Available via license: CC BY-NC-ND
Content may be subject to copyright.
Journal Pre-proof
Prevalence of Chronic Kidney Disease and Associated Factors
among Patients with Diabetes in Northwest Ethiopia: A
Hospital-Based Cross-Sectional Study
Hailemaryam Alemu MD , Workagegnehu Hailu MD ,
Aynshet Adane MD
PII: S0011-393X(20)30004-7
DOI: https://doi.org/10.1016/j.curtheres.2020.100578
Reference: CUTHRE 100578
To appear in: Current Therapeutic Research
Received date: 3 November 2019
Accepted date: 6 February 2020
Please cite this article as: Hailemaryam Alemu MD , Workagegnehu Hailu MD , Aynshet Adane MD ,
Prevalence of Chronic Kidney Disease and Associated Factors among Patients with Diabetes in North-
west Ethiopia: A Hospital-Based Cross-Sectional Study, Current Therapeutic Research (2020), doi:
https://doi.org/10.1016/j.curtheres.2020.100578
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition
of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of
record. This version will undergo additional copyediting, typesetting and review before it is published
in its final form, but we are providing this version to give early visibility of the article. Please note that,
during the production process, errors may be discovered which could affect the content, and all legal
disclaimers that apply to the journal pertain.
©2020 Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license.
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Prevalence of Chronic Kidney Disease and Associated Factors among Patients with
Diabetes in Northwest Ethiopia: A Hospital-Based Cross-Sectional Study
Hailemaryam Alemu, MD, Workagegnehu Hailu, MD*, and Aynshet Adane, MD
<organization>Department of Internal Medicine, College of Medicine and Health Sciences,
University of Gondar, <city>Gondar City, <country>Ethiopia
* Address correspondence to: Workagegnehu Hailu, MD, Department of Internal Medicine,
College of Medicine and Health Sciences, University of Gondar, PO Box 196, Gondar City,
Ethiopia.
E-mail address: workhailu@yahoo.com (W. Hailu).
© 2020 The Authors. Published by Elsevier, Inc. All rights reserved.
http://dx.xoi.org/10.1016.j.curtheres.2020.100578
ARTICLE INFO
Article history:
Accepted 6 February 2020
Key words:
chronic kidney disease
diabetes mellitus
Prevalence
northwest Ethiopia
ABSTRACT
Background: Chronic kidney disease (CKD) is increasingly recognized as a global health issue
and it affects 10% to 15% of the world population. Diabetes mellitus is the leading cause of end-
stage renal disease. More than 422 million adults in the world populations are living with
diabetes mellitus, 40% of whom will develop CKD. CKD in diabetes increases the risk of early
death and cardiovascular morbidity and mortality. There is a paucity of published data on the
prevalence of CKD and its associated factors among patients with diabetes in northwest Ethiopia.
Objective: The aim of this study is to determine the prevalence and factors associated with CKD
among patients with diabetes at University of Gondar Hospital, Northwest Ethiopia.
Methods: A hospital-based cross-sectional study was conducted from April 2 to July 31, 2018.
Using convenience sampling, a total of 272 consecutive patients with diabetes were recruited for
the study. Data regarding the patients’ sociodemographic information, clinical characteristics,
and laboratory parameters were collected using patient interview and review of medical records.
Serum creatinine was measured and used to calculate estimated glomerular filtration rate using
modification of diet in renal disease and chronic kidney disease epidemiology equations. Data
were analyzed using SPSS version 20. Bivariate and multivariate logistic regression analyses
were used to identify predictors of CKD in patients with diabetes.
Result: The prevalence of CKD, defined by estimated glomerular filtration rate <60
mL/min/1.73 m2, was found to be 17.3% and 14.3% by modification of diet in renal disease and
chronic kidney disease epidemiology equations, respectively. The proportion of stage 3 CKD by
modification of diet in renal disease equation was 14.7%, whereas the proportions of stage 4 and
stage 5 CKD were 2.2% and 0.4%, respectively. Among those who were diagnosed with CKD,
85.1% had pre-existing hypertension. Multivariate logistic regression analysis revealed that the
presence of retinopathy (adjusted odds ratio = 13; 95% CI, 4–36; P = 0.004), pre-existing
hypertension (adjusted odds ratio = 8.2; 95% CI, 2–23; P < 0.001), current systolic blood
pressure >140 mm Hg (adjusted odds ratio = 6; 95% CI, 4–22; P = 0.001), and duration of
diabetes >10 years (adjusted odds ratio = 3.2; 95% CI, 2–7; P = 0.004) were significantly
associated with CKD in patients with diabetes.
