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Saudi Journal of Kidney Diseases and Transplantation
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Year : 2013  |  Volume : 24  |  Issue : 3  |  Page : 534-541
Screening for chronic kidney diseases among an adult population

1 Department of Epidemiology, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh
2 Department of Hemodialysis and Transplant Unit, Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM) Hospital, Dhaka, Bangladesh
3 Department of Community Nutrition, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh
4 Department of Biochemistry and Cell Biology, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh

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Date of Web Publication24-Apr-2013


Chronic kidney disease (CKD) is now one of the major health problems all over the world and its early screening is vital to prevent the development of end-stage renal failure. This study was designed to evaluate the proportion of urban adults suffering from CKD as well as to have a preliminary idea about the determinants of this disorder. The screening program for CKD was arranged in a public place in Dhaka city, Bangladesh, and involved 634 adult partici­pants (>18 years of age) selected on first-come first-served basis. Socio-demographic, anthropometric, and clinical data were collected. Urinary protein was tested by the dipstick method, and serum glucose and creatinine were measured by an auto-analyzer. Estimated glomerular filtrate rate (eGFR) was calculated by using standard formula. CKD was diagnosed and classified accor­ding to the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines. A total of 12.8% of the subjects were found to have CKD of whom 2.7% were in Stage 1, 4.1% in Stage 2, and 6% were in Stage 3. The proportion was strongly influenced by age, with the highest prevalence (38.5%) found at 60 years and above. The CKD group showed higher body mass index, waist-hip ratio, and systolic blood pressure, compared with their non-CKD counterparts (P = 0.02). On multiple regression analysis (after adjustment of some confounding variables), age, random blood sugar, and education showed significant association with the development of CKD. A substantial number of urban adults in Dhaka were found to be unaware about the existence of CKD and large-scale prevention programs should be undertaken to reduce the classical risk factors of these disorders.

How to cite this article:
Fatema K, Abedin Z, Mansur A, Rahman F, Khatun T, Sumi N, Kobura K, Akter S, Ali L. Screening for chronic kidney diseases among an adult population. Saudi J Kidney Dis Transpl 2013;24:534-41

How to cite this URL:
Fatema K, Abedin Z, Mansur A, Rahman F, Khatun T, Sumi N, Kobura K, Akter S, Ali L. Screening for chronic kidney diseases among an adult population. Saudi J Kidney Dis Transpl [serial online] 2013 [cited 2021 Nov 29];24:534-41. Available from: https://www.sjkdt.org/text.asp?2013/24/3/534/111049

   Introduction Top

Chronic kidney disease (CKD) is a common progressive health problem that turns to a deadly disease contributing to significant mor­bidity and mortality and has now become a major public health burden. It is evident that all over the world, chronic diseases are increasing and no country in the world has enough resources to combat this situation.

The Third National Health and Nutrition Examination Survey (NHANES III) found dia­betes, hypertension, older age, and male sex to be positively associated with increased like­lihood of elevated serum creatinine levels. [1],[2] Studies have also shown that CKD is asso­ciated with a twofold to threefold higher risk of death in addition to a higher risk of dialysis recruitment and development of congestive heart failure or other cardiovascular events. [3] Several reports indicate that CKD is often undiagnosed and complications of later stages of CKD are often untreated. [4],[5],[6] Detection of CKD, particularly at early stages, is essential because therapeutic interventions are likely to be effective if they are implemented early in the course of the disease process. This can decrease the economic and social burden of kidney diseases and reduce the high morbidity and mortality associated with CKD.

The prevalence of CKD in different popula­tions, including the high-risk groups (e.g. older patients and patients with hypertension and/or diabetes), needs to be explored among various racial groups and in different environmental situations. Such data have not yet been pu­blished on Bangladeshi population. Taking ad­vantage of the observance of World Kidney Day recently, we conducted a screening pro­gram to find out the proportion of urban adults having CKD as well as to have a preliminary idea about the determinants of this disorder.

   Subjects and Methods Top

Study population

Adults (aged >18 years or more) attending the screening program in response to an open screening center in a busy public place of Dhaka City were included in the study. Based on first-come first-served basis, a total of 650 participants were selected among whom 634 completed the whole program successfully.

