Abstract
Hypertension is a risk factor for renal diseases, which, in turn, are precursors of hypertension. The authors assessed the prevalence and determinants of chronic kidney disease (CKD) among 336 hypertensive adult Cameroonians (mean age, 60.9±11.3 years; 63.4% women) at Yaoundé. Any participant with an estimated glomerular filtration rate <60 mL/min/1.73 m2 regardless of the equation used (Cockcroft‐Gault [CG], Modification of Diet in Renal Disease [MDRD], and Chronic Kidney Disease Epidemiology Collaboration [CKD‐EPI]) and/or dipstick proteinuria was reviewed 3 months later. Participants presented a high prevalence of diabetes (18.5%), dyslipidemia (17.6%), gout/hyperuricemia (10.7%), overweight/obesity (68.8%), self‐medication (37.5%), and alcohol consumption (33.3%). Hypertension was uncontrolled in 265 patients (78.9%). The prevalence of CKD was 49.7%, 50.0%, and 52.1% according to MDRD, CKD‐EPI, and CG equations, respectively. Advanced age, adiposity, and severity of hypertension were determinants of CKD . Nearly half of the hypertensive patients had CKD regardless of the estimators used, predicted by well‐known risk factors.
The prevalence of hypertension is growing worldwide, particularly in sub‐Saharan Africa (SSA) where most people with the disease remain undiagnosed, untreated, or inadequately treated.1, 2 Hypertension is also a major risk factor for cardiovascular and renal diseases and, furthermore, kidney diseases are precursors of hypertension.3
Chronic kidney disease (CKD) affects 10% of adults worldwide and poses a major public health and socioeconomic challenge.4, 5 Studies have revealed that Africans are at higher risk for CKD, which is three to four times more frequent in this ethnic group than in Caucasians, occurs prematurely, and progresses rapidly to end‐stage renal disease (ESRD).6, 7 Although a high prevalence of CKD have been reported in SSA settings, even higher figures ranging from 38.2% to 46.9% have been reported in high‐risk populations including hypertensive patients.8, 9, 10, 11, 12 Compared with Caucasians, Africans are at high risk for hypertensive nephrosclerosis (the second leading cause of ESRD), are five to eight times more likely to develop ESRD from hypertension, have renal involvement at a younger age, have blood pressure (BP) that responds less well to acute treatment, and have a faster decline in renal function at similar BP levels.3, 10, 13, 14, 15
In Cameroon, hypertension and other noncommunicable diseases (NCDs) including CKD are increasingly common, with unacceptably low awareness, treatment, and control rates.16, 17 However, CKD among people with hypertension, particularly those receiving routine care, has seldom been characterized. This has relevance in the context of highly advocated integrated approaches to the most common NCDs. Accordingly, we undertook this study to establish the prevalence and risk factors for CKD among hypertensive Cameroonians.
Methods
Study Setting and Design
Participants were recruited at the hypertension clinics of Yaoundé Central and University Teaching Hospitals—the two major hypertension clinics in the capital city of Cameroon. We included all Cameroonian adults (aged ≥18 years) with a medical diagnosis of hypertension. Participants provided written informed consent and were advised to take their medications as usual on the day of recruitment. This study was approved by the administrative authorities of the involved hospitals and the Cameroon national ethics committee.
