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Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus

  • Nephrology - Original Paper
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Abstract

Purpose

There are few studies on the establishment of diagnostic models for diabetic nephropathy (DN) in in type 2 diabetes mellitus (T2DM) patients based on biomarkers. This study was to establish a model for diagnosing DN in T2DM.

Methods

In this cross-sectional study, data were collected from the Second Hospital of Shijiazhuang between August 2018 to March 2021. Totally, 359 eligible participants were included. Clinical characteristics and laboratory data were collected. LASSO regression analysis was used to screen out diagnostic factors, and the selected factors were input into the decision tree for fivefold cross validation; then a diagnostic model was established. The performances of the diagnosis model were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The diagnostic performance of the model was also validated through risk stratifications.

Results

Totally, 199 patients (55.43%) were diagnosed with DN. Age, diastolic blood pressure (DBP), fasting blood glucose, insulin treatment, mean corpuscular hemoglobin concentration (MCHC), platelet distribution width (PDW), uric acid (UA), serum creatinine (SCR), fibrinogen (FIB), international normalized ratio (INR), and low-density lipoprotein cholesterol (LDL-C) were the diagnostic factors for DN in T2DM. The diagnostic model presented good performances, with the sensitivity, specificity, PPV, NPV, AUC, and accuracy being 0.849, 0.969, 0.971, 0.838, 0.965, and 0.903, respectively. The diagnostic model based on the stratifications also showed excellent diagnostic performance for diagnosing DN in T2DM patients.

Conclusion

Our diagnostic model with simple and accessible factors provides a noninvasive method for the diagnosis of DN.

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Data availability

All data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Magliano DJ, Chen L, Islam RM, Carstensen B, Gregg EW, Pavkov ME, Andes LJ, Balicer R, Baviera M, Boersma-van Dam E, Booth GL, Chan JCN, Chua YX, Fosse-Edorh S, Fuentes S, Gulseth HL, Gurevicius R, Ha KH, Hird TR, Jermendy G, Khalangot MD, Kim DJ, Kiss Z, Kravchenko VI, Leventer-Roberts M, Lin CY, Luk AOY, Mata-Cases M, Mauricio D, Nichols GA, Nielen MM, Pang D, Paul SK, Pelletier C, Pildava S, Porath A, Read SH, Roncaglioni MC, Lopez-Doriga Ruiz P, Shestakova M, Vikulova O, Wang KL, Wild SH, Yekutiel N, Shaw JE (2021) Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings. Lancet Diabetes Endocrinol 9(4):203–211. https://doi.org/10.1016/s2213-8587(20)30402-2

    Article  PubMed  Google Scholar 

  2. Magliano DJ, Islam RM, Barr ELM, Gregg EW, Pavkov ME, Harding JL, Tabesh M, Koye DN, Shaw JE (2019) Trends in incidence of total or type 2 diabetes: systematic review. BMJ (Clinical research ed) 366:l5003. https://doi.org/10.1136/bmj.l5003

    Article  PubMed  Google Scholar 

  3. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB, Huikuri HV, Johansson I, Jüni P, Lettino M, Marx N, Mellbin LG, Östgren CJ, Rocca B, Roffi M, Sattar N, Seferović PM, Sousa-Uva M, Valensi P, Wheeler DC (2020) 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J 41(2):255–323. https://doi.org/10.1093/eurheartj/ehz486

    Article  PubMed  Google Scholar 

  4. Quan KY, Yap CG, Jahan NK, Pillai N (2021) Review of early circulating biomolecules associated with diabetes nephropathy—ideal candidates for early biomarker array test for DN. Diabetes Res Clin Pract 182:109122. https://doi.org/10.1016/j.diabres.2021.109122

    Article  CAS  PubMed  Google Scholar 

  5. Gong P, Wang P, Pi S, Guo Y, Pei S, Yang W, Chang X, Wang L, Chen F (2021) Proanthocyanidins protect against cadmium-induced diabetic nephropathy through p38 MAPK and KEAP1/Nrf2 signaling pathways. Front Pharmacol 12:801048. https://doi.org/10.3389/fphar.2021.801048

    Article  CAS  PubMed  Google Scholar 

  6. Bülow RD, Boor P (2019) Extracellular matrix in kidney fibrosis: more than just a Scaffold. J Histochem Cytochem 67(9):643–661. https://doi.org/10.1369/0022155419849388

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Stehouwer CDA (2018) Microvascular dysfunction and hyperglycemia: a vicious cycle with widespread consequences. Diabetes 67(9):1729–1741. https://doi.org/10.2337/dbi17-0044