Conclusions: The prevalence of CKD in patients with diabetes is high and comparable with
previous studies from low- and middle-income countries. Pre-existing hypertension, current
systolic blood pressure >140 mm Hg, duration of diabetes >10 years, and presence of retinopathy
were significantly associated with CKD. Regular screening for CKD, retinopathy, and optimal
blood pressure management should be practiced. (Curr Ther Res Clin Exp. 2020; 81:XXX–
XXX)
© 2020 Elsevier HS Journals, Inc.
1
Introduction
Chronic kidney disease (CKD) is an increasing major global health problem.1
Globally, the estimated overall prevalence of CKD is 8% to 16%, and this corresponds to
nearly 500 million individuals, of whom 78% (387.5 million) reside in low- to middle-
income countries.2 CKD is associated with increased risks of mortality and cardiovascular
events.3
It is estimated that the incidence rates of CKD in low- to middle-income countries
might be up to 4 times higher than those observed in developed countries. In sub-Saharan
Africa, CKD more commonly affects individuals aged between 20 and 50 years, and the age
of onset of end-stage renal disease (ESRD) is 20 years earlier in populations of African
descent compared with other ethnic groups in Western countries (45 years vs 63 years).2,4
The magnitude of CKD in most parts of Africa is unknown mainly due to a shortage
of national registries and lack of community-based studies. Some studies from East Africa
and Egypt suggest that CKD is 3 or 4 times more frequent in the developing world. The
prevalence of diabetic nephropathy is estimated to be 6% to 16% in sub-Saharan Africa.5 A
systematic review showed the prevalence of CKD in Africa ranging from 2% to 41%.4,6
Globally, common causes of CKD are diabetes and hypertension. Diabetes causes
30% to 50% of all cases of CKD and ESRD worldwide. Screening for CKD based on
estimated glomerular filtration rate (eGFR) is recommended in patients with diabetes for
early identification and treatment.7
Tropical nephropathy (ie, glomerulonephritis) is still the main cause of CKD in sub-
Saharan Africa. Kidney disease associated with HIV infection was responsible for a majority
of ESRD burden in Africa.8
The World Health Organization estimates that, globally, 422 million adults older than
age 18 years (8.5% of the world adult population) were living with diabetes in 2014.
According to this estimate, 25 million adults in Africa (7.1%) live with diabetes.9 More than
40% of people with diabetes will develop CKD and a significant number will develop ESRD,
requiring renal replacement therapies.10 It estimated that by the year 2030, more than 70% of
patients with ESRD will be residents of developing countries.11 Patients with both diabetes
and CKD are at higher risk of cardiovascular morbidities and mortality, kidney failure, and
death when compared with those without CKD.12,13
Ethiopia is facing a double burden from communicable and noncommunicable
diseases. Noncommunicable diseases and injuries account for 52% of the total mortality in
2016 in Ethiopia.14 Although diabetes is becoming prevalent in Ethiopia, data on the
prevalence of CKD and determinant factors in patients with diabetes are scarce. Previous
hospital-based studies in Ethiopia showed a CKD prevalence of 18.2% and 23.8% and using
modification of diet in renal diseases (MDRD) and Cockroft-Gualt (CG) equations,
respectively, in patients with diabetes in Butajira Hospital.15
A similar hospital-based study performed in 2016 at Gondar University Hospital
showed the overall prevalence of CKD as 21.8%. In this study, only 3.9% of participants
have CKD defined by eGFR <60 mL/min/1.73 m2 using the MDRD equation, and the
majority (20.1%) have CKD defined by albuminuria using the urine dipstick test.16
Despite evidence showing the incidence of CKD in patients with diabetes is high in
other regions, there are limited data on the national prevalence and associated factors of CKD
in Ethiopia. Previous Ethiopian studies reported CKD prevalence using eGFR estimated with
MDRD and CG equations, whereas the chronic kidney disease epidemiology (CKD-EPI)
equation is the most widely used and accepted equation. Moreover, in the study performed at
Gondar University Hospital, the prevalence of CKD defined by eGFR is very low compared
with other regions, and this study excluded patients with cardiovascular disease (CVD).
Therefore, this study aimed to assess the prevalence and factors associated with CKD among
patients with diabetes, including those with CVD comorbidity, using both MDRD and CKD-
EPI equations.
Materials and Methods
Study settings and design
A hospital-based cross-sectional study was conducted from April 2 to July 31, 2018,
at the University of Gondar hospital. The hospital is located in northwest Ethiopia in the
historic town of Gondar, which is 750 km away from the capital, Addis Ababa. The hospital
has a bed capacity of 600 beds and serves as a tertiary referral hospital for a population
catchment area of nearly 7 million. A diabetes clinic that was established in 1985 provides
services to 3029 registered patients with diabetes mellitus. Around 120 patients with diabetes
are seen in the clinic every Tuesday and Friday. Routine evaluations include fasting blood
sugar measurements during each visit; annual retinal examination; and screening for CVD,
including echocardiography when needed. Simple routine urinalysis is performed for most
patients but quantitative proteinuria measurement is not routinely practiced due to
unavailability of tests. The hemoglobin A1c test is not readily available in the clinic.
