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 Table of Contents  
ORIGINAL ARTICLE
Year : 2013  |  Volume : 26  |  Issue : 1  |  Page : 18-22

The role of microalbuminuria in population screening for chronic kidney disease in an Egyptian village


1 Department of Public Health and Community Medicine, Faculty of Medicine Menoufia University, Menufia, Egypt
2 Department of Public Health and Community Medicine, National liver Institute, Menufia, Egypt

Date of Submission20-Feb-2013
Date of Acceptance18-Mar-2013
Date of Web Publication26-Jun-2014

Correspondence Address:
Zeinab A. Kasemy
MSc, Department of Public Health and Community Medicine, Faculty of Medicine, Menoufia University, Yassin Abdel Ghaffar St, Branched from Gamal Abd El Naser St, 32511 Menufia
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.7123/01.MMJ.0000429484.86621.34

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  Abstract 

Objectives

The study aimed at determining the prevalence of and risk factors for chronic kidney disease (CKD) using microalbuminuria (MA) as a screening test in a rural area in Gharbia Governorate.

Background

CKD is being increasingly recognized as a public health problem. There is still paucity of data on the prevalence of and risk factors for CKD by using MA as a screening test in the Middle East.

Methods

A randomly chosen sample of 320 apparently healthy individuals were interviewed in Shennera village in Gharbia Governorate using a structured questionnaire including information about risk factors for MA. They were also subjected to laboratory investigations including analyses of microalbumin, creatinine, and random blood sugar levels and lipid profile.

Results

The mean age of the studied sample was 35.7±13.7 years; 50.9% were male. Only 85% of the sample had sufficient income. The prevalence of MA was 14.4%. The prevalence of CKD (estimated glomerular filtration rate<60 ml/min/1.73 m2+MA) was 15.6%, but those with elevated creatinine levels comprised 2.8% of the studied group. MA showed an increasing trend with age. The prevalence of MA was significantly higher among diabetic patients, hypertensive patients, obese individuals, cardiovascular disease patients, and smokers. The independent predictor variables associated with the presence of MA in a mutually adjusted logistic regression model were diabetes (odds ratio=8.62, 95% confidence interval: 2.62–28.31) and hypertension (odds ratio=4.92, 95% confidence interval: 1.63–14.86).

Conclusion

Screening of MA seemed to be an easy, equally, or even more efficient method for early identification of significant numbers of individuals with CKD compared with other conventional methods, particularly among those with uncontrolled or undetected diseases such as diabetes or hypertension.

Keywords: chronic kidney disease, diabetes mellitus, early detection, hypertension, microalbuminuria, prevalence, screening


How to cite this article:
El Bahanasy RE, Mahrous OA, Abu Salem ME, El Batanony MA, Mourad WS, Kasemy ZA. The role of microalbuminuria in population screening for chronic kidney disease in an Egyptian village. Menoufia Med J 2013;26:18-22

How to cite this URL:
El Bahanasy RE, Mahrous OA, Abu Salem ME, El Batanony MA, Mourad WS, Kasemy ZA. The role of microalbuminuria in population screening for chronic kidney disease in an Egyptian village. Menoufia Med J [serial online] 2013 [cited 2020 Feb 17];26:18-22. Available from: http://www.mmj.eg.net/text.asp?2013/26/1/18/135422


  Introduction Top


According to the national kidney foundation, chronic kidney disease (CKD) is defined as kidney damage for 3 months or, more clearly, the presence of structural or functional abnormalities in the kidney, with or without a decrease in glomerular filtration rate (GFR), manifested by pathologic abnormalities or markers of kidney damage such as microalbuminuria (MA), abnormalities in the composition of blood or urine, or abnormalities in imaging tests 1. CKD passes through five stages: in stage 1 there is slightly diminished function or kidney damage manifested by MA with normal or relatively high GFR (≥90 ml/min/1.73 m2); in stage 2, there is mild reduction in GFR (60–89 ml/min/1.73 m2) with kidney damage; stage 3 is characterized by moderate reduction in GFR (30–59 ml/min/1.73 m2); in stage 4, there is severe reduction in GFR (15–29 ml/min/1.73 m2); and stage 5 is characterized by established kidney failure (GFR<15 ml/min/1.73 m2) and is called end-stage renal disease (ESRD) 2. Often, CKD is diagnosed as a result of screening of people known to be at risk for kidney problems, such as those with high blood pressure or diabetes. CKD may also be identified when it leads to one of its recognized complications, such as cardiovascular disease (CVD), anemia, or pericarditis 3. CKD is identified by a blood test for creatinine, but creatinine levels may be normal in the early stages of CKD, and the condition is discovered if urinalysis shows that there is loss of protein from the kidney, which is regarded as an independent marker for worsening of renal function (5). The incidence of ESRD in Egypt is growing and reached 79.9 patient/million population in the year 2001. The estimated number of ESRD patients increased to 35 751 in 2004 4. As the problem of CKD increases in Egypt, a community-based study was needed to investigate the role of MA as an early marker in the diagnosis of CKD and to estimate its prevalence.


