Skip Navigation


NDT Advance Access originally published online on November 26, 2007
Nephrology Dialysis Transplantation 2008 23(4):1265-1273; doi:10.1093/ndt/gfm790
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/4/1265    most recent
gfm790v1
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 arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Obermayr, R. P.
Right arrow Articles by Klauser-Braun, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Obermayr, R. P.
Right arrow Articles by Klauser-Braun, R.
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 e-mail: journals.permissions@oxfordjournals.org


Predictors of new-onset decline in kidney function in a general middle-european population

Rudolf P. Obermayr1, Christian Temml2, Maarten Knechtelsdorfer1, Georg Gutjahr3, Josef Kletzmayr1, Susanne Heiss1, Anton Ponholzer4, Stephan Madersbacher4, Rainer Oberbauer5 and Renate Klauser-Braun1

1 3rd Medical Department (Division of Nephrology, Diabetes and Hypertension), Donauspital, Sozialmedizinsches Zentrum Ost der Stadt Wien, Langobardenstrasse 122, A-1220, Vienna, Austria 2 Department of Health Prevention, Neutorgasse 15, A-1010, Vienna, Austria 3 Department of Statistics and Decision Support, University of Vienna, Universitätsstrasse 5, A-1090, Vienna, Austria 4 Department of Urology and Andrology, Donauspital, Sozialmedizinisches Zentrum Ost der Stadt Wien, Langobardenstrasse 122, A-1220, Vienna, Austria 5 Department of Nephrology, Krankenhaus der Elisabethinen, Fadingerstrasse 1, A-4010 Linz, Linz, Austria and, Medical University Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria

Rudolf P. Obermayr, 3rd Medical Department, Donauspital, Sozialmedizinisches Zentrum Ost der Stadt Wien, Langobardenstrasse 122, A-1220 Vienna, Austria (EU). Tel: +43-1-28802-5402; Fax: +43-1-28802-5480; E-mail: rudolf.obermayr{at}gmail.com



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Limited epidemiological data are available on predictors of new-onset kidney disease.

Methods. In this longitudinal cohort study, 17 375 apparently healthy volunteers of the general Viennese population (46.4% women, age range 20–84 years, men 20–89 years) performed a baseline examination at some time within the study period (1990–2005) and completed a median of two follow-up examinations [interquartile range (IQR) 1 to 4]; the median follow-up period was 7 years (IQR 4 to 11). The outcome of interest was the development of kidney disease, defined as a decrease of the glomerular filtration rate (GFR) <60 ml/min/1.73 m2 at the follow-up examinations [calculated by the abbreviated modification of diet in renal disease (MDRD) equation]. Logistic generalized estimating equations were used to analyse the relationship between the covariates and the outcome variable.

Results. The following parameters [odds ratios (OR) with 95% confidence intervals] predicted new-onset kidney disease: Age (increase by 5 years), OR = 1.36 (1.34–1.40); National Kidney Foundation-chronic kidney disease (NKF-CKD) stage 1 with proteinuria (+), OR = 1.39 (1.10–1.75); NKF-CKD stage 1 with proteinuria (≥++), OR = 2.07 (1.11–3.87); NKF-CKD stage 2 with proteinuria (+), OR = 2.71 (2.10–3.51); NKF-CKD stage 2 with proteinuria (≥++), OR = 3.80 (2.29–6.31); body mass index, OR = 1.04 (1.02–1.06); current-smoker, OR = 1.20 (1.01–1.43); performing no sports, OR = 1.57 (1.27–1.95); uric acid (increase by 2 mg/dl), OR = 1.69 (1.59–1.80); HDL-cholesterol (decrease by 10 mg/dl), OR = 1.12 (1.07–1.17); hypertension stage 1, OR = 1.35 (1.08–1.67); hypertension stage 2, OR = 2.01 (1.62–2.51); diabetes mellitus, OR = 1.44 (1.07–1.93).

Conclusions. Cardiovascular risk factors as well as NKF-CKD stages 1 and 2 and proteinuria, the more the higher and an entirely novel finding, performing no sports, predicted new-onset kidney disease.

Keywords: Caucasian middle-European population; epidemiology; estimated glomerular filtration rate; new-onset kidney disease; predictors



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 References
 
The incidence of end-stage renal disease (ESRD), and concomitantly the number of patients on renal replacement therapy, are increasing steadily [1–3]. Established cardiovascular risk factors are associated with ESRD, hypertension and diabetes being the leading causes [1–3].

