The prevalence of chronic kidney disease in the ... - Springer Link

Int Urol Nephrol (2012) 44:213–220 DOI 10.1007/s11255-011-9923-z

NEPHROLOGY – ORIGINAL PAPER

The prevalence of chronic kidney disease in the general population in Romania: a study on 60,000 persons Vasile Cepoi • Mihai Onofriescu • Liviu Segall Adrian Covic

•

Received: 11 February 2011 / Accepted: 15 February 2011 / Published online: 2 March 2011 Ó Springer Science+Business Media, B.V. 2011

Abstract Introduction Chronic kidney disease (CKD) is a major public health problem worldwide, due to its epidemic proportions and to its association with high cardiovascular risk. Therefore, screening for CKD is an increasingly important concept, aiming for early detection and prevention of progression and complications of this disease. Materials and methods We studied the prevalence of CKD in the adult population of Ias¸ i, the largest county in Romania, based on the results of a national general health screening program from 2007 to 2008. The patients were tested for CKD with serum creatinine and urinary dipstick. We used two different methods to estimate the glomerular filtration rate (eGFR): the simplified Modification of Diet in Renal Disease (MDRD) and the CKD Epidemiology Collaboration (CKD-EPI) equations. Based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria, we defined CKD as the presence of either V. Cepoi Department of Public Health, Ias¸ i, Romania M. Onofriescu L. Segall A. Covic Nephrology Clinic, ‘‘Dr. C. I. Parhon’’ Hospital, University of Medicine and Pharmacy ‘‘Gr. T. Popa’’, Ias¸ i, Romania L. Segall (&) Clinica Nefrologie, Spital ‘‘Dr. C. I. Parhon’’, B-dul Carol I nr. 50, Ias¸ i, Romania e-mail: [email protected]

eGFR \ 60 ml/min/1.73 m2 and/or dipstick proteinuria. The classification of CKD by stage was also done according to the KDIGO criteria. Results The study population included 60,969 people. The global prevalence of CKD was found to be 6.69% by the MDRD formula and 7.32% when using the CKD-EPI equation. The prevalence of CKD was much higher in women than in men: 9.09% versus 3.7%, by MDRD, and 9.32% versus 4.85%, by CKD-EPI. By age groups, the prevalence of CKD was 0.95% and 0.64% in persons aged 18–44 years old, 4.27% and 3.57% (45–64 years old), 13.36% and 15.34% (65–79 years old), and 23.59% and 34.56% ([80 years old), according to MDRD and CKD-EPI, respectively. By stages, the prevalence of CKD stage 3a (eGFR 59 to 45 ml/min/1.73 m2) was 5.72% by MDRD and 5.96% according to CKD-EPI, whereas the prevalence of stages 3b, 4, and 5 taken together (eGFR \ 45 ml/min/1.73 m2) was 0.96% (MDRD) and 1.35% (CKD-EPI). Patients with CKD were significantly older (71.0 years versus 53.7 years) and had lower levels of serum Hb, total cholesterol, and glutamic pyruvic transaminase, and significantly higher serum creatinine and blood glucose, in comparison with the individuals without CKD. Impaired fasting glucose (106 mg/dl) was found in the CKD population, but not in non-CKD individuals. Conclusions Our study is one of the largest ever reported on the prevalence of CKD worldwide, the first one in Romania, and one of the very few of its kind in Europe (particularly in Eastern Europe).

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The study showed that the prevalence of CKD in our country is around 7%, which is lower than in other countries; however, this could be underestimated due to population selection bias. The prevalence is similar with the MDRD and the CKD-EPI equations; it increases with age and is much higher in women than in men. Impaired fasting glucose was detected in CKD patients, a finding that should probably raise the awareness of the high cardiovascular risk associated with CKD. Keywords Chronic kidney disease Epidemiology eGFR MDRD CKD-EPI