  Participants and methods Top


Using the multistage random sampling technique, Shennera village in Gharbia Governorate was chosen from 28 governorates. It has eight districts, of which Santa district was selected. This district has 39 villages with a total population of about 80 000 individuals, from which Shennera village was finally selected with a total population of about 2100 individuals. The map of the village was obtained from the health unit, where a record of the households is maintained. The village was divided into squares. The studied square was randomly selected, and by systematic random sampling every third house was chosen. Participants aged 18 years or more were invited to participate in the study, which was started in April 2011 and continued for 1 month. All the participants provided informed consent.

Data collation

Each participant filled an investigator-administered questionnaire 5 that was piloted and validated to our setting. It contained questions on sociodemographics (age, sex, and educational status), smoking status, personal and family health, and lifestyle history (diabetes, hypertension, CVD, and CKD, levels of physical activity, income levels, and employment status). Age was categorized using a standard method 6. Diabetes was defined by a personal history of diabetes mellitus and/or by glucose levels. Similarly, hypertension was defined by personal history of hypertension and by blood pressure levels 5,6. A history of CVD was defined as the presence of any coronary heart disease, stroke, or peripheral vascular disease. History of smoking was also reported. All participants were seen at the screening facility in the village, and anthropometric measurements were taken (including weight, height, and waist and hip circumferences). Both systolic and diastolic blood pressure levels were measured on three occasions for patients without a history of hypertension.

Laboratory methods

Blood samples were taken for measurements of serum cholesterol, triglycerides, serum creatinine, plasma glucose, and hemoglobin. Patients were asked to bring morning urine samples on their visit to the screening site. They received instructions on how to collect a urine sample and to postpone collection in the event of fever, urinary tract infection, menstruation, or after engaging in heavy exercise. Urinary albumin excretion was expressed as albumin–creatinine ratio using standard guidelines 6, which was calculated for every participant. CKD was defined as an estimated glomerular filtration rate (eGFR) below 60 ml/min/1.73 m2+MA above 30 mg/g 5,6.

Statistical analysis

All analyses and calculations were performed using the SPSS statistical package, version 16.0 (SPSS Inc., Chicago, Illinois, USA). Descriptive analyses were used to characterize the participants on the basis of sociodemographic, lifestyle, and clinical factors. Qualitative data were presented as mean±SD. Independent sample t-tests were applied for comparison of group means; the χ2-test was applied for qualitative variables, and the Z-test was applied for proportions. Factors associated with MA were first determined using univariate logistic regression analysis. Variables significant at a P-value less than 0.05 on univariate analysis were used in the multivariate logistic regression model. A two-sided P-value less than 0.05 was considered statistically significant.


  Results Top


The studied group included 320 apparent healthy people; all underwent clinical and laboratory investigations for the presence of CKD. Sociodemographic characteristics of the studied population are shown in [Table 1].The overall mean age of the participants was 35.7±13.7 years. Male participants edcompris 50.9% and female participants comprised 49.1% of the group. In all, 85% of the participants had sufficient income, and 15% had insufficient income; 64.7% were married. The prevalence MA was 14.4% and low eGFR (<60 ml/min/1.73 m2) based on Modifying of Diet in Renal Disease (MDRD) criteria was present in 7.2% of patients. The prevalence of CKD (eGFR<60 ml/min/1.73 m2+MA) was high (15.6%), as in the Kidney Early Evaluation Program study (16%) [Figure 1], whereas 2.8% of participants had elevated serum creatinine values [Table 2]. This shows that the combination of MA and eGFR is more effective for early detection of CKD compared with elevated serum creatinine. Results showed that the prevalence of MA across the various age categories showed an increasing trend (P=0.001). No significant differences in the prevalence of MA was associated with sex (P=0.750) [Table 3]. Obese persons, hypertensive patients, diabetic patients, those with a personal history of CVD and bilharziasis, and those who received analgesics were significantly higher among MA cases than among those without MA [Table 4]. Positive history of smoking was significantly higher among MA cases than among those without MA (P=0.002). Of the total 24 participants with diabetes in our screening identified four new cases that did not have a previous history of diabetes. Also, out of the total 29 hypertensive patients, new five cases were identified by our screening [Table 4]. The independent predictor variables associated with the presence of MA in a mutually adjusted logistic regression model are shown in [Table 5], where diabetes [odds ratio (OR)=8.62, 95% confidence interval (CI): 2.62–28.31] and personal history of hypertension (OR=4.92, 95% CI: 1.63–14.86) were independent predictors of MA.
Figure 1: Prevalence of chronic kidney disease (CKD) and microalbuminuria among the studied participants.

