期刊论文详细信息
BMC Nephrology
Chronic kidney diseases in mixed ancestry south African populations: prevalence, determinants and concordance between kidney function estimators
Rajiv T Erasmus2  Andre P Kengne1  Mogamat S Hassan4  Megan A Rensburg2  Yandiswa Y Yako3  Tandi E Matsha3 
[1] NCRP for Cardiovascular and Metabolic Diseases, South African Medical Research Council, Cape Town, South Africa;Division of Chemical Pathology, Faculty of Medicine and Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa;Department of Biomedical Sciences, Faculty of Health and Wellness Science, Cape Peninsula University of Technology, Cape Town, South Africa;Department of Nursing and Radiography, Faculty of Health and Wellness Science, Cape Peninsula University of Technology, Cape Town, South Africa
关键词: South Africa;    Prevalence;    MDRD eGFR;    Cockroft-Gault eGFR;    CKD-EPI eGFR;   
Others  :  1082963
DOI  :  10.1186/1471-2369-14-75
 received in 2012-11-28, accepted in 2013-03-18,  发布年份 2013
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【 摘 要 】

Background

Population-based data on the burden of chronic kidney disease (CKD) in sub-Saharan Africa is still very limited. We assessed the prevalence and determinants of CKD, and evaluated the concordance of commonly advocated estimators of glomerular filtration rate (eGFR) in a mixed ancestry population from South Africa.

Methods

Participants were a population-based sample of adults selected from the Bellville-South community in the metropolitan city of Cape Town. eGFR was based on the Cockroft-Gault (CG), Modification of Diet in Kidney Disease (MDRD) and CKD Epidemiology Collaboration (CKD-EPI) equations (with and without adjustment for ethnicity). Kidney function staging used the Kidney Disease Outcome Quality Initiative (KDOQI) classification. Logistic regressions and kappa statistic were used to investigate determinants of CKD and assess the agreement between different estimators.

Results

The crude prevalence of CKD stage 3–5 was 14.8% for Cockcroft-Gault, 7.6% and 23.9% respectively for the MDRD with and without ethnicity correction, and 7.4% and 17.3% for the CKD-EPI equations with and without ethnicity correction. The highest agreement between GFR estimators was between MDRD and CKD-EPI equations, both with ethnicity correction, Kappa 0.91 (95% CI: 0.86-0.95), correlation coefficient 0.95 (95% CI: 0.94-0.96). In multivariable logistic regression models, sex, age and known hypertension were consistently associated with CKD stage 3–5 across the 5 estimators.

Conclusions

The prevalence of CKD stages greater than 3 is the highest reported in Africa. This study provides evidence for support of the CKD-EPI equation for eGFR reporting and CKD classification.