Introduction Chronic kidney disease (CKD) is considered today a major public health problem all over the world, due to its epidemic size and constantly increasing prevalence and its potentially severe, life-threatening complications. The most important dangers of CKD are two: first, its risk of progressing toward end-stage renal disease (ESRD), i.e., the point at which dialysis or renal transplantation are required for patients’ survival and second, its dreadful association with a particularly high risk of cardiovascular (CV) events and mortality [1, 2]. According to several recent surveys, the prevalence of CKD in the general population varies approximately between 10 and 20% [3–9]. The variations in prevalence are explained by differences not only in geographic area and ethnicity but also in the definitions of CKD and the methods to estimate renal function which were used in these studies. However, the prevalence is fairly similar in the Western world and in developing countries. Furthermore, when only at-risk populations—such as those with diabetes, hypertension or significant family history—are considered, the prevalence of CKD is two- to fourfold higher [10, 11]. In young and middle-aged people, the presence of CKD is associated with a 44% increase in premature CV disease and mortality [12]. The good news, however, is that CKD can be diagnosed by using simple laboratory tests and interventions to prevent or delay its progression and complications are both possible and effective [2]. In

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Int Urol Nephrol (2012) 44:213–220

these circumstances, many public health and academic authorities worldwide are becoming increasingly concerned with developing screening programs for the early diagnosis of CKD. A position statement published by the Kidney Disease Improving Global Outcomes (KDIGO) in 2007 [2] recommends that all countries should have a targeted screening program for CKD. This should include patients with hypertension, diabetes, CV disease, and possibly other groups, like families of patients with CKD, individuals over 60 years old, or those with hyperlipidemia, obesity, metabolic syndrome, smokers, patients treated with potentially nephrotoxic drugs, some chronic infectious diseases, and cancers. It is further recommended that methods for CKD screening should comprise both urine tests for proteinuria (two positive out of three tests being required for making the diagnosis) and a blood test for creatinine, to estimate the glomerular filtration rate (eGFR). The frequency of testing should be adjusted according to available guidelines and individual risk [2]. However, putting such programs into practice is hindered by several important issues. First, there is still not enough evidence that screening for CKD is cost-effective, as for other conditions like CV diseases or diabetes [13]. Second, ESRD—which is the main outcome of interest in CKD—has a low incidence with a less than 5% risk of progression from moderate CKD to ESRD [3]. Finally, the implementation of screening and treatment strategies for CKD is hampered by the limited number of nephrologists available and the already heavy workload in primary care [13]. Therefore, further research is necessary to better classify target groups for screening, to compare specificity and sensitivity of different screening tests and to identify those tests with the highest predictive value for progression of CKD, to define optimal timing interval for screening and surveillance, and to analyze costs, benefits and risks of screening programs [2, 13]. In Romania, the Minister of the Public Health Department and the President of the National Health Insurance House published in 2007 a Decision regarding the implementation of the National Program for the Evaluation of the Population’s Health State in Primary Care [14]. The aims of this program were (a) to detect major diseases in the general population, to identify their risk factors, and to facilitate their early diagnosis and surveillance, in


order to prevent premature deaths, (b) to improve the population’s health status by preventing and treating such diseases, (c) to improve survival and quality of life, aiming to reach the standards of the European Union, and (d) to increase the access to health care services of the entire population of Romania. Millions of Romanians took part in this program, in 2007 and 2008, and underwent a thorough evaluation of their health status by their family physicians. This evaluation included history taking, physical examination, and several laboratory investigations, aiming to detect major diseases and general risk factors. In this context, as well as for other diseases, a screening for CKD was included in persons at risk. This screening was based on serum creatinine and urinalysis. In our study, we analyzed the results of this CKD screening in the adult population of Ias¸ i, the largest of the 40 counties of Romania. Our aims were: (a) to calculate the prevalence of CKD by stages and by using two different methods of eGFR estimation—the simplified Modification of Diet in Renal Disease (MDRD) and the CKD Epidemiology Collaboration (CKD-EPI) equations and (b) to identify some laboratory abnormalities associated with CKD in this population.

Materials and methods Patients The study population consisted of all the inhabitants of Ias¸ i County, who participated in the National Program for the Evaluation of the Population’s Health State in Primary Care, in the period between June 2007 and December 2008. A number of 443,619 persons responded to the invitation of their family doctor to undergo a general physical examination, and in 405,774 of these, several basic laboratory investigations were additionally made, as judged appropriate and ordered by the physicians involved, in each and every individual case. Unfortunately, we were able to collect the data of only 130,940 of these subjects, among which 104,338 were adults (age [ 18 years). We finally selected for our analysis those in which both serum creatinine and dipstick urinalysis were performed, i.e., a number of 60,969 people.

