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Nephrology

A Nomogram for Predicting the Risk of CKD Based on Cardiometabolic Risk Factors

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Pages 4143-4154 | Received 27 Jun 2023, Accepted 15 Aug 2023, Published online: 11 Sep 2023

References

  • Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–830. doi:10.7326/0003-4819-158-11-201306040-00007
  • Bikbov B, Purcell CA, Levey AS. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–733. doi:10.1016/s0140-6736(20)30045-3
  • 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. doi:10.1016/s2213-8587(15)00040-6
  • Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073–2081. doi:10.1016/s0140-6736(10)60674-5
  • Tuot DS, Wong KK, Velasquez A, et al. CKD Awareness in the General Population: performance of CKD-Specific Questions. Kidney Medicine. 2019;1(2):43–50. doi:10.1016/j.xkme.2019.01.005
  • Webster AC, Nagler EV, Morton RL, Masson P. Chronic Kidney Disease. Lancet. 2017;389(10075):1238–1252. doi:10.1016/s0140-6736(16)32064-5
  • Yang C, Wang H, Zhao X, et al. CKD in China: evolving Spectrum and Public Health Implications. Am J Kidney Dis. 2020;76(2):258–264. doi:10.1053/j.ajkd.2019.05.032
  • D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi:10.1161/circulationaha.107.699579
  • Wang T, Lu J, Su Q, et al. Ideal Cardiovascular Health Metrics and Major Cardiovascular Events in Patients With Prediabetes and Diabetes. JAMA Cardiology. 2019;4(9):874–883. doi:10.1001/jamacardio.2019.2499
  • Lu J, He J, Li M, et al. Predictive Value of Fasting Glucose, Postload Glucose, and Hemoglobin A1c on Risk of Diabetes and Complications in Chinese Adults. Diabetes Care. 2019;42(8):1539–1548. doi:10.2337/dc18-1390
  • Joint Committee for Developing Chinese guidelines on Prevention and Treatment of Dyslipidemia in Adults. Chinese guidelines on prevention and treatment of dyslipidemia in adults. Zhonghua Xin Xue Guan Bing Za Zhi. 2007;35(5):390–419.
  • Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi:10.7326/0003-4819-150-9-200905050-00006
  • Hosmer DW. Assessing the Fit of the Model. Applied Logistic Regression. 2000;143–202.
  • Kshirsagar AV, Bang H, Bomback AS, et al. A simple algorithm to predict incident kidney disease. Arch Intern Med. 2008;168(22):2466–2473. doi:10.1001/archinte.168.22.2466
  • O’Seaghdha CM, Lyass A, Massaro JM, et al. A risk score for chronic kidney disease in the general population. Am J Med. 2012;125(3):270–277. doi:10.1016/j.amjmed.2011.09.009
  • Hippisley-Cox J, Coupland C. Predicting the risk of chronic Kidney Disease in men and women in England and Wales: prospective derivation and external validation of the QKidney Scores. BMC Fam Pract. 2010;11:49. doi:10.1186/1471-2296-11-49
  • Fraccaro P, van der Veer S, Brown B, et al. An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK. BMC Med. 2016;14:104. doi:10.1186/s12916-016-0650-2
  • Chien KL, Lin HJ, Lee BC, Hsu HC, Lee YT, Chen MF. A prediction model for the risk of incident chronic kidney disease. Am J Med. 2010;123(9):836–846.e2. doi:10.1016/j.amjmed.2010.05.010
  • Wen J, Hao J, Zhang Y, et al. Risk scores for predicting incident chronic kidney disease among rural Chinese people: a village-based cohort study. BMC Nephrol. 2020;21(1):120. doi:10.1186/s12882-020-01787-9
  • Bowe B, Xie Y, Xian H, Balasubramanian S, Al-Aly Z. Low levels of high-density lipoprotein cholesterol increase the risk of incident kidney disease and its progression. Kidney Int. 2016;89(4):886–896. doi:10.1016/j.kint.2015.12.034
  • Morton J, Zoungas S, Li Q, et al. Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study. Diabetes Care. 2012;35(11):2201–2206. doi:10.2337/dc12-0306
  • Lanktree MB, Thériault S, Walsh M, Paré G. HDL Cholesterol, LDL Cholesterol, and Triglycerides as Risk Factors for CKD: a Mendelian Randomization Study. Am J Kidney Dis. 2018;71(2):166–172. doi:10.1053/j.ajkd.2017.06.011
  • Miao L, Min Y, Qi B, et al. Causal effect between total cholesterol and HDL cholesterol as risk factors for chronic kidney disease: a Mendelian randomization study. BMC Nephrol. 2021;22(1):35. doi:10.1186/s12882-020-02228-3
  • Schaeffner ES, Kurth T, Curhan GC, et al. Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol. 2003;14(8):2084–2091. doi:10.1681/asn.V1482084
  • Liang X, Ye M, Tao M, et al. The association between dyslipidemia and the incidence of chronic kidney disease in the general Zhejiang population: a retrospective study. BMC Nephrol. 2020;21(1):252. doi:10.1186/s12882-020-01907-5
  • Zuo PY, Chen XL, Liu YW, Zhang R, He XX, Liu CY. Non-HDL-cholesterol to HDL-cholesterol ratio as an independent risk factor for the development of chronic kidney disease. Nutr Metab Cardiovasc Dis. 2015;25(6):582–587. doi:10.1016/j.numecd.2015.03.003
  • Wen J, Chen Y, Huang Y, et al. Association of the TG/HDL-C and Non-HDL-C/HDL-C Ratios with Chronic Kidney Disease in an Adult Chinese Population. Kidney Blood Press Res. 2017;42(6):1141–1154. doi:10.1159/000485861
  • Soto González A, Bellido D, Buño MM, et al. Predictors of the metabolic syndrome and correlation with computed axial tomography. Nutrition. 2007;23(1):36–45. doi:10.1016/j.nut.2006.08.019
  • Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev. 2010;23(2):247–269. doi:10.1017/s0954422410000144
  • Ramspek CL, Evans M, Wanner C, et al. Kidney Failure Prediction Models: a Comprehensive External Validation Study in Patients with Advanced CKD. J Am Soc Nephrol. 2021;32(5):1174–1186. doi:10.1681/asn.2020071077