Study participants
The source population included patients with diabetes who had hospital records of
diagnosis of diabetes mellitus with follow-up at the University of Gondar Hospital.
Inclusion criteria were older than age 18 years, patients with type 2 diabetes, and patients
with type 1 diabetes 5 years after their initial diagnosis. Participants with acute illness
requiring hospital admission, having recent or persistent diarrhea or vomiting, acute bleeding,
having hypertensive urgency or emergency, and pregnant women were excluded.
Sample size and sampling procedure
The sample size was calculated at a prevalence of 22% with a 95% CI and the degree
of precision of 5%. Using convenience sampling 272 study participants were recruited
consecutively. A total of 300 patients were approached with response rate of 90.6%.
Data collection
Data were collected through an investigator administered pretested questionnaire. The
questionnaire was pretested in 27 patients with diabetes for accuracy. Patients were
interviewed to obtain demographic data and risk factor variables. Patient records were
reviewed to obtain information on relevant medical history like duration of diabetes mellitus,
presence of hypertension diagnosis in the past, retinopathy, medication history, and
laboratory parameters such as fasting blood sugar level. The presence of comorbidities, such
as stroke or cardiac disease, was reviewed from patient medical records. Physical
examinations, including measurement of height, weight, and blood pressure, were performed.
Body mass index (BMI) was calculated using weight and height computed as weight in
kilograms divided by height in meters2. Obesity was defined as BMI >30. Blood pressure was
measured by general practitioners using sphygmomanometer and stethoscope. Measurements
were taken from the upper arm while placing the hand at heart level after the patients had
been sitting for more than 5 minutes. Hypertension was defined as systolic blood pressure
(SBP) >130 mm Hg or diastolic blood pressure >80 mg or current use of antihypertension
medication.
Laboratory measurements
Venous blood (2 mL) was taken from each patient to determine the serum creatinine
level and calculate eGFR. Serum creatinine level was measured in a single laboratory using
automated clinical chemistry analyzer (Pentra C400; Horiba France, Longjumeau Cedex,
France). Internal quality was assured by running control test every day before analyzing
patients’ sera. GFR was estimated using the MDRD equation GFR = 186 × [serum creatinine
(mg/dL)] – 1.154 × (age) – 0.203 × (0.742 for women) × (1.210 for African Americans), and
CKD-EPI equation, GFR (mL/min/1.73 m2) = 141 × min (SCr/κ, 1) α × max (SCr/κ, 1) –
1.209 × 0.993 age
Where α is –0.329 for women and –0.411 for men, min indicates the minimum of serum
creatinine (SCr) /κ , and κ is 0.7 for women and 0.9 for men, α is –0.248 for women and –
0.207 for men. Urine protein (ie, albumin) quantitative measurements were not available in
the setup and hence only functional estimation using serum creatinine level was used.
CKD is defined as eGFR <60 mL/min/1.73 m2. The stages of eGFR were categorized
based on the classification system established by the National Kidney Foundation Kidney
Disease Outcomes Quality Initiative classification where stage 3 = eGFR of 30 to 59
mL/min/1.73 m2, stage 4 = eGFR of 15 to 29 mL/min/1.73 m2, and stage 5 = eGFR of <15
mL/min/1.73 m2. Stage 3 was further classified into 3a (eGFR of 45–59.9 mL/min/1.73 m2)
and 3b (eGFR of 30–44.9 mL/min/1.73 m2).
The last 3 consecutive fasting blood sugar measurements were obtained from the
patient file to calculate average fasting blood sugar. Laboratory results were communicated
by telephone to those participants with eGFR <60 mL/min/1.73 m2, and these participants
were advised to have further follow-up and contact with the renal care clinic.
Statistical analysis
Data were entered into EPI info version 3.2 (Centers for Disease Control and
Prevention, Atlanta, Georgia) and analyzed using SPSS version 20 (IBM-SPSS Inc, Armonk,
NY). Normally distributed variables are summarized by their means (SD); median and range
are used for skewed data. Independent 2-sample test analysis was used to compare the means
of 2 variables. Cross-tabulation with 2 analysis was used for between-group comparisons of
CKD proportions. Descriptive statistics, including the proportion, mean, median, and
frequencies are depicted with tables and graphs. The association of independent variables
with the dependent variables was tested using stepwise binary logistic regression. Adjusted
odds ratio (AOR) with 95% CI was used to describe associations with P < 0.05 taken as
statistically significant.