The majority of epidemiologic studies related only individual risk factors to renal dysfunction and associated kidney disease: age [4], diabetes mellitus [5,6], hypertension [7,8], obesity [9,10], dyslipidaemia [11,12] and smoking [13–15]. Recent experimental and epidemiological evidence suggests a role of uric acid not only as a marker of reduced kidney function and an independent cardiovascular risk factor [16], but also as a risk factor for the development and progression of renal disease [17–19]. As is generally known, proteinuria has long been recognized as an important risk factor for progression of renal disease as well as an independent risk factor for cardiovascular morbidity and mortality [20–23].

Moreover, there is a bulk of evidence showing the beneficial effects of endurance and/or strength exercise training in patients with chronic diseases: improvement of blood pressure control [24,25], lipid profiles [26,27], glycosylated haemoglobin [26–28] and control of body weight [26]. In fact, little is known concerning the effect of physical exercise on risk factors for the development or progression of kidney disease [25,29,30].

The vast majority of studies concerning risk factors of kidney disease investigated patients who subsequently developed ESRD [31]. Only a few studies focused on earlier stages of kidney disease with the belief that early interventions might delay or prevent the progression to ESRD [32]. This is all the more important because a majority of adults with moderate kidney disease have coronary heart disease or risk equivalents [33].

The aim of this longitudinal cohort study was to identify risk factors at an earlier stage of kidney disease within an apparently healthy general adult Viennese population, i.e. representing a general Caucasian middle-European population, using an integral multivariable approach.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 References
 
Study design and population
It is common practice for the adult Viennese population to have the opportunity to perform yearly preventative medical checkups, due to invitations being issued by the health insurance companies. These examinations are usually performed by general practitioners.

Within the ongoing Vienna Health Screening Project, the general adult population of Vienna is invited by the Department of Health Prevention since 1990 to yearly extensive (study protocol) preventive medical checkups. Within this project, invitations are sent out by letter to e.g. companies, educational, social, governmental and non-governmental institutions (52.3% of all invitations were randomly selected using the Viennese company register file) as well as to households (47.7% of all invitations were randomly selected using the Austrian register of residents file) with the aim of obtaining a representative Viennese study cohort. In 1990, 6000 invitation letters were sent out to volunteers to participate in the study and subsequently 3000 letters were sent out every year until 2004. Overall 48 000 invitation letters were sent out; 38.0% volunteers accepted the study offer. Participants who performed the baseline examination were reminded annually by a letter to attend follow-up examinations. Until 2005, 18 239 apparently healthy volunteers (46.4% women, age range 20–84 years, men 20–89 years) performed the baseline examination at some time within the study period. Due to the study protocol, no preset time intervals for the follow-up examinations were demanded imperatively. Participants who attended at least one follow-up examination were included in the analysis, even if the patients died afterwards. In order to investigate predictors at an earlier stage of kidney disease, 329 participants with a baseline GFR <60 ml/min/1.73 m2 [calculated by the simplified modification of diet in renal disease (MDRD) equation] were excluded, as well as 534 participants who did not attend any follow-up examination (i.e. dropouts).

The institutional review board at the Department of Health Prevention, Vienna, Austria, approved the study. All participants gave informed consent according to the Helsinki declaration.

Assessments of risk factors
Assessment of medical history and physical examination was performed by specially trained general practitioners (GPs) with respect to the study protocol. Height (cm) and weight (kg) were measured by study nurses. Body mass index (BMI) was defined as weight in kilograms divided by the square of height in meters. Smoking status was defined as no-smoking, current-smoking or ex-smoking at baseline. Sports habits were classified as potentially preventive for health if the amount of endurance exercise exceeded 15 min per training unit and/or resistance exercise was performed due to a training protocol; furthermore physical exercise had to be performed continuously ≥2 times/week [34]. Blood pressure measurements were done according to the currently used guidelines of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7 [35]); two measurements of systolic and diastolic blood pressure were performed by the study nurses within a time interval of at least 15 min, and additionally one measurement was assessed by a GP. The arithmetic mean of the three measurements was used for statistical analysis. Evaluation of hypertension was performed using the JNC7 classification; normal blood pressure: systolic <120 mmHg and diastolic <80 mmHg; prehypertension: systolic = 120–139 mmHg or diastolic = 80–89 mmHg; hypertension stage 1: systolic = 140–159 mmHg or diastolic = 90–99 mmHg; hypertension stage 2: systolic ≥160 mmHg or diastolic ≥100 mmHg. Blood testing of fasting serum parameters included creatinine (mg/dl), glucose (mg/dl), uric acid (mg/dl), total cholesterol (mg/dl), HDL-cholesterol (mg/dl), LDL-cholesterol (mg/dl) and triglycerides (mg/dl). Participants with a fasting glucose level of 126 mg/dl or higher were defined as diabetic. Impaired fasting glucose was defined as a fasting glucose level between 100 and 125 mg/dl in the absence of diabetes [36]. Dipstick proteinuria was measured semi-quantitatively. Chronic kidney disease was classified using the National Kidney Foundation-chronic kidney disease (NKF-CKD) classification [37].