Screening protocol and evaluation criteria

All subjects completed a questionnaire, docu­menting their socio-demographic status (e.g. age, sex, income, occupation, and education), personal and family health history (e.g. hyper­tension, diabetes, and kidney disease), and lifestyle pattern (e.g. exercise and smoking), with the assistance of trained volunteers re­cruited from medical doctors, laboratory tech­nicians, nurses, and research assistants. Each participant underwent weight and height mea­surements, using a calibrated scale. Waist-hip ratio (WHR) was also measured. The body mass index (BMI) was calculated as weight (in kilograms) divided by height in square meters and categorized as per the cutoff value for Asian population.

Blood pressure (BP) was measured according to the guidelines presented in the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treat­ment of High Blood Pressure (JNC VII). [7] Hypertension was defined as systolic blood pressure (SBP) of 140 mmHg or diastolic BP (DBP) of 90 mmHg, or use of anti-hyperten­sive medications irrespective of the BP.

Participants were asked to collect a urine sample which was then used to detect proteins with urinary strips [8] by a trained person. Blood was collected to measure random blood sugar (RBS) and serum creatinine by using chemis­try auto-analyzer; serum glucose was measured by glucose oxidase method and serum creatinine by alkaline pictrate method. For determination of estimated glomerular filtration rate (eGFR), Modification of Diet in Renal Disease (MDRD) version 4 from the MDRD study was used. [9] According to the K/DOQI and Kidney Disease: Improving Global Outcomes (K/DIGO) guidelines to define CKD, we should have at least three months history or three positive test results. Since we conducted a screening pro­gram and tested only once, we used the term "likely CKD" instead of CKD.

Ethical consideration

The protocol was approved by the Ethical Committee of the Diabetic Association of Bangladesh.

   Statistical Analysis Top

Results are presented as numbers, percenta­ges, or means ± SD. Two-sample Student's t test and chi-square test were used for compari­son of means and proportions where appropriate.

The outcome under analysis was the presence of CKD, defined as above. Exposure variables that were considered included age, BP, dia­betes mellitus (DM), BMI, WHR, and edu­cation status. The crude (unadjusted) relation­ships between the exposure variables and the presence or absence of CKD were examined in univariate logistic regression analyses. Multi-variate logistic regression analysis was then done to evaluate the simultaneous effects of various exposure variables, with adjustment for the potential confounding effects of other factors. The above approaches were applied separately for CKD stages and for proteinuria. A multiple linear regression model was used to determine the independent association between the reduction of eGFR (<60 mL/min/1.73 m 2 ) and continuous variables such as age, SBP, DBP, DM, BMI, WHR, education status, and smoking.

   Results Top

Characteristics of the study population

[Table 1] shows the socio-demographic charac­teristics of the participants. The age range was between 18 and 70 years (mean ± SD, 37 ± 11). Most of the participants (88.3%) were males, as the program was conducted in front of the National Press Club, which is near the Secreta­riat, where male employees are more than fe­male employees. The participants who attended the screening program were from various pro­fessions. Only 2.4% of the participants were illiterate.
Table 1: Demographic characteristics of the study population (N = 634).

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Proportion of proteinuria

Proteinuria (regardless of kidney function) was detected in 8.2% (95% CI 6.06-10.33) of the participants. The frequency distribution of dip­stick proteinuria is given in [Table 2]. Among the subjects with proteinuria, 7% (n = 44) had isolated proteinuria while 1.3% (n = 8) had pro­teinuria with hematuria. Isolated hematuria, defined as dipstick hematuria ≥1+, was found in 15.2% (n = 97) of the total population.
Table 2: Distribution of degree of proteinuria among the study population.

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Proportion of likely CKD

The overall proportion of likely CKD was 12.8% (95% CI 10.3-15.3). Among the sub­jects with likely CKD, 6% subjects had re­duced eGFR, MDRD equation <60 mL/min/ 1.73 m 2 (CKD Stage 3). No subject was found to be having Stage 4 or Stage 5 CKD. The fre­quency distribution of different stages of CKD is given in [Table 3]. Furthermore, the propor­tion of various stages of likely CKD in dif­ferent target groups (whole population, dia­betic, hypertensive, obese, and overweight sub­jects) for three age categories (18-30, 31-45, and 46 years and above) is shown in [Table 4]. Overall, CKD became more prevalent with increasing age (46 years and above) in most of the target groups.
Table 3: Proportion of chronic kidney disease with staging among the study population.