Ethical consideration
Ethical approval for this research was obtained from the University of Gondar,
College of Health Science Institutional Research Ethics Committee. Participants were
informed about the procedures, the objectives, and the benefits of the research. Serum
creatinine was measured for free but there was no other incentive given to participants.
Informed written consent was obtained from each participant.
Results
Sociodemographic characteristics of participants
A total of 272 participants were included in the study. The mean (SD) age of
participants was 51.67 (13.75) years with the minimum and maximum age being 20 years and
81 years, respectively. The majority of the study participants were between ages 46 and 60
years (44.1% [n = 120]) and 23% (n = 64) were older than age 60 years. The proportion of
men and women was similar. Most study participants resided in an urban area (69.9% [n =
191]) and 174 (64%) had received formal education (Table 1).
Clinical characteristics of participants
The proportion of patients with type 2 and type 1 diabetes mellitus was 171 (62.9%)
and 101 (37.1%), respectively. There was equal sex distribution among patients with both
type 1 and type 2 diabetes.
The mean and median BMI of study participants was 23.79 and 23.2, respectively.
Seventy-one (26.1%) and 25 (9.2%) patients were overweight and obese, respectively, most
of whom were participants with type 2 diabetes. Most type 2 study patients were found in the
age range of 45 to 60 years and 23% were older than age 60 years. The mean (SD) duration
of diabetes was 9.05 (6.43) years with the minimum and maximum duration being 3 months
and 49 years, respectively.
Among all study participants, 46% had pre-existing hypertension and most of them
were taking 1 or 2 antihypertension medications. The proportion of hypertensive patients was
higher in type 2 patients than in type 1 patients (84.8% vs 15.2%). Regardless of therapy,
56% of all study participants had current SBP in the range above 140/90 mm Hg. We found
that 37.1% of patients with diabetes have an average fasting blood sugar level >170 mg/dL,
which is an indicator of poor glycemic control (Table 2). A small proportion of participants
had a history of smoking and alcohol consumption. There was a statistically significant
difference in the mean values of age, BMI, blood pressure, and duration of diabetes between
type 1 and type 2 participants (Table 3).
Prevalence of CKD
The prevalence of CKD (defined by eGFR <60 mL/min/1.73 m2) was found to be
17.3% (95% CI, 12.8%–21.8%) and 14.3% (95% CI, 9.8%–18.3%) using MDRD and CKD-
EPI equations, respectively. The prevalence of all the stages of CKD is shown in Table 4.
There was no statistically significant difference in the proportions of CKD between
the 2 types of diabetes: 13.9% in type 1 versus 19.2% in type 2 by MDRD and 10.9% in type
1 versus 16.4% in type 2 by CKD-EPI equation.
Factors associated with CKD according to MDRD and CKD-EPI equations
On bivariate analysis, factors significantly associated with CKD include the following
variables: age >60 years, presence of pre-existing hypertension, current SBP >140 mm Hg,
duration of diabetes >10 years, presence of retinopathy, presence of pre-existing
cardiovascular disease, current or past smoking, habitual use of analgesics, and no regular
exercise. On multivariate analysis, only 4 variables were found to have statistically
significant association with the presence of CKD by both MDRD and CKD-EPI equations:
the presence of retinopathy, pre-existing hypertension, current SBP, and duration of diabetes
>10 years (Table 5).
Patients with retinopathy were 14 (AOR = 14; 95% CI, 4–36; P < 0.001) times and 18
(AOR = 18; 95% CI, 6–40; P < 0.001) times more likely to develop CKD than having no
retinopathy using MDRD and CKD-EPI equations, respectively. The odds of CKD was 6
(AOR = 6; 95% CI, 4–22; P < 0.001) times higher among patients with diabetes having
current SBP >140 mm Hg compared with patients having SBP <140 mm Hg. Patients with
pre-existing hypertension and duration of diabetes >10 years were 8.2 (AOR = 8.2; 95% CI,
2–23; P < 0.004) and 3.2 (AOR = 3.2; 95% CI, 1.6–7; P = 0.004) times more likely to
develop CKD than their counterparts, respectively (Table 6).
1
Discussion
To the best of our knowledge, this study among the few studies in Ethiopia to assess
the prevalence of CKD in patients with diabetes using estimating equations based on serum
creatinine level. It is the first study to evaluate CKD prevalence using both MDRD and CKD-
EPI equations. A total of 272 patients with diabetes were included in the study and the
overall prevalence of CKD (defined by eGFR <60 mL/min/1.73 m2) was 17.3% and 14.3%
by MDRD and CKD-EPI equations, respectively. The majority of patients (9.6%), had stage
3a CKD and only 0.4% had stage 5 CKD.
