Causal relationship between inflammatory factors and diabetic nephropathy: a bidirectional Mendelian randomization study_Chinese Journal of Evidence-Based Medicine

Authors：

DONG Pengtao ¹ ,  WANG Zheng ² , LI Xiaoyu ³ ,  ZHANG Qing ² , FENG Xue ¹

1. Henan University of Chinese Medicine, Zhengzhou 450046, P. R. China;
2. The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, P. R. China;
3. School of Public Health, Zhengzhou University, Zhengzhou 450001, P. R. China;

Corresponding author：

WANG Zheng, Email: zxylc2013@163.com; ZHANG Qing, Email: zq_tcm@126.com

Keywords：

Inflammatory factors; Diabetic nephropathy; Mendelian randomization; Causal inference

DOI：

10.7507/1672-2531.202310170

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective This study applied Mendelian randomization to explore the potential causal relationship between inflammatory factors and diabetic nephropathy. Methods Summary-level data from genome-wide association studies of inflammatory factors and diabetic nephropathy were used, and inverse variance weighted analysis was used as the primary analytical method, complemented by results from weighted median, MR-Egger regression, simple model, and median model approaches. Sensitivity analysis was used to test the reliability of the MR analysis results. Results In the inverse variance weighted method, stem cell factor (OR=1.28, 95%CI 1.04 to 1.58, P=0.020) and interferon-γ (OR=1.36, 95%CI 1.10 to 1.70, P=0.005) were positively correlated with diabetic nephropathy, and diabetic nephropathy was positively correlated with interferon-inducible protein 10 (OR=0.90, 95%CI 0.83 to 0.98, P=0.012) were negatively correlated with diabetic nephropathy. Sensitivity analysis showed that MR analysis was reliable. Conclusion Stem cell factors and interferon-γ are associated with an increased risk of developing diabetic nephropathy, and diabetic nephropathy decreases the expression of interferon-inducible protein 10 in vivo. Our results demonstrate a potential causal relationship between inflammatory factors and the development of diabetic nephropathy. This finding is of clinical significance for the pre-diagnosis and treatment of diabetic nephropathy.

Citation： DONG Pengtao, WANG Zheng, LI Xiaoyu, ZHANG Qing, FENG Xue. Causal relationship between inflammatory factors and diabetic nephropathy: a bidirectional Mendelian randomization study. Chinese Journal of Evidence-Based Medicine, 2024, 24(5): 543-549. doi: 10.7507/1672-2531.202310170 Copy

