:10.1053/j.jrn.2006.01.004
   Web of Science ®Google Scholar
• Jarrell ZR, Smith MR, Hu X, et al. Plasma acylcarnitine levels increase with healthy aging. Aging (Albany NY). 2020;12(13):13555–13570. doi:10.18632/aging.103462
   Google Scholar
• Marsden D, Bedrosian CL, Vockley J. Impact of newborn screening on the reported incidence and clinical outcomes associated with medium- and long-chain fatty acid oxidation disorders. Genet Med. 2021;23(5):816–829. doi:10.1038/s41436-020-01070-0
   Web of Science ®Google Scholar
• Rocha H, Castineiras D, Delgado C, et al. Birth prevalence of fatty acid beta-oxidation disorders in Iberia. JIMD Rep. 2014;16:89–94.
   PubMedGoogle Scholar
• Almannai M, Alfadhel M, El-Hattab AW. Carnitine inborn errors of metabolism. Molecules. 2019;24(18):3251. doi:10.3390/molecules24183251
   PubMed Web of Science ®Google Scholar
• Giesbertz P, Ecker J, Haag A, Spanier B, Daniel H. An LC-MS/MS method to quantify acylcarnitine species including isomeric and odd-numbered forms in plasma and tissues. J Lipid Res. 2015;56(10):2029–2039. doi:10.1194/jlr.D061721
   PubMed Web of Science ®Google Scholar
• Xiang L, Wei J, Tian XY, et al. Comprehensive analysis of acylcarnitine species in db/db mouse using a novel method of high-resolution parallel reaction monitoring reveals widespread metabolic dysfunction induced by diabetes. Anal Chem. 2017;89(19):10368–10375. doi:10.1021/acs.analchem.7b02283
   Web of Science ®Google Scholar
• Mirzoyan K, Klavins K, Koal T, et al. Increased urine acylcarnitines in diabetic ApoE-/- mice: hydroxytetradecadienoylcarnitine (C14:2-OH) reflects diabetic nephropathy in a context of hyperlipidemia. Biochem Biophys Res Commun. 2017;487(1):109–115. doi:10.1016/j.bbrc.2017.04.026
   Web of Science ®Google Scholar
• Nkuipou-Kenfack E, Duranton F, Gayrard N, et al. Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease. PLoS One. 2014;9(5):e96955. doi:10.1371/journal.pone.0096955
   PubMed Web of Science ®Google Scholar
• McCoin CS, Knotts TA, Adams SH. Acylcarnitines–old actors auditioning for new roles in metabolic physiology. Nat Rev Endocrinol. 2015;11(10):617–625. doi:10.1038/nrendo.2015.129
   PubMed Web of Science ®Google Scholar
• Rebouche CJ. Kinetics, pharmacokinetics, and regulation of L-carnitine and acetyl-L-carnitine metabolism. Ann N Y Acad Sci. 2004;1033:30–41. doi:10.1196/annals.1320.003
   PubMed Web of Science ®Google Scholar
• Pande SV. A mitochondrial carnitine acylcarnitine translocase system. Proc Natl Acad Sci U S A. 1975;72(3):883–887. doi:10.1073/pnas.72.3.883
   Web of Science ®Google Scholar
• Guasch-Ferre M, Zheng Y, Ruiz-Canela M, et al. Plasma acylcarnitines and risk of cardiovascular disease: effect of Mediterranean diet interventions. Am J Clin Nutr. 2016;103(6):1408–1416. doi:10.3945/ajcn.116.130492
   Web of Science ®Google Scholar
• Yoshihisa A, Watanabe S, Yokokawa T, et al. Associations between acylcarnitine to free carnitine ratio and adverse prognosis in heart failure patients with reduced or preserved ejection fraction. ESC Heart Fail. 2017;4(3):360–364. doi:10.1002/ehf2.12176
   Web of Science ®Google Scholar
• McCann MR, George De la Rosa MV, Rosania GR, Stringer KA. L-carnitine and acylcarnitines: mitochondrial biomarkers for precision medicine. Metabolites. 2021;11(1):51. doi:10.3390/metabo11010051
   PubMed Web of Science ®Google Scholar
• Gunther SH, Khoo CM, Tai ES, et al. Serum acylcarnitines and amino acids and risk of type 2 diabetes in a multiethnic Asian population. BMJ Open Diabetes Res Care. 2020;8(1):e001315. doi:10.1136/bmjdrc-2020-001315
   Web of Science ®Google Scholar
• Aichler M, Borgmann D, Krumsiek J, et al. N-acyl taurines and acylcarnitines cause an imbalance in insulin synthesis and secretion provoking beta cell dysfunction in type 2 diabetes. Cell Metab. 2017;25(6):1334–1347. doi:10.1016/j.cmet.2017.04.012
   Web of Science ®Google Scholar
• Muilwijk M, Goorden SMI, Celis-Morales C, et al. Contributions of amino acid, acylcarnitine and sphingolipid profiles to type 2 diabetes risk among South-Asian Surinamese and Dutch adults. BMJ Open Diabetes Res Care. 2020;8(1):e001003. doi:10.1136/bmjdrc-2019-001003
   Web of Science ®Google Scholar