The 2016 US National Health and Nutrition Examination Survey (NHANES) (2007–
2012) cross-sectional analysis of adults with diabetes found the overall prevalence of stage 3
and above CKD using CKD-EPI equation was 18.9%. The prevalence of CKD using CKD-
EPI in this study is lower than NHANES survey data and this could be due to the difference
between the population characteristics, including age (51.6 years in this study vs 61.5 years in
the NHANES cohort), mean duration of diabetes (9 years in this study vs 13.5 years in the
NHANES cohort), different racial groups, and different settings where the studies were
conducted.17
The prevalence of CKD in patients with diabetes in eastern Mediterranean countries,
using MDRD, was 19.5%, which is similar to our study.18 A similar prevalence was reported
in Spain: 18% using eGFR by MDRD equation. However, the overall prevalence of CKD
was reported to be 27% in the Spanish study when both eGFR and albuminuria were used to
define CKD.19 Higher CKD prevalence was reported from studies in Pakistan (29.9%) and
India (48.8%) using eGFR and albuminuria.19,20 Our study did not use other markers of CKD
like albuminuria, and this might underestimate the prevalence of CKD in diabetic populations
in the study setting.
A systematic review from community-based studies on the prevalence of CKD in
Africa ranged between 2% to 41% and varied across the region. The estimated prevalence in
East Africa ranged from 7% to 15%.4
A 2014 hospital-based study in southern Ethiopia to determine the prevalence of CKD
in patients with diabetes found a prevalence of 18.2% and 23.8% by MDRD and CG
equations, respectively.15 Using the MDRD equation, the prevalence of CKD in our study and
southern Ethiopia is similar.
A similar hospital-based study performed in 2016 at Gondar University Hospital
(similar setting as the current study) showed an overall prevalence of CKD at 21.8%. In this
study, only 3.9% of participants had CKD defined by eGFR <60 mL/min/1.73m2 using the
MDRD equation and the majority (20.1%) had CKD defined by albuminuria using the urine
dipstick test.16 The prevalence of CKD in this report using the MDRD equation is lower than
the finding in our study (3.9% vs 17.3%). This could be due to the exclusion of patients with
diabetes and CVD in the previous study. Diabetes has systemic micro- and macrovascular
complications and patients with CVD are likely to have concomitant renal complications. We
used patient records to determine the presence of CVD, and this could even underestimate the
presence of important comorbidity.
Although not statistically significant, elderly patients have a higher proportion of
CKD (ie, 26.6%) (corrected OR = 2.7; P = 0.001) by MDRD and 25% by CKD-EPI. This is
consistent with other studies that showed advanced age as a consistent risk factor for the
development of CKD in patients with diabetes.21–23
In our study, pre-existing hypertension as well as current SBP >140 mm Hg are
strongly and independently associated with the presence of CKD. Among 125 patients with
hypertension and diabetes, 32.1% had CKD. Several prior studies in both developed and
developing countries support this finding.17,24,25 Among patients who developed CKD and
were taking antihypertension medications, most patients’ current blood pressure readings
were not in the target range. Physicians should emphasize optimization of hypertension
treatment to lower blood pressure so that the emergence and progression of CKD can be
delayed. Duration of diabetes >10 years is independently associated with the development of
CKD (29.3% vs 8.8%) and our study findings were consistent with others in this regard.2,17,26
Among 31 patients who were diagnosed with retinopathy, 74.2% developed CKD.
Retinopathy is independently associated with the presence of CKD. This finding is also
consistent with previous large-scale studies.27 This warrants screening for ophthalmic
complications in patients with diabetic kidney disease and vice versa.
In our study, there was no statistically significant association between CKD and level
of blood fasting blood sugar. Because this was a cross-sectional study, it is difficult to assess
long-term glycemic control and its effect on CKD. Another limitation of our study is that we
used the last fasting blood sugar levels and this might not represent the true glycemic control.
Hemoglobin A1c would have been a better option to assess glycemic control. This cross-
sectional study cannot assess the progression of renal impairment. We used only eGFR to
assess renal status, so the prevalence of CKD in the study population may be underestimated.
Neither estimating equation is validated in the Ethiopian population.
Limitations of the study
This hospital-based study might not reflect the true prevalence of CKD in patients
with diabetes in the community. We used eGFR <60 mL/min/1.73 m2 to define CKD and the
prevalence of CKD in the study group could be underestimated because we did not use
albuminuria or other structural abnormalities that define CKD. This lack of albuminuria
testing does not permit us to be more definitive about the diagnosis of diabetes-related kidney
disease, but it is our assumption that it is diabetes-related kidney disease.
Conclusions
The prevalence of CKD in the population we studied (defined as eGFR <60
mL/min/1.73 m2) was 17.3% and 14.3% using MDRD and CKD-EPI equations, respectively.