    Article  CAS  PubMed  Google Scholar 

  8. Wang G, Ouyang J, Li S, Wang H, Lian B, Liu Z, Xie L (2019) The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases. J Transl Med 17(1):264. https://doi.org/10.1186/s12967-019-2016-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ruiz-Ortega M, Rodrigues-Diez RR, Lavoz C, Rayego-Mateos S (2020) Special issue “Diabetic nephropathy: diagnosis, prevention and treatment.” J Clin Med 9(3):813. https://doi.org/10.3390/jcm9030813

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bermejo S, Pascual J, Soler MJ (2018) The current role of renal biopsy in diabetic patients. Minerva Med 109(2):116–125. https://doi.org/10.23736/s0026-4806.17.05446-5

    Article  PubMed  Google Scholar 

  11. Hogan JJ, Mocanu M, Berns JS (2016) The native kidney biopsy: update and evidence for best practice. Clin J Am Soc Nephrol CJASN 11(2):354–362. https://doi.org/10.2215/cjn.05750515

    Article  CAS  PubMed  Google Scholar 

  12. Zhang D, Ye S, Pan T (2019) The role of serum and urinary biomarkers in the diagnosis of early diabetic nephropathy in patients with type 2 diabetes. PeerJ 7:e7079. https://doi.org/10.7717/peerj.7079

    Article  PubMed  PubMed Central  Google Scholar 

  13. Thipsawat S (2021) Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: a review of the literature. Diab Vasc Dis Res 18(6):14791641211058856. https://doi.org/10.1177/14791641211058856

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yu D, Shang J, Cai Y, Wang Z, Zhao B, Zhao Z, Simmons D (2020) A low-cost laboratory-based method for predicting newly diagnosed biopsy-proven diabetic nephropathy in people with type 2 diabetes. Diabet Med 37(10):1728–1736. https://doi.org/10.1111/dme.14195

    Article  CAS  PubMed  Google Scholar 

  15. Chung JO, Cho DH, Chung DJ, Chung MY (2012) Associations between hemoglobin concentrations and the clinical characteristics of patients with type 2 diabetes. Korean J Intern Med 27(3):285–292. https://doi.org/10.3904/kjim.2012.27.3.285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Al-Rubeaan K, Siddiqui K, Alghonaim M, Youssef AM, AlNaqeb D (2018) The Saudi diabetic kidney disease study (Saudi-DKD): clinical characteristics and biochemical parameters. Ann Saudi Med 38(1):46–56. https://doi.org/10.5144/0256-4947.2018.03.01.1010

    Article  PubMed  PubMed Central  Google Scholar 

  17. Liu J, Liu X, Li Y, Quan J, Wei S, An S, Yang R, Liu J (2018) The association of neutrophil to lymphocyte ratio, mean platelet volume, and platelet distribution width with diabetic retinopathy and nephropathy: a meta-analysis. Biosci Rep. https://doi.org/10.1042/bsr20180172

  18. American Diabetes Association (2010) Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1):S62-69. https://doi.org/10.2337/dc10-S062

    Article  PubMed Central  Google Scholar 

  19. AlSahow A, AlHelal B, Alyousef A, AlQallaf A, Marzouq A, Nawar H, Fanous G, Abdelaty M, Bahbahani Y, AlRajab H, AlTerkait A, Ali H (2020) Renal data from the Arab world dialysis in Kuwait: 2013–2019. Saudi J Kidney Dis Transpl 31(4):826–830. https://doi.org/10.4103/1319-2442.292317

    Article  PubMed  Google Scholar 

  20. Zhang W, Liu X, Dong Z, Wang Q, Pei Z, Chen Y, Zheng Y, Wang Y, Chen P, Feng Z, Sun X, Cai G, Chen X (2022) New diagnostic model for the differentiation of diabetic nephropathy from non-diabetic nephropathy in Chinese patients. Front Endocrinol 13:913021. https://doi.org/10.3389/fendo.2022.913021

    Article  Google Scholar 

  21. Li H, Shen Y, Yu Z, Huang Y, He T, Xiao T, Li Y, Xiong J, Zhao J (2021) Potential role of the renal arterial resistance index in the differential diagnosis of diabetic kidney disease. Front Endocrinol 12:731187. https://doi.org/10.3389/fendo.2021.731187

    Article  Google Scholar 

  22. Liu MY, Chen XM, Sun XF, Zhou JH, Zhang XG, Zhu HY, Chen YZ, Liu SW, Wei RB, Tang L, Cai GY, Zhang L, Bai XY (2014) Validation of a differential diagnostic model of diabetic nephropathy and non-diabetic renal diseases and the establishment of a new diagnostic model. J Diabetes 6(6):519–526. https://doi.org/10.1111/1753-0407.12150

