Skip Navigation

Nephrology Dialysis Transplantation 2007 22(Supplement 9):ix8-ix18; doi:10.1093/ndt/gfm444
This Article
Right arrow Extract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by MacGregor, M. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by MacGregor, M. S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org


How common is early chronic kidney disease?

A Background Paper prepared for the UK Consensus Conference on Early Chronic Kidney Disease

Mark S. MacGregor

The John Stevenson Lynch Renal Unit, Crosshouse Hospital, NHS Ayrshire & Arran, Kilmarnock, KA2 0BE, Scotland

Correspondence to: Mark. S. MacGregor, Consultant Nephrologist, The John Stevenson Lynch Renal Unit, Crosshouse Hospital, NHS Ayrshire & Arran, Kilmarnock, KA2 0BE, Scotland. Email: Mark.MacGregor{at}aaaht.scot.nhs.uk



   Introduction
Top
 Introduction
How robust is estimated...
 What is the epidemiology...
 What are the implications...
 Conclusions
 References
 
Chronic disease of the kidneys has been described since the fifth century BC [1]. In modern times, it attracted labels such as chronic renal failure or chronic renal impairment. These terms are ill-defined, implying an unspecified degree of reduced function, present for an unspecified time. In 2002, the Kidney Disease Outcomes Quality Initiative (KDOQI) of the US National Kidney Foundation published a classification of chronic kidney disease (CKD) with explicit definitions (Table 1) [2]. This has been widely adopted in nephrology research and practice, and endorsed by guidelines including the Scottish Intercollegiate Guidelines Network [3], the Joint Specialty Committee on Renal Disease [4] and Kidney Disease: Improving Global Outcomes [5]. For this conference's purposes, early CKD encompasses stages 1–3, but not 4 and 5. The KDOQI classification has raised awareness of early CKD, and concern about the apparently high prevalence. This review presents the available evidence on CKD epidemiology. As the evidence relies on formula-based estimates of glomerular filtration rate (GFR), the limitations of these formulae are discussed. CKD is predominantly a disease of the elderly, so age-related changes in renal function are reviewed.


View this table:
[in this window]
[in a new window]

  		Table 1. The USA National Kidney Foundation's KDOQI classification [2]

 


   How robust is estimated GFR as a measure of early CKD?
Top
 Introduction
 How robust is estimated...
What is the epidemiology...
 What are the implications...
 Conclusions
 References
 
The KDOQI classification requires that the GFR is known, but formal GFR measurements are impractical for large epidemiological studies. In 1999, the Modification of Diet in Renal Disease (MDRD) study group published a formula (Box 1), which estimated GFR (eGFR) based on age, gender, race, serum creatinine, urea and albumin [6]. Omitting urea and albumin from the formula caused minimal loss of accuracy [7]. Further adaptation allowed calibration to a reference creatinine measurement method [8]. These formulae have been assessed in over 50 studies containing >16 000 subjects. Notably, they outperform the Cockcroft–Gault formula [9] and urinary creatinine clearance (Table 2) [6,10–22].


View this table:
[in this window]
[in a new window]

  		Table 2. Studies assessing the MDRD formula which have to some extent addressed calibration of the creatinine assay to the original MDRD laboratory

 

Box 1. Formulae used to predict glomerular filtration rate or creatinine clearance from serum creatinine

The Cockcroft–Gault equation

CCr = [(140 – age) x weight] / (0.814 x SCr) x 0.85(if female)

The MDRD 6-variable formula

eGFR = 170 x (0.011312 x SCr) –0.999 x age–0.176 x (2.8 x SUN) –0.170 x (0.1 x SAlb)0.318 x 0.762 (if female) x 1.180 (if black)

The MDRD 4-variable formula (or abbreviated formula)

eGFR = 186.3 x (0.011312 x SCr) –1.154 x age–0.203 x 0.742 (if female) x 1.212 (if black)

The ID-MS traceable MDRD formula (as used by UK NEQAS)

eGFR = 175 x [0.011312 x (SCr – c)/m]–1.154 x age–0.203 x 0.742 (if female) x 1.212 (if black)

CCr, creatinine clearance (ml/min); MDRD, Modification of Diet in Renal Disease; eGFR, estimated glomerular filtation rate (ml/min/1.73 m2); SCr, serum creatinine (µmol/l); SUN, serum urea (mmol/l); UUN, urine urea (mmol/d); SAlb, serum albumin (g/l); ID-MS, isotope dilution mass spectrometry; NEQAS, National External Quality Assessment Service; c and m, UK NEQAS correction factors to adjust a specific creatinine assay to ‘true’ serum creatinine.

 

Limitations of the MDRD formulae
Serum creatinine is usually measured by Jaffe-based methods, which typically report higher than the true value, due to the presence of interferents in serum [23,24]. The interferents have greatest impact when serum creatinine is low. Unfortunately, this is the level of interest when trying to measure eGFRs in early CKD (Figure 1). Assay calibration to the MDRD laboratory or a reference standard can mitigate this problem [8,17,25]. Patients with low muscle mass (e.g. older women) will have a lower creatinine for any given GFR, and are more affected by this issue. Even with assay calibration, the MDRD formula gives an increasingly unreliable estimate of GFR with higher GFRs [12–14,26–30]. Precision decreases, and bias becomes increasingly negative leading to underestimation of GFR (Figure 2), particularly at ≥60 ml/min/1.73 m2.


Figure 1
View larger version (17K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1. The eGFR predicted from serum creatinine in a theoretical white woman aged 70 years, using a creatinine assay calibrated to the MDRD laboratory. Also plotted is the actual eGFR in the same theoretical patient, estimated from a laboratory with a typical creatinine assay bias due to interferents of 20 µmol/l. The magnitude of eGFR error due to this bias is also plotted and increases dramatically with serum creatinine below 120 µmol/l. The graph is derived from a similar plot in [25]. eGFR, estimated glomerular filtration rate; MDRD, Modification of Diet in Renal Disease.

