Targeted metabolomics as a tool for the diagnosis of kidney disease in Type II diabetes mellitus

References

• Zhang L, Zhou Y, Zhou F, et al. Altered expression of long noncoding and messenger RNAs in diabetic nephropathy following treatment with rosiglitazone. Biol Med Res Int. 2020;2020:Article ID 1360843, 17 pages. doi:https://doi.org/10.1155/2020/1360843.
   Google Scholar
• Bjornstad P, Cherney D, Maahs DM. Early diabetic nephropathy in type 1 diabetes–new insights. Curr Opin Endocrinol Diabetes Obes. 2014;21:279.
   PubMedGoogle Scholar
• Kiconco R, Rugera SP, Kiwanuka GN. Microalbuminuria and traditional serum biomarkers of nephropathy among diabetic patients at Mbarara regional referral hospital in South Western Uganda. J Diabetes Res. 2019;2019:2019.
   Web of Science ®Google Scholar
• Mattix HJ, Hsu C-Y, Shaykevich S, et al. Use of the albumin/creatinine ratio to detect microalbuminuria: implications of sex and race. J Am Soc Nephrol. 2002;13:1034–1039.
   PubMed Web of Science ®Google Scholar
• Fesler P, Mimran A. Estimation of glomerular filtration rate: what are the pitfalls? Curr Hypertens Rep. 2011;13:116–121.
   PubMed Web of Science ®Google Scholar
• Wachtell K, Ibsen H, Olsen MH, et al. Albuminuria and cardiovascular risk in hypertensive patients with left ventricular hypertrophy: the LIFE study. Ann Intern Med. 2003;139:901–906.
   PubMed Web of Science ®Google Scholar
• Gross JL, De Azevedo MJ, Silveiro SP, et al. Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care. 2005;28:164–176.
   PubMed Web of Science ®Google Scholar
• Roshan B, Stanton RC. A story of microalbuminuria and diabetic nephropathy. Journal of nephropathology. 2013;2:234. .
   Google Scholar
• Zhang H, Zuo J-J, Dong S-S, et al. Identification of potential serum metabolic biomarkers of diabetic kidney disease: a widely targeted metabolomics study. J Diabetes Res. 2020;2020:Article ID 3049098, 11 pages. doi:https://doi.org/10.1155/2020/3049098.
   Google Scholar
• Wettersten HI, Weiss RH. Applications of metabolomics for kidney disease research: from biomarkers to therapeutic targets. Organogenesis. 2013;9:11–18.
   PubMed Web of Science ®Google Scholar
• Darshi M, Van Espen B, Sharma K. Metabolomics in diabetic kidney disease: unraveling the biochemistry of a silent killer. Am J Nephrol. 2016;44:92–103.
   PubMed Web of Science ®Google Scholar
• Makrecka M, Kuka J, Volska K, et al. Long-chain acylcarnitine content determines the pattern of energy metabolism in cardiac mitochondria. Mol Cell Biochem. 2014;395:1–10.
   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;12:108.
   Web of Science ®Google Scholar
• Shaw J. Fathoming Metabolism. The study of metabolites does an end run around genomics to provide telling clues to your future health. Harv Mag. 2011;3:27–31.
   Google Scholar
• Baker M. In biomarkers we trust? Nat Biotechnol. 2005;23:297–304.
   PubMed Web of Science ®Google Scholar
• Haukka JK, Sandholm N, Forsblom C, et al. Metabolomic profile predicts development of microalbuminuria in individuals with Type 1 diabetes. Sci Rep. 2018;8:13853.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Zhang S, Wang G. Metabolomic biomarkers in diabetic kidney diseases—a systematic review. J Diabetes Complications. 2015;29:1345–1351.
   PubMed Web of Science ®Google Scholar
• Liu -J-J, Ghosh S, Kovalik J-P, 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;2:470–480.
   PubMed Web of Science ®Google Scholar
• Miller WG, Jones GR. Estimated glomerular filtration rate; laboratory implementation and current global status. Adv Chronic Kidney Dis. 2018;25:7–13.