Measurements and definitions
Kidney function was estimated by GFR [38–40], which was calculated by the simplified MDRD equation [38]: GFR (ml/min/1.73 m2) = 186.3 x serum creatinine–1.154 x age–0.203 x (0.742 if female). Since the MDRD formula is sensitive to the creatinine assay methodology [42], the following correction formula was necessary (due to our creatinine assay methodology used) to convert measured creatinine to ‘MDRD creatinine’: MDRD creatinine (mg/dl) = –0.215 + 1.08 x measured creatinine [43].

Measurements, calibration, standardization and validation of serum chemistry parameters were performed in a single laboratory throughout the 16-year study period according to good laboratory practice guidelines. Glucose, creatinine, cholesterol, HDL-cholesterol and triglycerides were measured on the Hitachi 917® clinical chemistry analyzer using systemic reagents from Roche® Inc. LDL-cholesterol was measured using Friedewald's formula; inter-assay coefficient of variation (CV) was 2.4%. Glucose was measured by the hexokinase method (Gluco-quant®); intra-assay CV was 1.0% and inter-assay CV was 1.7%. Creatinine was measured by means of the kinetic Jaffé method; intra-assay CV was 0.7% and inter-assay CV was 2.3%. Cholesterol was measured by an enzymatic method (Cholesterol CHOD-PAP®); intra-assay CV was 0.8% and inter-assay CV was 1.7%. HDL-cholesterol was measured as a direct quantitative determination by an enzymatic assay (HDL-C-plus second generation); intra-assay CV was 0.8% and inter-assay CV was 1.8%. Triglycerides were measured by an enzymatic method (Triglycerides GPO-PAP®); intra-assay CV was 1.5% and inter-assay CV was 1.8%. Uric acid was measured by an enzymatic method (UA-plus®); intra-assay CV was 0.5% and inter-assay CV was 0.8%. Dipstick proteinuria was measured semi-quantitatively by a Combour-test®.

Statistical analysis
The outcome of interest (i.e. the dependent variable) was the development of kidney disease defined as a decrease of GFR <60 ml/min/1.73 m2 (i.e. NKF-CKD stage 3) at a follow-up examination. Covariates included age, sex, NKF-CKD stages 1 and 2, dipstick proteinuria (+) and (≥++), NKF-CKD stage 1 with proteinuria (+), NKF-CKD stage 1 with proteinuria (≥++), NKF-CKD stage 2 with proteinuria (+), NKF-CKD stage 2 with proteinuria (≥++), BMI, BMI categories, sports, current-smokers, ex-smokers, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, uric acid, fasting serum glucose, systolic blood pressure, diastolic blood pressure, JNC7 blood pressure categories [35] and modified diagnosis of diabetes mellitus categories [36]. Logistic generalized estimating equations (GEEs) were used to analyse the relationship between the covariates and the outcome variable, a method taking into account the dependence between repeated measurements within the same individual at different follow-up periods [44]. Since there was an significant correlation between the outcome variables on the same person and as the strength of these within-participant correlations decreased with increasing time between the subsequent examinations, an autoregressive correlation structure was used for the GEE models. First, all covariates, i.e. predictors, were univariably analysed after adjusting for age considering the known association of this parameter with GFR [4,38] and sex, taking into account the sex-related differences as shown in the baseline data (Table 1). Subsequently, a multivariable GEE model was performed with respect to statistical significance resulting from the univariable analyses. Since visual data inspection showed a rather nonlinear relationship between blood pressure as well as blood glucose and the odds for developing new-onset kidney disease, only the respective categorical terms were used for multivariable analysis. In addition, the study cohort is very similar, classified by the factors NKF-CKD stages as well as by the semi-quantitative amount of proteinuria. In order to avoid explaining the same statistical variation, only the combination of NKF-CKD stages with the proteinuria groups was included in the multivariable model. Covariates were retained if Wald tests gave a P < 0.05.


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

  		Table 1 Baseline characteristics of participants stratified by sex

 
All continuous data are presented as means ± standard deviations (SD). Categorical data are presented as percentages. A two-tailed P < 0.05 was considered to indicate statistical significance for the univariable analyses. All data analysis was done in the R environment for statistical computing, version 2.4 [45], using the ‘geepack library’, version 1.0–10 [46].