Data Collection
Final year undergraduate medical students collected data on the following: demographics (age, sex, and level of education), duration of hypertension, ongoing treatment, family history of CKD, comorbidities (diabetes, gout, human immunodeficiency virus [HIV], and viral hepatitis B and C), lifestyle characteristics, and practice of self‐medication. Anthropometric measurements and BP were taken three times and the average was used in all analyses. BP was measured in the office according to World Health Organization (WHO) guidelines18 using an automated sphygmomanometer (OMRON HEM705CP; Omron Matsusaka Co, Matsusaka, Japan)19 and appropriate cuff size on the right arm with participants in a sitting position after 30 minutes of rest. For each participant, we drew 3 mL of whole blood from an antecubital vein for serum creatinine (SCr) and fasting capillary glucose (after an overnight fast of at least 8 hours), and collected mid‐stream second morning urine for dipstick tests. The urine dipstick tests used were CombiScreen 7SL PLUS 7 test strips (Analyticon Biotechnologies AG, D‐35104 Lichentenfeis, Germany). Serum creatinine was measured with a kinetic modification of the Jaffé reaction using Human visual spectrophotometer (Human Gesellschaft, Biochemica und Diagnostica mbH, Wiesbaden, Germany) and the Beckman creatinine analyzer (Beckman CX systems instruments, Anaheim, CA, USA), and converted to standardized as SCrStandardized=0.95*SCrJaffe – 0.10.20, 21 In participants with an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and/or dipstick proteinuria, the chronicity was confirmed on another sample 3 months later.
Definitions and Calculations
eGFR (mL/min) was measured using the Cockcroft‐Gault (CG), the four‐variable Modification of Diet in Renal Disease (MDRD), and the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equations.22, 23, 24 CKD was defined by a confirmed positive dipstick proteinuria or albuminuria (at least traces) and/or eGFR <60 mL/min/1.73 m2. The Kidney Disease Improving Global Outcomes (KDIGO) guidelines were used to stage participants for GFR and albuminuria categories of CKD.25 The GFR categories included: G1 (eGFR ≥90), G2 (eGFR 60–89), G3 (eGFR 30–59), G4 (eGFR 15–29), and G5 (eGFR <15). The albuminuria categories of CKD were as follows: A1 (negative), A2 (traces to 1+ [30]), and A3 (at least 2+ [100]). The diagnosis of diabetes mellitus, dyslipidemia, and gout was made by the attending physician. Controlled BP was based on the average value of the three previous consultations' BP levels of <140 mm Hg for systolic BP and <90 mm Hg for diastolic BP. Hyperglycemia refers to a fasting capillary glucose level of at least 1.26 g/L. Self‐medication referred to recurrent use of medicines at least once every 3 months; these medicines included herbal medicines from African pharmacopeia and street medications, which are Western drugs usually of uncertain origins that are sold in shops and along market streets without any control.
Statistical Analysis
Data were analyzed using SAS/STAT version 9.1 software for Windows (SAS Institute Inc, Cary, NC). We have reported the results as means and standard deviations (SDs) and counts and percentages. Group comparisons used chi‐square tests and variants for qualitative variables, and Student t test and nonparametric equivalents for quantitative variables. Age‐ and sex‐adjusted logistic regression models were used to investigate the predictors of CKD, CKD stages G3 and G4, and albuminuria. A P value <.05 was used to characterize statistically significant results.
Sample Size Estimation
The study was planned with the intention of being able to investigate about 12 predictors of CKD. For such a purpose and assuming a ratio of eight participants with CKD per candidate predictor,26 a minimum of 96 participants with prevalent CKD were required. We further assumed the prevalence of CKD to be about 38% in our population, which is the bottom figure of reported prevalence of CKD in people with hypertension in SSA.8, 9, 10, 11, 12 Based on the above parameters, and considering a 95% probability of observing at least 96 participants with prevalent CKD, we needed to examine at least 288 participants with hypertension.
Results
Baseline Characteristics of the Study Population
A total of 336 hypertensive patients, 213 (63.4%) women, participated in the study. The mean age of participants was 60.9±11.3 years, and up to 10.4% did not have any formal education. As shown in Table 1, only 0.6% of the participants reported an existing family history of kidney disease although the group presented a high prevalence of CKD risk factors including diabetes (18.5%), dyslipidemia (17.6%), gout (10.7%), overweight/obesity (68.8%), self‐medication (37.5%), alcohol consumption (33.3%), and smoking (3.4%). There was a mild decrease in mean eGFR, which was almost similar across the various estimators, while study participants presented a high frequency of dipstick abnormalities including albuminuria (39.3%), hematuria (14.8%), and leukocyturia (8%) (Table 2).