 

Figure 2
View larger version (45K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2. Correlation of GFR measured by renal clearance of 125I-iothalamate (iGFR) with GFR estimated by the MDRD formula (eGFRMDRD). Note the increasing scatter and negative bias of eGFR as it rises above 60 ml/min/1.73 m2. This leads to a substantial number of false positives, i.e. patients mis-labelled as having a reduced GFR. Adapted from [14] with the permission of the American Society of Nephrology. MDRD, Modification of Diet in Renal Disease.

 
The MDRD formula works well in both sexes [6,12]. It contains a term for age, allowing for the expected decline in muscle mass. Performance seems at least as good in the over 65s [27,31], and into the 80s [32], but is ineffective in children [15,33]. The formula was developed in a mainly white US population [6], but is also effective in US blacks [10]. It performed less effectively in Chinese CKD patients, tending to overestimate GFR by 11–12 ml/min/1.73 m2. An additional correction factor may be required [34,35]. No studies have yet addressed Indo-Asians with CKD.

Increased body mass index (BMI) has relatively little impact on the accuracy of the formula [12,27,30,31,36]. Patients with a BMI >40 kg/m2 have not been separately assessed, although BMIs as high as 59 kg/m2 were included in these studies. One study [12] found a marked positive bias of 12 ml/min/1.73 m2 in patients with BMI <18.5 kg/m2. Only 6% of MDRD study patients were diabetic [6]. Subsequent studies show relatively poor performance of the formula in diabetics [13,28,30,37,38]. However, these focused on patients with preserved GFR. In diabetics with CKD3, the formula performs well [14].

Alternatives to the MDRD formula
Alternative creatinine-based formulae have been developed for blacks [10], diabetics [13], or the general population [26], but have little advantage over the MDRD formula. Cystatin C [39–41] has been suggested as an endogenous GFR marker since 1985 [42], but most studies have not shown a worthwhile advantage over MDRD eGFR [15,22,38,43–47]. Interestingly, cystatin C is a better predictor than eGFR of cardiovascular events [48,49] and of all-cause mortality [50–52]. Of course, this is not synonymous with being a better GFR marker.



   What is the epidemiology of early CKD in the UK?
Top
 Introduction
 How robust is estimated...
 What is the epidemiology...
What are the implications...
 Conclusions
 References
 
Numerous publications detail the epidemiology of dialysis [53–57], telling us a lot about treatment provision, but little about disease prevalence. An ideal survey would select a large cross-section of the population randomly (perhaps with oversampling of groups such as ethnic minorities), and assess their kidney function on at least two occasions. As a minimum, GFR, dipstick urinalysis and a laboratory measure of proteinuria would be required, thus allowing CKD prevalence to be defined. By following the population longitudinally, incidence could also be defined, together with the associated outcomes such as dialysis or death. Such a study has yet to be carried out in the UK, or indeed anywhere else.

Most studies utilized laboratory or clinical databases created during routine clinical practice. These have two main flaws: they assume an absence of disease in patients who have not been bled (an unproven assumption which may lead to an underestimate of prevalence); and they are usually based on a single eGFR result which will tend to overestimate prevalence (patients being bled for a reason, perhaps as a result of acute illness associated with transient renal dysfunction). Population surveys are more reliable, but are also usually based on a single blood or urine sample. This is less of an issue in subjects who are not suffering from acute illness, but will still have an impact due to biological and analytical variation. Sampling bias may also be an issue, if high-risk populations are excluded or if recruitment rates are low (this may lead to an overestimate or underestimate of prevalence depending on whether unhealthy individuals were more or less likely to take part).

CKD stage 3 epidemiology in the UK
To estimate the need for dialysis (Table 3), early studies searched clinical records for patients with a markedly raised serum urea or creatinine [58–65]. These patients had CKD5, with one study also detecting some with CKD4 [65]. As a single threshold value for males and females was used, need in females (and the elderly) was underestimated. Nevertheless, the estimates are reassuringly similar to modern estimates.


View this table:
[in this window]
[in a new window]

  		Table 3. UK studies on the prevalence of CKD

 
Modern studies assessed the epidemiology of early CKD using serum creatinine thresholds or more recently eGFR. A Southampton study searched laboratory databases for all serum creatinines >150 µmol/l over 2 years [66]. Prevalent patients with raised serum creatinines in the preceding 2–4 years were excluded. Chronicity was established by subsequent persistence of renal dysfunction for ≥6 months. They were unable to categorize 43% of patients as acute or chronic, and allocated those cases, after casenote review of 21% of them. Annual incidence of raised serum creatinine was 0.17%. Table 3 shows the annual incidence by CKD stage. These eGFR analyses were carried out post hoc, and given the creatinine threshold, will be an underestimate of CKD3 incidence, especially in women and the elderly. Despite that, a pronounced age-related gradient was noted: annual incidence of 0.01% in 20–29 year olds rising to 1.2% in over 80 year olds. Higher rates were also noted with increased deprivation.

An East Kent study searched laboratory databases over 1 year [67], using different creatinine thresholds for men and women, which approximated to the same eGFR (32–47 ml/min/1.73 m2). Attempts were made to exclude acute renal dysfunction. They found a prevalence of 0.56%, which again will be a marked underestimate of CKD3, particularly in the elderly. Median eGFR was 28 ml/min/1.73 m2, ranging from 4 to 43 ml/min/1.73 m2. Again, a marked age-related gradient was seen, reaching 6.1% prevalence in over 80 year olds. Prevalence was higher in women of almost all ages (60.8 vs 39.2% overall).