• Limonte CP, Valo E, Montemayor D, et al. A targeted multiomics approach to identify biomarkers associated with rapid eGFR decline in type 1 diabetes. Am J Nephrol. 2020;51(10):839–848. doi:10.1159/000510830
   Web of Science ®Google Scholar
• Fort PE, Rajendiran TM, Soni T, et al. Diminished retinal complex lipid synthesis and impaired fatty acid beta-oxidation associated with human diabetic retinopathy. JCI Insight. 2021;6(19):e152109. doi:10.1172/jci.insight.152109
   Web of Science ®Google Scholar
• Li X, Li Y, Liang Y, Hu R, Xu W, Liu Y. Plasma targeted metabolomics analysis for amino acids and acylcarnitines in patients with prediabetes, type 2 diabetes mellitus, and diabetic vascular complications. Diabetes Metab J. 2021;45(2):195–208. doi:10.4093/dmj.2019.0209
   Web of Science ®Google Scholar
• Zhao S, Feng XF, Huang T, et al. The association between acylcarnitine metabolites and cardiovascular disease in Chinese patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne). 2020;11:212. doi:10.3389/fendo.2020.00212
   Web of Science ®Google Scholar
• Xia FY, Zhu L, Xu C, et al. Plasma acylcarnitines could predict prognosis and evaluate treatment of IgA nephropathy. Nutr Metab (Lond). 2019;16:2. doi:10.1186/s12986-018-0328-1
   Web of Science ®Google Scholar
• Afshinnia F, Rajendiran TM, Soni T, et al. Impaired beta-oxidation and altered complex lipid fatty acid partitioning with advancing CKD. J Am Soc Nephrol. 2018;29(1):295–306. doi:10.1681/ASN.2017030350
   Web of Science ®Google Scholar
• Thongnak L, Pongchaidecha A, Lungkaphin A. Renal lipid metabolism and lipotoxicity in diabetes. Am J Med Sci. 2020;359(2):84–99. doi:10.1016/j.amjms.2019.11.004
   PubMed Web of Science ®Google Scholar
• Bremer J. Carnitine as a fatty acid carrier in intermediary metabolism. Nature. 1962;196(4858):993–994. doi:10.1038/196993a0
   Web of Science ®Google Scholar
• Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017;13(5):269–284. doi:10.1038/nrneph.2017.30
   PubMed Web of Science ®Google Scholar
• van der Kloet FM, Tempels FW, Ismail N, et al. Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study). Metabolomics. 2012;8(1):109–119. doi:10.1007/s11306-011-0291-6
   PubMed Web of Science ®Google Scholar
• Chuang W-H, Arundhathi A, Lu C, et al. Altered plasma acylcarnitine and amino acid profiles in type 2 diabetic kidney disease. Metabolomics. 2016;12(6):108. doi:10.1007/s11306-016-1049-y
   Web of Science ®Google Scholar
• Khamis MM, Adamko DJ, El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom Rev. 2017;36(2):115–134. doi:10.1002/mas.21455
   PubMed Web of Science ®Google Scholar
• Lains I, Gantner M, Murinello S, et al. Metabolomics in the study of retinal health and disease. Prog Retin Eye Res. 2019;69:57–79. doi:10.1016/j.preteyeres.2018.11.002
   PubMed Web of Science ®Google Scholar
• Jang C, Chen L, Rabinowitz JD. Metabolomics and isotope tracing. Cell. 2018;173(4):822–837. doi:10.1016/j.cell.2018.03.055
   PubMed Web of Science ®Google Scholar
• Rebholz CM, Zheng Z, Grams ME, et al. Serum metabolites associated with dietary protein intake: results from the Modification of Diet in Renal Disease (MDRD) randomized clinical trial. Am J Clin Nutr. 2019;109(3):517–525. doi:10.1093/ajcn/nqy202
   Web of Science ®Google Scholar
• Hirschel J, Vogel M, Baber R, et al. Relation of whole blood amino acid and acylcarnitine metabolome to age, sex, BMI, puberty, and metabolic markers in children and adolescents. Metabolites. 2020;10(4):149. doi:10.3390/metabo10040149
   Web of Science ®Google Scholar
• Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353–367. doi:10.1038/s41580-019-0108-4
   PubMed Web of Science ®Google Scholar
• Chen KH, Chen YL, Tang HY, et al. Dietary leucine supplement ameliorates hepatic steatosis and diabetic nephropathy in db/db mice. Int J Mol Sci. 2018;19(7):1921. doi:10.3390/ijms19071921
   Web of Science ®Google Scholar
• Quan W, Jiao Y, Xue C, et al. The effect of exogenous free N(epsilon)-(Carboxymethyl)lysine on diabetic-model Goto-Kakizaki rats: metabolomics analysis in serum and urine. J Agric Food Chem. 2021;69(2):783–793. doi:10.1021/acs.jafc.0c06445
   PubMed Web of Science ®Google Scholar
• Zahoor I, Rui B, Khan J, Datta I, Giri S. An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview. Cell Mol Life Sci. 2021;78(7):3181–3203. doi:10.1007/s00018-020-03733-2
   Web of Science ®Google Scholar