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Table 1: Sociodemographic characteristics of the studied population

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Table 2: Prevalence of elevated creatinine and classification of chronic kidney disease based on National Kidney Foundation, 2002, among the participants

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Table 3: Distribution of microalbuminuria among the participants regarding their age and sex

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Table 4: Distribution of risk factors for microalbuminuria among the participants

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Table 5: Multivariate stepwise binary logistic regression of the risk factors for microalbuminuria

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  Discussion Top


Screening for CKD is usually performed using one of two measures: impaired eGFR and/or increased MA. Interestingly, it was recently shown that individuals with increased levels of MA are at risk for ESRD, irrespective of their eGFR level. These data suggest that screening for CKD using MA might be more effective than screening using impaired eGFR alone to identify individuals at risk for developing ESRD. In this study, the prevalence of CKD based on the eGFR MDRD-3 equation was 7.2%. The prevalence did not change substantially when we used the Cockcroft–Gault equation and the new CKD–EPI equation for estimation of GFR. The diagnosis of CKD in this study by the presence of MA rather than by reduced GFR indicates that people may experience kidney damage before their eGFR decreases below 60 ml/min/1.73 m2. The majority of studied patients were classified into CKD stages 1 and 2. This is lower than other reported prevalences from different parts of the world. For example, the overall prevalence in Beijing (China) was 13.0% 7. 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). Abo Zenah et al. 7 provided a first description of increased MA levels in young adult Arab individuals from Saudi Arabia and showed in his study a similar prevalence for MA. The present results indicated that MA is more common in the elderly, with the prevalence showing an increasing trend with progression of age. This is also in agreement with the results of some other epidemiological studies 8–11. No significant differences in the prevalence of CKD with sex were noted. This is in agreement with the results of the study by Gouda et al. 12 who conducted a cross-sectional study among participants of the Egypt Information, Prevention, and Treatment of Chronic Kidney Diseases (EGYPT-CKD) Program, a population-based screening program for MA and CKD in Damanhour (Egypt), in which 417 individuals were screened. In this study, 10.6% of participants were positive for MA; the prevalence of MA across the various age categories showed an increasing trend (P=0.001) and no significant differences across the prevalence of MA with sex (P=0.43) were noted 12. This is also in agreement with the results of the study by Alsuwaida et al. (2010) who conducted a cross-sectional study on 491 participants in the Kingdom of Saudi Arabia (SEEK-Saudi investigators); in this study the overall prevalence of MA was 5.3%. There was no significant difference between male and female patients regarding the presence of MA on the basis of the urine dipstick test. However, the youngest age group (18–30 years) showed the highest prevalence of MA (30.8%) (P=0.042) 13. The prevalence of diabetic, hypertensive, obese, and CVD patients and of persons who received analgesics was significantly higher among MA cases than among those without MA. This is also consistent with the findings of the study by Alsuwaida et al. 13, in which a similar higher prevalence of older patients (P=0.046) and those with higher serum fasting glucose (P<0.001) was found among patients with MA. The independent predictor variables associated with the presence of MA in a logistic regression model showed that diabetes and hypertension were independent predictor variables. This is in agreement with the results of the study by Abdulkareem et al. (2010) in which the independent predictor variables associated with the presence of MA in a logistic regression model showed age (OR=1.055, 95% CI: 1.01–1.10), hypertension (OR=1.04, 95% CI: 1.102–1.07), and personal history of CVD (OR=2.34, 95% CI: 2.31–18.1) 13. There was a high prevalence of bilharziasis (P=0.001) among participants with MA [Table 4]. This is in agreement with a study conducted by Sobh et al. 14 on 240 patients who were found to be free of any secondary cause other than schistosomiasis, which could explain their MA levels. The albumin–creatinine ratio, which was used in this study to define MA, has good reliability and has been recommended for population screening. Further, collection of early-morning spot urine gives a reasonable estimate of the 24-h urinary excretion of albumin and has been shown to be a reliable index of MA in population-based studies 15, regardless of the relatively small sample size used; yet, it demonstrated the feasibility of the MA test in screening and early detection of CKD in the rural population. However, further studies are also recommended with a larger sample size and more sophisticated sampling techniques to evaluate the prevalence and risk factors for MA and early diagnosis of CKD.