Table 1.
Clinical Characteristics by BP Control
| Characteristics | Overall | Controlled BP Group | Uncontrolled BP Group | P Value |
|---|---|---|---|---|
| No. (%) | 336 (100) | 71 (21.1) | 265 (78.9) | |
| Mean age, y (SD) | 60.9 (11.3) | 64.0 (10.6) | 60.1 (11.4) | .01 |
| Men:women, No. | 123:213 | 20:51 | 103:162 | .097 |
| Level of education, No. (%) | ||||
| None | 35 (10.4) | 9 (12.7) | 26 (9.8) | .614 |
| Primary | 121 (36.0) | 24 (33.8) | 97 (36.6) | |
| Secondary | 124 (36.9) | 29 (40.8) | 95 (35.8) | |
| Higher | 56 (16.7) | 9 (12.7) | 47 (17.7) | |
| Familial history of kidney disease, No. (%) | ||||
| No | 328 (97.6) | 70 (98.6) | 258 (97.4) | .596 |
| Yes | 2 (0.6) | 0 (0) | 2 (0.8) | |
| Unknown | 6 (1.8) | 1 (1.4) | 5 (1.9) | |
| Diabetes, No. (%) | 62 (18.5) | 4 (5.6) | 58 (21.9) | .001 |
| Gout, No. (%) | 36 (10.7) | 8 (11.3) | 28 (10.6) | .505 |
| Dyslipidemia, No. (%) | 59 (17.6) | 44 (16.6) | 15 (21.1) | .374 |
| HIV infection, No. (%) | ||||
| No | 306 (91.1) | 69 (97.2) | 237 (89.4) | .026 |
| Yes | 12 (3.6) | 0 (0) | 12 (4.5) | |
| Unknown | 18 (5.4) | 2 (2.8) | 16 (6.0) | |
| Current or former tobacco use, No. (%) | 13 (3.9) | 3 (4.2) | 10 (3.8) | .542 |
| Current or former alcohol use, No. (%) | 112 (33.3) | 26 (36.6) | 86 (32.5) | .508 |
| Self‐medication, No. (%) | 126 (37.5) | 25 (35.2) | 101 (38.1) | .654 |
| Mean duration of hypertension, y (SD) | 6.7 (7.9) | 8.3 (8.6) | 6.2 (7.6) | .047 |
| Mean SBP, mm Hg (SD) | 154 (26) | 126 (8) | 162 (23) | <.001 |
| Mean DBP, mm Hg (SD) | 90 (15) | 76 (7) | 94 (14) | <.001 |
| Hypertension treatment, No. (%) | ||||
| ACE inhibitor | 192 (57.1) | 49 (69.0) | 143 (54.0) | .023 |
| ARB | 9 (2.7) | 2 (2.8) | 7 (2.6) | >.999 |
| CCB | 132 (39.3) | 24 (33.8) | 108 (40.7) | .287 |
| Diuretics | 9 (2.7) | 2 (2.8) | 7 (2.6) | .086 |
| Blocker | 42 (12.5) | 9 (12.7) | 33 (12.4) | .960 |
| Central‐acting drugs | 2 (0.6) | 0 (0) | 2 (0.7) | >.999 |
| Median (25–75th percentiles) number of antihypertensive drugs | 2 (1–2) | 2 (2–2) | 2 (1–2) | .120 |
| Mean BMI, kg/m2 (SD) | 29.4 (14.8) | 27.7 (5.1) | 29.9 (16.4) | .282 |
| BMI ≥25 kg/m2, No. (%) | 231 (68.8) | 48 (67.6) | 183 (69.1) | .815 |
Abbreviations: ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; CCB, calcium channel blocker; DBP, diastolic blood pressure; HIV, human immunodeficiency virus; SBP, systolic blood pressure; SD, standard deviation.
