Research progress of metabonomics in early diagnosis of diabetic kidney disease_West China Medical Journal

Authors：

YE Binbin , YANG Kai ,  DU Yan

Department of Clinical Laboratory, the First Affiliated Hospital of Kunming Medical University & Yunnan Province Clinical Research Center for Laboratory Medicine, Kunming, Yunnan 650032, P. R. China;

Corresponding author：

DU Yan, Email: duyan_m@139.com

Keywords：

Metabonomics; diabetic kidney disease; early diagnosis

DOI：

10.7507/1002-0179.202211039

Video：

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Diabetic kidney disease, as a common complication of diabetes, is one of the main causes of end-stage renal disease. Because of the rapid progress of its course and the limited means of treatment, it is of great clinical significance to seek biomarkers from early diagnosis for the treatment of diabetic kidney disease. At present, there are limited methods for early diagnosis of diabetic kidney disease. As a widely used research method, metabonomics can detect metabolites in diseases and provide biomarkers for disease diagnosis and prognosis. This article summarizes the changes of amino acids, lipids, organic acids and other metabolites in blood or urine of patients with diabetic kidney disease.

Citation： YE Binbin, YANG Kai, DU Yan. Research progress of metabonomics in early diagnosis of diabetic kidney disease. West China Medical Journal, 2023, 38(4): 603-607. doi: 10.7507/1002-0179.202211039 Copy