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2.	Li HD, You YK, Shao BY, et al. Roles and crosstalks of macrophages in diabetic nephropathy. Front Immunol, 2022, 13: 1015142.
3.	Liu Y, Lv Y, Zhang T, et al. T cells and their products in diabetic kidney disease. Front Immunol, 2023, 14: 1084448.
4.	Moreno JA, Gomez-Guerrero C, Mas S, et al. Targeting inflammation in diabetic nephropathy: a tale of hope. Expert Opin Investig Drugs, 2018, 27(11): 917-930.
5.	Barutta F, Bellini S, Corbetta B, et al. The future of diabetic kidney disease management: what to expect from the experimental studies. J Nephrol, 2020, 33(6): 1151-1161.
6.	Aryan Z, Ghajar A, Faghihi-Kashani S, et al. Baseline high-sensitivity c-reactive protein predicts macrovascular and microvascular complications of type 2 diabetes: a population-based study. Ann Nutr Metab, 2018, 72(4): 287-295.
7.	Chung CH, Fan J, Lee EY, et al. Effects of tumor necrosis factor-α on podocyte expression of monocyte chemoattractant protein-1 and in diabetic nephropathy. Nephron Extra, 2015, 5(1): 1-18.
8.	黄恺琪, 盛泓沁, 张燕媚, 等. 2型糖尿病肾病湿证与外周血炎症因子的相关性研究. 时珍国医国药, 2022, 33(12): 2965-2970.
9.	Jin Q, Liu T, Qiao Y, et al. Oxidative stress and inflammation in diabetic nephropathy: role of polyphenols. Front Immunol, 2023, 14: 1185317.
10.	Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008, 27(8): 1133-1163.
11.	Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA, 2017, 318(19): 1925-1926.
12.	Ahola-Olli AV, Würtz P, Havulinna AS, et al. Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am J Hum Genet, 2017, 100(1): 40-50.
13.	Zhang Z, Wang S, Ren F, et al. Inflammatory factors and risk of meningiomas: a bidirectional Mendelian-randomization study. Front Neurosci, 2023, 17: 1186312.
14.	Yeung CHC, Au Yeung SL, Fong SSM, et al. Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study. Diabetologia, 2019, 62(5): 789-799.
15.	Shen Z, Qiu B, Chen L, et al. Common gastrointestinal diseases and chronic obstructive pulmonary disease risk: a bidirectional Mendelian randomization analysis. Front Genet, 2023, 14: 1256833.
16.	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.
17.	Asghar S, Asghar S, Shahid S, et al. Metabolic syndrome in type 2 diabetes mellitus patients: prevalence, risk factors, and associated microvascular complications. Cureus, 2023, 15(5): e39076.
18.	Xiang M, Wang Y, Gao Z, et al. Exploring causal correlations between inflammatory cytokines and systemic lupus erythematosus: a Mendelian randomization. Front Immunol, 2023, 13: 985729.
19.	Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
20.	Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol, 2015, 44(2): 512-525.
21.	Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 2017, 32(5): 377-389.
22.	Hemani G, Tilling K, Davey SG. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet, 2017, 13(11): e1007081.
23.	Matsushita K, Coresh J, Sang Y, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol, 2015, 3(7): 514-525.
24.	Fried LF, Emanuele N, Zhang JH, et al. Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med, 2013, 369(20): 1892-1903.
25.	Perkovic V, de Zeeuw D, Mahaffey KW, et al. Canagliflozin and renal outcomes in type 2 diabetes: results from the CANVAS program randomised clinical trials. Lancet Diabetes Endocrinol, 2018, 6(9): 691-704.
26.	Peng Y, Zhou M, Yang H, et al. Regulatory mechanism of M1/M2 macrophage polarization in the development of autoimmune diseases. Mediators Inflamm, 2023, 2023: 8821610.
27.	Tang SCW, Yiu WH. Innate immunity in diabetic kidney disease. Nat Rev Nephrol, 2020, 16(4): 206-222.
28.	Wada J, Makino H. Inflammation and the pathogenesis of diabetic nephropathy. Clin Sci (Lond), 2013, 124(3): 139-152.
29.	Tang PM, Nikolic-Paterson DJ, Lan HY. Macrophages: versatile players in renal inflammation and fibrosis. Nat Rev Nephrol, 2019, 15(3): 144-158.
30.	安英, 李强, 隋春红, 等. 促炎细胞因子在糖尿病肾病患者中的作用. 中国民康医学, 2016, 28(14): 44-47.
31.	Matsushima K, Yang D, Oppenheim JJ. Interleukin-8: an evolving chemokine. Cytokine, 2022, 153: 155828.
32.	Liu SY, Chen J, Li YF. Clinical significance of serum interleukin-8 and soluble tumor necrosis factor-like weak inducer of apoptosis levels in patients with diabetic nephropathy. J Diabetes Investig, 2018, 9(5): 1182-1188.
33.	Yaribeygi H, Atkin SL, Sahebkar A. Interleukin-18 and diabetic nephropathy: a review. J Cell Physiol, 2019, 234(5): 5674-5682.
34.	Su H, Lei CT, Zhang C. Interleukin-6 signaling pathway and its role in kidney disease: an update. Front Immunol, 2017, 8: 405.
35.	Lu TC, Wang ZH, Feng X, et al. Knockdown of Stat3 activity in vivo prevents diabetic glomerulopathy. Kidney Int, 2009, 76(1): 63-71.
36.	Chen LH, Advani SL, Thai K, et al. SDF-1/CXCR4 signaling preserves microvascular integrity and renal function in chronic kidney disease. PLoS One, 2014, 9(3): e92227.
37.	周礼香, 张若鹏, 董兆梅. 颗粒细胞中干细胞因子对卵母细胞及胚胎质量的评估. 中国性科学, 2022, 31(10): 65-68.
38.	Silva GE, Costa RS, Ravinal RC, et al. Mast cells, TGF-beta1 and alpha-SMA expression in IgA nephropathy. Dis Markers, 2008, 24(3): 181-190.
39.	Ravinal RC, Costa RS, Coimbra TM, et al. Mast cells, TGF-beta1 and myofibroblasts expression in lupus nephritis outcome. Lupus, 2005, 14(10): 814-821.
40.	郭丹丹, 李琳, 吕莉娜, 等. 肥大细胞和干细胞因子在糖尿病肾病患者肾组织中的浸润表达及其意义. 中国中西医结合肾病杂志, 2021, 22(8): 682-686,.
41.	胥雪玲, 刘军, 杨爱华, 等. 高糖诱导人肾小管上皮细胞高表达干细胞因子. 中国组织工程研究, 2022, 26(1): 96-100.
42.	丁天皓, 廖成成. IFN-γ在口腔鳞状细胞癌组织中的表达及功能研究进展. 重庆医学, 2023, 52(3): 446-450.
43.	Ivashkiv LB. IFNγ: signalling, epigenetics and roles in immunity, metabolism, disease and cancer immunotherapy. Nat Rev Immunol, 2018, 18(9): 545-558.
44.	徐伟俊, 刘琪, 孙潇君, 等. 补肾宣肺方对慢性支气管炎急性发作患者的IP-10、Rantes及炎症因子的影响. 中华中医药学刊, 2024.

