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Prognostic value of metabolic syndrome in renal structural changes in type 2 diabetes

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Abstract

Purpose

To investigate the prognostic value of metabolic syndrome (MetS) and its relationship with renal structure changes in patients with type 2 diabetes and associated diabetic nephropathy (DN).

Methods

411 Chinese patients with type 2 diabetes and biopsy-confirmed DN were enrolled in this retrospective study. MetS was defined according to the modified criteria of the 2005 International Diabetes Federation. Baseline demographics and clinical information at the time of renal biopsy were extracted from the hospital’s electronic medical records system. Renal pathological findings were assessed according to Renal Pathology Society system. Univariate and multivariate logistic regression analyses were performed to define the pathological covariates associated with MetS. A competing risk model, with death as the competing risk, was used to estimate the sub-distribution hazard ratio (SHR) of MetS for end-stage kidney disease (ESKD).

Results

224 (55%) patients had MetS. Patients with MetS had poor renal function and more severe interstitial fibrosis tubular atrophy scores (IFTA) than those without MetS. Multivariate logistic regression analysis revealed that IFTA was significantly associated with MetS (odds ratio per score increase 1.45, 95% confidence interval [CI] 1.02–2.05). Of the patients with DN at risk, 40% of patients progressed to ESKD. After adjusting for renal function and pathological parameters, the presence of MetS was an independent predictor for progression to ESKD (SHR 1.93, 95% CI 1.34–2.79). The SHRs for progression to ESKD also increased as the number of MetS components increased. Additionally, adding the IFTA scores improved the prognostic power of a model that only contained MetS and clinical covariates for predicting future ESKD.

Conclusion

MetS is an independent prognostic predictor of ESKD in patients with T2D and DN, while adding the IFTA scores increased the prognostic value of MetS for renal outcome.

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Data availability

Datasets are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K et al (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract 157:107843. https://doi.org/10.1016/j.diabres.2019.107843

    Article  PubMed  Google Scholar 

  2. Sinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R (2020) Diabetes and global ageing among 65–99-year-old adults: findings from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract 162:108078. https://doi.org/10.1016/j.diabres.2020.108078

    Article  PubMed  Google Scholar 

  3. Zhang L, Long J, Jiang W, Shi Y, He X, Zhou Z, Li Y, Yeung RO, Wang J, Matsushita K et al (2016) Trends in chronic kidney disease in China. N Engl J Med 375:905–906. https://doi.org/10.1056/NEJMc1602469

    Article  PubMed  Google Scholar 

  4. Marchesini G, Forlani G, Cerrelli F, Manini R, Natale S, Baraldi L, Ermini G, Savorani G, Zocchi D, Melchionda N (2004) WHO and ATPIII proposals for the definition of the metabolic syndrome in patients with type 2 diabetes. Diabet Med 21:383–387. https://doi.org/10.1111/j.1464-5491.2004.01115.x

    Article  CAS  PubMed  Google Scholar 

  5. Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD (2011) Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol 6:2364–2373. https://doi.org/10.2215/CJN.02180311

    Article  PubMed  PubMed Central  Google Scholar 

  6. Okpechi IG, Pascoe MD, Swanepoel CR, Rayner BL (2007) Microalbuminuria and the metabolic syndrome in non-diabetic black Africans. Diabetes Vasc Dis Res 4:365–367

    Article  Google Scholar 

  7. Kittiskulnam P, Thokanit NS, Katavetin P, Susanthitaphong P, Srisawat N, Praditpornsilpa K, Tungsanga K, Eiam-Ong S (2018) The magnitude of obesity and metabolic syndrome among diabetic chronic kidney disease population: a nationwide study. PLoS One 13:e0196332. https://doi.org/10.1371/journal.pone.0196332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Eirin A, Woollard JR, Ferguson CM, Jordan KL, Tang H, Textor SC, Lerman A, Lerman LO (2017) The metabolic syndrome induces early changes in the swine renal medullary mitochondria. Transl Res 184(45–56):e9. https://doi.org/10.1016/j.trsl.2017.03.002