  Conclusion Top


Screening of MA seemed to be an easy, equally, and even more efficient method for early identification of individuals with CKD compared with other conventional methods, particularly for those with uncontrolled or undetected diseases such as diabetes or hypertension.[15]

 
  References Top

1.MacGregor MS, Boag DE, Innes A.Chronic kidney disease: evolving strategies for detection and management of impaired renal function.QJM2006;99:365–375.  Back to cited text no. 1
    
2..KDOQI clinical practice guidelines for chronic kidney disease. 2002; Available at: http://www.kidney.org/professionals/KDOQI/guidelines_ckd [Accessed 29 October 2009].  Back to cited text no. 2
    
3.Mitch WEGoldman L, Ausiello D.Chronic kidney disease.Cecil medicine2007:23rd ed..Philadelphia, PA:W.B. Saunders Elsevier.  Back to cited text no. 3
    
4.Afifi AM, Mady GE, Ahmad AA, El-Sharkawy ME, Aly AR, Khalil HH.Pattern of renal diseases among elderly Egyptians patients with acute or chronic renal diseases in Ain Shams University and Nasser Institute Hospitals, Cairo, Egypt.J Egypt Soc Parasitol2005;35:911–924.  Back to cited text no. 4
    
5.Levey AS, Coresh J, Bolton K, Culleton B, Harvey KS, Ikizler TA, et al..K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification.Am J Kidney Dis2002;39Suppl 1S1–S266.  Back to cited text no. 5
    
6.Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al..Chronic kidney disease as a global public health problem: approaches and initiatives – a position statement from Kidney Disease Improving Global Outcomes.Kidney Int2007;72:247–259.  Back to cited text no. 6
    
7.Abo Zenah H, El Benayan A, El Nahas AM.Prevalence of increased albumin excretion rate in young Saudi adults.Nephon Clin Pract2008;108:c155–c162.  Back to cited text no. 7
    
8.Cirillo M, Senigalliesi L, Laurenzi M, Alfieri R, Stamler J, Stamler R, et al..Microalbuminuria in nondiabetic adults: relation of blood pressure, body mass index, plasma cholesterol levels and smoking: the Gubbio Population Study.Arch Intern Med1998;158:1933–1939.  Back to cited text no. 8
    
9.Kramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D.Obesity and prevalent and incident CKD: the hypertension detection and follow-up program.Am J Kidney Dis2005;46:587–594.  Back to cited text no. 9
    
10.Fox CS, Larson MG, Leip EP, Culleton B, Wilson PWF, Levy D.Predictors of new-onset kidney disease in a community-based population.J Am Med Assoc2004;291:844–850.  Back to cited text no. 10
    
11.Valensi P, Assayag M, Busby M, Pariès J, Lormeau B, Attali JR.Microalbuminuria in obese patients with or without hypertension.Int J Obes1996;20:574–579.  Back to cited text no. 11
    
12.Gouda Z, Mashaal G, Bello AK, El Attar A, El Kemmry T, El Reweny A, et al..Egypt Information, Prevention and Treatment of Chronic Kidney Disease (EGIPT-CKD) programme: prevalence and risk factors for microalbuminuria among the relatives of patients with CKD in Egypt.Saudi J Kidney Dis Transpl2011;22:1055–1063.  Back to cited text no. 12
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13.Alsuwaida AO, Farag YM, Al Sayyari AA, Mousa D, Alhejaili F, Al Harbi A, et al..Epidemiology of chronic kidney disease in the Kingdom of Saudi Arabia (SEEK-Saudi investigators) – a pilot study.Saudi J Kidney Dis Transpl2010;21:1066–1072.  Back to cited text no. 13
    
14.Sobh MA, Moustafa FE, Sally SM, Deelder AM, Ghoniem MA.Characterisation of kidney lesions in early schistosomal-specific nephropathy.Nephrol Dial Transplant1988;3:392–398.  Back to cited text no. 14
    
15.Gansevoort RT, Verhave JC, Hillege HL, Burgerhof JGM, Bakker SJL, De Zeeuw D, et al..The validity of screening based on spot morning urine samples to detect subjects with microalbuminuria in the general population.Kidney Int Suppl2005;67:S28–S35.  Back to cited text no. 15
    


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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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