Table 2.
Biological Characteristics by BP Control
| Characteristics | Overall | Controlled BP Group | Uncontrolled BP Group | P Value |
|---|---|---|---|---|
| No. (%) | 336 (100) | 71 (21.1) | 265 (78.9) | |
| Mean fasting capillary glucose, g/L (SD) | 1.01 (0.40) | 0.89 (0.19) | 1.05 (0.43 | .004 |
| Hyperglycemia, No. (%) | 48 (14.3) | 3 (4.2) | 45 (17.0) | .006 |
| Dipstick abnormalities, No. (%) | ||||
| Albuminuria | 132 (39.3) | 25 (35.2) | 107 (40.4) | .319 |
| Hematuria | 50 (14.9) | 13 (18.3) | 37 (14.0) | .361 |
| Leukocyturia | 27 (8.0) | 4 (5.6) | 23 (8.7) | .402 |
| Mean serum creatinine, mg/L (SD) | 13.0 (10.4) | 11.7 (5.1) | 13.4 (11.4) | .229 |
| Mean creatinine clearance, mL/min/1.73 m2 (SD) | ||||
| MDRD | 71.0 (29.1) | 71.3 (31.1) | 70.9 (28.7) | .928 |
| CG | 72.8 (35.0) | 68.0 (29.9) | 74.1 (36.2) | .193 |
| CKD‐EPI | 70.1 (26.0) | 68.9 (21.7) | 70.5 (27.0) | .660 |
Abbreviations: BP, blood pressure; CG, Cockroft‐Gault; CKD‐EPI, Chronic Kidney Disease Epidemiology Collaboration; MDRD, Modification of Diet in Renal Disease; SD, standard deviation.
The mean systolic BP was 154±26 mm Hg and diastolic BP was 90±15 mm Hg. Equivalent figures were 126±8 mm Hg and 76±7 mm Hg for the controlled group and 162±23 mm Hg and 94±14 mm Hg for the uncontrolled group (all P<.001). The duration of hypertension was 6.7±7.9 years in the global population, 8.3±8.6 years in the controlled group, and 6.2 years in the uncontrolled group (P=.047). The median (25–75th percentiles) number of antihypertensive drugs was two (1–2), with no statistical difference between the two groups (P=.120) (Table 1).
Hypertension was uncontrolled in 265 patients (78.9%) and was significantly associated with younger age (P=.01), shorter duration of the disease (P=.047), uncontrolled diabetes (P=.006), HIV infection (P=.026), and lower use of angiotensin‐converting enzyme inhibitors (P=.023) (Tables 1 and 2).
Prevalence and Correlates of CKL As Well As Albuminuria and G3 to G5 GFR Categories
Nearly half of patients had CKD regardless the estimators, with prevalence rates of 49.7%, 50.0%, and 52.1% according to MDRD, CKD‐EPI, and CG equations, respectively (Table 3 and Table SI). Using albuminuria and GFR categories of the KDIGO classification of CKD, 116 patients (34.5%) had albuminuria A2 to A3, among whom 21 (6.2%) and 95 (28.3%) were in categories A2 and A3, respectively. Similarly, 109 patients (32.5%) were in G3 to G5 categories according to MDRD and CKD‐EPI equations, and 113 patients (33.6%) according to the CG formula (Tables 3 and Table SI). With GFR estimated by the MDRD equation, the prevalence of G3, G4, and G5 stages was 28.3%, 2.1%, and 2.1%, respectively. Equivalent figures were 27.7%, 3.0%, and 1.8% with the CKD‐EPI equation, and 25.3%, 6.8%, and 1.5% with the CG formula.
Table 3.
