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The global burden of chronic kidney disease: estimates, variability and pitfalls

Key Points

  • Chronic kidney disease (CKD) is presently defined by a matrix of biomarkers, specifically, glomerular filtration rate (GFR) <60 ml/min/1.73 m2 and albuminuria in the context of manifestations of kidney damage and duration >3 months

  • GFR can be measured or estimated but both of these approaches to assess kidney function have drawbacks when applied to epidemiologic studies of the population prevalence of CKD

  • Failure to apply the >3 month duration requirement for CKD diagnosis can lead to an overestimation of CKD prevalence, especially when age, sex, ethnicity and diet are not taken into account

  • Data from multiple sources indicate that CKD is a common disorder that contributes markedly to morbidity and mortality; however, the prevalence of CKD shows wide variations between and within specific geographic locations

  • In many nations, including the USA, the prevalence of CKD and newly treated end-stage renal disease (ESRD) is stable; future trends in ESRD prevalence will likely depend on changes in access to treatment, population demographics and mortality

Abstract

Chronic kidney disease (CKD) is currently defined by abnormalities of kidney structure or function assessed using a matrix of variables — including glomerular filtration rate (GFR), thresholds of albuminuria and duration of injury — and is considered by many to be a common disorder globally. However, estimates of CKD prevalence vary widely, both within and between countries. The reasons for these variations are manifold, and include true regional differences in CKD prevalence, vagaries of using estimated GFR (eGFR) for identifying CKD, issues relating to the use of set GFR thresholds to define CKD in elderly populations, and concerns regarding the use of one-off testing for assessment of eGFR or albuminuria to define the prevalence of CKD in large-scale epidemiological studies. Although CKD is common, the suggestion that its prevalence is increasing in many countries might not be correct. Here, we discuss the possible origins of differences in estimates of CKD prevalence, and present possible solutions for tackling the factors responsible for the reported variations in GFR measurements. The strategies we discuss include approaches to improve testing methodologies for more accurate assessment of GFR, to improve awareness of factors that can alter GFR readouts, and to more accurately stage CKD in certain populations, including the elderly.

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Figure 1: Non-GFR determinants that affect estimated GFR.

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References

  1. Brück, K. et al. CKD prevalence varies across the European general population. J. Am. Soc. Nephrol. 27, 2135–2147 (2016).

    Article  PubMed  Google Scholar 

  2. Okparavero, A. et al. Prevalence and complications of chronic kidney disease in a representative elderly population in Iceland. Nephrol. Dial. Transplant. 31, 439–447 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. De Nicola, L. et al. Prevalence and cardiovascular risk profile of chronic kidney disease in Italy: results of the 2008–2012 National Health Examination Survey. Nephrol. Dial. Transplant. 30, 806–814 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Stanifer, J. W. et al. The epidemiology of chronic kidney disease in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob. Health 2, e174–e181 (2014).

    Article  PubMed  Google Scholar 

  5. Mills, K. T. et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 88, 950–957 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brück, K. et al. Methodology used in studies reporting chronic kidney disease prevalence: a systematic literature review. Nephrol. Dial. Transplant. 30, iv6–iv16 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ebert, N. et al. Prevalence of reduced kidney function and albuminuria in older adults: the Berlin Initiative Study. Nephrol. Dial. Transplant. http://dx.doi.org/10.1093/ndt/gfw079 (2016).

  8. Coresh, J. et al. Prevalence of chronic kidney disease in the United States. JAMA 298, 2038–2047 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am. J. Kidney Dis. 39 (2 Suppl. 1), S1–S266 (2002).

  10. Anders, H.-J., Jayne, D. R. W. & Rovin, B. H. Hurdles to the introduction of new therapies for immune-mediated kidney diseases. Nat. Rev. Nephrol. 12, 205–216 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. Suppl. 3, 1–150 (2013).

  12. Zdrojewski, Ł. et al. Prevalence of chronic kidney disease in a representative sample of the Polish population: results of the NATPOL 2011 survey. Nephrol. Dial. Transplant. 31, 433–439 (2016).