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Table 4: Proportion of chronic kidney disease in different study groups categorized according to age

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Determinants of CKD

Age, SBP, diabetes, BMI, and WHR were found to be significant risk indicators for the development of CKD in both the univariate and multivariate models [Table 5]. SBP (<90 mmHg), presence of diabetes (>7.8 mmol/L), BMI of <18.5 or >23.01, and WHR (high risk >1.0 for male, >0.85 for female) were shown to be the risk factors for the occurrence of CKD. The risk for developing CKD was al­most twofold higher in subjects with high SBP (adjusted OR 2.1, 95% CI 1.04-4.4; P = 0.04), high RBS (adjusted OR 2.1, 95% CI 1.0-4.2; P = 0.03), BMI > 23.0 kg/m 2 , and high WHR (adjusted OR 2.0, 95% CI 1.1-3.9; P = 0.04).
Table 5: Determinants of chronic kidney disease (univariate and multivariate analyses).

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We used logistic regression to quantify the effects of age, SBP, DBP, RBS, BMI, WHR, education, and smoking with CKD. After ad­justing for potential confounders in the multi-variate model by using all variables, age, RBS, and education showed significant association with the development of CKD [Table 6].
Table 6: Risk indicators associated with chronic kidney diseases.

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   Discussion Top

The epidemic of end-stage renal disease (ESRD) is a major public health problem world­wide. This has led to concerns about the burden of mild to moderate CKD in the gene­ral population. Stages 1 and 2 of CKD appear to be very common as recently reported in the National Kidney Foundation (NKF) K/DOQI guidelines. [10] In our study, we found that 2.7% of likely CKD subjects were in Stage 1, 4.1% were in Stage 2, and 6.0% were in Stage 3. We did not find any individuals in stages 4 and 5, and the overall proportion of likely CKD was around 12.8%. This is quite high in a random screening program. This figure supports the use of targeted screening in identifying large numbers of subjects at risk for CKD. The most alarming situation is that only 1.9% people knew that they had some sort of kidney disease although our assessment revealed that 12.8% of the people had some sort of abnormal renal function. Conversely, patients with stages 4 and 5 CKD are usually symptomatic and, thus, probably did not take part in the screening pro­gram. On the other hand, since patients in the early stages of CKD are asymptomatic, they do not take any measures to treat the disease which then progresses over time to ESRD. It is well documented that adverse outcomes of CKD can be prevented or delayed through early detection and early intervention. This screening program shows the effectiveness of identifying a large number of individuals in early stages of CKD, which is alarming for a developing country like Bangladesh.

In our study, 8.2% of the participants showed ≥+1 positive dipstick proteinuria. This value is lower than the 15% found in Congo [11] and 11% found in Bolivia, [12] but almost similar (8.7%) to NHANES III. [2] This discrepancy between studies may be partly due to various defini­tions (microalbuminuria vs. dipstick protei­nuria) and criteria of selection (random vs. without random sample and/or high-risk popu­lation vs. whole population) applied in each survey. As it is a key prognostic finding and has a significant beneficial role for early diag­nosis of CKD, screening of urine for protei­nuria using the dipstick test is being used successfully. [13]

From the history, we found that most of our participants with likely CKD were in the age group of 45 years and above (29.3%). Although older age is associated with increased rates of complications from CKD, [5] age >60 years was an independent predictor of inadequate control of BP in CKD. Only 14.8% of the people sur­veyed knew that they had high BP. However, on recording the BP as part of the survey, we found that 25.2% were hypertensive, which is an alarming situation because hypertension is a risk factor for both CKD and cardiovascular disease (CVD).

About 11.5% of the surveyed people told that they were diabetic, although after measuring the RBS, we found that 8.5% of the people were hyperglycemic. Using the Asian BMI criteria, [14] almost 59.4% of our participants were overweight and obese. Moreover, 64.2% participants showed an association between likely CKD and obesity and being overweight. It is of interest to note that general obesity was found to have almost twofold higher risk for developing CKD in subjects with BMI >23.0 kg/m 2 in the present study. We also observed that the group with likely CKD showed higher BMI (24.7 ± 3.5), higher WHR (0.93 ± 0.06), and higher SBP (122 ± 20.7), compared to their non-CKD counterparts. As already stated, CKD should be regarded as a major compli­cation of overweight and obesity, regardless of whether this association is independent or through the influence of diabetes, hyperten­sion, CVD, metabolic syndrome, and high fructose intake. [15] In this study, it may indicate that general obesity as well as CKD with obesity is on the rise among the city dwellers in Bangladesh.