    Article  CAS  PubMed  Google Scholar 

  23. Miao DD, Pan EC, Zhang Q, Sun ZM, Qin Y, Wu M (2017) Development and validation of a model for predicting diabetic nephropathy in Chinese people. Biomed Environ Sci BES 30(2):106–112. https://doi.org/10.3967/bes2017.014

    Article  CAS  PubMed  Google Scholar 

  24. Li L, Yang Y, Zhu X, Xiong X, Zeng L, Xiong S, Jiang N, Li C, Yuan S, Xu H, Liu F, Sun L (2020) Design and validation of a scoring model for differential diagnosis of diabetic nephropathy and nondiabetic renal diseases in type 2 diabetic patients. J Diabetes 12(3):237–246. https://doi.org/10.1111/1753-0407.12994

    Article  PubMed  Google Scholar 

  25. Kamijo-Ikemori A, Sugaya T, Yasuda T, Kawata T, Ota A, Tatsunami S, Kaise R, Ishimitsu T, Tanaka Y, Kimura K (2011) Clinical significance of urinary liver-type fatty acid-binding protein in diabetic nephropathy of type 2 diabetic patients. Diabetes Care 34(3):691–696. https://doi.org/10.2337/dc10-1392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhou XW, Jiang J, Ren W, Fei YY, Peng L, Jiang JL, Lan L, Ye SD (2018) Related factors of diabetic nephropathy in patients with type 1 diabetes mellitus. Zhonghua Yi Xue Za Zhi 98(30):2403–2406. https://doi.org/10.3760/cma.j.issn.0376-2491.2018.30.007

    Article  CAS  PubMed  Google Scholar 

  27. Cai W, Li J, Xu JX, Liu Y, Zhang W, Xiao JR, Zhu LY, Liu JY (2015) Association of 2184AG polymorphism in the RAGE gene with diabetic nephropathy in Chinese patients with type 2 diabetes. J Diabetes Res 2015:310237. https://doi.org/10.1155/2015/310237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Martin CL, Albers JW, Pop-Busui R (2014) Neuropathy and related findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care 37(1):31–38. https://doi.org/10.2337/dc13-2114

    Article  CAS  PubMed  Google Scholar 

  29. Lachin JM, White NH, Hainsworth DP, Sun W, Cleary PA, Nathan DM (2015) Effect of intensive diabetes therapy on the progression of diabetic retinopathy in patients with type 1 diabetes: 18 years of follow-up in the DCCT/EDIC. Diabetes 64(2):631–642. https://doi.org/10.2337/db14-0930

    Article  CAS  PubMed  Google Scholar 

  30. de Kort H, de Koning EJ, Rabelink TJ, Bruijn JA, Bajema IM (2011) Islet transplantation in type 1 diabetes. BMJ (Clin Res Ed) 342:d217. https://doi.org/10.1136/bmj.d217

    Article  Google Scholar 

  31. Mi AE, Abdallah N, Eldars W (2021) Mean platelet volume and platelet distribution width correlate with microvascular complications in Egyptian People with type 2 diabetes mellitus. Curr Diabetes Rev 17(8):e080621193947. https://doi.org/10.2174/1573399817666210608121024

    Article  CAS  PubMed  Google Scholar 

  32. Tessari P, Kiwanuka E, Barazzoni R, Vettore M, Zanetti M (2006) Diabetic nephropathy is associated with increased albumin and fibrinogen production in patients with type 2 diabetes. Diabetologia 49(8):1955–1961. https://doi.org/10.1007/s00125-006-0288-2

    Article  CAS  PubMed  Google Scholar 

  33. Matuszewski W, Stefanowicz-Rutkowska MM, Szychlińska M, Bandurska-Stankiewicz E (2020) Differences in risk factors for diabetic retinopathy in type 1 and type 2 diabetes mellitus patients in North-East Poland. Medicina (Kaunas) 56(4):177. https://doi.org/10.3390/medicina56040177

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Endocrinology, The Second Hospital of Shijiazhuang, No. 53, Huaxi Road, Shijiazhuang, 050000, People’s Republic of China

    Yuwei Xing, Xuejiao Chai, Guang Cao & Geng Wei

  2. Department of Endocrinology, The Third Hospital of Hebei Medical University, Shijiazhuang, 050000, People’s Republic of China

    Kuanzhi Liu

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Contributions

YX designed the study and wrote the manuscript. XC, KL and GC collected, analyzed, and interpreted the data. YX critically reviewed, edited, and approved the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yuwei Xing.

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Conflict of interest

The authors have declared that no conflict of interest exists.

Ethical approval

The study was approved by the ethics committee of the Second Hospital of Shijiazhuang (No. Sey2021005).

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Xing, Y., Chai, X., Liu, K. et al. Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus. Int Urol Nephrol 56, 1439–1448 (2024). https://doi.org/10.1007/s11255-023-03815-7

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