1. 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.
2. Li HD, You YK, Shao BY, et al. Roles and crosstalks of macrophages in diabetic nephropathy. Front Immunol, 2022, 13: 1015142.
3. Liu Y, Lv Y, Zhang T, et al. T cells and their products in diabetic kidney disease. Front Immunol, 2023, 14: 1084448.
4. Moreno JA, Gomez-Guerrero C, Mas S, et al. Targeting inflammation in diabetic nephropathy: a tale of hope. Expert Opin Investig Drugs, 2018, 27(11): 917-930.
5. Barutta F, Bellini S, Corbetta B, et al. The future of diabetic kidney disease management: what to expect from the experimental studies. J Nephrol, 2020, 33(6): 1151-1161.
6. Aryan Z, Ghajar A, Faghihi-Kashani S, et al. Baseline high-sensitivity c-reactive protein predicts macrovascular and microvascular complications of type 2 diabetes: a population-based study. Ann Nutr Metab, 2018, 72(4): 287-295.
7. Chung CH, Fan J, Lee EY, et al. Effects of tumor necrosis factor-α on podocyte expression of monocyte chemoattractant protein-1 and in diabetic nephropathy. Nephron Extra, 2015, 5(1): 1-18.
8. 黄恺琪, 盛泓沁, 张燕媚, 等. 2型糖尿病肾病湿证与外周血炎症因子的相关性研究. 时珍国医国药, 2022, 33(12): 2965-2970.
9. Jin Q, Liu T, Qiao Y, et al. Oxidative stress and inflammation in diabetic nephropathy: role of polyphenols. Front Immunol, 2023, 14: 1185317.
10. Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008, 27(8): 1133-1163.
11. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA, 2017, 318(19): 1925-1926.
12. Ahola-Olli AV, Würtz P, Havulinna AS, et al. Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am J Hum Genet, 2017, 100(1): 40-50.
13. Zhang Z, Wang S, Ren F, et al. Inflammatory factors and risk of meningiomas: a bidirectional Mendelian-randomization study. Front Neurosci, 2023, 17: 1186312.
14. Yeung CHC, Au Yeung SL, Fong SSM, et al. Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study. Diabetologia, 2019, 62(5): 789-799.
15. Shen Z, Qiu B, Chen L, et al. Common gastrointestinal diseases and chronic obstructive pulmonary disease risk: a bidirectional Mendelian randomization analysis. Front Genet, 2023, 14: 1256833.
16. 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.
17. Asghar S, Asghar S, Shahid S, et al. Metabolic syndrome in type 2 diabetes mellitus patients: prevalence, risk factors, and associated microvascular complications. Cureus, 2023, 15(5): e39076.
18. Xiang M, Wang Y, Gao Z, et al. Exploring causal correlations between inflammatory cytokines and systemic lupus erythematosus: a Mendelian randomization. Front Immunol, 2023, 13: 985729.
19. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
20. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol, 2015, 44(2): 512-525.
21. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 2017, 32(5): 377-389.
22. Hemani G, Tilling K, Davey SG. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet, 2017, 13(11): e1007081.
23. Matsushita K, Coresh J, Sang Y, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol, 2015, 3(7): 514-525.