    Article  PubMed  Google Scholar 

  13. De Nicola, L. & Zoccali, C. Chronic kidney disease prevalence in the general population: heterogeneity and concerns. Nephrol. Dial. Transplant. 31, 331–335 (2016).

    Article  PubMed  Google Scholar 

  14. Knight, E. L. et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int. 65, 1416–1421 (2004).

    Article  CAS  PubMed  Google Scholar 

  15. Melsom, T. et al. Estimated GFR is biased by non-traditional cardiovascular risk factors. Am. J. Nephrol. 41, 7–15 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Schei, J. et al. Residual associations of inflammatory markers with eGFR after accounting for measured GFR in a community-based cohort without CKD. Clin. J. Am. Soc. Nephrol. 11, 280–286 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Smith, H. W. in The Kidney: Structure and Function in Health and Disease 231–238 (Oxford Univ. Press, 1951).

    Google Scholar 

  18. Denker, M. et al. Chronic Renal Insufficiency Cohort Study (CRIC): overview and summary of selected findings. Clin. J. Am. Soc. Nephrol. 10, 2073–2083 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Eriksen, B. O. et al. GFR normalized to total body water allows comparisons across genders and body sizes. J. Am. Soc. Nephrol. 22, 1517–1525 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Eriksen, B. O. et al. Cystatin C is not a better estimator of GFR than plasma creatinine in the general population. Kidney Int. 78, 1305–1311 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Schaeffner, E. S. et al. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann. Intern. Med. 157, 471–481 (2012).

    Article  PubMed  Google Scholar 

  22. Inker, L. A. et al. Midlife blood pressure and late-life GFR and albuminuria: an elderly general population cohort. Am. J. Kidney Dis. 66, 240–248 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Melsom, T. et al. Prediabetes and risk of glomerular hyperfiltration and albuminuria in the general nondiabetic population: a prospective cohort study. Am. J. Kidney Dis. 67, 841–850 (2016).

    Article  PubMed  Google Scholar 

  24. Delanaye, P. & Cohen, E. P. Formula-based estimates of the GFR: equations variable and uncertain. Nephron Clin. Pr. 110, c48–c53 (2008).

    Article  CAS  Google Scholar 

  25. Coresh, J., Eknoyan, G. & Levey, A. S. Estimating the prevalence of low glomerular filtration rate requires attention to the creatinine assay calibration. J. Am. Soc. Nephrol. 13, 2811–2812 (2002).

    Article  PubMed  Google Scholar 

  26. Cockcroft, D. W. & Gault, M. H. Prediction of creatinine clearance from serum creatinine. Nephron 16, 31–41 (1976).

    Article  CAS  PubMed  Google Scholar 

  27. Stevens, L. A. et al. Evaluation of the modification of diet in renal disease study equation in a large diverse population. J. Am. Soc. Nephrol. 18, 2749–2757 (2007).

    Article  PubMed  Google Scholar 

  28. Piéroni, L. et al. A multicentric evaluation of IDMS-traceable creatinine enzymatic assays. Clin. Chim. Acta 412, 2070–2075 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pottel, H. et al. A new estimating glomerular filtration rate equation for the full age spectrum. Nephrol. Dial. Transplant. 31, 798–806 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bjork, J. et al. Accuracy of GFR estimating equations combining standardized cystatin C and creatinine assays: a cross-sectional study in Sweden. Clin. Chem. Lab. Med. 53, 403–414 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Delanaye, P., Cavalier, E., Cristol, J.-P. & Delanghe, J. R. Calibration and precision of serum creatinine and plasma cystatin C measurement: impact on the estimation of glomerular filtration rate. J. Nephrol. 27, 467–475 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Kuster, N. et al. Enzymatic creatinine assays allow estimation of glomerular filtration rate in stages 1 and 2 chronic kidney disease using CKD-EPI equation. Clin. Chim. Acta 428, 89–95 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Delanaye, P., Cavalier, E., Maillard, N., Krzesinski, J.-M. & Mariat, C. Creatinine calibration in NHANES: is a revised MDRD study formula needed? Am. J. Kidney Dis. 51, 709 (2008).