    Article  CAS  Google Scholar 

  9. Alexander MP, Patel TV, Farag YMK, Florez A, Rennke HG, Singh AK (2009) Kidney pathological changes in metabolic syndrome: a cross-sectional study. Am J Kidney Dis 53:751–759. https://doi.org/10.1053/j.ajkd.2009.01.255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhang X, Li Z-L, Woollard JR, Eirin A, Ebrahimi B, Crane JA, Zhu X-Y, Pawar AS, Krier JD, Jordan KL et al (2013) Obesity-metabolic derangement preserves hemodynamics but promotes intrarenal adiposity and macrophage infiltration in swine renovascular disease. Am J Physiol Renal Physiol 305:F265–F276. https://doi.org/10.1152/ajprenal.00043.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhao L, Zou Y, Zhang J, Zhang R, Ren H, Li L, Guo R, Zhang J, Liu F (2020) Serum transferrin predicts end-stage renal disease in type 2 diabetes mellitus patients. Int J Med Sci 17:2113–2124. https://doi.org/10.7150/ijms.46259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhao L, Zhang J, Lei S, Ren H, Zou Y, Bai L, Zhang R, Xu H, Li L, Zhao Y et al (2020) Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end-stage renal disease. J Diabetes. https://doi.org/10.1111/1753-0407.13150

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhao L, Ren H, Zhang J, Cao Y, Wang Y, Meng D, Wu Y, Zhang R, Zou Y, Xu H et al (2020) Diabetic retinopathy, classified using the lesion-aware deep learning system, predicts diabetic end-stage renal disease in Chinese patients. Endocr Pract 26:429–443. https://doi.org/10.4158/EP-2019-0512

    Article  PubMed  Google Scholar 

  14. Zhao L, Liu F, Li L, Zhang J, Wang T, Zhang R, Zhang W, Yang X, Zeng X, Wang Y et al (2021) Solidified glomerulosclerosis, identified using single glomerular proteomics, predicts end-stage renal disease in Chinese patients with type 2 diabetes. Sci Rep 11:4658. https://doi.org/10.1038/s41598-021-83856-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart J-C, James WPT, Loria CM, Smith SC (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120:1640–1645. https://doi.org/10.1161/CIRCULATIONAHA.109.192644

    Article  CAS  PubMed  Google Scholar 

  16. Chen J, Kong X, Jia X, Li W, Wang Z, Cui M, Xu D (2017) Association between metabolic syndrome and chronic kidney disease in a Chinese urban population. Clin Chim Acta 470:103–108. https://doi.org/10.1016/j.cca.2017.05.012

    Article  CAS  PubMed  Google Scholar 

  17. Alberti KG, Zimmet P, Shaw J, Group IDFETFC (2005) The metabolic syndrome–a new worldwide definition. Lancet 366:1059–1062. https://doi.org/10.1016/S0140-6736(05)67402-8

    Article  PubMed  Google Scholar 

  18. Tervaert TW, Mooyaart AL, Amann K, Cohen AH, Cook HT, Drachenberg CB, Ferrario F, Fogo AB, Haas M, de Heer E et al (2010) Pathologic classification of diabetic nephropathy. J Am Soc Nephrol 21:556–563. https://doi.org/10.1681/ASN.2010010010

    Article  PubMed  Google Scholar 

  19. Zhao L, Wang X, Wang T, Fan W, Ren H, Zhang R, Zou Y, Xu H, Zhang J, Wu Y et al (2020) Associations between high-altitude residence and end-stage kidney disease in Chinese patients with type 2 diabetes. High Alt Med Biol 21:396–405. https://doi.org/10.1089/ham.2020.0076

    Article  CAS  PubMed  Google Scholar 

  20. Zhao L, Li L, Ren H, Zou Y, Zhang R, Wang S, Xu H, Zhang J, Liu F (2020) Association between serum alkaline phosphatase and renal outcome in patients with type 2 diabetes mellitus. Ren Fail 42:818–828. https://doi.org/10.1080/0886022x.2020.1804402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhang J, Zhang R, Wang Y, Li H, Han Q, Wu Y, Wang T, Liu F (2019) The level of serum albumin is associated with renal prognosis in patients with diabetic nephropathy. J Diabetes Res 2019:7825804. https://doi.org/10.1155/2019/7825804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kuo IC, Lin HY-H, Niu S-W, Hwang D-Y, Lee J-J, Tsai J-C, Hung C-C, Hwang S-J, Chen H-C (2016) Glycated hemoglobin and outcomes in patients with advanced diabetic chronic kidney disease. Sci Rep 6:20028. https://doi.org/10.1038/srep20028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhu X, Xiong X, Yuan S, Xiao L, Fu X, Yang Y, Tang C, He L, Liu F, Sun L (2016) Validation of the interstitial fibrosis and tubular atrophy on the new pathological classification in patients with diabetic nephropathy: a single-center study in China. J Diabetes Complicat 30:537–541. https://doi.org/10.1016/j.jdiacomp.2015.12.002