24. Fried LF, Emanuele N, Zhang JH, et al. Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med, 2013, 369(20): 1892-1903.
25. Perkovic V, de Zeeuw D, Mahaffey KW, et al. Canagliflozin and renal outcomes in type 2 diabetes: results from the CANVAS program randomised clinical trials. Lancet Diabetes Endocrinol, 2018, 6(9): 691-704.
26. Peng Y, Zhou M, Yang H, et al. Regulatory mechanism of M1/M2 macrophage polarization in the development of autoimmune diseases. Mediators Inflamm, 2023, 2023: 8821610.
27. Tang SCW, Yiu WH. Innate immunity in diabetic kidney disease. Nat Rev Nephrol, 2020, 16(4): 206-222.
28. Wada J, Makino H. Inflammation and the pathogenesis of diabetic nephropathy. Clin Sci (Lond), 2013, 124(3): 139-152.
29. Tang PM, Nikolic-Paterson DJ, Lan HY. Macrophages: versatile players in renal inflammation and fibrosis. Nat Rev Nephrol, 2019, 15(3): 144-158.
30. 安英, 李强, 隋春红, 等. 促炎细胞因子在糖尿病肾病患者中的作用. 中国民康医学, 2016, 28(14): 44-47.
31. Matsushima K, Yang D, Oppenheim JJ. Interleukin-8: an evolving chemokine. Cytokine, 2022, 153: 155828.
32. Liu SY, Chen J, Li YF. Clinical significance of serum interleukin-8 and soluble tumor necrosis factor-like weak inducer of apoptosis levels in patients with diabetic nephropathy. J Diabetes Investig, 2018, 9(5): 1182-1188.
33. Yaribeygi H, Atkin SL, Sahebkar A. Interleukin-18 and diabetic nephropathy: a review. J Cell Physiol, 2019, 234(5): 5674-5682.
34. Su H, Lei CT, Zhang C. Interleukin-6 signaling pathway and its role in kidney disease: an update. Front Immunol, 2017, 8: 405.
35. Lu TC, Wang ZH, Feng X, et al. Knockdown of Stat3 activity in vivo prevents diabetic glomerulopathy. Kidney Int, 2009, 76(1): 63-71.
36. Chen LH, Advani SL, Thai K, et al. SDF-1/CXCR4 signaling preserves microvascular integrity and renal function in chronic kidney disease. PLoS One, 2014, 9(3): e92227.
37. 周礼香, 张若鹏, 董兆梅. 颗粒细胞中干细胞因子对卵母细胞及胚胎质量的评估. 中国性科学, 2022, 31(10): 65-68.
38. Silva GE, Costa RS, Ravinal RC, et al. Mast cells, TGF-beta1 and alpha-SMA expression in IgA nephropathy. Dis Markers, 2008, 24(3): 181-190.
39. Ravinal RC, Costa RS, Coimbra TM, et al. Mast cells, TGF-beta1 and myofibroblasts expression in lupus nephritis outcome. Lupus, 2005, 14(10): 814-821.
40. 郭丹丹, 李琳, 吕莉娜, 等. 肥大细胞和干细胞因子在糖尿病肾病患者肾组织中的浸润表达及其意义. 中国中西医结合肾病杂志, 2021, 22(8): 682-686,.
41. 胥雪玲, 刘军, 杨爱华, 等. 高糖诱导人肾小管上皮细胞高表达干细胞因子. 中国组织工程研究, 2022, 26(1): 96-100.
42. 丁天皓, 廖成成. IFN-γ在口腔鳞状细胞癌组织中的表达及功能研究进展. 重庆医学, 2023, 52(3): 446-450.
43. Ivashkiv LB. IFNγ: signalling, epigenetics and roles in immunity, metabolism, disease and cancer immunotherapy. Nat Rev Immunol, 2018, 18(9): 545-558.
44. 徐伟俊, 刘琪, 孙潇君, 等. 补肾宣肺方对慢性支气管炎急性发作患者的IP-10、Rantes及炎症因子的影响. 中华中医药学刊, 2024.

Chinese Journal of Evidence-Based Medicine

Causal relationship between inflammatory factors and diabetic nephropathy: a bidirectional Mendelian randomization study

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