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Saudi Journal of Kidney Diseases and Transplantation
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ORIGINAL ARTICLE Table of Contents   
Year : 2010  |  Volume : 21  |  Issue : 6  |  Page : 1066-1072
Epidemiology of chronic kidney disease in the Kingdom of Saudi Arabia (SEEK-Saudi investigators) - A pilot study


1 Renal Division, King Saud University, Riyadh, Saudi Arabia
2 Renal Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
3 Nephrology Division, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
4 Department of Nephrology, Riyadh Armed Forces Hospital, Riyadh, Saudi Arabia
5 Department of Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia
6 Department of Internal Medicine, Security Forces Hospital Program, Riyadh, Saudi Arabia
7 King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia

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Date of Web Publication4-Nov-2010
 

   Abstract 

There are no available data about the prevalence of chronic kidney disease (CKD) and its risk factors in the general population of the kingdom of Saudi Arabia. To estimate the prevalence of CKD and its associated risk factors in the Saudi population, we conducted a pilot community-based screening program in commercial centers in Riyadh, Saudi Arabia. Candidates were interviewed and blood and urine samples were collected. Participants were categorized to their CKD stage according to their estimated Modification of Diet in Renal Disease (MDRD3)-based, the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and the presence of albuminuria. The sample comprised 491 (49.9% were males) adult Saudi nationals. The mean age was 37.4 ± 11.3 years. The over­all prevalence of CKD was 5.7% and 5.3% using the MDRD-3 and CKD-EPI glomerular filtration equations, respectively. Gender, age, smoking status, body mass index, hypertension and diabetes mel­litus were not significant predictors of CKD in our cohort. However, CKD was significantly higher in the older age groups, higher serum glucose, waist/hip ratio and blood pressure. Only 7.1% of the CKD patients were aware of their CKD status, while 32.1% were told that they had protein or blood in their urine and 10.7% had known kidney stones in the past. We conclude that prevalence of CKD in the young Saudi population is around 5.7%. Our pilot study demonstrated the feasibility of screening for CKD. Screening of high-risk individuals is likely to be the most cost-effective strategy to detect CKD patients.

How to cite this article:
Alsuwaida AO, Farag YM, Al Sayyari AA, Mousa D, Alhejaili F, Al-Harbi A, Housawi A, Mittal BV, Singh AK. Epidemiology of chronic kidney disease in the Kingdom of Saudi Arabia (SEEK-Saudi investigators) - A pilot study. Saudi J Kidney Dis Transpl 2010;21:1066-72

How to cite this URL:
Alsuwaida AO, Farag YM, Al Sayyari AA, Mousa D, Alhejaili F, Al-Harbi A, Housawi A, Mittal BV, Singh AK. Epidemiology of chronic kidney disease in the Kingdom of Saudi Arabia (SEEK-Saudi investigators) - A pilot study. Saudi J Kidney Dis Transpl [serial online] 2010 [cited 2019 May 21];21:1066-72. Available from: http://www.sjkdt.org/text.asp?2010/21/6/1066/72293

   Introduction Top


Chronic kidney disease (CKD) is emerging as an important problem worldwide. [1] However, data on the burden of CKD in the Arab world remains poorly understood. The kingdom of Saudi Arabia is the largest country in the Ara­bian Peninsula in Southwest Asia. It has an estimated population of 28 million, including approximately 5.5 million non-nationals. [2] Data available on the exact incidence and prevalence of chronic kidney disease is limited to patients with end-stage renal disease. In the annual report of the Saudi Center for Organ Transplantation (SCOT), [3] the incidence of dialysis in the king­dom of Saudi Arabia was 136 new cases per million population (pmp). This compares to 360 pmp in the United States, [4] 585 pmp in Europe [5] and to 163 pmp in India. [6]

The SEEK-Saudi study (Screening and Early Evaluation of Kidney Disease) is aimed at eva­luating the burden of CKD and its predictors in the kingdom of Saudi Arabia using standar­dized GFR prediction equations. The study also aimed to demonstrate the feasibility of perfor­ming a community-based screening.