   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 References
 
A total of 17 375 volunteers (46.4% women, age range 20–84 years, men 20–89 years) were assessed longitudinally. They performed the baseline examination and completed a median of two follow-up examinations (IQR 1 to 4), i.e. a total of 59 142 follow-up examinations. The median follow-up period was 7 years (IQR 4 to 11); the mean follow-up period was 7.2 ± 3.9 years (Figure 1). Participants who dropped out did not show systematic differences in their baseline characteristics compared to the total cohort. Overall 190 participants died within the study period.


Figure 1
View larger version (23K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1 Until 2005, 17 375 apparently healthy volunteers (46.4% women, age range 20–84 years, men 20–89 years) performed a baseline examination at some time within the study period and completed a median of two follow-up examinations [interquartile range (IQR) 1 to 4]. The median follow-up period was 7 years (IQR 4 to 11) and the mean follow-up period was 7.2 ± 3.9 years. Altogether, a total of 17 375 baseline examinations and 59 142 follow-up examinations were used for analysis.

 
Baseline characteristics of participants are shown in Table 1. According to the NKF-CKD classification [37], at baseline 91.4% of the participants (women and men) were classified as apparently free of kidney disease, and 8.6% of the volunteers were classified into NKF-CKD stages 1 (n = 841; 4.8%) or 2 (n = 648; 3.8%). A total of 329 participants were excluded at baseline, because 309 volunteers were classified into NKF-CKD stage 3, 6 into NKF-CKD stage 4 and 14 into NKF-CKD stage 5, respectively.

BMI was about 7% higher in men compared to women. Systolic and diastolic blood pressure were about 9% higher in men compared to women, fasting serum glucose was about 5% higher and uric acid was about 40% higher, HDL-cholesterol was about 20% lower and triglycerides were about 30% higher in men as well. Proteinuria, smoking status and sports performance showed only slight differences in women versus men.

Logistic GEEs for individual predictors were performed after adjusting for age and sex. The outcome of interest was the development of kidney disease defined as a decrease of GFR <60 ml/min/1.73 m2 at the follow-up examinations. Each individual's last follow-up examination was used for a detailed description of the gradual progression of CKD (Table 2). Overall, 268 (1.5%) participants developed NKF-CKD stage 3; 8 (0.05%) participants NKF-CKD stage 4 and 12 (0.07%) participants NKF-CKD stage 5 at the last follow-up examination.


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

  		Table 2 Progression of CKD and decline in eGFR during the whole study period

 
Results are presented as odds ratios (OR) with a 95% confidence interval (CI) for all individual predictors as shown in Table 3.


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

  		Table 3 Individual predictors of developing chronic kidney disease (defined as a GFR <60 ml/min/1.73 m2)

 
Subsequently, a multivariable GEE model was performed with respect to statistical significance resulting from the univariable analyses of the individual predictors as shown in Table 4.


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

  		Table 4 Multivariable predictors of developing chronic kidney disease (defined as a GFR < 60 ml/min/1.73 m2)

 


   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
References
 
In a large apparently healthy adult Viennese cohort showing baseline characteristics very similar to data from respective Viennese community-based studies [47–49], established cardiovascular risk factors predicted the development of new-onset kidney disease. This is the first study on this issue performed within a general middle-European population using an integral multivariable approach that focused on an earlier stage of kidney disease with the belief that early interventions might delay or prevent sequelae. Therefore the predictors of the multivariable model are subsequently discussed in detail (Table 4).

First, the relation between age and GFR is well known and due to the physiological process of ageing [4,38,50], the 80% higher odds concerning female sex were unexpected. This was an issue to investigate in more detail, since the prevalence of ESRD is not higher in women compared to men in Austria [3]. A plausible mathematical explanation to this finding could be that sex appears in the numerator of the MDRD equation, which results in lower GFR values for women. In order to untie this finding, we replicated the approach of Fox et al. [32], who used the fifth sex-specific percentile of the baseline GFR as the cut-off limit at the follow-up examinations of their study sample to define new-onset kidney disease: the mean baseline GFR at the fifth percentile (i.e. cut-off limit at the follow-up examinations) was 67 ml/min/1.73 m2 for women and 73 ml/min/1.73 m2 for men. Consequently, the resulting age-adjusted sex-specific OR (if female) for developing new-onset kidney disease was 0.88 (0.82 to 0.93), which confirms the results of the Framingham study [32]. In fact, little is known concerning the impact of sex on new-onset kidney disease: Domrongkitchaiporn et al. [51] did not include the terms age and sex in the statistical analysis because of the presence of both variables in the MDRD formula; Yamagata et al. [50] performed a sex-stratified analysis. So far, we suggest that the impact of sex on new-onset kidney disease should be interpreted retentively, especially when different cut-off limits for the classification of CKD were used.

