1. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol 2017;12:2032–2045.

2. Jin DC. Analysis of mortality risk from Korean hemodialysis registry data 2017. Kidney Res Clin Pract 2019;38:169–175.

3. Kim HJ, Kim SS, Song SH. Glomerular filtration rate as a kidney outcome of diabetic kidney disease: a focus on new antidiabetic drugs. Korean J Intern Med 2022;37:502–519.

4. Kim DW, Song SH. Sarcopenia in chronic kidney disease: from bench to bedside. Korean J Intern Med 2023;38:303–321.

5. Johansen KL, Chertow GM, Foley RN, et al. US Renal Data System 2020 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis 2021;77(4 Suppl 1):A7–A8.

6. Tong LL, Adler SG. Diabetic kidney disease treatment: new perspectives. Kidney Res Clin Pract 2022;41(Suppl 2):S63–S73.

7. Maruno S, Tanaka T, Nangaku M. Exploring molecular targets in diabetic kidney disease. Kidney Res Clin Pract 2022;41(Suppl 2):S33–S45.

8. Stevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–830.

9. Fiorentino M, Bolignano D, Tesar V, et al. Renal biopsy in patients with diabetes: a pooled meta-analysis of 48 studies. Nephrol Dial Transplant 2017;32:97–110.

10. Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care 2005;28:164–176.

11. Jawa A, Kcomt J, Fonseca VA. Diabetic nephropathy and retinopathy. Med Clin North Am 2004;88:1001–36xi.

12. Umanath K, Lewis JB. Update on diabetic nephropathy: core curriculum 2018. Am J Kidney Dis 2018;71:884–895.

13. Jiang G, Luk AOY, Tam CHT, et al. Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes. Kidney Int 2019;95:178–187.

14. Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab 2017;25:43–56.

15. Jang C, Chen L, Rabinowitz JD. Metabolomics and isotope tracing. Cell 2018;173:822–837.

16. Dumas ME. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes. Mol Biosyst 2012;8:2494–2502.

17. Breit M, Weinberger KM. Metabolic biomarkers for chronic kidney disease. Arch Biochem Biophys 2016;589:62–80.

18. Dubin RF, Rhee EP. Proteomics and metabolomics in kidney disease, including insights into etiology, treatment, and prevention. Clin J Am Soc Nephrol 2020;15:404–411.

19. Davies R. The metabolomic quest for a biomarker in chronic kidney disease. Clin Kidney J 2018;11:694–703.

20. Lim JH, Chung BH, Lee SH, et al. Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection. Korean J Intern Med 2022;37:520–533.

21. Jin Q, Ma RCW. Metabolomics in diabetes and diabetic complications: insights from epidemiological studies. Cells 2021;10:2832.

22. Sakashita M, Tanaka T, Inagi R. Metabolic changes and oxidative stress in diabetic kidney disease. Antioxidants (Basel) 2021;10:1143.

23. Jung CY, Yoo TH. Novel biomarkers for diabetic kidney disease. Kidney Res Clin Pract 2022;41(Suppl 2):S46–S62.

24. Hirayama A, Nakashima E, Sugimoto M, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem 2012;404:3101–3109.

25. Xia JF, Liang QL, Hu P, Wang YM, Li P, Luo GA. Correlations of six related purine metabolites and diabetic nephropathy in Chinese type 2 diabetic patients. Clin Biochem 2009;42:215–220.

26. 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;77:1279–1284.

27. Han LD, Xia JF, Liang QL, et al. Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta 2011;689:85–91.

28. Pena MJ, Lambers Heerspink HJ, Hellemons ME, et al. Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus. Diabet Med 2014;31:1138–1147.

29. Kim DW, Kim HJ, Seong EY, et al. Virtual diagnosis of diabetic nephropathy using metabolomics in place of kidney biopsy: the DIAMOND study. Diabetes Res Clin Pract 2023;205:110986.

30. 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.

31. 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.

32. Colombo M, Looker HC, Farran B, et al. Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes. Diabetologia 2019;62:156–168.