   Methods Top


In March 2008 and for 2 months thereafter, camps in two large commercial centers in Riyadh, the capital of the kingdom of Saudi Arabia, were conducted (total of three camps). Saudi nationals aged ≥18 years of age were invited to participate in the study. All the par­ticipants provided informed consent. Screening data were collected on sociodemographic cha­racteristics, medical history and medications. The presence of CKD and cardiovascular di­sease, its risk factors and related complica­tions were also addressed in the questionnaire. Pertinent family history was also documented. Anthropometric measures (weight, height, mid­abdominal and hip circumference), resting blood pressure and heart rate were measured. We used the guidelines of the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC 7). [7] Two consecutive standardized blood pressure measurements were recorded with the person seated using a mer­cury sphygmomanometer. The anthropometric measurements were performed according to the Anthropometric Standardization Reference manual. [8] Participants were requested to visit the laboratory while fasting so that fresh urine and blood samples were collected. Blood and urine specimens were processed for the deter­mination of random blood glucose, serum crea­tinine, hemoglobin, microalbuminuria, hema­turia and pyuria. We used urine dipstick Bayer Multistix 10 SG. Serum creatinine concen­tration was determined by the buffered kinetic Jaffe reaction without deproteinaization using a Cobas® 6000 analyzer, and was traceable with isotope dilution mass spectrometry (IDMS). In addition, the estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD3) formula (eGFR = 175 × (serum creatinine in mg/dL) -1.154 × age -0.203 × (0.742 if female) × (1.21 if black) (3). The eGFR was re-calculated using the CKD-EPI [9] and the Cockcroft-Gault predi­cation equation. Individuals with eGFR values <60 mL/min per 1.73 m 2 or those having albu­minuria ≥1+ were defined as having CKD. We used the National Kidney Foundation (NKF) criteria for CKD. [10] Strata for the presentation of statistics include gender (male or female), age group (18 to 30, 31 to 40, 41 to 50, 51 to 60, or 61 + years), diabetes status (yes or no) according to the American Diabetes Associa­tion (fasting blood glucose ≥126 mg/dL, random blood glucose ≥200 mg/dL, on diabetes medi­cation or self-reported diabetes mellitus) and hypertension status (yes or no), either with ele­vated blood pressure on anti-hypertensive medi­cations or self-reported hypertension.


   Statistical Analysis Top


The group with CKD and those without were compared for frequencies of certain demogra­phic factors. The Student's "t"-test was used to evaluate continuous variables and the Chi­square and Fisher's exact tests for categorical parameters. The significance level was set at P <0.05. The mean values were reported as the mean ± standard deviation (SD).


   Results Top


A total of 494 subjects participated in the study; three participants were excluded because they were either below 18 years of age (n = 2) or had a history of kidney transplant (n = 1). The mean age of the participants included in the analysis (n = 491) was 37.41 ± 11.3 years. There were 49.9% males (n = 245). The mean body mass index was 29.24 ± 5.7 kg/m 2 . Other baseline characteristics are shown in [Table 1] and [Table 2].
Table 1 :Demographic characteristic distribution by CKD status.

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Table 2 :Mean values of selected clinical and laboratory screening data collected in SEEK-Saudi and by
chronic kidney disease (CKD) status.


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Estimation of Kidney Function

We calculated eGFR using the MDRD-3, the CKD-EPI and the Cockcroft-Gault equations [Table 3]. The mean eGFR was 107.77 ± 23.97 mL/min/1.73 m 2 . The overall prevalence of mildly decreased kidney function (GFR 60-89 mL/min per 1.73 m 2 ) was 20.8%. Low GFR was more common in males (61.8%) than in females (38.2%) (P = 0.008). Mildly decreased GFR was highest among the age groups 31 to 40 years and 41 to 50 years, 29.4% and 38.2% respectively (P = 0.000). The prevalence of decreased GFR (GFR 15 to 59 mL/min per 1.73 m 2 ) was 0.4% and exclusively in females. None of the participants had stage 5 CKD.
Table 3 :Stages of chronic kidney disease (CKD) with estimated glomerular filtration rate (eGFR) measured by different equations or estimates.

