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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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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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DOI: https://doi.org/10.1007/s11255-021-03051-x