Characteristics According to Albuminuria, GFR Categories, and CKD Based on the Four‐Variable MDRD Equation
| Variable | Albuminuria‐Based CKD | GFR‐Based CKD Stages | CKD | ||||||
|---|---|---|---|---|---|---|---|---|---|
| A1 | A2 to A3 | P Value | G1 and G2 | G3 to G5 | P Value | No | Yes | P Value | |
| Participants, No. (%) | 220 (65.5) | 116 (34.5) | 227 (67.5) | 109 (32.5) | 169 (50.3) | 167 (49.7) | |||
| Women, No. (%) | 151 (68.6) | 62 (53.4) | .009 | 142 (62.5) | 71 (65.1) | .717 | 111 (65.7) | 102 (61.1) | .428 |
| Mean age, y (SD) | 60.5 (11.4) | 61.7 (11.1) | .369 | 59.1 (11.4) | 64.6 (10.1) | <.001 | 58.6 (11.4) | 63.2 (10.7) | <.001 |
| Diabetes, No. (%) | 35 (15.9) | 27 (23.3) | .105 | 35 (15.4) | 27 (24.8) | .05 | 28 (16.6) | 34 (20.3) | .401 |
| Gout, No. (%) | 20 (9.1) | 16 (13.8) | .197 | 20 (8.8) | 16 (14.7) | .131 | 14 (8.3) | 22 (13.2) | .161 |
| Dyslipidemia, No. (%) | 40 (18.2) | 19 (16.4) | .764 | 44 (19.4) | 15 (13.8) | .224 | 34 (20.1) | 25 (15.0) | .252 |
| Smoking, No. (%) | 9 (4.1) | 4 (4.4) | >.999 | 10 (4.4) | 3 (2.7) | .559 | 8 (4.7) | 5 (3.0) | .573 |
| HIV, No. (%) | 9 (4.1) | 3 (2.6) | .193 | 10 (4.4) | 2 (1.8) | .297 | 8 (4.7) | 4 (2.4) | .171 |
| Self‐medication, No. (%) | 84 (38.2) | 42 (36.2) | .813 | 86 (37.9) | 40 (36.7) | .904 | 65 (38.4) | 61 (36.5) | .736 |
| Mean duration hypertension, y (SD) | 7.1 (8.0) | 5.8 (7.5) | .164 | 6.1 (7.9) | 7.8 (7.6) | .053 | 6.4 (7.9) | 6.9 (7.8) | .551 |
| Mean SBP, mm Hg (SD) | 152.0 (23.8) | 158.6 (28.4) | .025 | 152.4 (23.4) | 158.3 (29.5) | .047 | 151.9 (23.4) | 156.8 (27.6) | .080 |
| Mean DBP, mm Hg (SD) | 89.7 (13.8) | 91.8 (16.1) | .227 | 89.9 (14.4) | 91.5 (15.1) | .345 | 89.7 (14.0) | 91.7 (15.2) | .387 |
| Controlled hypertension, No. (%) | 48 (21.8) | 23 (19.8) | .779 | 50 (22.0) | 21 (19.3) | .669 | 36 (21.3) | 35 (20.9) | >.999 |
| Antihypertensive drugs ≥3, No. (%) | 40 (18.2) | 23 (19.8) | .749 | 40 (17.6) | 23 (21.1) | .557 | 30 (17.7) | 33 (19.8) | .965 |
| Mean BMI, kg/m2 (SD) | 29.6 (17.7) | 28.9 (5.9) | .696 | 30.2 (17.6) | 27.6 (4.8) | .131 | 30.2 (20.0) | 28.6 (5.6) | .310 |
| BMI ≥25 kg/m2, No. (%) | 146 (66.4) | 85 (73.3) | .217 | 157 (69.2) | 74 (67.9) | .803 | 112 (66.3) | 119 (71.2) | .348 |
| Mean fasting capillary glucose, g/L (SD) | 0.99 (0.4) | 1.04 (0.3) | .371 | 0.99 (0.3) | 1.04 (0.4) | .254 | 1.00 (0.4) | 1.02 (0.4) | .578 |
Abbreviations: BMI, body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; GFR, glomerular filtration rate; HIV, human immunodeficiency virus; MDRD, Modification of Diet in Renal Disease; SBP, systolic blood pressure; SD, standard deviation.