By using the MDRD equation, the prevalence of CKD in this study was similar to most
previously published studies. The presence of retinopathy, pre-existing hypertension, and
longer duration of diabetes is associated with the presence of CKD. Routine CKD screening
should be implemented in patients with diabetes for early detection and delayed progression
of CKD. We also recommend screening all patients with diabetes for retinopathy and
hypertension, and optimizing blood pressure goals to prevent CKD. Further large survey
studies using additional markers like albuminuria and imaging are needed to know the exact
burden of CKD in patients with diabetes.
Acknowledgments
Funding for this research was obtained from the College of Medicine and Health
Sciences, University of Gondar. The Internal Medicine Specialty Clinic analyzed the serum
creatinine levels at a subsidized cost.
The authors thank Dr Ermias Diro for his encouragement and guidance in preparing
the manuscript as well as the study participants for their willingness to participate in this
study.
Drs Hailu, Alemu and Adane were all involved in the conception and design of the
study. Drs Hailu and Alemu analyzed the data and prepared the draft manuscript. All authors
critically revised the content of the manuscript and read and approved the final version.
Conflicts of Interest
The authors have indicated that they have no conflicts of interest regarding the
content of this article.
References
1. 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 Int. 2007;72(3):247–59.
2. Stanifer JW, Muiru A, Jafar TH, Patel UD. Chronic kidney disease in low- and
middle-income countries. Nephrol Dial Transplant. 2016;31(6):868–74.
3. Naghavi M, Wang H, Lozano R, Davis A, Liang X, Zhou M, et al. Global, regional,
and national age-sex specific all-cause and cause-specific mortality for 240 causes of death,
1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet
[Internet]. 2015;385(9963):117–71. Available from: http://dx.doi.org/10.1016/S0140-
6736(14)61682-2
4. Abd Elhafeez S, Bolignano D, D’Arrigo G, Dounousi E, Tripepi G, Zoccali C.
Prevalence and burden of chronic kidney disease among the general population and high-risk
groups in Africa: A systematic review. BMJ Open. 2018;8(1).
5. Naicke S. End-stage renal disease in Sub-Saharan Africa. Kidney Int Suppl.
2013;3:161–3.
6. Stanifer JW, Jing B, Tolan S, Helmke N, Mukerjee R, Naicker S, et al. The
epidemiology of chronic kidney disease in sub-Saharan Africa: A systematic review and
meta-analysis. Lancet Glob Heal [Internet]. 2014;2(3):e174–81. Available from:
http://dx.doi.org/10.1016/S2214-109X(14)70002-6
7. Persson F, Rossing P. Diagnosis of diabetic kidney disease: state of the art and future
perspective. Kidney Int Suppl [Internet]. 2018;8(1):2–7. Available from:
https://doi.org/10.1016/j.kisu.2017.10.003
8. Jha V, Wang AYM, Wang H. The impact of CKD identification in large countries:
The burden of illness. Nephrol Dial Transplant. 2012;27(SUPPL. 3):32–8.
9. WHO. Global report on diabetes. Isbn [Internet]. 2016;978:92–4. Available from:
http://www.who.int/about/licensing/copyright_form/index.html%0Ahttp://www.who.int/abou
t/licensing/
10. Gheith O, Othman N, Nampoory N, Halimb M, Al-Otaibi T. Diabetic kidney disease:
difference in the prevalence and risk factors worldwide. J Egypt Soc Nephrol Transplant.
2016;16(3):65.
11. Yirsaw BD. Chronic kidney disease in sub-Saharan Africa: Hypothesis for research
demand. Ann Afr Med. 2012;11(2):119–20.
12. Saely C, Vonbank A, Lins C, Zanolin D, Leiherer A, Rein P, et al. Type 2 Diabetes,
Chronic Kidney Disease, and Mortality in Patients With Established Cardiovascular Disease.
J Am Coll Cardiol [Internet]. 2018;71(11):A1841. Available from:
http://dx.doi.org/10.1016/S0735-1097(18)32382-9
13. Hamada S, Gulliford MC. Multiple risk factor control, mortality and cardiovascular
events in type 2 diabetes and chronic kidney disease: A population-based cohort study. BMJ
Open. 2018;8(5).
14. FMoH. Addressing the Impact of Noncommunicable Diseases and Injuries in
Ethiopia. 2018;(November). Available from: http://www.ncdipoverty.org/ethiopia-report/
15. Fiseha T. Prevalence of Chronic Kidney Disease and Associated Risk Factors among
Diabetic Patients in Southern Ethiopia. Am J Heal Res. 2014;2(4):216.
16. Damtie S, Biadgo B, Baynes HW, Ambachew S, Melak T, Asmelash D, et al. Chronic
Kidney Disease and Associated Risk Factors Assessment among Diabetes Mellitus Patients at
A Tertiary Hospital, Northwest Ethiopia. Ethiop J Health Sci. 2018;28(6):691–700.