    Article  PubMed  Google Scholar 

  35. Selvin, E. et al. Calibration of serum creatinine in the National Health and Nutrition Examination Surveys (NHANES) 1988–1994, 1999–2004. Am. J. Kidney Dis. 50, 918–926 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Boutten, A. et al. Enzymatic but not compensated Jaffe methods reach the desirable specifications of NKDEP at normal levels of creatinine. Results of the French multicentric evaluation. Clin. Chim. Acta 419, 132–135 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. White, C. A. et al. The impact of interlaboratory differences in cystatin C assay measurement on glomerular filtration rate estimation. Clin. J. Am. Soc. Nephrol. 6, 2150–2156 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Delanaye, P. et al. Analytical study of three cystatin C assays and their impact on cystatin C-based GFR-prediction equations. Clin. Chim. Acta 398, 118–124 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. González-Antuña, A. et al. Determination of Cystatin C in human serum by isotope dilution mass spectrometry using mass overlapping peptides. J. Proteomics 112, 141–155 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Blirup-Jensen, S., Grubb, A., Lindstrom, V., Schmidt, C. & Althaus, H. Standardization of Cystatin C: development of primary and secondary reference preparations. Scand. J. Clin. Lab. Invest. Suppl. 241, 67–70 (2008).

    Article  CAS  PubMed  Google Scholar 

  41. Ebert, N. et al. Cystatin C standardization decreases assay variation and improves assessment of glomerular filtration rate. Clin. Chim. Acta 456, 115–121 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Eckfeldt, J. H., Karger, A. B., Miller, W. G., Rynders, G. P. & Inker, L. A. Performance in measurement of serum Cystatin C by laboratories participating in the College of American Pathologists 2014 CYS Survey. Arch. Pathol. Lab. Med. 139, 888–893 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Delanaye, P. et al. Estimation of GFR by different creatinine- and cystatin-C-based equations in anorexia nervosa. Clin. Nephrol. 71, 482–491 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Stevens, L. A. et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 75, 652–660 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Naour, N. et al. Potential contribution of adipose tissue to elevated serum cystatin C in human obesity. Obesity (Silver Spring) 17, 2121–2126 (2009).

    Article  CAS  Google Scholar 

  46. Fricker, M., Wiesli, P., Brandle, M., Schwegler, B. & Schmid, C. Impact of thyroid dysfunction on serum cystatin C. Kidney Int. 63, 1944–1947 (2003).

    Article  CAS  PubMed  Google Scholar 

  47. Larsson, A., Akerstedt, T., Hansson, L.-O. & Axelsson, J. Circadian variability of cystatin C, creatinine, and glomerular filtration rate (GFR) in healthy men during normal sleep and after an acute shift of sleep. Chronobiol. Int. 25, 1047–1061 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Levey, A. S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann. Intern. Med. 130, 461–470 (1999).

    Article  CAS  PubMed  Google Scholar 

  49. Bouquegneau, A. et al. Creatinine-based equations for the adjustment of drug dosage in an obese population. Br. J. Clin. Pharmacol. 81, 349–361 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bouquegneau, A. et al. Modification of Diet in Renal Disease versus Chronic Kidney Disease Epidemiology Collaboration equation to estimate glomerular filtration rate in obese patients. Nephrol. Dial. Transplant. 28, iv122–iv130 (2013).

    Article  PubMed  Google Scholar 

  51. Lemoine, S. et al. Accuracy of GFR estimation in obese patients. Clin. J. Am. Soc. Nephrol. 9, 720–727 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Froissart, M., Rossert, J., Jacquot, C., Paillard, M. & Houillier, P. Predictive performance of the modification of diet in renal disease and Cockcroft–Gault equations for estimating renal function. J. Am. Soc. Nephrol. 16, 763–773 (2005).

    Article  PubMed  Google Scholar 

  53. White, S. L., Polkinghorne, K. R., Atkins, R. C. & Chadban, S. J. 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. Am. J. Kidney Dis. 55, 660–670 (2010).