We observed that around 56.7% of the study population had completed secondary education but health-related awareness was very poor. About 20.3% of the participants were smokers. Smoking was associated with an almost two­fold higher risk for developing CKD [Table 6]. Smoking remains one of the most important renal risk factors which is modifiable, and it is a burning public health concern among deve­loped as well as developing countries.

All the participants in the study were reques­ted to collect their reports from the Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM) Hospital after two weeks. Those who had abnormal results were suggested re­gular follow-up in the nephrology, cardiology, or diabetes clinic of the BIRDEM Hospital.

The main limitation of this screening pro­gram is that the participants were investigated only once. Another limitation is that in a screening program, people participate volun­tarily, so a selection bias may exist. It is also understood that persons who participate in screening programs tend to be those who are health conscious and better educated or aware. This study did not address the specific cause of proteinuria in individual subjects and it needs further clarification. In the K/DOQI [10] and K/DIGO [16] guidelines, the definition of CKD requires the persistence of kidney damage for at least three months. Hence, the single mea­surement of proteinuria and eGFR in our study might overestimate its prevalence and needs follow-up. We reported hypertension, DM, and kidney disease on the basis of the investi­gations on the day of screening. Because of lack of resources, we used RBS for diagnosis of diabetes, instead of glucose tolerance test which is used as a confirmation test.

In summary, a substantial number of urban adults in Dhaka are unaware about the exis­tence of CKD and large-scale prevention pro­grams should be undertaken to reduce the classical risk factors of these disorders.

   Acknowledgments Top

Special thanks to Prof. Meguid El Nahas and Dr. Mohammad Parvez Hossain, Sheffield Kidney Institute, UK. We acknowledge the important contribution of the laboratory staff, field workers, volunteers, Biomedical Research Group of BIRDEM for having made this work possible. Most of all, we are grateful to all the subjects who participated in the study.

   References Top

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3.Lei HH, Perneger TV, Klag MJ, Whelton PK, Coresh J. Familial aggregation of renal disease in a population-based case-control study. J Am Soc Nephrol 1998;7:1270-6.  Back to cited text no. 3
4.The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complica­tions Trial Research Group. N Engl J Med 1993;329:977-86.  Back to cited text no. 4
5.The United Kingdom Prospective Diabetes Study Group. UK Prospective Diabetes Study 33: Intensive blood glucose control with sulphonylureas or insulin compared with conven­tional treatment and risk of complications in patients with type 2 diabetes. Lancet 1998;352: 837-53.  Back to cited text no. 5
6.Ohkubo Y, Kishikawa H, Araki E, et al. Inten­sive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin dependent diabetes mellitus: A randomized, prospective 6-year study. Diabetes Res Clin Pract 1995;18:103-17.  Back to cited text no. 6
7.Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Com­mittee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 report. JAMA 2003;289:2560-72.  Back to cited text no. 7
8.Yamagata K, Iseki K, Nitta K, et al. Chronic kidney disease perspectives in Japan and the importance of urinalysis screening. Clin Exp Nephrol 2008;12:1-8.  Back to cited text no. 8
9.Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modi­fication of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461-70.  Back to cited text no. 9
10.K/DOQI: Clinical practice guidelines for chro­nic kidney disease: Evaluation, classification, and stratification. Kidney Disease Outcome Quality Initiative. Am J Kidney Dis 2002;39(2 Suppl):S21-66.  Back to cited text no. 10
11.Sumaili EK, Nseka NM, Lepira FB, et al. Screening for Proteinuria and Chronic Kidney Disease Risk Factors in Kinshasa: A World Kidney Day 2007 Study. Nephron Clin Pract 2008;110:c220-8.  Back to cited text no. 11
12.Plata R, Silva C, Yahuita J, Perez L, Schieppati A, Remuzzi G. The first clinical and epidemiological programme on renal disease in Bolivia: A model for prevention and early diagnosis of renal diseases in the developing countries. Nephrol Dial Transplant 1998;13:3034-6.  Back to cited text no. 12
13.Iseki K, Ikemiya Y, Iseki C, Takishita S. Proteinuria and the risk of developing end stage renal disease. Kidney Int 2003;63:1468-74.  Back to cited text no. 13
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Correspondence Address:
Liaquat Ali
Department of Biochemistry & Cell Biology, Bangladesh University of Health Sciences (BUHS) and Vice-Chancellor, BUHS, Dhaka
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DOI: 10.4103/1319-2442.111049

PMID: 23640626

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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]

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