33. 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;76:511–520.

34. Kwon S, Hyeon JS, Jung Y, et al. Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance. Kidney Res Clin Pract 2023;42:445–459.

35. 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;85:1214–1224.

36. Kramer CK, Leitão CB, Pinto LC, Silveiro SP, Gross JL, Canani LH. Clinical and laboratory profile of patients with type 2 diabetes with low glomerular filtration rate and normoalbuminuria. Diabetes Care 2007;30:1998–2000.

37. MacIsaac RJ, Tsalamandris C, Panagiotopoulos S, Smith TJ, McNeil KJ, Jerums G. Nonalbuminuric renal insufficiency in type 2 diabetes. Diabetes Care 2004;27:195–200.

38. Hirakawa Y, Yoshioka K, Kojima K, et al. Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics. Sci Rep 2022;12:16287.

39. Wang Z, Zhang J, Wang L, et al. Glycine mitigates renal oxidative stress by suppressing Nox4 expression in rats with streptozotocin-induced diabetes. J Pharmacol Sci 2018;137:387–394.

40. Yuan Y, Huang L, Yu L, et al. Clinical metabolomics characteristics of diabetic kidney disease: a meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls. Diabetes Metab Res Rev 2024;40:e3789.

41. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology 2021;128:1580–1591.

42. Jian Q, Wu Y, Zhang F. Metabolomics in diabetic retinopathy: from potential biomarkers to molecular basis of oxidative stress. Cells 2022;11:3005.

43. Zhu XR, Yang FY, Lu J, et al. Plasma metabolomic profiling of proliferative diabetic retinopathy. Nutr Metab (Lond) 2019;16:37.

44. Chen L, Cheng CY, Choi H, et al. Plasma metabonomic profiling of diabetic retinopathy. Diabetes 2016;65:1099–1108.

45. 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:e0241365.

46. Tomofuji Y, Suzuki K, Kishikawa T, et al. Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes. Commun Med (Lond) 2023;3:5.

47. KP , Kumar J A, Rai S, et al. Predictive value of serum sialic Acid in type-2 diabetes mellitus and its complication (nephropathy). J Clin Diagn Res 2013;7:2435–2437.

48. El-Sayed MS, El Badawy A, Abdelmoneim RO, Mansour AE, Khalil ME, Darwish K. Relationship between serum sialic acid concentration and diabetic retinopathy in Egyptian patients with type 2 diabetes mellitus. Benha Med J 2018;35:257–263.

49. Li MN, Qian SH, Yao ZY, et al. Correlation of serum N-Acetylneuraminic acid with the risk and prognosis of acute coronary syndrome: a prospective cohort study. BMC Cardiovasc Disord 2020;20:404.

50. Hu X, Chen S, Ye S, Chen W, Zhou Y. New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era. Front Immunol 2024;15:1342837.

51. Sha Q, Lyu J, Zhao M, et al. Multi-omics analysis of diabetic nephropathy reveals potential new mechanisms and drug targets. Front Genet 2020;11:616435.

52. Wu IW, Tsai TH, Lo CJ, et al. Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease. NPJ Digit Med 2022;5:166.

53. Di Minno A, Gelzo M, Caterino M, Costanzo M, Ruoppolo M, Castaldo G. Challenges in metabolomics-based tests, biomarkers revealed by metabolomic analysis, and the promise of the application of metabolomics in precision medicine. Int J Mol Sci 2022;23:5213.

54. Beale DJ, Pinu FR, Kouremenos KA, et al. Review of recent developments in GC-MS approaches to metabolomics-based research. Metabolomics 2018;14:152.

55. Tolstikov V, Moser AJ, Sarangarajan R, Narain NR, Kiebish MA. Current status of metabolomic biomarker discovery: impact of study design and demographic characteristics. Metabolites 2020;10:224.

56. Bartroff J, Song J. Sequential tests of multiple hypotheses controlling false discovery and nondiscovery rates. Seq Anal 2020;39:65–91.