    Article  PubMed  Google Scholar 

  54. Delanaye, P. et al. Creatinine-or cystatin C-based equations to estimate glomerular filtration in the general population: impact on the epidemiology of chronic kidney disease. BMC Nephrol. 14, 57 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ponte, B. et al. Determinants and burden of chronic kidney disease in the population-based CoLaus study: a cross-sectional analysis. Nephrol. Dial. Transplant. 28, 2329–2339 (2013).

    Article  PubMed  Google Scholar 

  56. Fraser, S. D. et al. Exploration of chronic kidney disease prevalence estimates using new measures of kidney function in the health survey for England. PLoS ONE 10, e0118676 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Juutilainen, A. et al. Comparison of the MDRD Study and the CKD-EPI Study equations in evaluating trends of estimated kidney function at population level: findings from the National FINRISK Study. Nephrol. Dial. Transplant. 27, 3210–3217 (2012).

    Article  CAS  PubMed  Google Scholar 

  58. Rothenbacher, D. et al. Prevalence and determinants of chronic kidney disease in community-dwelling elderly by various estimating equations. BMC Public Health 12, 343 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Stengel, B. et al. Epidemiology and prognostic significance of chronic kidney disease in the elderly — the Three-City prospective cohort study. Nephrol. Dial. Transplant. 26, 3286–3295 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Van Pottelbergh, G. et al. The glomerular filtration rate estimated by new and old equations as a predictor of important outcomes in elderly patients. BMC Med. 12, 27 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Mandelli, S. et al. Mortality prediction in the oldest old with five different equations to estimate glomerular filtration rate: the Health and Anemia Population-based Study. PLoS ONE 10, e0136039 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Grams, M. E. et al. Trends in the prevalence of reduced GFR in the United States: a comparison of creatinine- and cystatin c-based estimates. Am. J. Kidney Dis. 62, 253–260 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lujambio, I. et al. Estimation of glomerular filtration rate based on serum cystatin C versus creatinine in a Uruguayan population. Int. J. Nephrol. 2014, 837106 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Glaser, N., Deckert, A., Phiri, S., Rothenbacher, D. & Neuhann, F. Comparison of various equations for estimating GFR in Malawi: how to determine renal function in resource limited settings? PLoS ONE 10, e0130453 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Eriksen, B. O. & Ingebretsen, O. C. In chronic kidney disease staging the use of the chronicity criterion affects prognosis and the rate of progression. Kidney Int. 72, 1242–1248 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Benghanem Gharbi, M. et al. Chronic kidney disease, hypertension, diabetes, and obesity in the adult population of Morocco: how to avoid 'over'- and 'under'-diagnosis of CKD. Kidney Int. 89, 1363–1371 (2016).

    Article  PubMed  Google Scholar 

  67. Eriksen, B. O. & Ingebretsen, O. C. The progression of chronic kidney disease: a 10-year population-based study of the effects of gender and age. Kidney Int. 69, 375–382 (2006).

    Article  CAS  PubMed  Google Scholar 

  68. Delanaye, P. et al. Are the creatinine-based equations accurate to estimate glomerular filtration rate in African American populations? Clin. J. Am. Soc. Nephrol. 6, 906–912 (2011).

    Article  PubMed  Google Scholar 

  69. van Deventer, H. E., George, J. A., Paiker, J. E., Becker, P. J. & Katz, I. J. Estimating glomerular filtration rate in black South Africans by use of the modification of diet in renal disease and Cockcroft–Gault equations. Clin. Chem. 54, 1197–1202 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Flamant, M. et al. Performance of GFR estimating equations in African Europeans: basis for a lower race-ethnicity factor than in African Americans. Am. J. Kidney Dis. 62, 182–184 (2013).

    Article  PubMed  Google Scholar 

  71. Anker, N. et al. Racial disparities in creatinine-based kidney function estimates among HIV-infected adults. Ethn. Dis. 26, 213–220 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Delanaye, P., Cavalier, E., Mariat, C., Krzesinski, J.-M. & Rule, A. D. Estimating glomerular filtration rate in Asian subjects: where do we stand? Kidney Int. 80, 439–440 (2011).