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In this population, we calculated the eGFR, by using two different methods: the simplified version of the MDRD formula [15] and the CKD-EPI formula [16]. Based on the Kidney Disease Improving Global Outcomes (KDIGO) definition and classification of CKD [17], we first divided our population simply into ‘‘no CKD’’ (defined as eGFR C 60 ml/min/1.73 m2 and no proteinuria on dipstick) and ‘‘CKD’’ (i.e., eGFR \ 60 ml/min/1.73 m2 and/or presence of proteinuria). For more detailed analysis, we further divided by eGFR the group without CKD into those [90 ml/min/1.73 m2 and those with 89–60 ml/min/ 1.73 m2, and then the group with CKD into 59–45 ml/min/ 1.73 m2 (stage 3a) and \45 ml/min/1.73 m2 (stages 3b ? 4 ? 5). To study the links between eGFR and other factors, we also used several laboratory data, including serum hemoglobin (Hb), blood glucose, total cholesterol, triglycerides, and glutamic pyruvic transaminase (GPT), where available. Statistics The statistic analysis was performed using the SPSS 17.0 software (SPSS Inc., Chicago, IL). The variables were expressed as mean ± standard deviation (SD). The comparisons between groups were made with ANOVA (the Games-Howell post hoc test), and the correlations between variables were tested using the Spearman’s coefficient of rank correlation. The comparison between non-CKD and CKD groups was made using the independent samples t test. Values of P \ 0.05 were considered statistically significant.

Results The population included in the analysis consisted of 60,969 persons. Their mean age was 55.1 ± 15.4 years and 44.58% were men. The population’s distribution by eGFR, using each of the two estimating methods, MDRD and CKD-EPI, is shown in Table 1. The prevalence of CKD was found to be 6.69% by the MDRD formula and 7.32% when using the CKDEPI equation. By gender, the prevalence was much higher in women than in men: 9.09% versus 3.7%, by MDRD, and 9.32% versus 4.85%, by CKD-EPI. Patients with CKD were significantly older than those without CKD (71.0 years vs. 53.7 years). By

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Table 1 Population’s distribution (no. of persons) by eGFR range groups (ml/min/1.73 m2) CKD-EPI 89–60 59–30 29–15 \ 15 Total (MDRD)

C90

MDRD C90 89–60

24511

2093

0

0

0

26604

3835 26017

646

0

0

30498

59–30

0

29–15

0

257 3457

36

0

3750

0

79

4

83

\ 15 0 0 0 Total 28346 28367 4103 (CKD-EPI)

0 115

34 38

34 60969

0

age groups, the prevalence of CKD was 0.95% and 0.64% in those aged 18–44 years old, 4.27% and 3.57% (45–64 years old), 13.36% and 15.34% (65–79 years old), and 23.59% and 34.56% ([80 years old), according to MDRD and CKD-EPI eGFR estimations, respectively. By stages, the prevalence of CKD stage 3a was 5.72% by MDRD and 5.96% according to CKD-EPI, whereas the prevalence of stages 3b, 4, and 5, taken together, was 0.96% (MDRD) and 1.35% (CKD-EPI) (Table 2a, b). When looking at various laboratory data, we found that patients with CKD had significantly lower serum Hb, total cholesterol, and GPT, and significantly higher serum creatinine and blood glucose, in comparison with the individuals without CKD. Detailed results, distributed by eGFR range groups, are given in Table 2 a, b. Furthermore, in bivariate analysis, we found significant correlations between eGFR and all of these above-mentioned factors (Table 3).