The NEOERICA project [68,69], searched primary care databases in Surrey, Kent and Manchester for reduced eGFRs. Of the total population, 26% had a serum creatinine, rising to 62% of those aged 75–85 years. The prevalence of CKD3 was 4.6%. Again, a striking relationship with age was noted, and again the prevalence was higher amongst women (male-to-female ratio 0.45). A Northern Irish study searched laboratory databases, finding a similar CKD3 prevalence of 4.8% (reported in abstract [70]). Neither of these studies calibrated their creatinine assay against the MDRD laboratory, nor is it clear what efforts were made to exclude acute renal dysfunction. In another NEORICA report (abstract only [71]), samples were calibrated with the MDRD laboratory, with a worrying increase in CKD3 prevalence to 9.2%.

Some studies examined high-risk groups. Two London studies searched primary care databases for a raised serum creatinine in patients aged 50–75 years with hypertension and/or diabetes. All the patients identified would have had CKD3 or worse, but the same cut-off was used for men and women. In the first study [72], 5.7% of all hypertensives and 6.3% of all diabetics had a raised creatinine. However, only 53% had been bled. The second study [73] also invited the 37% of hypertensive and diabetic patients who had no recent blood result for screening (52% attended), and found a raised creatinine in 7.4% of those screened compared with 8.7% of clinical samples. Most studies assume that unbled patients have a zero prevalence of renal disease, but this study does not support that contention, at least in hypertensives and diabetics. Using a shared primary–secondary care electronic patient record in Salford [74], 20.4% of diabetics were found to have CKD3 (equating to 0.7% of the total population). Studies of the impact of ethnicity in the UK setting are needed. One population-based study (abstract only [75]) showed no difference in CKD3 prevalence between Indo-Asians and Northern Europeans, despite the higher prevalence of end-stage renal disease amongst Indo-Asians [54].

CKD stage 3 epidemiology in other countries
It is worth dwelling on the important US third National Health and Nutrition Examination Study (NHANES III) [76]. It was a cross-sectional, stratified, clustered sample of the non-institutionalized population from 1988 to 1994, with a 74% acceptance rate. Certain groups such as the young, the elderly and ethnic minorities were deliberately over-sampled. Sampling weights are then applied to give population prevalences. Non-diabetic adult NHANES III subjects were found to have a surprisingly high 12.3% CKD3 prevalence using the MDRD formula. However, after adjustment of the creatinine assay to the MDRD laboratory, prevalence fell to 3.2% [77], demonstrating the critical importance of calibration. A subsequent analysis of the NHANES III adult subjects including diabetics, showed a CKD3 prevalence of 4.3% and has become the most widely accepted estimate of CKD prevalence [78]. Both analyses demonstrated a marked increase in prevalence with age (Figure 3). Prevalence was higher in women, but not after adjustment for age. It should be noted that NHANES III excluded institutionalized patients, and is thus likely to be an underestimate.


Figure 3
View larger version (26K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 3. Prevalence of different degrees of reduced GFR in the non-diabetic white US population (A and B), based on NHANES III data, after adjustment of the assay to the MDRD laboratory. Reproduced with permission from [77]. NHANES III, third National Health and Nutrition Examination Survey; MDRD: Modification of Diet in Renal Disease.

 
Several other epidemiological surveys have been carried out with CKD3 prevalence estimated at 8.1–40% in studies without calibration, or those using Cockcroft–Gault estimates (Table 4) [79–83]. In a Norwegian study with calibration [84], CKD3 prevalence was 4.2%, surprisingly high given the lower rates of obesity and diabetes than in the US. Both the calibrated and uncalibrated studies confirmed an increased prevalence in women [79,81,83,84] and the elderly [79,81–84]. Reliable prevalences from developing countries would be of interest.


View this table:
[in this window]
[in a new window]

  		Table 4. Non-UK studies on the epidemiology of CKD

 
Given the challenges of managing large numbers of CKD patients, it is noteworthy that in a Californian study [85], the prevalence of CKD3B (i.e. 30–44 ml/min/1.73 m2) was only 22% of CKD3A (i.e. 45–59 ml/min/1.73 m2). If extrapolated to the NHANES III data, this would give a population prevalence of 3.5% CKD3A but only 0.8% CKD3B.

CKD stages 1 and 2
Categorizing patients as CKD3 simply requires two eGFRs >90 days apart [2]. However, CKD1 or 2 also require evidence of kidney damage: ‘markers of kidney damage include abnormalities in the composition of the blood or urine or abnormalities in imaging tests’ [2]. CKD1 and 2 prevalence is inevitably more poorly defined because of this requirement. Clinical or laboratory database studies are particularly unhelpful, because of the low rate of testing and recording of urinalysis [68,69,72–74].

Differentiating CKD1 and 2 is fraught because of the limitations of eGFR in this range, and merging the prevalence estimates is probably more sensible. Proteinuria includes microalbuminuria, which although appropriate in type 1 diabetes mellitus, may not be appropriate for other patients. Microalbuminuria in non-diabetics is a marker of increased cardiovascular morbidity and mortality, [86,87] but it is not clear that it represents actual kidney disease. Nevertheless, in small studies, microalbuminuria was associated with a GFR decline in non-diabetic hypertensives [88] and in the general population [89]. It remains unclear which marker of proteinuria is the best predictor of outcomes, and assay standardization is another challenge.

NHANES III suggests a prevalence of 3.3% CKD1 and 3% CKD2 based on the presence of microalbuminuria [78], falling to 0.32 and 0.37% if only macro-albuminuria is included. Based on microalbuminuria, Norwegian [84] and Spanish [82] studies give similar prevalences (Table 4). Using the presence of proteinuria and/or microscopic haematuria, an Australian study showed prevalences of 0.9% CKD1 and 2% CKD2 [79]. None of these studies incorporate eGFR, microscopic haematuria, microalbuminuria and structural renal abnormalities, so are all underestimates of the true prevalence.