Advanced age was the only factor significantly associated with the presence of CKD and G3 to G5 categories of CKD regardless of the equation used (all P<.001), while albuminuria was associated with female sex (P=.009). Raised systolic BP was associated with albuminuria (P=.025) and G3 to G5 categories of CKD estimated by MDRD (P=.047) and CKD‐EPI (P=.043) equations. Longer duration of hypertension was associated with G3 to G5 categories of CKD‐EPI–diagnosed CKD (P=.03), while the presence of diabetes was associated with G3 to G5 categories of CKD estimated by CG (P=.008) and MDRD (P=.05) equations. As expected, low body mass index (BMI) was associated with G3 to G5 categories of CKD estimated by the CG formula (Table 3 and Table SI).
Predictors of CKD As Well As Albuminuria and G3 to G5 GFR Categories in Age‐ and Sex‐Adjusted Logistic Regressions
Table 4 and Table SII show the age‐ and sex‐adjusted predictors of CKD as well as albuminuria and the G3 to G5 GFR categories of CKD based on each of the three estimators. With the exception of albuminuria, advanced age was consistently and positively associated with all these outcomes, with the magnitude of the effects per year increase in age being 4% (2%–6%) to 6% (3%–8%) for CKD and 5% (2%–7%) to 9% (6%–12%) for G3 to G5 categories of CKD. Raised systolic BP was significantly and positively associated with most of the outcomes, while female sex was significantly and negatively associated with albuminuria (odds ratio [OR], 0.53; 95% confidence interval [CI], 0.33–0.84 [P=.007]). Increasing BMI was significantly and negatively associated with G3 to G5 categories of CKD estimated by the CG equation and as a consequence, overweight/obesity was associated with lower odds of these categories of CKD (OR, 0.48; 95% CI, 0.28–0.82 [P=.007]). The presence of dyslipidemia was significantly and negatively associated with G3 to G5 categories of CKD estimated by CG (OR, 0.49; 95% CI, 0.24–0.99 [P=.048]), while the increasing number of antihypertensive drugs was significantly and positively associated with these stages using the same equation (OR, 1.44; 95% CI, 1.06–1.94 [P=.007]).
Table 4.
Predictors of Albuminuria and G3 to G5 Categories and CKD Based on the Four‐Variable MDRD Equation in Age‐ and Sex‐Adjusted Logistic Regressions
| Variable | Albuminuria | CKD Stages G3 to G5 | CKD | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | OR (95% CI) | P Value | OR (95% CI) | P Value | |
| Women | 0.53 (0.33–0.84) | .007 | 1.20 (0.73–1.95) | .475 | 0.86 (0.55–1.36) | .525 |
| Age, y | 1.01 (0.99–1.03 | .462 | 1.05 (1.02–1.07) | <.001 | 1.04 (1.02–1.06) | <.001 |
| Diabetes | 1.43 (0.81–2.55) | .220 | 1.66 (0.92–2.98) | .092 | 1.11 (0.63–1.97) | .709 |
| Gout | 1.29 (0.62–2.66) | .495 | 1.75 (0.84–3.66) | .134 | 1.45 (0.70–3.03) | .315 |
| Dyslipidemia | 0.86 (0.47–1.57) | .617 | 0.63 (0.33–1.22) | .173 | 0.66 (0.37–1.19) | .168 |
| Smoking | 0.65 (0.19–2.21) | .489 | 0.73 (0.19–2.83) | .649 | 0.63 (0.19–2.04) | .444 |
| Self‐medication | 0.88 (0.55–1.42) | .609 | 0.96 (0.59–1.56) | .870 | 0.89 (0.57–1.40) | .626 |
| Duration of hypertension, y | 0.98 (0.95–1.01) | .159 | 1.01 (0.98–1.04) | .371 | 1.00 (0.97–1.02) | .774 |
| Number of antihypertensive drugs | 1.02 (0.78–1.32) | .882 | 1.26 (0.96–1.66) | .101 | 1.08 (0.84–1.38) | .559 |
| SBP, mm Hg | 1.01 (1.00–1.02) | .018 | 1.01 (1.00–1.02) | .031 | 1.01 (1.00–1.02) | .049 |
| DBP, mm Hg | 1.01 (0.99–1.03) | .204 | 1.01 (1.00–1.03) | .080 | 1.01 (1.00–1.03) | .127 |
| BMI, kg/m2 | 1.00 (0.98–1.02) | .953 | 0.97 (0.93–1.01) | .200 | 0.99 (0.98–1.01) | .553 |
| BMI ≥25 kg/m2 | 1.64 (0.97–2.77) | .063 | 1.18 (0.70–1.98) | .543 | 1.63 (0.99–2.67) | .053 |
| Fasting capillary glucose | 1.20 (0.67–2.08) | .526 | 1.28 (0.72–2.28) | .390 | 1.06 (0.61–1.84) | .826 |