1.	Bonner R, Albajrami O, Hudspeth J, et al. Diabetic kidney disease. Prim Care, 2020, 47(4): 645-659.
2.	刘彦玲, 张丛, 王强, 等. 糖尿病肾病代谢组学差异代谢物的研究进展. 药物评价研究, 2019, 42(1): 187-193.
3.	Anders HJ, Huber TB, Isermann B, et al. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol, 2018, 14(6): 361-377.
4.	Jung CY, Yoo TH. Pathophysiologic mechanisms and potential biomarkers in diabetic kidney disease. Diabetes Metab J, 2022, 46(2): 181-197.
5.	Yamanouchi M, Furuichi K, Hoshino J, et al. Nonproteinuric diabetic kidney disease. Clin Exp Nephrol, 2020, 24(7): 573-581.
6.	Zhang S, Li X, Luo H, et al. Role of aromatic amino acids in pathogeneses of diabetic nephropathy in Chinese patients with type 2 diabetes. J Diabetes Complications, 2020, 34(10): 107667.
7.	Qi Q, Li J, Yu B, et al. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies. Gut, 2022, 71(6): 1095-1105.
8.	Zhang F, Guo R, Cui W, et al. Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren Fail, 2021, 43(1): 980-992.
9.	Jin Q, Ma RCW. Metabolomics in diabetes and diabetic complications: insights from epidemiological studies. Cells, 2021, 10(11): 2832.
10.	Wu MH, Lin CN, Chiu DT, et al. Kynurenine/tryptophan ratio predicts angiotensin receptor blocker responsiveness in patients with diabetic kidney disease. Diagnostics (Basel), 2020, 10(4): 207.
11.	Liu S, Li L, Lou P, et al. Elevated branched-chain α-keto acids exacerbate macrophage oxidative stress and chronic inflammatory damage in type 2 diabetes mellitus. Free Radic Biol Med, 2021, 175: 141-154.
12.	Zhang X, Liu D, He Y, et al. Branched chain amino acids protects rat mesangial cells from high glucose by modulating TGF-β1 and BMP-7. Diabetes Metab Syndr Obes, 2019, 12: 2433-2440.
13.	Zhou C, Zhang Q, Lu L, et al. Metabolomic profiling of amino acids in human plasma distinguishes diabetic kidney disease from type 2 diabetes mellitus. Front Med (Lausanne), 2021, 8: 765873.
14.	Saleem T, Dahpy M, Ezzat G, et al. The profile of plasma free amino acids in type 2 diabetes mellitus with insulin resistance: association with microalbuminuria and macroalbuminuria. Appl Biochem Biotechnol, 2019, 188(3): 854-867.
15.	Mbhele T, Tanyanyiwa DM, Moepya RJ, et al. Relationship between amino acid ratios and decline in estimated glomerular filtration rate in diabetic and non-diabetic patients in South Africa. Afr J Lab Med, 2021, 10(1): 1398.
16.	Luo Q, Liang W, Zhang Z, et al. Compromised glycolysis contributes to foot process fusion of podocytes in diabetic kidney disease: Role of ornithine catabolism. Metabolism, 2022, 134: 155245.
17.	Cao P, Huang B, Hong M, et al. Association of amino acids related to urea cycle with risk of diabetic nephropathy in two independent cross-sectional studies of Chinese adults. Front Endocrinol (Lausanne), 2022, 13: 983747.
18.	Abdelsattar S, Kasemy ZA, Elsayed M, et al. Targeted metabolomics as a tool for the diagnosis of kidney disease in type Ⅱ diabetes mellitus. Br J Biomed Sci, 2021, 78(4): 184-190.
19.	Gao Z, Chen X. Fatty acid β-oxidation in kidney diseases: perspectives on pathophysiological mechanisms and therapeutic opportunities. Front Pharmacol, 2022, 13: 805281.