Is CKD becoming more common?
The prevalence of renal replacement therapy (RRT) has risen steadily in the developed world since its inception in 1960 [53–57]. It is therefore commonly said that CKD prevalence is rising. However, dialysis registries record treatment provision rather than the disease prevalence. Much of the rise in the UK dialysis population is due to relaxation of selection criteria for treatment. Furthermore, there is a surprising lack of connection between RRT incidence and CKD prevalence in different populations. For example, US blacks and whites have a similar prevalence of CKD3, whereas RRT incidence in blacks exceeds that in whites 4-fold [90]. A similar phenomenon is seen with gender.

The rising prevalence of diabetes and obesity might lead to an increase in CKD, but equally, a falling incidence of vascular disease and improved treatment of diabetes, hypertension and vascular disease may have the opposite effect. Recognition of CKD has improved dramatically since the introduction of eGFR reporting and CKD guidelines, but this represents ascertainment bias rather than a true increase in prevalence.

There is no information about secular trends in CKD in the UK. NHANES demonstrated that CKD3 and CKD4 increased from 2% in 1976–1980 to 2.5% in 1988–1994 amongst 20–74 year olds [90]. However, there was no difference between the prevalence in 1988–1994 (4.2%) compared with 1999–2000 (3.7%) in a separate analysis of ≥20 year olds [91]. There was, however, a significant increase in CKD1 and CKD2 from 4.4% to 5.6%.



   What are the implications for the ageing population?
Top
 Introduction
 How robust is estimated...
 What is the epidemiology...
 What are the implications...
Conclusions
 References
 
Cross-sectional studies show increasing prevalence of reduced eGFR with age (Figure 3). Although consistent with a progressive age-related decline in GFR, these studies cannot answer the question of whether that is pathological or simply ageing. It is typically stated that GFR declines at ~1 ml/min/year from the age of 40 years, based on cross-sectional studies using gold-standard GFR methods from the mid-twentieth century. The seminal study of Davies and Shock measured GFR with inulin clearance in 70 men [92], and showed a decline from 121 ml/min/1.73 m2 in the fifth decade to 65 ml/min/1.73 m2 in the ninth decade. One problem with this study (and others) is that little allowance was made for the impact of ill health. Almost all the subjects were hospitalized, and many of the older patients had atherosclerosis or hypertension (Figure 4). Separating the effect of age from disease is thus difficult.


Figure 4
View larger version (17K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 4. The relationship between inulin clearance and age, in a cross-sectional study of hospitalized patients. Redrawn from original data in [93]. Patients with hypertension, renal disease or cardiovascular disease have been separately identified. Simple best fit lines through the ‘healthy’ patients (declining at 0.76 ml/min/year) and those with comorbidity (1.14 ml/min/year) are shown. HT, hypertension. GFR, glomerular filtration rate.

 
Two cross-sectional studies examined healthy volunteers [93] or potential kidney donors [94]. The first found an inulin GFR of 104 ml/min/1.73 m2 in those under 40 years, declining at 0.4 ml/min/year thereafter, and the second using 51Cr-EDTA showed a GFR of 103 ml/min/1.73 m2 declining after the age of 40 years at 0.9 ml/min/year. A third study examining healthy elderly, hypertensives and patients with cardiac failure found inulin GFRs of 103, 103 and 92 ml/min/1.73 m2, respectively [95]. Although lower than young volunteers (121 ml/min/1.73 m2), 65% of healthy or hypertensive subjects were within the normal range compared with 29% of heart failure patients. All subjects had similar protein intakes. Protein intake may decline in the elderly, causing a reversible decline in GFR [96], a potential confounder in other studies.

Necropsy studies show a reduction in renal mass and number of glomeruli in older patients, but again are flawed by not excluding patients with comorbidity (reviewed in reference [97]). Little glomerulosclerosis was seen in older living kidney donors [93], or in older necropsy subjects with only mild atherosclerosis [98]. In individuals who died from non-vascular causes, kidney weight was not reduced with age, if adjusted for body surface area [99].

The Baltimore Longitudinal Study of Ageing measured urinary creatinine clearance in 884 ambulatory subjects, followed for up to 24 years [100–102]. In subjects with no hypertension, renal or vascular disease, GFR declined at 0.75 ml/min/year, with no decline in 36%. The rate of decline accelerated with age, but creatinine clearance in the ninth decade was still 94 ml/min/1.73 m2. A Scandinavian study found no significant decline in GFR (measured by51Cr-EDTA) between 75 and 79 years of age [103].

It seems clear that GFR declines with age, but in the absence of comorbidities the rate of decline may be less than previously thought, such that GFR need not decline to a clinically significant level. As CKD prevalence increases with age, it is commonly assumed that an ageing population will become an increasing public health burden. However, the rising elderly population is due not only to a demographic bulge, but also to increased longevity. If increased longevity is a result of improved health, and if CKD is mainly due to comorbidities rather than ageing per se, then the prevalence of CKD may not increase as predicted.



   Conclusions
Top
 Introduction
 How robust is estimated...
 What is the epidemiology...
 What are the implications...
 Conclusions
References
 
So how common is early CKD? Prevalence estimates of CKD1 and CKD2 combined range from 2.9% to 7.0%, but no study adequately assessed proteinuria, haematuria and structural abnormalities. CKD3 prevalence from two population studies with creatinine assay calibration was 4.2–4.3%. UK clinical and laboratory database estimates are similar at 4.6–4.8%, but one troubling report suggests a higher prevalence at 9.2%. Prevalence is dramatically associated with age, with 30.5% of men and 37.5% of women in their 80s having CKD3. Prevalence is also increased in diabetics. Local prevalence figures will, therefore, be determined by the local population age and sex structure and prevalence of comorbidities. Large-scale longitudinal epidemiological studies are warranted to define incidence, prevalence and outcomes of CKD more clearly. Better markers of early kidney dysfunction would be valuable. Given the crucial impact of age, the limited studies of ageing and kidney function need to be expanded, in both the healthy elderly and those with comorbidity.