| Hyperglycemia | 1.51 (0.80–2.83) | .201 | 1.60 (0.84–3.05) | .156 | 1.10 (0.58–2.06) | .771 |
| Controlled hypertension | 0.92 (0.52–1.63) | .769 | 0.69 (0.38–1.24) | .215 | 0.85 (0.50–1.47) | .574 |
Abbreviations: BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; DBP, diastolic blood pressure; MDRD, Modification of Diet in Renal Disease; OR, odds ratio; SBP, systolic blood pressure.
Discussion
Our study among this group of hypertensive Cameroonians revealed that nearly half had CKD regardless of the estimators used. Based on the KDIGO classification of CKD,25 about one third of participants had albuminuria A2 to A3 categories and were in stages CKD G3 to G5 regardless of the equation used to estimate GFR. Albuminuria and G3 to G5 categories of CKD were predicted by advanced age, raised systolic BP, adiposity, and increased number of antihypertensive drugs.
Across estimators of GFR, the CG equation diagnosed more participants with CKD while CKD‐EPI and MDRD diagnosed the same proportion of participants with CKD, largely in line with existing and extensively discussed reports from the general population.8, 27, 28 Although none of these equations has been validated in Africans populations, the similar yields of the MDRD and CKD‐EPI equations suggest their possible applicability in this population, based on the results of previous studies and guidelines recommendations, while awaiting development of an appropriate CKD equation for Africans.25, 27, 28
We observed that nearly half of the study participants presented with CKD, a high prevalence of CKD similar to data reported in other SSA countries ranging from 38.2% to 46.9% depending on the definition of CKD, and the estimation method of GFR and proteinuria.9, 10, 11, 12 This could be related to the high proportion of those with uncontrolled hypertension and the high prevalence of endemic infections including HIV, hepatitis B and C virus, and bacterial and parasitic infections, which were not screened in this study. It can also be favored by the severity of the disease, as suggested by the association of prevalent CKD with raised systolic BP and increased number of antihypertensive drugs used, despite the fact that nearly 60% of patients were taking an angiotensin‐converting enzyme inhibitor or an angiotensin receptor blocker, in line with guidelines.29, 30, 31 It is well documented that hypertensive patients with longer duration of the disease and difficulties in achieving BP control and who are less respondent to acute BP treatment or require increasing numbers of antihypertensive drugs are more likely to have associated target organ damage and underlying kidney disease.3, 11, 12, 32 All the above features were predictors of CKD in our patients. Moreover, the high frequency of well‐known CKD initiation and progression of risk factors observed in this population could further contribute to the observed high prevalence as reported in previous SSA studies.9, 10
The African ethnicity of our participants is another explanation of the high prevalence of CKD. Indeed, Africans are at high risk for hypertension‐associated renal disease, which is the second worldwide leading cause of ESRD.3 Compared with Caucasians, they are five to eight times more likely to develop ESRD from hypertension‐associated renal diseases and show a faster decline in renal function at similar BP levels.3, 13, 14 However, with the late referral of CKD patients to the nephrologist in this setting, mostly at an ESRD stage when kidney biopsy is less contributive in determining the cause of CKD,33 the high frequency of CKD attributed to hypertension‐associated renal diseases in Africans could be related to MYH9 gene polymorphisms. This may be associated with focal and segmental sclerosis and may explain the poor response to BP control in those with a clinical diagnosis of hypertension‐associated renal diseases.34, 35