17. Wu B, Bell K, Stanford A, Kern DM, Tunceli O, Vupputuri S, et al. Understanding
CKD among patients with T2DM: Prevalence, temporal trends, and treatment patterns—
NHANES 2007–2012. BMJ Open Diabetes Res Care. 2016;4(1).
18. Coll-De-Tuero G, Mata-Cases M, Rodriguez-Poncelas A, Pepió JMA, Roura P,
Benito B, et al. Chronic kidney disease in the type 2 diabetic patients: Prevalence and
associated variables in a random sample of 2642 patients of a Mediterranean area. BMC
Nephrol. 2012;13(1).
19. Rodriguez-Poncelas A, Coll-De Tuero G, Turrò-Garriga O, Barrot-De La Puente J,
Franch-Nadal J, Mundet-Tuduri X. Impact of chronic kidney disease on the prevalence of
cardiovascular disease in patients with type 2 diabetes in Spain: PERCEDIME2 study. BMC
Nephrol. 2014;15(1):2–9.
20. Rajput R, Kumar KMP, Seshadri K, Agarwal P, Talwalkar P, Kotak B, et al.
Prevalence of Chronic Kidney Disease (CKD) in Type 2 Diabetes Mellitus Patients: START-
India Study. J Diabetes Metab. 2017;08(02).
21. Lou Arnal LM, Campos Gutiérrez B, Cuberes Izquierdo M, Gracia García O, Turón
Alcaine JM, Bielsa García S, et al. Prevalence of chronic kidney disease in patients with type
2 diabetes mellitus treated in primary care. Nefrologia. 2010;30(5):552–6.
22. Ferguson T, Tulloch-Reid M, Younger-Coleman N, Wright-Pascoe R, Boyne M,
Soyibo A, et al. Prevalence of Chronic Kidney Disease among Patients Attending a Specialist
Diabetes Clinic in Jamaica. West Indian Med J. 2015;64(3).
23. Wachukwu CM, Emem-Chioma PC, Wokoma FS, Oko-Jaja RI. Prevalence of risk
factors for chronic kidney disease among adults in a university community in Southern
Nigeria. Pan Afr Med J. 2015;21:1–5.
24. Maria Faye, Ahmed Tall Lemrabott, Mouhamadou Moustapha Cissé KF, Younoussa
Keita, Alioune A. Ngaide, Alassane Mbaye, El Hadji Fary Ka, Abdou Niang, Abdoul Kane
BD. Prevalence and Risk Factors of Chronic Kidney Disease in an African Semi-urban Area:
Results from a Cross-sectional Survey in Gueoul, Senegal. Saudi J Kidney Dis Transplant.
2017;28(6):1389–96.
25. Anand S, Zheng Y, Montez-Rath ME, Wei WJ, Perico N, Carminati S, et al. Do
attributes of persons with chronic kidney disease differ in low-income and middle-income
countries compared with high-income countries? Evidence from population-based data in six
countries. BMJ Glob Heal. 2017;2(4):1–9.
26. Stanifer JW, Cleland CR, Makuka GJ, Egger JR, Maro V, Maro H, et al. Prevalence,
risk factors, and complications of diabetes in the Kilimanjaro region: A population-based
study from Tanzania. PLoS One. 2016;11(10):1–13.
27. Rodríguez-Poncelas A, Xavier M-T, Miravet-Jiménez S, Casellas A, Barrot-De La
Puente JF, Franch-Nadal J, et al. Chronic Kidney Disease and Diabetic Retinopathy in
Patients with Type 2 Diabetes. PLoS One. 2016;11(2):1–10.
Table 1
Sociodemographic characteristics of participants (N = 272).
Characteristic
n
%
Age category, y
18-30
26
9.6
31-45
62
22.8
46-60
120
44.1
61-90
64
23.5
Sex
Male
137
50.4
Female
135
49.6
Residence
Urban
190
69.9
Rural
82
30.1
Marital status
Married
215
79
Single/widowed/divorced
57
21
Education level
No formal school
98
36
Elementary school and less
77
28.3
High school and more
97
35.7
Monthly income, ETB
<1500
153
56.3
>1500
119
43.8
Occupation
Not government employed
191
70.2
Government employed
81
29.8
ETB=Ethiopian birr; at the time of writing, $1 = 32ETB.
Table 2 Clinical characteristics of participants (N = 272).