Conflict of interest statement. None declared.



   References
Top
 Introduction
 How robust is estimated...
 What is the epidemiology...
 What are the implications...
 Conclusions
 References
 

  1. Hippocrates. Aphorisms. Section VII. 34.
  2. Kidney Disease Outcomes Quality Initiative. Clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. Am J Kidney Dis (2002) 39(Suppl 1):S46–S75.[CrossRef]
  3. Scottish Intercollegiate Guidelines Network. Diagnosis and management of chronic kidney disease. National meeting draft (2006) Edinburgh: SIGN.
  4. Joint Specialty Committee on Renal Medicine. Chronic kidney disease in adults: UK guidelines for identification, management and referral (2006) London: Royal College of Physicians of London.
  5. Levey AS, Eckardt K-U, Tsukamoto Y, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int (2005) 67:2089–2100.[CrossRef][ISI][Medline]
  6. Levey AS, Bosch JP, Lewis JB, et al. 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 Int Med (1999) 130:461–470.[Abstract/Free Full Text]
  7. Levey AS, Greene T, Kusek JW, Beck GJ. A simplified equation to predict glomerular filtration rate from serum creatinine. J Am Soc Nephrol (2000) 11:A0828.
  8. Levey AS, Coresh J, Greene J, et al. Expressing the MDRD study equation for estimating GFR with IDMS traceable (gold standard) serum creatinine values. J Am Soc Nephrol (2005) 16:69A.[CrossRef]
  9. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron (1976) 16:31–41.[ISI][Medline]
  10. Lewis J, Agodoa L, Cheek D, et al. African-American Study of Hypertension and Kidney Disease. Comparison of cross-sectional renal function measurements in African Americans with hypertensive nephrosclerosis and of primary formulas to estimate glomerular filtration rate. Am J Kidney Dis (2001) 38:744–753.[ISI][Medline]
  11. Mariat C, Alamartine E, Barthelemy J-C, et al. Assessing renal graft function in clinical trials: can tests predicting glomerular filtration rate substitute for a reference method? Kidney Int (2004) 65:289–297.[CrossRef][ISI][Medline]
  12. Froissart M, Rossert J, Jacquot C, et al. Predictive performance of the modification of diet in renal disease and Cockcroft-Gault equations for estimating renal function. J Am Soc Nephrol (2005) 16:763–773.[Abstract/Free Full Text]
  13. Ibrahim H, Mondress M, Tello A, et al. An alternative formula to the Cockcroft-Gault and the modification of diet in renal diseases formulas in predicting GFR in individuals with type 1 diabetes. J Am Soc Nephrol (2005) 16:1051–1060.[Abstract/Free Full Text]
  14. Poggio ED, Wang X, Greene T, et al. Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol (2005) 16:459–466.[Abstract/Free Full Text]
  15. Grubb A, Nyman U, Bjork J, et al. Simple cystatin C-based prediction equations for glomerular filtration rate compared with the modification of diet in renal disease prediction equation for adults and the Schwartz and the Counahan-Barratt prediction equations for children. Clin Chem (2005) 51:1420–1431.[Abstract/Free Full Text]
  16. Rule AD, Torres VE, Chapman AB, et al. Comparison of methods for determining renal function decline in early autosomal dominant polycystic kidney disease: the consortium of radiologic imaging studies of polycystic kidney disease cohort. J Am Soc Nephrol (2006) 17:854–862.[Abstract/Free Full Text]
  17. Hallan S, Asberg A, Lindberg M, Johnsen H. Validation of the Modification of Diet in Renal Disease formula for estimating GFR with special emphasis on calibration of the serum creatinine assay. Am J Kidney Dis (2004) 44:84–93.[CrossRef][ISI][Medline]
  18. Poggio ED, Wang X, Weinstein DM, et al. Assessing glomerular filtration rate by estimation equations in kidney transplant recipients. Am J Transplant (2006) 6:100–108.[CrossRef][ISI][Medline]
  19. White C, Akbari A, Hussain N, et al. Estimating glomerular filtration rate in kidney transplantation: a comparison between serum creatinine and cystatin C-based methods. J Am Soc Nephrol (2005) 3763–3770.
  20. Ibrahim HN, Rogers T, Tello A, Matas A. The performance of three serum creatinine-based formulas in estimating GFR in former kidney donors. Am J Transplant (2006) 6:1479–1485.[CrossRef][ISI][Medline]
  21. Poggio ED, Nef PC, Wang X, et al. Performance of the Cockcroft-Gault and modification of diet in renal disease equations in estimating GFR in ill hospitalized patients. Am J Kidney Dis (2005) 46:242–252.[CrossRef][ISI][Medline]
  22. Risch L, Huber AR. Assessing glomerular filtration rate in renal transplant recipients by estimates derived from serum measurements of creatinine and cystatin C. Clinica Chimica Acta (2005) 356:204–211.[CrossRef][ISI][Medline]
  23. Miller WG, Myers GL, Ashwood ER, et al. Creatinine measurement: state of the art in accuracy and inter-laboratory harmonization. Arch Pathol Lab Med (2005) 129:297–304.[ISI][Medline]
  24. Murthy K, Stevens LA, Stark PC, Levey AS. Variation in the serum creatinine assay calibration: a practical application to glomerular filtration rate estimation. Kidney Int (2005) 68:1884–1887.[CrossRef][ISI][Medline]
  25. Coresh J, Astor BC, McQuillan G, et al. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis (2002) 39:920–929.[CrossRef][ISI][Medline]