As expected, NKF-CKD stages 1 and 2 (which by definition refers to the existence of initial CKD at baseline), and the more the higher proteinuria, were revealed as the major predictors for progression of CKD [20,21,32,50].

Little is known regarding the effect of physical exercise on risk factors for the development or progression of kidney disease [25,29,30]. It seems plausible that the well-examined beneficial effects of endurance and/or resistance exercise in chronic disease (e.g. reduction of blood pressure, insulin resistance and BMI, improvement of the serum lipid profile) should play their role in chronic kidney disease as well. Indeed, as an entirely novel finding, participants who did not perform sports showed 57% higher odds for developing new-onset kidney disease.

Current-smoking at baseline induced 20% higher odds for the development of new-onset kidney disease compared to non-smoking [32,50]. The mechanisms of smoking-induced renal damage are only partly understood; probably they comprise haemodynamic (e.g. increase in blood pressure and presumably intraglomerular pressure) and chronic effects (e.g. endothelial cell dysfunction) [14].

Remarkably, uric acid at baseline was associated with 69% higher odds (with an increase of 2 mg/dl) to develop new-onset kidney disease. Recent experimental and epidemiological evidence suggests a role of uric acid not only as a marker of reduced kidney function [52] and an independent cardiovascular risk factor [16], but also as a risk factor for the development of renal disease [17–19]. Additionally, independent nephrotoxic effects of elevated uric acid are described, e.g. entry of uric acid into both endothelial and vascular smooth muscle cells, resulting in local inhibition of endothelial nitric oxide levels, stimulation of vascular smooth muscle cell proliferation and stimulation of vasoactive and inflammatory mediators [16,53]. An evaluation of the role of uric acid as a potential risk factor would require a single-factor analysis using a confounder model from an epidemiological point of view. Yet this was not the primary aim of our study.

An increase in BMI of 1 kg/m2 was associated with 4% higher odds for developing incident kidney disease. This is a really weak effect and probably due to the near normal BMI values of our rather young study population. However, obesity was revealed as a relevant risk factor with hazard ratios of 1.42 (men) and 1.56 (women) within a Japanese cohort [50].

The same applies for HDL-cholesterol, which (in this study cohort) was also in the normal range. A decrease of 10 mg/dl showed 12% higher odds for developing new-onset kidney disease, which we considered as a weak effect. Nevertheless, low HDL-cholesterol [54] was shown as a risk factor for the development of renal dysfunction.

Blood pressure at baseline was in the normal and prehypertensive range in ~70% of the population (Table 1). Visual data inspection showed a rather nonlinear relationship between blood pressure and the odds for developing new-onset kidney disease with only modest increases over the normal and prehypertensive ranges and strong increases for the hypertensive ranges. Therefore, blood pressure groups were used in the multivariable model instead of linear terms for systolic and diastolic blood pressure. As expected, stage 1 hypertension predicted new-onset kidney disease with 35% higher odds and stage 2 with 101% higher odds [8,32,50].

The same visual data inspection with its consequences applies for fasting serum glucose and the diagnosis of diabetes mellitus. In this study cohort, the number of participants having diabetes mellitus were few (Table 1). Nevertheless, having diabetes mellitus predicted the development of new-onset kidney disease with 44% higher odds with a wide CI. Two large epidemiological studies reported considerably higher odds for developing new-onset kidney disease [32,51], both having a remarkably higher prevalence of diabetics within their study cohorts; in contrast, another large study [50] showed hazard ratios of 0.71 (men) and 0.76 (women) for patients having untreated diabetes mellitus (probably showing hyperfiltration) and hazard ratios of 1.20 (men) and 1.12 (women) for patients having treated long-lasting diabetes, respectively.

Strength of the study: this study has the advantage of a large sample of participants apparently healthy at baseline and the use of an integral multivariable approach concerning established and rather novel cardiovascular risk factors. We examined risk factors altogether that led to new-onset kidney disease, where interventions could be efficacious for delaying the continuous progress of the decline in kidney function. Moreover, early interventions could be important since a majority of adults with moderate kidney disease have coronary heart disease or risk equivalents [55].

