Causal relationships between blood metabolites and diabetic nephropathy in east Asian populations: a Mendelian randomization study_Chinese Journal of Evidence-Based Medicine

Authors：

LI Qihu ¹ , WANG Qian ² , ZHOU Yu ¹ , DENG Weizhu ² , WANG Xiaolong ² , WANG Fuling ² ,  CHEN Xiangmei ² ,  DONG Zheyi ²

1. School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, P. R. China;
2. Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, P. R. China;

Corresponding?author：

CHEN Xiangmei, Email: xmchen301@126.com; DONG Zheyi, Email: shengdai26@163.com

Keywords：

Diabetic kidney disease; Blood metabolites; Mendelian randomization; East Asian populations

DOI：

10.7507/1672-2531.202507134

Video：

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Objective To investigate the potential causal relationships between blood metabolites and diabetic kidney disease (DKD) in East Asian populations and to identify ethnicity-specific metabolic risk factors. Methods We utilized summary-level data from genome-wide association studies (GWAS) comprising 112 blood metabolites in a Chinese population and DKD in an East Asian population. Genetic variants significantly associated with metabolites were selected as instrumental variables. We performed bidirectional two-sample Mendelian randomization (MR) analyses, with primary causal estimates derived using the inverse variance weighted (IVW) method. Sensitivity analyses included MR-Egger, weighted median, and Bayesian-weighted MR (BWMR). Metabolites showing suggestive evidence of association were further investigated using multivariable MR and colocalization analysis. Robustness of findings was assessed via Cochran’s Q test for heterogeneity, the MR-Egger intercept test for horizontal pleiotropy, and leave-one-out analysis. Results Forward MR analysis identified suggestive associations between elevated levels of tyrosine, glutamine, leucine, and the albumin-to-globulin ratio with DKD risk. In multivariable MR analysis, leucine (OR=0.084, P=0.026) and the albumin-to-globulin ratio (OR=0.161, P=0.048) retained statistically significant independent protective effects against DKD, whereas the associations for tyrosine and glutamine were attenuated. Colocalization analysis did not support the presence of shared underlying causal genetic variants between the identified metabolites and DKD. Sensitivity analyses revealed no significant evidence of heterogeneity or horizontal pleiotropy. Reverse MR analysis did not suggest the presence of reverse causation. Conclusion Our findings suggest that leucine and the albumin-to-globulin ratio may act as independent protective factors against DKD in East Asian populations. These results provide new genetic insights into the ethnicity-specific metabolic mechanisms contributing to DKD.

Citation： LI Qihu, WANG Qian, ZHOU Yu, DENG Weizhu, WANG Xiaolong, WANG Fuling, CHEN Xiangmei, DONG Zheyi. Causal relationships between blood metabolites and diabetic nephropathy in east Asian populations: a Mendelian randomization study. Chinese Journal of Evidence-Based Medicine, 2026, 26(6): 676-682. doi: 10.7507/1672-2531.202507134 Copy