Discussions Our study is the first CKD screening study in Romania and one of the largest ever performed in Europe. In Europe, several screening programs for CKD have been accomplished in the past few decades, most of them in Western countries. In 2010, among the 27 European Union member countries, national surveys on the prevalence of CKD in adults were available in only 12 countries [18]. The criterion for the diagnosis

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of CKD in these surveys varied from serum creatinine above a certain cutoff (mostly [150 lmol/l) to eGFR \ 60 ml/min/1.73 m2 and to a positive urine dipstick or even the presence of microalbuminuria [19]. According to the Kidney Disease Outcomes Quality Initiative (K/DOQI) definition and classification of CKD [20], an eGFR \ 60 ml/min/1.73 m2 only allows detection of CKD stages 3, 4, and 5, whereas making the diagnosis of stages 1 and 2 requires evidence of kidney damage, such as proteinuria. However, in 2008, de Jong et al. [19] could retrieve only three studies that had measured both proteinuria and eGFR: Prevention of Renal and Vascular Endstage Disease (PREVEND), performed in the Netherlands in 1997 [21], North Trøndelag Health Study (HUNT), in Norway, 1995 [22], and Estudio Epidemiologico de la Insuficiencia Renal en Espan˜a (EPIRCE), in Spain, 2004 [23]. Of these three studies, only HUNT included a large population sample (n = 65,181), whereas the other two, particularly EPIRCE, were considerably smaller (n = 3432 in PREVEND and n = 237 in EPIRCE). Therefore, with almost 61,000 participants, our study is one of the largest of its kind ever conducted in Europe, comparable to the Norwegian HUNT study, and the first of this size in an Eastern European country. The results of PREVEND, HUNT, and EPIRCE showed that the prevalence of CKD by stage is comparable in the three European countries, ranging from 5.1 to 7.0% for stages 1 and 2 combined, from 4.5 to 5.3% for stage 3, and much lower for stage 4, from 0.1 to 0.4%. These figures are similar to those reported by the Third National Health and Nutrition Examination Survey (NHANES III) in the United States, which showed that 6.3% of the general population has stage 1 or 2 CKD, 4.3% has stage 3, 0.2% has stage 4, and 0.2% has stage 5 [3]. According to the studies where only eGFR was considered, the prevalence of stage 3–5 CKD also appears to be fairly similar across European countries, ranging from 3.57% (Norway) to 7.2% (Germany) in men, and from 6.2% (Italy) to 10.2% (Iceland) in women. Our study showed a slightly lower prevalence of CKD in Romania, compared to other European [18] and non-European countries [3–9]. When looking at the prevalence of CKD estimated by using the MDRD equation versus by using the CKDEPI equation, we found that the former method tends to underestimate this prevalence in comparison with the latter. This finding is somewhat surprising, with regard to other studies. The Modification of Diet in Renal


217

Table 2 (a) Distribution of age and laboratory data (serum levels) by eGFR (MDRD). (b) Distribution of age and laboratory data (serum levels) by eGFR (CKD-EPI) [90

89–60

\60 (CKD)

59–45 (CKD 3a)

\45 (CKD 3b ? 4 ? 5)

N (%)

26505 (43.47%)

30389 (49.84%)

4075 (6.68%)

3488 (5.72%)

587 (0.96%)

Age (years)

50 ± 16.59a

57.5 ± 14.9b

68.7 ± 11.69c

68 ± 11.73d

72.6 ± 10.6e

GPT (IU/l)

28.4 ± 28.17a

27.1 ± 23.12b

24.5 ± 18.35c

eGFR—MDRD (ml/min/1.73 m2) (a)

Cholesterol (mg/dl)

207.2 ± 47.47

f

Triglycerides (mg/dl)

129.9 ± 108.01

Glucose (mg/dl)

96.7 ± 27.91a

b

219 ± 47.39 g

133.7 ± 96.26

h

100.2 ± 29.36b

a

25 ± 18.86d c

b

21.3 ± 14.53e d

208.7 ± 53.6f

211.6 ± 49.77

212 ± 49.17

131.5 ± 97.46

130.6 ± 97.68

138.1 ± 95.84

106.6 ± 34.91c

106.4 ± 34.38i

108.2 ± 37.9i

Creatinine (mg/dl)

0.7 ± 0.13

0.9 ± 0.14

1.2 ± 0.57

1.1 ± 0.17

1.9 ± 1.24e

Hb (g/dl)

14.2 ± 1.41a

13.6 ± 1.39b

13.3 ± 1.37c

13.4 ± 1.34d

12.9 ± 1.45e

[90

eGFR—CKD-EPI (ml/min/1.73 m2) (b) N (%)

c

d

\60 (CKD)