20.	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.
21.	Feng Q, Li Y, Yang Y, et al. Urine metabolomics analysis in patients with normoalbuminuric diabetic kidney disease. Front Physiol, 2020, 11: 578799.
22.	Fijałkowski M, Stępniewska J, Domański M, et al. The role of eicosanoids in renal diseases - potential therapeutic possibilities. Acta Biochim Pol, 2018, 65(4): 479-486.
23.	Mota-Zamorano S, Robles NR, Lopez-Gomez J, et al. Plasma and urinary concentrations of arachidonic acid-derived eicosanoids are associated with diabetic kidney disease. EXCLI J, 2021, 20: 698-708.
24.	Morita Y, Kurano M, Sakai E, et al. Simultaneous analyses of urinary eicosanoids and related mediators identified tetranor-prostaglandin E metabolite as a novel biomarker of diabetic nephropathy. J Lipid Res, 2021, 62: 100120.
25.	Kaewin S, Changsorn K, Sungkaworn T, et al. Fungus-derived 3-hydroxyterphenyllin and candidusin a ameliorate palmitic acid-induced human podocyte injury via anti-oxidative and anti-apoptotic mechanisms. Molecules, 2022, 27(7): 2109.
26.	Chang W, Hatch GM, Wang Y, et al. The relationship between phospholipids and insulin resistance: from clinical to experimental studies. J Cell Mol Med, 2019, 23(2): 702-710.
27.	Mitrofanova A, Fontanella AM, Merscher S, et al. Lipid deposition and metaflammation in diabetic kidney disease. Curr Opin Pharmacol, 2020, 55: 60-72.
28.	Yoshioka K, Hirakawa Y, Kurano M, et al. Lysophosphatidylcholine mediates fast decline in kidney function in diabetic kidney disease. Kidney Int, 2022, 101(3): 510-526.
29.	Huang J, Covic M, Huth C, et al. Validation of candidate phospholipid biomarkers of chronic kidney disease in hyperglycemic individuals and their organ-specific exploration in leptin receptor-deficient db/db mouse. Metabolites, 2021, 11(2): 89.
30.	Mu X, Yang M, Ling P, et al. Acylcarnitines: can they be biomarkers of diabetic nephropathy?. Diabetes Metab Syndr Obes, 2022, 15: 247-256.
31.	Esmati P, Najjar N, Emamgholipour S, et al. Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy. J Diabetes Metab Disord, 2021, 20(1): 591-599.
32.	Afshinnia F, Nair V, Lin J, et al. Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians. JCI Insight, 2019, 4(21): e130317.
33.	Lunyera J, Diamantidis CJ, Bosworth HB, et al. Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease. Metabolomics, 2021, 18(1): 5.
34.	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 Press Res, 2019, 44(2): 245-263.
35.	郑丹娜, 何强. 尿液标志物在糖尿病肾病早期诊断中的研究进展. 中国中西医结合肾病杂志, 2020, 21(6): 553-555.
36.	Sanchez M, Roussel R, Hadjadj S, et al. Plasma concentrations of 8-hydroxy-2’-deoxyguanosine and risk of kidney disease and death in individuals with type 1 diabetes. Diabetologia, 2018, 61(4): 977-984.
37.	Peng X, Wang X, Shao X, et al. Serum metabolomics benefits discrimination kidney disease development in type 2 diabetes patients. Front Med (Lausanne), 2022, 9: 819311.
38.	Linnan B, Yanzhe W, Ling Z, et al. In situ metabolomics of metabolic reprogramming involved in a mouse model of type 2 diabetic kidney disease. Front Physiol, 2021, 12: 779683.
39.	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.