    Article  PubMed  Google Scholar 

  73. Teo, B. W. et al. The choice of estimating equations for glomerular filtration rate significantly affects the prevalence of chronic kidney disease in a multi-ethnic population during health screening. Nephrology (Carlton) 14, 588–596 (2009).

    Article  Google Scholar 

  74. Inker, L. A. et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367, 20–29 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Pottel, H., Hoste, L., Delanaye, P., Cavalier, E. & Martens, F. Demystifying ethnic/sex differences in kidney function: is the difference in (estimating) glomerular filtration rate or in serum creatinine concentration? Clin. Chim. Acta 413, 1612–1617 (2012).

    Article  CAS  PubMed  Google Scholar 

  76. Inker, L. A. et al. GFR estimation using β-trace protein and β2-microglobulin in CKD. Am. J. Kidney Dis. 67, 40–48 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Warnock, D. G. Estimated glomerular filtration rate: fit for what purpose? Nephron 134, 43–49 (2016).

    Article  CAS  PubMed  Google Scholar 

  78. Glassock, R. J. & Winearls, C. The global burden of chronic kidney disease: how valid are the estimates? Nephron Clin. Pract. 110, c39–c46 (2008).

    Article  PubMed  Google Scholar 

  79. Ene-Iordache, B. et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob. Health 4, e307–e319 (2016).

    Article  PubMed  Google Scholar 

  80. Zhang, L. et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet 379, 815–822 (2012).

    Article  PubMed  Google Scholar 

  81. Stanifer, J. W., Muiru, A., Jafar, T. H. & Patel, U. D. Chronic kidney disease in low- and middle-income countries. Nephrol. Dial. Transplant. 31, 868–874 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Glassock, R., Delanaye, P. & El Nahas, M. An age-calibrated classification of chronic kidney disease. JAMA 314, 559–560 (2015).

    Article  CAS  PubMed  Google Scholar 

  83. Delanaye, P., Glassock, R. J., Pottel, H. & Rule, A. D. An age-calibrated definition of chronic kidney disease: rationale and benefits. Clin. Biochem. Rev. 37, 17–26 (2016).

    PubMed  PubMed Central  Google Scholar 

  84. Levey, A. S., Inker, L. A. & Coresh, J. Chronic kidney disease in older people. JAMA 314, 557–558 (2015).

    Article  CAS  PubMed  Google Scholar 

  85. Pottel, H., Hoste, L. & Delanaye, P. Abnormal glomerular filtration rate in children, adolescents and young adults starts below 75 mL/min/1.73 m2. Pediatr. Nephrol. 30, 821–828 (2015).

    Article  PubMed  Google Scholar 

  86. Foley, R. N., Wang, C., Snyder, J. J. & Collins, A. J. Cystatin C levels in U.S. adults, 1988–1994 versus 1999–2002: NHANES. Clin. J. Am. Soc. Nephrol. 4, 965–972 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Murphy, D. et al. Trends in prevalence of chronic kidney disease in the United States. Ann. Intern. Med. 165, 473–481 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Saran, R. et al. US Renal Data System 2015 annual data report: epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 67, A7–A8 (2016).

    Article  Google Scholar 

  89. Centers for Disease Control and Prevention. Diabetes public health resource. CDC www.cdc.gov/diabetes/statistics/prevalence_national.htm (2015).

  90. Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 386, 743–800 (2015).

  91. GBD 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385, 117–171 (2014).

  92. Tuot, D. S. et al. Variation in patients' awareness of CKD according to how they are asked. Clin. J. Am. Soc. Nephrol. 11, 1566–1573 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  93. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence and years live with disability for 310 diseases and injuries; 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 388, 1545–1602 (2016).

  94. Luyckx, V. A. et al. Effect of fetal and child health on kidney development and long-term risk of hypertension and kidney disease. Lancet 382, 273–283 (2013).

    Article  PubMed  Google Scholar 

  95. Brenner, B. M. & Mackenzie, H. S. Nephron mass as a risk factor for progression of renal disease. Kidney Int. Suppl. 63, S124–S127 (1997).