   PubMed Web of Science ®Google Scholar
• Schneider MF, Muñoz A, Ku E, et al. Estimation of albumin-creatinine ratio from protein-creatinine ratio in urine of children and adolescents with CKD. Am J Kidney Dis. 2020. DOI:https://doi.org/10.1053/j.ajkd.2020.07.015.
   Google Scholar
• Oraby M. Preliminary results of Egypt experience for use of tandem mass spectrometry for expanded metabolic screening. J Appl Sci Res. 2009;5:1425–1435.
   Google Scholar
• Rinaldo P, Cowan TM, Matern D. Acylcarnitine profile analysis. Genet Med. 2008;10:151–156.
   PubMed Web of Science ®Google Scholar
• Christou C, Gika HG, Raikos N, et al. GC-MS analysis of organic acids in human urine in clinical settings: a study of derivatization and other analytical parameters. J Chromatogr B. 2014;964:195–201.
   PubMed Web of Science ®Google Scholar
• Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–1379.
   PubMed Web of Science ®Google Scholar
• Bujang MA, Baharum N. Sample size guideline for correlation analysis. World. 2016;3:1.
   Google Scholar
• Ratner B. The correlation coefficient: its values range between+ 1/− 1, or do they? J Targeting, Meas Anal Marketing. 2009;17:139–142.
   Google Scholar
• Wajner M, Sitta A, Kayser A, et al. Screening for organic acidurias and aminoacidopathies in high-risk Brazilian patients: eleven-year experience of a reference center. Genet Mol Biol. 2019;42:178–185.
   PubMed Web of Science ®Google Scholar
• Su W, Cao R, He YC, et al. Crosstalk of hyperglycemia and dyslipidemia in diabetic kidney disease. Kidney Diseases. 2017;3:171–180.
   Google Scholar
• Farahat TM, Elsaeed GK, Gazareen SS, et al. Prevalence of proteinuria among type 2 diabetic patients in Menoufia governorate, Egypt. Menoufia Med J. 2014;27:363.
   Google Scholar
• Kumar MA, Bitla ARR, Raju K, et al. Branched chain amino acid profile in early chronic kidney disease. Saudi J Kidney Dis Transpl. 2012;23:1202.
   PubMedGoogle Scholar
• Nakamura H, Jinzu H, Nagao K, et al. Plasma amino acid profiles are associated with insulin, C-peptide and adiponectin levels in type 2 diabetic patients. Nutr Diabetes. 2014;4:e133–e.
   PubMed Web of Science ®Google Scholar
• Kahl S, Roden M. Amino acids—lifesaver or killer in patients with diabetes? Nat Rev Endocrinol. 2018;14:449–451.
   PubMed Web of Science ®Google Scholar
• Mihalik SJ, Goodpaster BH, Kelley DE, et al. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity. 2010;18:1695–1700.
   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;19:144.
   PubMed Web of Science ®Google Scholar
• Tang X, You J, Liu D, et al. 5-hydroxyhexanoic acid predicts early renal functional decline in Type 2 diabetes patients with microalbuminuria. Kidney Blood Pressure Res. 2019;44:245–263.
   PubMed Web of Science ®Google Scholar
• Sharma K, Karl B, Mathew AV, et al. Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease. J Am Soc Nephrol. 2013;24:1901–1912.
   PubMed Web of Science ®Google Scholar
• Campion CG, Sanchez-Ferras O, Batchu SN. Potential role of serum and urinary biomarkers in diagnosis and prognosis of diabetic nephropathy. Can J Kidney Health Dis. 2017;4:2054358117705371.
   Google Scholar
• Kim M, Kim M, Han JY, et al. The metabolites in peripheral blood mononuclear cells showed greater differences between patients with impaired fasting glucose or type 2 diabetes and healthy controls than those in plasma. Diab Vasc Dis Res. 2017;14:130–138.
   PubMed Web of Science ®Google Scholar
• Adams SH, Hoppel CL, Lok KH, et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid β-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J Nutr. 2009;139:1073–1081.
   PubMed Web of Science ®Google Scholar
• Roberts LD, Souza AL, Gerszten RE, et al. Targeted metabolomics. Curr Protoc Mol Biol. 2012;98:30.2. 1-.2. 24.
   Google Scholar