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5.	Kondoh H, Kameda M, Yanagida M. Whole blood metabolomics in aging research. Int J Mol Sci, 2020, 22(1): 175.
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16.	Lutz SM, Wu AC, Hokanson JE, et al. Caution against examining the role of reverse causality in Mendelian randomization. Genet Epidemiol, 2021, 45(5): 445-454.
17.	Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
18.	Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology, 2017, 28(1): 30-42.
19.	Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 2017, 32(5): 377-389.
20.	Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol, 2016, 40(4): 304-314.
21.	Zhao J, Ming J, Hu X, et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics, 2020, 36(5): 1501-1508.
22.	Lu X, Perkins DL. Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures. BMC Bioinformatics, 2007, 8: 157.
23.	Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res, 2012, 21(3): 223-242.
24.	Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet, 2014, 10(5): e1004383.
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26.	Cohen JF, Chalumeau M, Cohen R, et al. Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol, 2015, 68(3): 299-306.
27.	Pietzner M, Stewart ID, Raffler J, et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021, 27(3): 471-479.
28.	Noerman S, Landberg R. Blood metabolite profiles linking dietary patterns with health-toward precision nutrition. J Intern Med, 2023, 293(4): 408-432.
29.	Bar N, Korem T, Weissbrod O, et al. A reference map of potential determinants for the human serum metabolome. Nature, 2020, 588(7836): 135-140.
30.	Luo HH, Li J, Feng XF, et al. Plasma phenylalanine and tyrosine and their interactions with diabetic nephropathy for risk of diabetic retinopathy in type 2 diabetes. BMJ Open Diabetes Res Care, 2020, 8(1): e000877.
31.	Nasri M, Adibhesami G, Mahdavifard S, et al. Exogenous glutamine ameliorates diabetic nephropathy in a rat model of type 2 diabetes mellitus through its antioxidant and anti-inflammatory activities. Arch Physiol Biochem, 2023, 129(2): 363-372.
32.	Zhang H, Jia Y, Cooper JJ, et al. Common variants in glutamine: fructose-6-phosphate amidotransferase 2 (GFPT2) gene are associated with type 2 diabetes, diabetic nephropathy, and increased GFPT2 mRNA levels. J Clin Endocrinol Metab, 2004, 89(2): 748-755.
33.	Gao X, Hou R, Li X, et al. The association between leucine and diabetic nephropathy in different gender: a cross-sectional study in Chinese patients with type 2 diabetes. Front Endocrinol (Lausanne), 2021, 11: 619422.
34.	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.
35.	Lin SP, Chen CM, Chiu SH, et al. Associations of dietary protein intake and amino acid patterns with the risk of diabetic kidney disease in adults with type 2 diabetes: a cross-sectional study. Nutrients, 2025, 17(13): 2168.
36.	Hong Q, Zhang L, Fu J, et al. LRG1 promotes diabetic kidney disease progression by enhancing TGF- β-induced angiogenesis. J Am Soc Nephrol, 2019, 30(4): 546-562.
37.	Hong Q, Cai H, Zhang L, et al. Modulation of transforming growth factor-β-induced kidney fibrosis by leucine-rich α-2 glycoprotein-1. Kidney Int, 2022, 101(2): 299-314.
38.	Gibb B, Giebelmann R, Martin S, et al. Nachweis von haptoglobinen und isoagglutininen im harn bei diabetischer angiopathie: ein beitrag zur frage der glomerul?ren filtration h?hermolekularer proteine. Diabetologia, 1966, 1(3-4): 219-227.
39.	Liu M, Yang Y, Liu Y, et al. Serum branched chain amino acids: an effective indicator of diabetic kidney disease. Front Endocrinol (Lausanne), 2023, 14: 1269633.
40.	Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol, 2014, 10(12): 723-736.