  26. Rule AD, Larson TS, Bergstralh EJ, et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med (2004) 141:929–937.[Abstract/Free Full Text]
  27. Verhave JC, Fesler P, Ribstein J, et al. Estimation of renal function in subjects with normal serum creatinine levels: influence of age and body mass index. Am J Kidney Dis (2005) 46:233–241.[CrossRef][ISI][Medline]
  28. Rossing P, Rossing K, Gaede P, et al. Monitoring kidney function in type 2 diabetic patients with incipient and overt diabetic nephropathy. Diabetes Care (2006) 29:1024–1030.[Abstract/Free Full Text]
  29. Mahajan S, Mukhiya GK, Singh R, et al. Assessing suitability for renal donation: can equations predicting glomerular filtration rate substitute for a reference method in the Indian population? Nephron (2005) 101:c128–c133.[CrossRef][Medline]
  30. Rigalleau V, Lasseur C, Perlemoine C, et al. Cockcroft-Gault formula is biased by body weight in diabetic patients with renal impairment. Metabolism: Clin Exp (2006) 55:108–112.
  31. Bosma RJ, Doorenbos CR, Stegeman CA, et al. Predictive performance of renal function equations in renal transplant recipients: an analysis of patient factors in bias. Am J Transplant (2005) 5:2193–2203.[CrossRef][ISI][Medline]
  32. Lamb EJ, Webb MC, Simpson DE, et al. Estimation of glomerular filtration rate in older patients with chronic renal insufficiency: is the modification of diet in renal disease formula an improvement? J Am Geriatr Soc (2003) 51:1012–1017.[CrossRef][ISI][Medline]
  33. Pierrat A, Gravier E, Saunders C, et al. Predicting GFR in children and adults: a comparison of the Cockcroft-Gault, Schwartz, and Modification of Diet in Renal Disease formulas. Kidney Int (2003) 64:1425–1436.[CrossRef][ISI][Medline]
  34. Zuo L, Ma Y-C, Zhou Y-H, et al. Application of GFR-estimating equations in Chinese patients with chronic kidney disease. Am J Kidney Dis (2005) 45:463–472.[CrossRef][ISI][Medline]
  35. Ma Y-C, Zuo L, Chen J-H, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol (2006) 17:2937–2944.[Abstract/Free Full Text]
  36. Saracino A, Morrone LF, Suriano V, et al. A simple method for correcting overestimated glomerular filtration rate in obese subjects evaluated by the Cockcroft and Gault formula: a comparison with 51Cr EDTA clearance. Clin Nephrol (2004) 62:97–103.[ISI][Medline]
  37. Vervoort G, Willems HL, Wetzels JF. Assessment of glomerular filtration rate in healthy subjects and normoalbuminuric diabetic patients: validity of a new (MDRD) prediction equation. Nephrol Dial Transplant (2002) 17:1909–1913.[Abstract/Free Full Text]
  38. Macisaac RJ, Tsalamandris C, Thomas MC, et al. Estimating glomerular filtration rate in diabetes: a comparison of cystatin-C- and creatinine-based methods. Diabetologia (2006) 49:1686–1689.[CrossRef][ISI][Medline]
  39. Newman DJ, Cystatin C. Ann Clin Biochem (2002) 39:89–104.[CrossRef][ISI][Medline]
  40. Filler G, Bökenkamp A, Hofmann W, et al. Cystatin C as a marker of GFR – history, indications, and future research. Clin Biochem (2005) 38:1–8.[CrossRef][ISI][Medline]
  41. Shlipak MG, Praught ML, Sarnak MJ. Update on cystatin C: new insights into the importance of mild kidney dysfunction. Curr Opin Nephrol Hypertens (2006) 15:270–275.[ISI][Medline]
  42. Simonsen O, Grubb A, Thysell H. The blood serum concentration of cystatin C (gamma-trace) as a measure of the glomerular filtration rate. Scand J Clin Lab Invest (1985) 45:97–101.[ISI][Medline]
  43. Van Den Noortgate NJ, Janssens WH, Delanghe JR, et al. Serum cystatin C concentration compared with other markers of glomerular filtration rate in the old old. J Am Geriatr Soc (2002) 50:1278–1282.[CrossRef][ISI][Medline]
  44. Harmoinen A, Lehtimaki T, Korpela M, et al. Diagnostic accuracies of plasma creatinine, cystatin C, and glomerular filtration rate calculated by the Cockcroft-Gault and Levey (MDRD) formulas. Clin Chem (2003) 49:1223–1235.[Free Full Text]
  45. Sjostrom P, Tidman M, Jones I. Determination of the production rate and non-renal clearance of cystatin C and estimation of the glomerular filtration rate from the serum concentration of cystatin C in humans. Scand J Clin Lab Invest (2005) 65:111–124.[ISI][Medline]
  46. Hojs R, Bevc S, Ekart R, et al. Serum cystatin C as an endogenous marker of renal function in patients with mild to moderate impairment of kidney function. Nephrol Dial Transplant (2006) 21:1855–1862.[Abstract/Free Full Text]
  47. Poge U, Gerhardt T, Stoffel-Wagner B, et al. Cystatin C-based calculation of glomerular filtration rate in kidney transplant recipients. Kidney Int (2006) 70:204–210.[CrossRef][ISI][Medline]
  48. Jernberg T, Lindahl B, James S, et al. Cystatin C: a novel predictor of outcome in suspected or confirmed non-ST-elevation acute coronary syndrome. Circulation (2004) 110:2342–2348.[Abstract/Free Full Text]
  49. Koenig W, Twardella D, Brenner H, Rothenbacher D. Plasma concentrations of cystatin C in patients with coronary heart disease and risk for secondary cardiovascular events: more than simply a marker of glomerular filtration rate. Clin Chem (2005) 51:321–327.[Abstract/Free Full Text]