    Article  Google Scholar 

  24. Chuang SM, Shih HM, Chien MN, Liu SC, Wang CH, Lee CC (2019) Risk factors in metabolic syndrome predict the progression of diabetic nephropathy in patients with type 2 diabetes. Diabetes Res Clin Pract 153:6–13. https://doi.org/10.1016/j.diabres.2019.04.022

    Article  PubMed  Google Scholar 

  25. Shih H-M, Chuang S-M, Lee C-C, Liu S-C, Tsai M-C (2020) Addition of metabolic syndrome to albuminuria provides a new risk stratification model for diabetic kidney disease progression in elderly patients. Sci Rep 10:6788. https://doi.org/10.1038/s41598-020-63967-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang J, Han Q, Zhao L, Zhang J, Wang Y, Wu Y, Wang T, Zhang R, Grung P, Xu H et al (2019) Identification of clinical predictors of diabetic nephropathy and non-diabetic renal disease in Chinese patients with type 2 diabetes, with reference to disease course and outcome. Acta Diabetol 56:939–946. https://doi.org/10.1007/s00592-019-01324-7

    Article  PubMed  Google Scholar 

  27. Nargesi AA, Zhang L, Tang H, Jordan KL, Saadiq IM, Textor SC, Lerman LO, Eirin A (2019) Coexisting renal artery stenosis and metabolic syndrome magnifies mitochondrial damage, aggravating poststenotic kidney injury in pigs. J Hypertens 37:2061–2073. https://doi.org/10.1097/HJH.0000000000002129

    Article  CAS  PubMed  Google Scholar 

  28. Eddy AA (1998) Interstitial fibrosis in hypercholesterolemic rats: role of oxidation, matrix synthesis, and proteolytic cascades. Kidney Int 53:1182–1189. https://doi.org/10.1046/j.1523-1755.1998.00889.x

    Article  CAS  PubMed  Google Scholar 

  29. Ruan XZ, Varghese Z, Moorhead JF (2009) An update on the lipid nephrotoxicity hypothesis. Nat Rev Nephrol 5:713–721. https://doi.org/10.1038/nrneph.2009.184

    Article  CAS  PubMed  Google Scholar 

  30. Luk AO, So WY, Ma RC, Kong AP, Ozaki R, Ng VS, Yu LW, Lau WW, Yang X, Chow FC et al (2008) Metabolic syndrome predicts new onset of chronic kidney disease in 5,829 patients with type 2 diabetes: a 5-year prospective analysis of the Hong Kong Diabetes Registry. Diabetes Care 31:2357–2361. https://doi.org/10.2337/dc08-0971

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhu Q, Scherer PE (2018) Immunologic and endocrine functions of adipose tissue: implications for kidney disease. Nat Rev Nephrol 14:105–120. https://doi.org/10.1038/nrneph.2017.157

    Article  CAS  PubMed  Google Scholar 

  32. Perri A, Vizza D, Lupinacci S, Toteda G, De Amicis F, Leone F, Gigliotti P, Lofaro D, La Russa A, Bonofiglio R (2016) Adiponectin secreted by tubular renal cells during LPS exposure worsens the cellular inflammatory damage. J Nephrol 29:185–194. https://doi.org/10.1007/s40620-015-0220-2

    Article  CAS  PubMed  Google Scholar 

  33. Sanches FMR, Avesani CM, Kamimura MA, Lemos MM, Axelsson J, Vasselai P, Draibe SA, Cuppari L (2008) Waist circumference and visceral fat in CKD: a cross-sectional study. Am J Kidney Dis 52:66–73. https://doi.org/10.1053/j.ajkd.2008.02.004

    Article  PubMed  Google Scholar 

  34. Birjmohun RS, van Leuven SI, Levels JH, van’t Veer C, Kuivenhoven JA, Meijers JC, Levi M, Kastelein JJ, van der Poll T, Stroes ES (2007) High-density lipoprotein attenuates inflammation and coagulation response on endotoxin challenge in humans. Arterioscler Thromb Vasc Biol 27:1153–1158. https://doi.org/10.1161/ATVBAHA.106.136325