Limitations of the study: this study may have been subject to a survival bias because participants had to attend follow-up examinations and 534 participants dropped out. Though unlikely (they showed very similar baseline data compared to the study cohort), it is possible that those individuals may have developed more severe risk factors after the baseline examination and could have had rapid disease progression in the interim. This is certainly a limitation and may have led to underestimation of some findings. It is noteworthy that this study population was predominantly middle-aged compared to the general age distribution in Vienna [41]. For that reason, the multivariable analysis was performed for volunteers aged ≤65 years, too. The resulting ORs were very similar to the results from the total cohort. Moreover, using the surrogate parameter the estimated GFR calculated by the MDRD formula to represent the severity of kidney disease is not a gold standard method, but it must be considered the method of choice within large epidemiologic studies, because of its simplicity and low cost. Correction of routine measured creatinine to ‘MDRD-creatinine’ is mandatory [42] and was performed by the correction formula of Hallan et al. [43], who used exactly the same creatinine assay methodology as we did.

In conclusion, cardiovascular risk factors as well as NKF-CKD stages 1 and 2 and proteinuria, the more the higher, predicted the development of new-onset kidney disease. A rather novel finding was the role of uric acid as a predictor in this context; implementation of a confounder model could possibly confirm its role as a risk factor. An entirely novel finding was that performing no sports was revealed as an important predictor of new-onset kidney disease.