89–60

59–45 (CKD 3a)

\45 (CKD 3b ? 4 ? 5)

28242 (46.32%)

28265 (46.36%)

4462 (7.32%)

3636 (5.96%)

826 (1.35%)

Age (years)

46.9 ± 15.09a

60.5 ± 13.79b

71 ± 10.97c

70.2 ± 11.09d

74.6 ± 9.62e

GPT (IU/l)

28.7 ± 27.79a

26.8 ± 23.3b

24 ± 18.04c

24.4 ± 18.12d

22.1 ± 17.56e

Cholesterol (mg/dl)

207.4 ± 47.5f

211.5 ± 49.33c

212.5 ± 48.76d

206.7 ± 51.86f

130.5 ± 91.97

129.9 ± 90.34

133.4 ± 99.94

106.1 ± 33.51c

105.8 ± 32.87i

107.8 ± 36.17i

Triglycerides (mg/dl)

129.9 ± 106.77

Glucose (mg/dl)

96.3 ± 27.27a

Creatinine (mg/dl)

0.8 ± 0.14

Hb (g/dl)

219.6 ± 47.34b g

134.2 ± 97.38

100.8 ± 30.15b

a

14.1 ± 1.43

h

b

0.9 ± 0.14 a

13.7 ± 1.39

1.2 ± 0.54 b

c

13.3 ± 1.36

1.1 ± 0.17 c

a

significantly different from 89–60, \60, 59–45, and \45 ml/min/1.73 m2 (P \ 0.0001)

b

significantly different from [90, \60, 59–45, and \ 45 ml/min/1.73 m2 (P \ 0.0001)

c

significantly different from [90 to 89–60 ml/min/1.73 m2 (P \ 0.0001)

d

significantly different from [90, 89–60, to \45 ml/min/1.73 m2 (P \ 0.0001)

e

significantly different from [90, 89–60, to 59–45 ml/min/1.73 m2 (P \ 0.0001)

f

significantly different from 89–60, to 59–45 ml/min/1.73 m2 (P \ 0.0001)

g

significantly different from 89 to 60 ml/min/1.73 m2 (P \ 0.0001)

h

significantly different from [90 ml/min/1.73 m2 (P \ 0.0001)

i

significantly different from [90 to 89–60 ml/min/1.73 m2 (P \ 0.0001)

d

1.7 ± 1.08e d

13 ± 1.41e

13.4 ± 1.34

Table 3 Correlations (Spearman’s rho) of eGFR with age and laboratory data Age CKD-EPI MDRD

GPT

Glucose

Cholesterol

Triglycerides

Hb

-0.579**

0.033**

-0.191**

-0.119**

-0.062**

0.217**

**

**

**

**

**

0.255**

-0.318

0.018

-0.159

-0.119

-0.064

** Significant correlation

Disease (MDRD) formula, first published in 1999 [24] and later simplified [25], is currently the most widely used GFR-estimating equation. As it was developed in patients with CKD, this formula is very reliable in

persons with GFR \ 60 ml/min/1.73 m2, as well as in the elderly, but it tends to underestimate GFR in those with normal or near-normal renal function [26–29]. The more recent CKD Epidemiology Collaboration