Characteristic
n
%
Type of diabetes
1
101
37.1
2
171
62.9
Duration of diabetes, y
≥10
113
41.5
<10
159
58.5
Drug for diabetes
Insulin only
134
49.3
Oral agent only
107
39.3
Insulin + oral agent
31
11.4
Body mass index category
Underweight
25
9.2
Normal weight
151
55.5
Overweight
71
26.1
Obese
25
9.2
Pre-existing hypertension
Present
125
46
Absent
147
59.6
Stage of current systolic blood
pressure
Normal
72
26.5
Elevated
48
17.6
Stage 1
100
36.8
Stage 2
52
19.1
Stage* of current diastolic blood
pressure, mm Hg
60–79
113
41.5
80–89
109
40.1
90–109
50
18.4
Presence of retinopathy
Present
31
11.4
Absent/not known
241
88.6
Cardiovascular disease
Present
40
14.7
Absent
232
85.23
Type of cardiovascular disease
Congestive heart failure/
hypertensive heart disease
12
4.4
Ischemic heart disease
17
6.3
Peripheral artery disease
2
0.7
Stroke
3
1.1
Other†
6
2.2
None
232
85.3
Previous known kidney disease
Present
7
2.6
Absent
265
97.4
Family history of kidney disease
Present
3
1.1
Absent/unknown
269
98.9
Noncardiovascular illness
Present
20
7.4
Absent
268
98.5
*Diastolic blood pressure stages are defined as normal (60–79 mm Hg), stage 1 (80–89 mm
Hg), or stage 2 (90–109 mm Hg).
†Other types of cardiovascular disease include degenerative valvular heart disease and
dyslipidemia.
Table 3
Behavioral characteristics of participants (N = 272).
Characteristic
n
%
Smoking status
11
4
Smoker (current or past)
Nonsmoker
261
96
Exercise sessions, wk
None
170
62.5
2 or more
102
37.5
Dietary habit of fruits and
vegetables
None/occasional
188
69.1
Regular consumer
84
30.9
Alcohol consumption, drinks/d
None or <2
213
78.3
More than 2
59
21.7
Habitual analgesia use
Yes
17
6.3
No
255
93.8
Traditional medicine use
Yes
7
2.6
No
265
97.4
Table 4
Mean values of age, body mass index (BMI), blood pressure (BP), and duration of diabetes mellitus
(DM) type 1 or type 2.
Variable
Type 1 DM*
Type 2 DM*
Mean difference (95% CI)
P value
Age, y
41.0 (13)
57.0 (9.8)
–16.7 (–19.4 to 13.9)
< 0.001
Systolic BP
121.0 (15.6)
134.8(17)
–13.48 (–17.5 to –9.2)
< 0.001
Diastolic BP
75.6 (8)
79.0 (8)
–3.8 (–5.9 to 1.7)
< 0.001
BMI
21.0 (2.7)
25.0 (3.8)
–4.0 (–4.9 to 3.2)
< 0.001
Duration of diabetes, y
11.0 (6.5)
7.0 (6)
3.4 (1.8 to 5.0)
< 0.001
*Values are presented as mean (SD).
Table 5
Proportion of different stages of chronic kidney disease (CKD) according to modified diet in renal
disease (MDRD) and chronic kidney disease-epidemiology (CKD-EPI) equations (N = 272).
Stage of CKD
Description
eGFR, mL/min/1.73 m2
MDRD*
CKD-EPI*
Stage 3a
Mild to moderately decreased
45–59.9
26 (9.6)
18 (6.6)
Stage 3b
Moderate to severely decreased
30–44.9
14 (5.1)
14 (5.1)
Stage 4
Severely decreased
15–29.9
6 (2.2)
7 (2.6)
Stage 5
ESRD
≤15
1 (0.4)
1 (0.4)
Total
47 (17.3)
40 (14.7)
eGFR = estimated glomerular filtration rate; ESRD = end-stage renal disease.
*Values are presented as n (%).
Table 6
Factors associated with chronic kidney disease (CKD) according to modified diet in renal diseases
(MDRD) and chronic kidney disease-epidemiology (CKD-EPI) equations (N = 272).
Factor
CKD by MDRD
CKD-EPI
AOR
95% CI
P value
AOR
95% CI
P value
Age, y
>60
1.3
0.5–2.9
0.08
1.2
0.4–2
0.09
<60
Pre-existing HTN
Yes
8.2
2–23
< 0.001
8.3
2–24
< 0.001
No
Type of DM
1
0.8
0.2–1.2
0.3
0.9
0.3–1.5
0.4
2
Duration of DM, y
≥10
3.2
1.6–7
0.004
3.2
1.5–7
0.004
<10
Retinopathy
Present
14
4–36
< 0.001
18
6–40
< 0.001
Absent
CVD
Present
2
0.9–5
0.1
2.1
0.8–5
0.1
Absent
Current SBP, mm Hg
>140
6
4–22
< 0.001
7.2
5–28
< 0.001
<140
Habitual analgesic
use
Yes
2.2
0.8–7
0.2
2
0.7–7
0.25
No
CVD = cardiovascular disease; DM = diabetes mellitus; HTN = hypertension; SBP = systolic blood
pressure.