   Acknowledgments
 
The authors are particularly grateful to Alexander Lapin, MD, Head of the Institute of Medical and Chemical Laboratory Analysis at Sozialmedizinisches Zentrum Sophienspital, Vienna, Austria, for the implementation of all laboratory analysis in accordance with the good laboratory practice guidelines. The study is financially supported by the Department of Health Prevention and thus is receiving a full state subsidy by the government of Vienna.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. US Renal Data System. USRDS. Annual Data Report, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2003.
  2. Stengel B, Billon S, Van Dijk PC, et al. Trends in the incidence of renal replacement therapy for end stage renal disease in Europe, 1990–1999. Nephrol Dial Transplant (2003) 18:1824–1833.[Abstract/Free Full Text]
  3. Kramar R, Oberbauer R. Austrian Dialysis and Transplantation Registry (OEDTR), Austrian Society of Nephrology, Annual Report 2005.
  4. Lindeman RD, Tobin J, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc (1985) 33:278–285.[Web of Science][Medline]
  5. Ritz E, Orth SR. Nephropathy in patients with type 2 diabetes mellitus. N Engl J Med (1999) 341:1127–1133.[Free Full Text]
  6. Adler AI, Stevens RJ, Manley SE, et al. Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int (2003) 63:225–232.[CrossRef][Web of Science][Medline]
  7. Young JH, Klag MJ, Munter P, et al. Blood pressure and decline in kidney function: findings from the systolic hypertension in the elderly program (SHEP). J Am Soc Nephrol (2002) 13:2776–2788.[Abstract/Free Full Text]
  8. Haroun MK, Jaar BG, Hoffman SC, et al. Risk factors for chronic kidney disease: a prospective study of 23,435 men and women in Washington County, Maryland. J Am Soc Nephrol (2003) 14:2934–2941.[Abstract/Free Full Text]
  9. Kramer H, Luke A, Bidani A, et al. Obesity and prevalent and incident CKD: the Hypertension Detection and Follow-Up Program. Am J Kidney Dis (2005) 46:587–594.[CrossRef][Web of Science][Medline]
  10. Gelber RP, Kurth T, Kausz AT, et al. Association between body mass index and CKD in apparently healthy men. Am J Kidney Dis (2005) 46:871–880.[CrossRef][Web of Science][Medline]
  11. Muntner P, Coresh J, Smith JC, et al. Plasma lipids and the risk of developing renal dysfunction: the atherosclerosis risk in community study. Kidney Int (2000) 58:293–301.[CrossRef][Web of Science][Medline]
  12. Schaeffner ES, Kurth T, Curhan GC, et al. Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol (2003) 14:2084–2091.[Abstract/Free Full Text]
  13. Ejerblad E, Fored CM, Lindblad P, et al. Association between cigarette smoking and chronic renal failure in a nationwide population-based case-control study. J Am Soc Nephrol (2004) 14:2178–2185.[Web of Science]
  14. Orth SR. Effects of smoking on systemic and intrarenal hemodynamics: influence on renal function. J Am Soc Nephrol (2004) 15:S58–S63.[Abstract/Free Full Text]
  15. Pinto-Sietsma SJ, Mulder J, Janssen WM, et al. Smoking is related to albuminuria and abnormal renal function in nondiabetic persons. Ann Intern Med (2000) 133:585–591.[Abstract/Free Full Text]
  16. Fang J, Alderman MH. Serum uric acid and cardiovascular mortality. The NHANES I epidemiologic follow-up study, 1971–1992. JAMA (2000) 283:2404–2410.[Abstract/Free Full Text]
  17. Kang DH, Nakagawa T, Feng L, et al. A role of uric acid in the progression of renal disease. J Am Soc Nephrol (2002) 13:2888–2897.[Abstract/Free Full Text]
  18. Johnson RJ, Segal MS, Srinivas T, et al. Essential hypertension, progressive renal disease, and uric acid: a pathogenetic link? J Am Soc Nephrol (2005) 16:1909–1919.[Abstract/Free Full Text]
  19. Nakagawa T, Kang D-H, Feig D, et al. Unearthing uric acid: an ancient factor with recently found significance in renal and cardiovascular disease. Kidney Int (2006) 69:1722–1725.[CrossRef][Web of Science][Medline]
  20. Venkat K. Proteinuria and microalbuminuria in adults: significance, evaluation, and treatment. South Med J (2004) 97:969–979.[CrossRef][Web of Science][Medline]
  21. DE Leeuw PW, Thijs L, Birkenhäger WH, et al. Prognostic significance of renal function in elderly patients with isolated systolic hypertension: results from the Syst-Eur trial. J Am Soc Nephrol (2002) 13:2213–2222.[Abstract/Free Full Text]
  22. Gerstein HC, Mann JF, Yi Q, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA (2001) 286:421–426.[Abstract/Free Full Text]
  23. Hillege HL, Fidler V, Diercks GF, et al. Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population. Circulation (2002) 106:1777–1782.[Abstract/Free Full Text]
  24. Pescatello LS, Franklin BA, Fagard R, et al. American College of Sports Medicine position stand. Exercise and hypertension. Med Sci Sports Exerc (2004) 36:533–553.
  25. Boyce ML, Robergs RA, Avasthi PS, et al. Exercise training by individuals with predialysis renal failure: cardiorespiratory endurance, hypertension and renal function. Am J Kidney Dis (1997) 30:180–192.[Web of Science][Medline]
  26. Margareta Eriksson K, Westborg CJ, et al. A randomized trial of lifestyle interventions in primary health care for the modification of cardiovascular risk factors. Scand J Public Health (2006) 34:435–461.
  27. Cauza E, Hanusch-Enserer U, Strasser B, et al. The metabolic effects of long term exercise in type 2 diabetes patients. Wien Med Wochenschr (2006) 156:515–519.[CrossRef][Medline]
  28. Albright A, Franz M, Hornsby G, et al. American College of Sports Medicine position stand. Exercise and type 2 diabetes. Med Sci Sports Exerc (2000) 32:1345–1360.
  29. Castaneda C, Gordon PL, Uhlin KL, et al. Resistance training to counteract the catabolism of a low protein diet in patients with chronic renal insufficiency. A randomized, controlled trial. Ann Intern Med (2001) 135:965–976.[Abstract/Free Full Text]