  50. Shlipak MG, Sarnak MJ, Katz R, et al. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med (2005) 352:2049–2060.[Abstract/Free Full Text]
  51. Shlipak MG, Wassel Fyr CL, Chertow GM, et al. Cystatin C and mortality risk in the elderly: the Health, Aging, and Body Composition Study. J Am Soc Nephrol (2006) 17:254–261.[Abstract/Free Full Text]
  52. Shlipak MG, Katz R, Fried LF, et al. Cystatin-C and mortality in elderly persons with heart failure. J Am Coll Cardiol (2005) 45:268–271.[Abstract/Free Full Text]
  53. Scottish Renal Association. Scottish Renal Registry report, 2001. (2003) Edinburgh: Information and Statistics Division.
  54. UK Renal Registry. The eighth annual report. (2006) Bristol: Renal Association.
  55. ERA-EDTA Registry. ERA-EDTA Registry 2004 Annual Report. (2006) Amsterdam, The Netherlands: Academic Medical Centre.
  56. Australia and New Zealand Dialysis and Transplant Registry. ANZDATA Registry report 2004. (2005) Adelaide, South Australia.
  57. United States Renal Data System. 2005 Annual Data Report: Atlas of end-stage renal disease in the United States (2005) Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.
  58. Branch RA, Clark GW, Cochrane AL, et al. Incidence of uraemia and requirements for maintenance haemodialysis. Br Med J (1971) 1:249–254.[ISI][Medline]
  59. Pendreigh DM, Howitt LF, Macdougall AI, et al. Survey of chronic renal failure in Scotland. Lancet (1972) 1:304–307.[ISI][Medline]
  60. McGeown MG. Chronic renal failure in Northern Ireland, 1968–70. A prospective survey. Lancet (1972) 1:307–310.[ISI][Medline]
  61. McCormick M, Navarro V. Prevalence of chronic renal failure and access to dialysis. Int J Epidemiol (1973) 2:247–255.[Abstract/Free Full Text]
  62. Dombey SL, Sagar D, Knapp MS. Chronic renal failure in Nottingham and requirements for dialysis and transplant facilities. Br Med J (1975) 2:484–485.[ISI][Medline]
  63. Feest TG, Mistry CD, Grimes DS, Mallick NP. Incidence of advanced chronic renal failure and the need for end stage renal replacement therapy. Br Med J (1990) 301:897–900.[ISI][Medline]
  64. McGeown MG. Prevalence of advanced renal failure in Northern Ireland. Br Med J (1990) 301:900–903.[ISI][Medline]
  65. Khan IH, Catto GRD, Edward N, MacLeod AM. Chronic renal failure: factors influencing nephrology referral. Q J Med (1994) 87:559–564.[ISI]
  66. Drey N, Roderick P, Mullee M, Rogerson M. A population-based study of the incidence and outcomes of diagnosed chronic kidney disease. Am J Kidney Dis (2003) 42:677–684.[CrossRef][ISI][Medline]
  67. John R, Webb M, Young A, Stevens PE. Unreferred chronic kidney disease: a longitudinal study. Am J Kidney Dis (2004) 43:825–835.[CrossRef][ISI][Medline]
  68. De Lusignan S, Chan T, Stevens P, et al. Identifying patients with chronic kidney disease from general practice computer records. Family Practice (2005) 22:234–241.[Abstract/Free Full Text]
  69. Anandarajah S, Tai T, De Lusignan, et al. The validity of searching routinely collected general practice computer data to identify patients with chronic kidney disease (CKD): a manual review of 500 medical records. Nephrol Dial Transplant (2005) 20:2089–2096.[Abstract/Free Full Text]
  70. Reaney EA, Maxwell AP, Savage G, et al. Implications of using estimated glomerular filtration rate (eGFR) versus serum creatinine for identifying and referring patients with CKD. Renal Association, April 2005. Abstract RA5305. http://www.triangle3.org.uk/cgi-bin/absdb/absdb_view.cgi?AbstractID=RA5305.
  71. Stevens PE, O’Donaghue DJ, Klebe B, et al. Calibrating creatinine assays to the MDRD Cleveland Clinic laboratory significantly influences estimates of the prevalence of CKD in England. Renal Association, May 2006. Abstract RA6328. http://www.triangle3.org.uk/cgi-bin/absdb/absdb_view.cgi?AbstractID=RA6328.
  72. Kissmeyer L, Kong C, Cohen J, et al. Community nephrology: audit of screening for renal insufficiency in a high risk population. Nephrol Dial Transplant (1999) 14:2150–2155.[Abstract/Free Full Text]
  73. Ellis PA, Cairns HS. Renal impairment in elderly patients with hypertension and diabetes. Q J Med (2001) 94:261–265.[ISI]
  74. Middleton RJ, Foley RN, Hegarty J, et al. The unrecognized prevalence of chronic kidney disease in diabetes. Nephrol Dial Transplant (2006) 21:88–92.[Abstract/Free Full Text]
  75. Lightstone L, Roderick P, Mullee M, et al. Systematic screening of patients in primary care to compare the prevalence of chronic kidney disease (CKD) in UK Indian Asians (IA) and Northern Europeans (NE). Renal Association, September 2004. Abstract RA4603. http://www.triangle3.org.uk/cgi-bin/absdb/absdb_view.cgi?AbstractID=RA4603.
  76. Clase CM, Garg AX, Kiberd BA. Prevalence of low glomerular filtration rate in nondiabetic Americans: Third National Health and Nutrition Examination Survey (NHANES III). J Am Soc Nephrol (2002) 13:1338–1349.[Abstract/Free Full Text]
  77. Clase CM, Garg AX, Kiberd BA. Estimating the prevalence of low glomerular filtration rate requires attention to the creatinine assay calibration: reply from the authors. J Am Soc Nephrol (2002) 13:2812–2816.[ISI]