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Prevalence of Albuminuria

The overall prevalence of macroalbuminuria (by urine dipstick) was 5.3%. There was no sig­nificant difference between males and females regarding the presence of macroalbuminuria by urine dipstick. However, the youngest age group (18 to 30 years) in our cohort had the highest prevalence of macroalbuminuria, of 30.8% (P = 0.042).

Prevalence of CKD

We observed that the overall CKD preva­lence of all stages was 5.7%. The prevalence of CKD stages 1, 2 and 3 was 3.5%, 1.6% and 0.6%, respectively. Comparison between the two groups (participants with CKD versus par­ticipants without CKD) is presented in [Table 1] and [Table 2]. There was no significant difference between the two groups by age, gender, level of education, smoking status, hypertension and obesity. There was moderate negative correla­tion (Spearman's rho = -0.209) between the eGFR and the number of smoking years, but was not strong enough to reach statistical significance (P = 0.06). There was a higher CKD prevalence in the older age group (P = 0.046). The CKD group had a higher serum fasting glucose level (P < 0.000) and a lower hemo­globin level (P = 0.02).

We performed a sensitivity analysis [Table 3] using the Cockcroft-Gault equation and the new CKD-EPI equation for estimation of GFR. When using the CG equation, prevalence of CKD was found to be 8.6%. The overall CKD prevalence of all stages was 5.3%. The per­centage of CKD patients decreased, especially in the higher age groups (41 to 50 years and 51 to 60 years).

Awareness of CKD status and CKD symptoms

Only 7.1% of the CKD patients knew that they had CKD, while 32.1% were told that they have protein or blood in their urine and 10.7% had known kidney stones in the past [Table 4].
Table 4 :Awareness of chronic kidney disease (CKD) status or CKD symptoms.

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   Discussion Top


In our study, the overall prevalence of CKD was 5.7% (based on the eGFR MDRD-3 equa­tion). The prevalence did not change substan­tially when we used the Cockcroft-Gault equa­tion and the new CKD-EPI equation for esti­mation of GFR. The overall prevalence of mildly decreased kidney function (GFR 60 to 89 mL/min per 1.73 m 2 ) was 20.8%. Older age and higher fasting serum glucose were sug­gested to be risk factors for CKD.

This study is a part of the Global SEEK Project; Screening and Early Evaluation of Kidney disease, and used the methodology that was employed in India and Thailand. CKD in this cohort was largely explained by the presence of albuminuria rather than reduced GFR. This indicates that people may experience kidney damage before their eGFR decreases below 60 mL/min per 1.73 m 2 . The majority of subjects were classified into CKD stages 1 and 2. This is lower than other reported prevalence from different parts of the world. For example, the overall prevalence in Beijing, China, was 13.0%. [11] In USA, the prevalence rate over 1999-2004 was 13.1% in all four stages of CKD (1.8%, 3.2%, 7.7% and 0.35% for stages 1, 2, 3 and 4, respectively). [12] A population of American Indians and Alaska Natives was screened for CKD as part of the Kidney Early Evaluation Program (KEEP). In this popula­tion, the prevalence of any CKD was found to be 29% and of low GFR (defined as <60 mL/ min/1.73 m 2 ) was 17%. (Jolly). The low preva­lence of CKD in our study compared with other studies might be explained by the fact that the mean age in our study population was lower. The strongest predictor of low GFR in the KEEP study was older age (61+ years OR 8.42, 95% CI 5.92-11.98). [13] In the study from Beijing, the prevalence rose from 10% in the age group 18-39 years to 30.5% in those >70 years of age. [11]