【 授权许可】

   
2013 Matsha et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002, 39:S1-S266.
  • [2]Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J: CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009, 150:604-612.
  • [3]Wali RK: Aspirin and the prevention of cardiovascular disease in chronic kidney disease: time to move forward? J Am Coll Cardiol 2010, 56:966-968.
  • [4]Schieppati A, Remuzzi G: Chronic renal disease as a public health problem: epidemiology, social, and economic implications. Kidney Int Suppl 2005, 68:7-10.
  • [5]Eggers PW: Has the incidence of end-stage renal disease in the USA and other countries stabilized? Curr Opin Nephrol Hypertens 2011, 20:241-245.
  • [6]Bidani AK, Griffin KA: Chronic kidney disease: blood-pressure targets in chronic kidney disease. Nat Rev Nephrol 2011, 7:128-130.
  • [7]Naicker S: End-stage renal disease in sub-Saharan Africa. Ethn Dis 2009, 19:S1-S15.
  • [8]Arogundade FA, Barsoum RS: CKD prevention in Sub-Saharan Africa: a call for governmental, nongovernmental, and community support. Am J Kidney Dis 2008, 51:515-523.
  • [9]Sumaili EK, Cohen EP, Zinga CV, Krzesinski JM, Pakasa NM, Nseka NM: High prevalence of undiagnosed chronic kidney disease among at-risk population in Kinshasa, the Democratic Republic of Congo. BMC Nephrol 2009, 10:18. BioMed Central Full Text
  • [10]van Deventer HE, George JA, Paiker JE, Becker PJ, Katz IJ: Estimating glomerular filtration rate in black South Africans by use of the modification of diet in renal disease and Cockcroft-Gault equations. Clin Chem 2008, 54:1197-1202.
  • [11]Eastwood JB, Kerry SM, Plange-Rhule J, Micah FB, Antwi S, Boa FG, Banerjee D, Emmett L, Miller MA, Cappuccio FP: Assessment of GFR by four methods in adults in Ashanti, Ghana: the need for an eGFR equation for lean African populations. Nephrol Dial Transplant 2010, 25:2178-2187.
  • [12]Madala ND, Nkwanyana N, Dubula T, Naiker IP: Predictive performance of eGFR equations in South Africans of African and Indian ancestry compared with 99mTcDTPA imaging. Int Urol Nephrol 2012, 44:847-855.
  • [13]Halle MP, Kengne AP, Ashuntantang G: Referral of patients with kidney impairment for specialist care in a developing country of sub-Saharan Africa. Ren Fail 2009, 31:341-348.
  • [14]Stevens LA, Coresh J, Feldman HI, Greene T, Lash JP, Nelson RG, Rahman M, Deysher AE, Zhang YL, Schmid CH, Levey AS: Evaluation of the modification of diet in renal disease study equation in a large diverse population. J Am Soc Nephrol 2007, 18:2749-2757.
  • [15]Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, Nelson RG, Van Deventer M, Wang HY, Zuo L, Zhang YL, Levey AS: Evaluation of the chronic kidney disease epidemiology collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int 2011, 79:555-562.
  • [16]de Wit E, Delport W, Rugamika CE, Meintjes A, Möller M, van Helden PD, Seoighe C, Hoal EG: Genome-wide analysis of the structure of the South African Coloured Population in the Western Cape. Hum Genet 2010, 128:145-153.
  • [17]Zemlin AE, Matsha TE, Hassan MS, Erasmus RT: HbA1c of 6,5% to diagnose Diabetes Mellitus – Does it work for us? - The Bellville South Africa Study. PLoS One 2011, 6(8):e22558.
  • [18]Matsha T, Hassan MS, Kidd M, Erasmus RT: The 30-year cardiovascular risk profile of south Africans with diagnosed diabetes, undiagnosed diabetes, Pre-diabetes or normoglycaemia. The Bellville-south Africa study. Cardiovasc J Afr 2012, 23:5-11.
  • [19]Chalmers J, MacMahon S, Mancia G, Whitworth J, Beilin L, Hansson L, Neal B, Rodgers A, Ni Mhurchu C, Clark T: 1999 World health organization-international society of hypertension guidelines for the management of hypertension. Guidelines sub-committee of the world health organization. Clin Exp Hypertens 1999, 21:1009-1060.
  • [20]Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998, 15:539-553.
  • [21]Cockcroft DW, Gault MH: Prediction of creatinine clearance from serum creatinine. Nephron 1976, 16:31-41.
  • [22]Du Bois D, Du Bois EF: A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 1916, 17:863-871.
  • [23]Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999, 130:461-470.
  • [24]Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F, Chronic Kidney Disease Epidemiology Collaboration: Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006, 145:247-254.
  • [25]Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J: CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009, 150:604-612.
  • [26]Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, Hogg RJ, Perrone RD, Lau J, Eknoyan G: National Kidney Foundation.National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003, 139:137-147. Erratum in: Ann Intern Med 2003; 139: 605
  • [27]Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJL, Lozano R, Inoue M: Age standardization of rates: a new WHO standard. (GPE discussion paper series no. 31). Geneva: World Health Organization; 2001.
  • [28]White SL, Polkinghorne KR, Atkins RC, Chadban SJ: Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle) Study. Am J Kidney Dis 2010, 55:660-670.
  • [29]Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS: Prevalence of chronic kidney disease in the United States. JAMA 2007, 298:2038-2047.
  • [30]Neugarten J, Acharya A, Silbiger SR: Effect of gender on the progression of nondiabetic renal disease: a meta-analysis. J Am Soc Nephrol 2000, 11:319-329.
  • [31]Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis 2003, 41:1-12.
  • [32]Coresh J, Byrd-Holt D, Astor BC, Briggs JP, Eggers PW, Lacher DA, Hostetter TH: Chronic kidney disease awareness, prevalence, and trends among U.S. adults, 1999 to 2000. J Am Soc Nephrol 2005, 16:180-188.
  • [33]Chadban SJ, Briganti EM, Kerr PG, Dunstan DW, Welborn TA, Zimmet PZ, Atkins RC: Prevalence of kidney damage in Australian adults: the AusDiab kidney study. J Am Soc Nephrol 2003, 14(7 Suppl 2):S131-S138.
  • [34]Silbiger SR, Neugarten J: The role of gender in the progression of renal disease. Adv Ren Replace Ther 2003, 10:3-14.
  • [35]Veriava Y, DuToit E, Lawley CG: Hypertension as a cause of end stage renal failure in South Africa. J Hum Hypertens 1990, 4:379-383.
  • [36]Crews DC, Plantinga LC, Miller ER 3rd, Saran R, Hedgeman E, Saydah SH, Williams DE, Powe NR: Centers for disease control and prevention chronic kidney disease surveillance team. Prevalence of chronic kidney disease in persons with undiagnosed or prehypertension in the united states. Hypertension 2010, 55:1102-1109.
  • [37]Davids MR: Chronic kidney disease – the silent epidemic. CME 2007, 25:378-382.
  • [38]Tsimihodimos V, Mitrogianni Z, Elisaf M: Dyslipidemia associated with Chronic Kidney Disease. The Open Cardiovascular Medicine Journal 2011, 5:41-48.
  • [39]Hunley TE, Ma L, Kon V: Scope and mechanisms of obesity related renal disease. Curr Opin Nephrol Hypertens 2010, 19:227-234.
  • [40]Noble E, Johnson DW, Gray N, Hollett P, Hawley CM, Campbell SB, Mudge DW, Isbel NM: The impact of automated eGFR reporting and education on nephrology service referrals. Nephrol Dial Transplant 2008, 23:3845-3850.
  • [41]Hemmelgarn BR, Zhang J, Manns BJ: Nephrology visits and health care resource use before and after reporting estimated glomerular filtration rate. JAMA 2010, 303:1151-1158.
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