  30. Castaneda C, Gordon PL, Parker RC, et al. Resistance training to reduce the malnutrition–inflammation complex syndrome of chronic kidney disease. Am J Kidney Dis (2004) 43:607–616.[CrossRef][Web of Science][Medline]
  31. Iseki K, Ikemiya Y, Fukiyama K. Risk factors of end stage renal disease and serum creatinine in a community based mass-screening. Kidney Int (1997) 51:850–854.[Web of Science][Medline]
  32. Fox CS, Larson GM, Leip EP, et al. Predictors of new-onset kidney disease in a community-based population. JAMA (2004) 291:844–850.[Abstract/Free Full Text]
  33. Verhave JC, Hillere HL, Burgerhof JGM, et al. (for the PREVEND Study Group) The association between atherosclerotic risk factors and renal function in the general population. Kidney Int (2005) 67:1967–1973.[CrossRef][Web of Science][Medline]
  34. Pollock ML, Gaesser GA, Butcher JD, et al. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Position stand. Med Sci Sports Exerc (1998) 30:975–991.
  35. Chobanian AV, Bakris GL, Black HR, et al. and the National High Blood Pressure Education Program Coordinating Committee. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The JNC7 report. JAMA (2003) 289:2560–2571.[Abstract/Free Full Text]
  36. Genuth S, Alberti KG, Bennett P, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care (2003) 26:3160–3167.[Free Full Text]
  37. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. Am J Kidney Dis (2002) 39(Suppl_1):S1–S266.[CrossRef][Web of Science][Medline]
  38. Levey AS, Bosch JP, Lewis JB, et al. (Modification of Diet in Renal Disease Study Group). A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med (1999) 130:461–470.[Abstract/Free Full Text]
  39. Stevens LA, Coresh J, Greene T, et al. Assessing kidney function—measured and estimated glomerular filtration rate. N Engl J Med (2006) 354:2473–2483.[Free Full Text]
  40. Coresh J, Stevens LA. Kidney function estimation equations: where do we stand? Curr Opinion Nephrol Hypertens (2006) 15:276–284.[Web of Science][Medline]
  41. 2004) Statisik Austria. Volkszaehlung. 001, Hauptergebnisse II—Wien. Verlag Bundesanstalt Statistik Austria, Vienna, Austria.
  42. Van Biesen W, Vanholder R, Veys N, et al. The importance of standardization of creatinine in the implementation of guidelines and recommendations for CKD: implications for CKD management programs. Nephrol Dial Transplant (2006) 21:77–83.[Abstract/Free Full Text]
  43. Hallan S, Asberg A, Lindberg M, et al. 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][Web of Science][Medline]
  44. Liang KY, Zeger S. Longitudinal data analysis using generalized linear models. Biometrika (1986) 73:13–22.[Abstract/Free Full Text]
  45. R Development Core Team (2007). R: a language environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org(7 September 2007, date last accessed).
  46. Yan J. Yet another package for generalized estimating equations. R-News (2002) 2/3:12–14.
  47. Dorner T, Rieder A. Risk management of coronary heart disease-prevention. Wien Med Wschr (2004) 154:257–265.[CrossRef][Medline]
  48. Dorner T, Rathmanner T, Lechleitner M, et al. Public health aspects of diabetes mellitus—epidemiology, prevention strategies, policy implications: the first Austrian diabetes report. Wien Klin Wschr (2006) 118:513–519.[CrossRef][Medline]
  49. Kiefer I, Kunze U, Mitsche N. Obesity in Austria: epidemiologic and social medicine aspects. Acta Med Austriaca (1998) 25:126–128.[Web of Science][Medline]
  50. Yamagata K, Ishida K, Sairenchi T, et al. Risk factors for chronic kidney disease in a community-based population: a 10-year follow-up study. Kidney Int (2007) 71:159–166.[CrossRef][Web of Science][Medline]
  51. Domrongkitchaiporn S, Sritara P, Kitiyakara C, et al. Risk factors for development of decreased kidney function in a southeast Asian population: a 12-year cohort study. J Am Soc Nephrol (2005) 16:791–799.[Abstract/Free Full Text]
  52. Roch-Ramel F, Diezi J. Renal transport of organic anions and uric acid. In: Diseases of the Kidney, 6th edn—Schrier RW, Gottschalk CE, eds. (1996) 231. Boston: Little Brown.
  53. Sanchez-Lozada LG, Tapia E, Santamaria J, et al. Mild hyperuricemia induces vasoconstriction and maintains glomerular hypertension in normal and remnant kidney rats. Kidney Int (2005) 67:237–247.[CrossRef][Web of Science][Medline]
  54. Muntner P, Coresh J, Smith J, et al. Plasma lipids and risk of developing renal dysfunction: The Atherosclerosis Risk in Communities Study. Kidney Int (2000) 58:93–101.[CrossRef]
  55. Hyre AD, Fox CS, Astor BC, et al. The impact of reclassifying moderate CKD as a coronary heart disease risk equivalent on the number of US adults recommended for lipid-lowering treatment. Am J Kidney Dis (2007) 49:37–45.[CrossRef][Web of Science][Medline]
Received for publication: 14. 7.07
Accepted in revised form: 10.10.07


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
 Ann Clin BiochemHome page
E. J Lamb, F. MacKenzie, and P. E Stevens
How should proteinuria be detected and measured?
Ann Clin Biochem, May 1, 2009; 46(3): 205 - 217.
[Abstract] [Full Text] [PDF]

Home page
 J. Am. Soc. Nephrol.Home page
R. P. Obermayr, C. Temml, G. Gutjahr, M. Knechtelsdorfer, R. Oberbauer, and R. Klauser-Braun
Elevated Uric Acid Increases the Risk for Kidney Disease
J. Am. Soc. Nephrol., December 1, 2008; 19(12): 2407 - 2413.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/4/1265    most recent
gfm790v1
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 arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Obermayr, R. P.
Right arrow Articles by Klauser-Braun, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Obermayr, R. P.
Right arrow Articles by Klauser-Braun, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?