1. KDOQI. KDOQI clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease. Am J Kidney Dis, 2007, 49(2 Suppl 2): S12-S154.
2. Afkarian M, Sachs MC, Kestenbaum B, et al. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol, 2013, 24(2): 302-308.
3. Fox CS, Matsushita K, Woodward M, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet, 2012, 380(9854): 1662-1673.
4. Lu J, Zhao W, Chen T, et al. Influence of guideline adherence and parameter control on the clinical outcomes in patients with diabetic nephropathy. BMJ Open Diabetes Res Care, 2020, 8(1): e001166.
5. Kondoh H, Kameda M, Yanagida M. Whole blood metabolomics in aging research. Int J Mol Sci, 2020, 22(1): 175.
6. Chen Y, Lu T, Pettersson-Kymmer U, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet, 2023, 55(1): 44-53.
7. He J, Gai J. Genome-wide association studies (GWAS). Methods Mol Biol, 2023, 2638: 123-146.
8. Mulder S, Perco P, Oxlund C, et al. Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes. Transl Res, 2020, 222: 17-27.
9. Birney E. Mendelian randomization. Cold Spring Harb Perspect Med, 2022, 12(4): a041302.
10. Rasooly D, Peloso GM. Two-sample multivariable mendelian randomization analysis using R. Curr Protoc, 2021, 1(12): e335.
11. Richmond RC, Davey Smith G. Mendelian randomization: concepts and scope. Cold Spring Harb Perspect Med, 2022, 12(1): a040501.
12. Liu X, Tong X, Zou Y, et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat Genet, 2022, 54(1): 52-61.
13. Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet, 2021, 53(10): 1415-1424.
14. Hemani G, Zheng J, Elsworth B, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife, 2018, 7: e34408.
15. Kamat MA, Blackshaw JA, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics, 2019, 35(22): 4851-4853.
16. Lutz SM, Wu AC, Hokanson JE, et al. Caution against examining the role of reverse causality in Mendelian randomization. Genet Epidemiol, 2021, 45(5): 445-454.
17. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
18. Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology, 2017, 28(1): 30-42.
19. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 2017, 32(5): 377-389.
20. Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol, 2016, 40(4): 304-314.
21. Zhao J, Ming J, Hu X, et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics, 2020, 36(5): 1501-1508.
22. Lu X, Perkins DL. Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures. BMC Bioinformatics, 2007, 8: 157.
23. Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res, 2012, 21(3): 223-242.
24. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet, 2014, 10(5): e1004383.
25. Gronau QF, Wagenmakers EJ. Limitations of Bayesian leave-one-out cross-validation for model selection. Comput Brain Behav, 2019, 2(1): 1-11.
26. Cohen JF, Chalumeau M, Cohen R, et al. Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol, 2015, 68(3): 299-306.
27. Pietzner M, Stewart ID, Raffler J, et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021, 27(3): 471-479.
28. Noerman S, Landberg R. Blood metabolite profiles linking dietary patterns with health-toward precision nutrition. J Intern Med, 2023, 293(4): 408-432.
29. Bar N, Korem T, Weissbrod O, et al. A reference map of potential determinants for the human serum metabolome. Nature, 2020, 588(7836): 135-140.
30. Luo HH, Li J, Feng XF, et al. Plasma phenylalanine and tyrosine and their interactions with diabetic nephropathy for risk of diabetic retinopathy in type 2 diabetes. BMJ Open Diabetes Res Care, 2020, 8(1): e000877.
31. Nasri M, Adibhesami G, Mahdavifard S, et al. Exogenous glutamine ameliorates diabetic nephropathy in a rat model of type 2 diabetes mellitus through its antioxidant and anti-inflammatory activities. Arch Physiol Biochem, 2023, 129(2): 363-372.
32. Zhang H, Jia Y, Cooper JJ, et al. Common variants in glutamine: fructose-6-phosphate amidotransferase 2 (GFPT2) gene are associated with type 2 diabetes, diabetic nephropathy, and increased GFPT2 mRNA levels. J Clin Endocrinol Metab, 2004, 89(2): 748-755.
33. Gao X, Hou R, Li X, et al. The association between leucine and diabetic nephropathy in different gender: a cross-sectional study in Chinese patients with type 2 diabetes. Front Endocrinol (Lausanne), 2021, 11: 619422.
34. 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.
35. Lin SP, Chen CM, Chiu SH, et al. Associations of dietary protein intake and amino acid patterns with the risk of diabetic kidney disease in adults with type 2 diabetes: a cross-sectional study. Nutrients, 2025, 17(13): 2168.
36. Hong Q, Zhang L, Fu J, et al. LRG1 promotes diabetic kidney disease progression by enhancing TGF- β-induced angiogenesis. J Am Soc Nephrol, 2019, 30(4): 546-562.
37. Hong Q, Cai H, Zhang L, et al. Modulation of transforming growth factor-β-induced kidney fibrosis by leucine-rich α-2 glycoprotein-1. Kidney Int, 2022, 101(2): 299-314.
38. Gibb B, Giebelmann R, Martin S, et al. Nachweis von haptoglobinen und isoagglutininen im harn bei diabetischer angiopathie: ein beitrag zur frage der glomerul?ren filtration h?hermolekularer proteine. Diabetologia, 1966, 1(3-4): 219-227.
39. Liu M, Yang Y, Liu Y, et al. Serum branched chain amino acids: an effective indicator of diabetic kidney disease. Front Endocrinol (Lausanne), 2023, 14: 1269633.
40. Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol, 2014, 10(12): 723-736.

Chinese Journal of Evidence-Based Medicine

Causal relationships between blood metabolites and diabetic nephropathy in east Asian populations: a Mendelian randomization study

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