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(CKD-EPI) equation, published in 2009 [30], was derived from both CKD and non-CKD populations, and it demonstrated lesser bias, greater accuracy, and lower CKD prevalence than MDRD, in several studies [26, 31, 32]. For example, in a research on a large population sample from an outpatient facility, the number of individuals identified by MDRD as having CKD decreased by 10% overall and by 35% in the group \ 60 years old when CKD-EPI was used instead [33]. A screening study in Belgium showed that the prevalence of CKD stage 3 was significantly higher with MDRD than with CKD-EPI (11% vs. 8%) [34]. Comparing the two equations in the population from the Atherosclerosis Research in Communities (ARIC) Study, a community-based cohort of African American and white individuals aged 45–64 years, Matsushita et al. found that 43.5% of participants with CKD stage 3a (eGFR 59–45 mL/min/1.73 m2) according to the MDRD formula were reclassified to no CKD (eGFR [ 60 mL/min/1.73 m2) by the CKD-EPI equation. [35]. In our study, patients with CKD were significantly older (71.0 years vs. 53.7 years) than those without CKD, which is an expected finding, considering that advanced age is a well-known risk factor for CKD [3, 36]. Regarding gender distribution, we found that the prevalence of CKD was much higher in women than in men. A female predominance among CKD patients has also been reported in other European countries, such as Belgium, England, Iceland, and Norway [18]. However, the higher prevalence of CKD in women is in contradiction with end-stage renal disease (ESRD) statistics, where men are more affected than women [18]. This discrepancy could be explained, at least in part, by the naturally lower eGFR in women, which probably leads to an overestimation of CKD in this group [18, 19]. Our patients with CKD had significantly lower levels of serum Hb, total cholesterol, and GPT, and significantly higher serum creatinine and blood glucose, in comparison with the individuals without CKD. The decrease in serum Hb in parallel with the GFR is common knowledge and needs no special comment. However, the relation of kidney disease with the blood concentrations of glucose, cholesterol, and GPT are somewhat more difficult to interpret. It is known for several years now that CKD is often associated with the so-called ‘‘metabolic syndrome,’’ which is defined by central obesity and a cluster of

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several abnormalities, including at least two of the following: raised triglycerides ([150 mg/dl) or specific lipid-lowering treatment, reduced HDL-cholesterol (\40 mg/dl), increased blood pressure ([130/ 85 mm Hg) or antihypertensive treatment, and raised fasting plasma glucose ([100 mg/dl) [37]. This is generally thought to be a two-way relationship, with increased insulin resistance as its crucial pathophysiologic link [38–41]. We found that our CKD population had impaired fasting glucose (106 mg/dl), by the definition of the American Diabetes Association [42]. Additionally, we found an inverse correlation between serum triglycerides and eGFR, although there was no significant difference in this regard between people with and those without CKD. However, in the absence of data on body mass index, blood pressure, and HDL-cholesterol, it is impossible for us to say whether a true metabolic syndrome was actually present in this CKD population. On the other hand, the observed small but significant decrease in total cholesterol in patients with CKD is likely the expression of incipient protein-energy wasting, which is another very common complication of this multifaceted disease [43, 44]. Unfortunately, our study also had several limitations, which must be pointed out. First, due to deficiencies in the reporting system, we were not able to collect data from all the participants in the National Program, which would have greatly increased the importance of our results. Second, our population was somewhat selected, as the measurement of serum creatinine was not taken in all participants, but only in those judged to be ‘‘at risk’’ of CKD by their family physicians. Moreover, there were no universal criteria to define the concept of ‘‘risk’’; therefore, these were left at each physician’s free choice and they remained obscure to the authors of this study. A selection bias of the participants may also have occurred for another reason: patients previously known as having chronic diseases (including CKD, diabetes, and cardiovascular diseases, among others) were not included in the program if they had already been examined in the three months prior to the start of the program. Therefore, a number of patients were lost, and the true prevalence of CKD in the general population may have been underestimated in our study. Third, we had no knowledge on the methods used for serum creatinine measurement, and it is likely that these methods were not uniform


among laboratories. Fourth, we could not obtain any clinical data concerning the participants, such as family history of CKD, blood pressure, body mass index, smoking status, or presence of comorbid conditions like diabetes or cardiovascular disease. Such information would have been very useful to us in order to investigate significant factors associated with CKD in this population. Finally, as this was a cross-sectional study, we only had a single serum creatinine and urinalysis evaluation in each patient. Since the diagnosis of CKD requires the persistence of renal abnormalities for at least three months, it is possible that cases of acute kidney injury or transient proteinuria were erroneously classified as CKD. In conclusion, despite its limitations, our study is one of the largest ever reported on the prevalence of CKD worldwide, the first one in Romania, and one of the very few of its kind in Europe. The study showed that the prevalence of CKD in our country is around 7%, which is lower than in other countries, although this could be an underestimation due to population selection bias. The prevalence is similar with the MDRD and the CKD-EPI equations; it increases with age and is much higher in women than in men. Impaired fasting glucose was detected in CKD patients, a finding that should probably raise the awareness of the high cardiovascular risk associated with CKD.

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