  78. Coresh J, Astor BC, Greene T, et al. 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.[ISI][Medline]
  79. Chadban SJ, Briganti EM, Kerr PG, et al. Prevalence of kidney damage in Australian adults: the AusDiab kidney study. J Am Soc Nephrol (2003) 14:S131–S138.[Abstract/Free Full Text]
  80. Iseki K, Kinjo K, Iseki C, Takishita S. Relationship between predicted creatinine clearance and proteinuria and the risk of developing ESRD in Okinawa, Japan. Am J Kidney Dis (2004) 44:806–814.[CrossRef][ISI][Medline]
  81. Amato D, Alvarez-Aguilar C, Castaneda-Limones R, et al. Prevalence of chronic kidney disease in an urban Mexican population. Kidney Int (2005) 68(Suppl 97):S11–S17.
  82. Otero A, Gayoso P, Garcia F, De Francisco AL. The EPIRCE Study Group. Epidemiology of chronic renal disease in the Galician population: results of the pilot Spanish EPIRCE study. Kidney Int (2005) 68(Suppl 99):S16–S19.[CrossRef]
  83. Viktorsdottir O, Palsson R, Andresdottir MB, et al. Prevalence of chronic kidney disease based on estimated glomerular filtration rate and proteinuria in Icelandic adults. Nephrol Dial Transplant (2005) 20:1799–1807.[Abstract/Free Full Text]
  84. Hallan SI, Coresh J, Astor BC, et al. International comparison of the relationship of chronic kidney disease prevalence and ESRD risk. J Am Soc Nephrol (2006) 17:2275–2284.[Abstract/Free Full Text]
  85. Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med (2004) 351:1296–1305.[Abstract/Free Full Text]
  86. Gerstein HC, Mann JFE, Yi Q, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and non-diabetic individuals. JAMA (2001) 286:421–426.[Abstract/Free Full Text]
  87. Bigazzi R, Bianchi S, Baldari D, Campese VM. Microalbuminuria predicts cardiovascular events and renal insufficiency in patients with essential hypertension. J Hypertens (1998) 16:1325–1333.[CrossRef][ISI][Medline]
  88. Hillege HL, Fidler V, Diercks GFH, et al. Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population. Circulation (2002) 106:1777–1782.[Abstract/Free Full Text]
  89. Verhave JC, Gansevoort RT, Hillege HL, et al. An elevated urinary albumin excretion predicts de novo development of renal function impairment in the general population. Kidney Int (2004) 66(Suppl 92):S18–S21.
  90. Hsu C-Y, Vittinghoff E, Lin F, Shlipak MG. The incidence of end-stage renal disease is increasing faster than the prevalence of chronic renal insufficiency. Ann Intern Med (2004) 141:95–101.[Abstract/Free Full Text]
  91. Coresh J, Byrd-Holt D, Astor BC, et al. Chronic kidney disease awareness, prevalence, and trends amongst US adults, 1999–2000. J Am Soc Nephrol (2005) 16:180–188.[Abstract/Free Full Text]
  92. Davies DF, Shock NW. Age changes in glomerular filtration rate, effective renal plasma flow, and tubular excretory capacity in adult males. J Clin Invest (1950) 29:496–507.[ISI][Medline]
  93. Hoang K, Tan JC, Derby G, et al. Determinants of glomerular hypofiltration in aging humans. Kidney Int (2003) 64:1417–1424.[CrossRef][ISI][Medline]
  94. Grewal GS, Blake GM. Reference data for51Cr-EDTA measurements of the glomerular filtration rate derived from live kidney donors. Nucl Med Commun (2005) 26:61–65.[CrossRef][ISI][Medline]
  95. Fliser D, Franek E, Joest M, et al. Renal function in the elderly: impact of hypertension and cardiac function. Kidney Int (1997) 51:1196–1204.[ISI][Medline]
  96. Lew SQ, Bosch JP. Effect of diet on creatinine clearance and excretion in young and elderly healthy subjects and in patients with renal disease. J Am Soc Nephrol (1991) 2:856–865.[Abstract]
  97. Lindeman RD, Goldman R. Anatomic and physiologic age changes in the kidney. Exp Gerontol (1986) 21:379–406.[CrossRef][ISI][Medline]
  98. Kasiske BL. Relationship between vascular disease and age-associated changes in the human kidney. Kidney Int (1987) 1153–1159.
  99. Kasiske BL, Umen AJ. The influence of age, sex, race, and body habitus on kidney weight in humans. Arch Pathol Lab Med (1986) 110:55–60.[ISI][Medline]
  100. Rowe JW, Andres R, Tobin JD, et al. The effect of age in creatinine clearance in men: a cross sectional and longitudinal study. J Gerontol (1976) 31:155–163.[ISI][Medline]
  101. Lindeman RD, Tobin JD, Shock NW. Association between blood pressure and the rate of decline in renal function with age. Kidney Int (1984) 26:861–868.[ISI][Medline]
  102. Lindeman RD, Tobin JD, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc (1985) 33:278–285.[ISI][Medline]
  103. Larsson M, Jagenburg R, Landahl S. Renal function in an elderly population. A study of S-creatinine, 51Cr-EDTA clearance, endogenous creatinine clearance and maximal tubular water reabsorption. Scand J Clin Lab Invest (1986) 46:593–598.[ISI][Medline]

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 QJMHome page
A. Almond, S. Siddiqui, S. Robertson, J. Norrie, and C. Isles
Comparison of combined urea and creatinine clearance and prediction equations as measures of residual renal function when GFR is low
QJM, August 1, 2008; 101(8): 619 - 624.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Extract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by MacGregor, M. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by MacGregor, M. S.