    Article  CAS  PubMed  Google Scholar 

  35. Lanktree MB, Theriault S, Walsh M, Pare G (2018) HDL cholesterol, LDL cholesterol, and triglycerides as risk factors for CKD: a mendelian randomization study. Am J Kidney Dis 71:166–172. https://doi.org/10.1053/j.ajkd.2017.06.011

    Article  CAS  PubMed  Google Scholar 

  36. Vaziri ND (2016) HDL abnormalities in nephrotic syndrome and chronic kidney disease. Nat Rev Nephrol 12:37–47. https://doi.org/10.1038/nrneph.2015.180

    Article  CAS  PubMed  Google Scholar 

  37. Yin QH, Zhang R, Li L, Wang YT, Liu JP, Zhang J, Bai L, Cheng JQ, Fu P, Liu F (2016) Exendin-4 ameliorates lipotoxicity-induced glomerular endothelial cell injury by improving ABC transporter A1-mediated cholesterol efflux in diabetic apoE knockout mice. J Biol Chem 291:26487–26501. https://doi.org/10.1074/jbc.M116.730564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Maxwell PH, Ferguson DJ, Nicholls LG, Johnson MH, Ratcliffe PJ (1997) The interstitial response to renal injury: fibroblast-like cells show phenotypic changes and have reduced potential for erythropoietin gene expression. Kidney Int 52:715–724. https://doi.org/10.1038/ki.1997.387

    Article  CAS  PubMed  Google Scholar 

  39. Hashimoto Y, Tanaka M, Kimura T, Kitagawa N, Hamaguchi M, Asano M, Yamazaki M, Oda Y, Toda H, Nakamura N et al (2015) Hemoglobin concentration and incident metabolic syndrome: a population-based large-scale cohort study. Endocrine 50:390–396. https://doi.org/10.1007/s12020-015-0587-9

    Article  CAS  PubMed  Google Scholar 

  40. Parving HH, Lehnert H, Bröchner-Mortensen J, Gomis R, Andersen S, Arner P (2001) The effect of irbesartan on the development of diabetic nephropathy in patients with type 2 diabetes. N Engl J Med 345:870–878

    Article  CAS  Google Scholar 

  41. Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HAW, Livingstone SJ, Charlton-Menys V, DeMicco DA, Fuller JH (2009) Effects of atorvastatin on kidney outcomes and cardiovascular disease in patients with diabetes: an analysis from the Collaborative Atorvastatin Diabetes Study (CARDS). Am J Kidney Dis 54:810–819. https://doi.org/10.1053/j.ajkd.2009.03.022

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by the National Natural Science Foundation of China [Grant numbers 81970626 and 81670662]; Key Research and Development Project of Sichuan Science and Technology Department [Grant number 19ZDYF1273], Postdoctoral Research Foundation of Sichuan University [Grant number 2021SCU12029]. The funding source played no role in study design, data analysis, and manuscript writing or submission.

Funding

This study was supported by the National Natural Science Foundation of China [Grant numbers 81970626 and 81670662]; Key Research and Development Project of Sichuan Science and Technology Department [Grant number 19ZDYF1273], Postdoctoral Research Foundation of Sichuan University [Grant number 2021SCU12029]. The funding source played no role in study design, data analysis, and manuscript writing or submission.

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All authors participated in the conceive, design of the manuscript. All authors have read and approved the final manuscript; LZ analyzed the data, interpreted the results and drafted the manuscript. FL analyzed and interpreted data, edited/revised the manuscript. FL and NT approved the final version of the manuscript. HX and LL performed pathological reviewing. HR, YZ, YW, YZ, YW carried out the data collecting and recording. ZC, MEC, NT, JZ, LB, LZ, LT, QS, and SL contributed to the discussion. FL is the guarantor of this work and had full access to all the data in this study, and takes responsibility for the integrity of the data.

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Correspondence to Nanwei Tong or Fang Liu.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the institutional review board at the West China Hospital of Sichuan University.

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Zhao, L., Zou, Y., Bai, L. et al. Prognostic value of metabolic syndrome in renal structural changes in type 2 diabetes. Int Urol Nephrol 54, 2005–2014 (2022). https://doi.org/10.1007/s11255-021-03051-x

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