    CAS  PubMed  Google Scholar 

  96. Shannon, J. A. & Smith, H. W. The excretion of inulin, xylose, and urea by normal and phorizinized man. J. Clin. Invest. 14, 393–401 (1935).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hendrix, J. P., Westfall, B. B. & Richards, A. N. Quantitative studies of the composition of glomerular urine. XIV. The glomerular excretion of insulin in frogs and necturi. J. Biol. Chem. 116, 735–747 (1937).

    Google Scholar 

  98. Delanaye, P. et al. Iohexol plasma clearance for measuring glomerular filtration rate in clinical practice and research: a review. Part 1: how to measure glomerular filtration rate with iohexol? Clin. Kidney J. 9, 682–699 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Soveri, I. et al. Measuring GFR: a systematic review. Am. J. Kidney Dis. 64, 411–424 (2014).

    Article  PubMed  Google Scholar 

  100. Delanaye, P. & Mariat, C. The applicability of eGFR equations to different populations. Nat. Rev. Nephrol. 9, 513–522 (2013).

    Article  CAS  PubMed  Google Scholar 

  101. Levey, A. S. et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann. Intern. Med. 145, 247–254 (2006).

    Article  CAS  PubMed  Google Scholar 

  102. Grubb, A. et al. Generation of a new cystatin C-based estimating equation for glomerular filtration rate by use of 7 assays standardized to the international calibrator. Clin. Chem. 60, 974–986 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. Delanghe, J. R. & Speeckaert, M. M. Creatinine determination according to Jaffe — what does it stand for? NDT Plus 4, 83–86 (2011).

    PubMed  PubMed Central  Google Scholar 

  104. Perrone, R. D., Madias, N. E. & Levey, A. S. Serum creatinine as an index of renal function: new insights into old concepts. Clin. Chem. 38, 1933–1953 (1992).

    CAS  PubMed  Google Scholar 

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All authors contributed to writing the article, to the discussion of the article's content and to review and/or editing of the manuscript before submission.

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Supplementary information

Supplementary information S1 (table)

Prevalence counts (×1000) of CKD according to stage in 2015 (PDF 68 kb)

Supplementary information S2 (table)

Percentage change 2005–2015 total counts/age standardized prevalence rates (PDF 69 kb)

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Glossary

Jaffe assays

Used for measuring serum creatinine levels. Jaffe assays are based on a colourimetric reaction using alkaline picrate; the colour change is directly proportional to the creatinine concentration. Unfortunately, the accuracy of the assay can be affected by interfering factors such as haemolysis, hyperbilirubinemia, lipaemia and non-creatinine chromogens such as glucose, acetoacetate and ascorbic acid.

IDMS traceable

A serum creatinine value that has been obtained using an assay that has been calibrated to single standardized serum creatinine using reference materials traceable to the primary reference material at the National Institute of Standards. The value is described as being traceable to isotope dilution mass spectrometry (IDMS) because the assay calibration is based on IDMS.

Regression to the mean

Regression to (or toward) the mean is the phenomenon that if the level of a variable is extreme on first measurement, it will tend to be closer to the mean on second measurement, whereas if the level is extreme on second measurement, it will tend to have been closer to the mean on first measurement.

Renal senescence

The gradual deterioration of renal function that is characteristic of most complex lifeforms. Senescence can refer either to cellular senescence or to senescence of the whole organ. Cellular senescence is commonly believed to underlie organ senescence. Anatomically, renal senescence is characterized by global glomerulosclerosis, tubulointerstitial fibrosis and arteriolosclerosis.

Disability-adjusted life years

A measure of overall disease burden, expressed as the number of years lost owing to ill-health, disability or early death.

Generic CKD

A classification of chronic kidney disease (CKD) in which a definite cause is not identified or in which all causes (known or unknown) are included.

Ascertainment bias

A systematic distortion in measuring the true frequency of a phenomenon owing to the way in which the data are collected.

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Glassock, R., Warnock, D. & Delanaye, P. The global burden of chronic kidney disease: estimates, variability and pitfalls. Nat Rev Nephrol 13, 104–114 (2017). https://doi.org/10.1038/nrneph.2016.163

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  • DOI: https://doi.org/10.1038/nrneph.2016.163

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