When we used the CKD-EPI equation, the prevalence of CKD in our study was 5.3%. In addition, when we remodeled our data using the CKD-EPI equation, the evaluation of cor­relations with specific risk factors was similar to the data generated from the MDRD-3 equa­tion [Table 1],[Table 2] and [Table 3]. Sensitivity analysis for the prevalence of CKD was also performed using the Cockcroft-Gault (CG) equation to generate an estimate of creatinine clearance. Prevalence of CKD was higher (8.6%) using the CG equation. Nevertheless, regardless of the equations used to classify CKD, the burden of CKD was predominant in stage 1 (76.9%, 60.7% and 57.1% using the CKD-EPI, MDRD­3 and CG equations, respectively). The factors associated with the presence of CKD in our group included older age group (P = 0.046) and higher serum fasting glucose (P < 0.000). CKD was also found in patients with a higher waist/hip ratio (P = 0.022) and higher systolic and diastolic blood pressure (P = 0.002 and P = 0.001, respectively). Although smoking status was not significantly different (P = 0.115) bet­ween the CKD and non-CKD groups, there was a moderately negative correlation (Spear­man's rho = -0.209) between the eGFR and the number of smoking years, but this was not strong enough to reach statistical significance (P = 0.06). The association between cigarette smoking and CKD has been observed in seve­ral previous reports. [14],[15] This is particularly noteworthy in the Saudi Arabian population since there is a higher prevalence of current cigarette smoking [16] compared with previous studies among males in Saudi Arabia 5 years ago (4.7%). [17]

We also observed a strong association bet­ween the serum fasting glucose level and CKD. Although the prevalence of diabetes mellitus in Saudi Arabia is among the highest in the world, [18] we believe that our study was not sufficiently powered to confirm the association between CKD and diabetes mellitus. More­over, we enrolled relatively healthy and young subjects.

Obesity is associated with CKD, and we did observe this in our cohort, since our CKD sub­jects manifested significantly larger waist cir­cumference than the non-CKD group (P = 0.004). However, since BMI ≥30 is the criteria for obesity, it is possible that our study was not powered enough to detect the effect of obesity. This is supported by the fact that 60.7% and 42.5% of the CKD and non-CKD subjects were obese, respectively.

We observed a significantly higher preva­lence of CKD (25%) in participants above 60 years of age in comparison with the younger age groups, in addition to diabetes mellitus (58.33%,) and hypertension (58.33%) [Figure 1]. Finally, we detected a low awareness of the CKD status and CKD symptoms in our cohort. This implies false awareness.
Figure 1 :Participants detected with chronic kidney disease (CKD) among the different age groups.

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Our study has several limitations. The most important is that bias may have been intro­duced because of the sampling technique used in our study. We used a camp-based module in this pilot study that could have biased toward the urban population and healthier participants. A large study is currently planned. This could be an explanation for why our CKD estimates are lower than those presented in earlier stu­dies. The US National Health and Nutrition Examination Survey (NHANES) used a repre­sentative cross-sectional, multistage, clustered probability samples of the US civilian non­institutionalized population and reported a CKD prevalence of 18.3%. [19] On the other hand, the Kidney Early Evaluation Program (KEEP) offered a free health camp-based screening program for individuals at increased risk of de­veloping kidney disease and reported an even higher prevalence of 26.2%. Because the ma­jority of the population in Saudi Arabia lives in urban areas and large cities, we believe that our numbers are likely to be representative of the urban Saudi population. In our pilot study, we measured albuminuria on only one occasion. Since there is a significant variation in the albumin excretion rates, our estimate of the rate of albuminuria may be imprecise. Further­more, other possible limiting factors include a small sample size and restriction of our study to only Saudi nationals.

We conclude that our pilot study does de­monstrate the feasibility of the screening and early detection of CKD in a Saudi population. However, our data should generalize the Saudi population with caution, and we plan to con­duct further studies with a larger sample size and more sophisticated sampling techniques to evaluate the prevalence and risk factors for CKD.


   Acknowledgement Top


Dr. Ajay K. Singh is the Chair of the SEEK­Global Steering Committee. The SEEK-Saudi investigators would like to thank Magdy A. Abd El-Hameed, Product Manager - Eprex, Janssen-Cilag branch in the Gulf Cooperation Council (GCC) countries for his generous sup­port throughout the project period. We also would like to thank Dania Daye, MD-PhD student and HHMI-NIBIB* Interfaces Scholar at the University of Pennsylvania School of Medicine, for her instrumental contribution in the CKD-EPI eGFR equation results.