The reported high prevalence of adiposity in this study could be an additional contributor to the high prevalence of CKD observed elsewhere.9, 10 Obesity is a well‐known risk factor for CKD, which leads to albuminuria probably secondary to the hyperfiltration state with focal and segmental glomerulosclerosis lesions.25 We also observed that advanced age was predictive of prevalent CKD as well described in the literature.9, 10, 25 This could be related to arterial stiffness, abnormalities of vascular reactivity, and self‐medication including anti‐inflammatory drugs.3
Study Limitations and Strengths
The present study has some limitations including the semiquantitative assessment of urinary albumin excretion using dipsticks, the nonvalidation of any of the equations used in Africans populations, the nonsystematic screening of participants for endemic infections conferring high risk for CKD such as HIV infection, hepatitis B and C viral infection, and the nonexhaustive assessment of socioeconomic status, which has been shown to be associated with CKD.9, 36, 37 However, this study to our knowledge is the first in SSA to provide data on the epidemiology of kidney disease among hypertensive patients using the three estimators of GFR with a 3‐month confirmation of the chronicity according to the KDIGO guidelines for CKD screening.25 It also provides a complete picture of CKD prevalence including albuminuria and GFR categories as well as their determinants in the African population with hypertension.
Conclusions
This study revealed that nearly half of hypertensive patients had CKD regardless of the estimators used. The high prevalence of CKD as well as albuminuria and advanced stages of CKD was predicted by advanced age, raised systolic BP, adiposity, and increased number of antihypertensive drugs. These results invite actions for adequate management of hypertension, systematic screening of hypertensive patients for CKD, and early referral to nephrologists for optimal management.
Disclosures
The authors report no specific funding in relation to this research and no conflicts of interest to disclose.
Authors' Contributions
FFK: Conception and design of the study, supervision of data collection, interpretation of data, and drafting of the manuscript. APK: Data analysis and interpretation and drafting of the manuscript. CTM: Conception and design of the study, data collection, and critical revision of the manuscript. MPH: Conception and design of the study and critical revision of the manuscript. EY: Conception and design of the study and critical revision of the manuscript. KBN: Conception and design of the study and critical revision of the manuscript. All authors approved the final manuscript.
Supporting information
Table SI. Characteristics according to glomerular filtration rate categories and chronic kidney disease based on Cockroft‐Gault and CKD‐EPI equations.
Table SII. Predictors of G3 to G5 categories and chronic kidney disease based on Cockroft‐Gault and CKD‐EPI equations in age‐ and sex‐adjusted logistic regressions.
Acknowledgments
We thank the staff of the Yaoundé Central and University Teaching Hospitals hypertension clinics and the biochemistry laboratory technicians of the Yaoundé University Teaching Hospital.
J Clin Hypertens (Greenwich). 2016;18:408–414. 10.1111/jch.12781. © 2016 Wiley Periodicals, Inc.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table SI. Characteristics according to glomerular filtration rate categories and chronic kidney disease based on Cockroft‐Gault and CKD‐EPI equations.
Table SII. Predictors of G3 to G5 categories and chronic kidney disease based on Cockroft‐Gault and CKD‐EPI equations in age‐ and sex‐adjusted logistic regressions.