1. Bonner R, Albajrami O, Hudspeth J, et al. Diabetic kidney disease. Prim Care, 2020, 47(4): 645-659.
2. 刘彦玲, 张丛, 王强, 等. 糖尿病肾病代谢组学差异代谢物的研究进展. 药物评价研究, 2019, 42(1): 187-193.
3. Anders HJ, Huber TB, Isermann B, et al. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol, 2018, 14(6): 361-377.
4. Jung CY, Yoo TH. Pathophysiologic mechanisms and potential biomarkers in diabetic kidney disease. Diabetes Metab J, 2022, 46(2): 181-197.
5. Yamanouchi M, Furuichi K, Hoshino J, et al. Nonproteinuric diabetic kidney disease. Clin Exp Nephrol, 2020, 24(7): 573-581.
6. Zhang S, Li X, Luo H, et al. Role of aromatic amino acids in pathogeneses of diabetic nephropathy in Chinese patients with type 2 diabetes. J Diabetes Complications, 2020, 34(10): 107667.
7. Qi Q, Li J, Yu B, et al. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies. Gut, 2022, 71(6): 1095-1105.
8. Zhang F, Guo R, Cui W, et al. Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren Fail, 2021, 43(1): 980-992.
9. Jin Q, Ma RCW. Metabolomics in diabetes and diabetic complications: insights from epidemiological studies. Cells, 2021, 10(11): 2832.
10. Wu MH, Lin CN, Chiu DT, et al. Kynurenine/tryptophan ratio predicts angiotensin receptor blocker responsiveness in patients with diabetic kidney disease. Diagnostics (Basel), 2020, 10(4): 207.
11. Liu S, Li L, Lou P, et al. Elevated branched-chain α-keto acids exacerbate macrophage oxidative stress and chronic inflammatory damage in type 2 diabetes mellitus. Free Radic Biol Med, 2021, 175: 141-154.
12. Zhang X, Liu D, He Y, et al. Branched chain amino acids protects rat mesangial cells from high glucose by modulating TGF-β1 and BMP-7. Diabetes Metab Syndr Obes, 2019, 12: 2433-2440.
13. Zhou C, Zhang Q, Lu L, et al. Metabolomic profiling of amino acids in human plasma distinguishes diabetic kidney disease from type 2 diabetes mellitus. Front Med (Lausanne), 2021, 8: 765873.
14. Saleem T, Dahpy M, Ezzat G, et al. The profile of plasma free amino acids in type 2 diabetes mellitus with insulin resistance: association with microalbuminuria and macroalbuminuria. Appl Biochem Biotechnol, 2019, 188(3): 854-867.
15. Mbhele T, Tanyanyiwa DM, Moepya RJ, et al. Relationship between amino acid ratios and decline in estimated glomerular filtration rate in diabetic and non-diabetic patients in South Africa. Afr J Lab Med, 2021, 10(1): 1398.
16. Luo Q, Liang W, Zhang Z, et al. Compromised glycolysis contributes to foot process fusion of podocytes in diabetic kidney disease: Role of ornithine catabolism. Metabolism, 2022, 134: 155245.
17. Cao P, Huang B, Hong M, et al. Association of amino acids related to urea cycle with risk of diabetic nephropathy in two independent cross-sectional studies of Chinese adults. Front Endocrinol (Lausanne), 2022, 13: 983747.
18. Abdelsattar S, Kasemy ZA, Elsayed M, et al. Targeted metabolomics as a tool for the diagnosis of kidney disease in type Ⅱ diabetes mellitus. Br J Biomed Sci, 2021, 78(4): 184-190.
19. Gao Z, Chen X. Fatty acid β-oxidation in kidney diseases: perspectives on pathophysiological mechanisms and therapeutic opportunities. Front Pharmacol, 2022, 13: 805281.
20. 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.
21. Feng Q, Li Y, Yang Y, et al. Urine metabolomics analysis in patients with normoalbuminuric diabetic kidney disease. Front Physiol, 2020, 11: 578799.
22. Fijałkowski M, Stępniewska J, Domański M, et al. The role of eicosanoids in renal diseases - potential therapeutic possibilities. Acta Biochim Pol, 2018, 65(4): 479-486.
23. Mota-Zamorano S, Robles NR, Lopez-Gomez J, et al. Plasma and urinary concentrations of arachidonic acid-derived eicosanoids are associated with diabetic kidney disease. EXCLI J, 2021, 20: 698-708.
24. Morita Y, Kurano M, Sakai E, et al. Simultaneous analyses of urinary eicosanoids and related mediators identified tetranor-prostaglandin E metabolite as a novel biomarker of diabetic nephropathy. J Lipid Res, 2021, 62: 100120.
25. Kaewin S, Changsorn K, Sungkaworn T, et al. Fungus-derived 3-hydroxyterphenyllin and candidusin a ameliorate palmitic acid-induced human podocyte injury via anti-oxidative and anti-apoptotic mechanisms. Molecules, 2022, 27(7): 2109.
26. Chang W, Hatch GM, Wang Y, et al. The relationship between phospholipids and insulin resistance: from clinical to experimental studies. J Cell Mol Med, 2019, 23(2): 702-710.
27. Mitrofanova A, Fontanella AM, Merscher S, et al. Lipid deposition and metaflammation in diabetic kidney disease. Curr Opin Pharmacol, 2020, 55: 60-72.
28. Yoshioka K, Hirakawa Y, Kurano M, et al. Lysophosphatidylcholine mediates fast decline in kidney function in diabetic kidney disease. Kidney Int, 2022, 101(3): 510-526.
29. Huang J, Covic M, Huth C, et al. Validation of candidate phospholipid biomarkers of chronic kidney disease in hyperglycemic individuals and their organ-specific exploration in leptin receptor-deficient db/db mouse. Metabolites, 2021, 11(2): 89.
30. Mu X, Yang M, Ling P, et al. Acylcarnitines: can they be biomarkers of diabetic nephropathy?. Diabetes Metab Syndr Obes, 2022, 15: 247-256.
31. Esmati P, Najjar N, Emamgholipour S, et al. Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy. J Diabetes Metab Disord, 2021, 20(1): 591-599.
32. Afshinnia F, Nair V, Lin J, et al. Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians. JCI Insight, 2019, 4(21): e130317.
33. Lunyera J, Diamantidis CJ, Bosworth HB, et al. Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease. Metabolomics, 2021, 18(1): 5.
34. 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 Press Res, 2019, 44(2): 245-263.
35. 郑丹娜, 何强. 尿液标志物在糖尿病肾病早期诊断中的研究进展. 中国中西医结合肾病杂志, 2020, 21(6): 553-555.
36. Sanchez M, Roussel R, Hadjadj S, et al. Plasma concentrations of 8-hydroxy-2’-deoxyguanosine and risk of kidney disease and death in individuals with type 1 diabetes. Diabetologia, 2018, 61(4): 977-984.
37. Peng X, Wang X, Shao X, et al. Serum metabolomics benefits discrimination kidney disease development in type 2 diabetes patients. Front Med (Lausanne), 2022, 9: 819311.
38. Linnan B, Yanzhe W, Ling Z, et al. In situ metabolomics of metabolic reprogramming involved in a mouse model of type 2 diabetic kidney disease. Front Physiol, 2021, 12: 779683.
39. 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.

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