*HHMI: Howard Hughes Medical Institute, NIBIB: National Institute of Biomedical Imaging and Bioengineering

Disclosure

The study was funded by a research grant from the Janssen-Cilag branch in the Gulf Cooperation Council (GCC) and institutional funds from the College of Medicine Research Center at King Saud University. Dr. Ajay K. Singh reports having received research support and consulting fees from Amgen Inc, Johnson and Johnson, Roche, AMAG Pharmaceuticals Inc. and Watson Pharmaceuticals. He is on the Speakers Bureau for Watson Pharmaceuticals and Johnson and Johnson.

 
   References Top

1.Schoolwerth AC, Engelgau MM, Hostetter TH, et al. Chronic kidney disease: A public health problem that needs a public health action plan. Prev Chronic Dis 2006 Apr [Accessed November 25, 2009]. Available from: URL: http://www.cdc.gov/pcd/issues/2006/apr/05_01 05.htm.   Back to cited text no. 1
    
2.CIA World Fact book. http://www.cia.gov/library/publications/the-world-factbook/geos/tz.html accessed September 25, 2008.  Back to cited text no. 2
    
3.The annual report of The Saudi Center for Organ Transplantation (SCOT). http://www.scot.org.sa/annual-report.html accessed September 25, 2009.  Back to cited text no. 3
    
4.U.S. Renal Data System, USRDS 2008 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2008.  Back to cited text no. 4
    
5.Grassmann A, Gioberge S, Moeller S, Brown G. ESRD patients in 2004: Global overview of patient numbers, treatment modalities and associated trends. Nephrol Dial Transplant 2005;20:2587-93.  Back to cited text no. 5
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6.Modi GK, Jha V. The incidence of end-stage renal disease in India: A population-based study. Kidney Int 2006;70:2131-3.  Back to cited text no. 6
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7.The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7). http://www.nhlbi.nih.gov/guidelines/hypertension/ accessed September 20, 2008.  Back to cited text no. 7
    
8.Anthropometric Standardization Reference Manual by Timothy G. Lohman. ISBN 0873223314.  Back to cited text no. 8
    
9.Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150(9):604-12.  Back to cited text no. 9
    
10.National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis 2002;39(2 Suppl 1):S1-266.  Back to cited text no. 10
    
11.Zhang L, Zhang P, Wang F, et al. Prevalence and factors associated with CKD: A population study from Beijing. Am J Kidney Dis 2008; 51:373-84.  Back to cited text no. 11
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12.Coresh J, Selvin E, Stevens LA, et al. Preva-lence of chronic kidney disease in the United States. JAMA 2007;298(17):2038-47.  Back to cited text no. 12
    
13.Jolly SE, Li S, Chen SC, et al. Risk Factors for Chronic Kidney Disease among American Indians and Alaska Natives - Findings from the Kidney Early Evaluation Program. Am J Nephrol 2008;29(5):440-6.  Back to cited text no. 13
    
14.Shankar A, Klein R, Klein B. The Association among smoking, heavy drinking, and chronic kidney disease. Am J Epidemiol 2006;164(3): 263-71.  Back to cited text no. 14
    
15.Hallan S, de Mutsert R, Carlsen S, Dekker FW, Aasarod K, Holmen J. Obesity, smoking, and physical inactivity as risk factors for CKD: Are men more vulnerable? Am J Kidney Dis 2006; 47(3):396-405.  Back to cited text no. 15
    
16.Abdalla AM, Al-Kaabba AF, Saeed AA, Abdulrahman BM, Raat H. Gender differences in smoking behavior among adolescents in Saudi Arabia. Saudi Med J 2007;28(7):1102-8.  Back to cited text no. 16
    
17.Global Youth Tobacco Survey Collaborating Group. Differences in worldwide tobacco use by gender: Findings from the Global Youth Tobacco Survey. J Sch Health 2003;73(6):207-15.  Back to cited text no. 17
    
18.International Diabetes Federation Website, Facts and Figures. http://www.idf.org/Facts_and_Figures accessed July 12, 2008.  Back to cited text no. 18
    
19.Bakris GL, McCullough PA, Collins AJ. Executive summary: Kidney Early Evaluation Program (KEEP) 2008 annual data report. Am J Kidney Dis 2009;53(4 Suppl 4):S1-2.  Back to cited text no. 19
    

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Correspondence Address:
Ajay K Singh
Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115
USA
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PMID: 21060175

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