Abstract
Background
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality among patients with advanced chronic kidney disease (CKD). However, data on its prevalence and patterns in Sub-Saharan Africa remain scarce.
Purpose
To determine the prevalence and spectrum of CVD among patients with advanced CKD in Johannesburg, South Africa.
Patients and Methods
This retrospective study enrolled adults aged ≥18 years with advanced CKD attending the renal clinic at Chris Hani Baragwanath Academic Hospital between 2009 and 2018. Demographic, clinical, laboratory, electrocardiographic, and echocardiographic variables were extracted from medical records. CVD was defined as heart failure, ischemic heart disease, arrhythmias, non-rheumatic valvular heart disease, pericardial disease, stroke, or peripheral vascular disease, supplemented by clinical, electrocardiographic, or echocardiographic findings. Patients were grouped by their CVD status. Multivariable logistic regression identified independent factors associated with CVD.
Results
Among 300 participants (mean age of 55.3 ± 15.0 years, 54.3% males), CVD prevalence was 58.7%. The mean age at CVD diagnosis was 46.6 ± 13.7 years, with a median interval of 8.3 months (IQR, 2.5–27.6) between the onset of CKD and CVD diagnosis. Patients with CVD were younger than those without (52.8 ± 13.8 vs 58.8 ± 16.0 years; p<0.001), had higher diastolic blood pressure (88.5 ± 20.6 vs 81.3 ± 17.6 mmHg; p = 0.002), and a higher prevalence of obesity (54.6% vs 38.7%; p<0.001). Predominant CVD manifestations were ischemic ECG changes (95.1%) and diastolic dysfunction with heart failure (45.5%). Higher diastolic blood pressure (aOR 1.04; 95% CI 1.00–1.08; p = 0.029) and reduced estimated GFR (aOR 0.96; 95% CI 0.94–0.99; p = 0.022) were independent predictors of CVD.
Conclusion
CVD is highly prevalent in advanced CKD and occurs at a young age. Early cardiovascular risk assessment and integrated cardio-renal management strategies are essential in this high-risk population.
Keywords: chronic kidney disease, cardiovascular disease, prevalence, echocardiogram, estimated glomerular filtration rate, heart failure
Introduction
Chronic kidney disease (CKD) is a major global health problem associated with substantial morbidity and mortality, affecting approximately 10% of the world’s population.1 Cardiovascular disease (CVD) is known as the leading cause of death among patients with CKD, and its prevalence increases with declining renal function.2 The Spanish MERENA study of patients with stage 3–4 CKD not yet on dialysis found a CVD prevalence of 39.1%, whereas a multicentre Japanese study found a lower prevalence of 26.8%.3,4 In contrast, higher prevalence rates have been documented in South India (50.1%) and Saudi Arabia (56.3%).5,6 In sub-Saharan Africa (SSA), the burden of CVD among patients with CKD remains high, at 69.8% in Cameroon and 54.4% in Uganda.7,8
The elevated CVD risk in CKD is multifactorial, driven by both traditional risk factors, such as hypertension, diabetes mellitus, and dyslipidemia, and CKD-specific factors, including anemia, inflammation, and albuminuria, which act synergistically to worsen cardiovascular outcomes.9 However, data on the prevalence and pattern of CVD among patients with CKD in South Africa remain limited.10 This is concerning given the rising prevalence of CKD, which is largely attributed to hypertension, diabetes, HIV (human immunodeficiency virus), and other lifestyle-related diseases.11,12 Therefore, early recognition and management of CVD risk factors in this population are critical. This study aimed to determine the prevalence and spectrum of CVD among patients with advanced CKD attending tertiary renal clinic in Johannesburg, South Africa.
Materials and Methods
Study Design, Setting, and Participants
This was a retrospective cross-sectional study conducted in the Renal Outpatient Department of the Chris Hani Baragwanath Academic Hospital (CHBAH) between January 1, 2009, and December 31, 2018.
Inclusion and Exclusion Criteria
Patients aged 18 years and older with CKD stage G4–G5, and those on hemodialysis and peritoneal dialysis, were included. Patients who had acute kidney injury, kidney transplants, pregnancy, significant missing data, rheumatic valvular heart diseases, congenital heart diseases, and earlier stages of CKD (G1–G3) were excluded.
Data Collection
A total of 1970 outpatient records of patients with advanced CKD were screened. After applying the eligibility criteria, 1,670 records were excluded, leaving 300 participants eligible for the final analysis (Figure 1). Data on demographics, clinical characteristics, biochemical parameters, medication use, electrocardiographic findings (arrhythmias, ischemic changes, and left ventricular hypertrophy), and echocardiographic features (ejection fraction, diastolic dysfunction, left ventricular hypertrophy, and valvular abnormalities) were collected and entered into research electronic data capture (REDCap), a secure web-based data management platform hosted by the University of the Witwatersrand, South Africa. Electrocardiograms and echocardiograms were performed as part of routine clinical care and interpreted by qualified cardiologists in accordance with standard clinical practice at the institution. The reported findings, documented in the medical records, were used for analysis.
Figure 1.
Flow diagram showing the selection of participants included in the study. Medical records from patients with chronic kidney disease were screened (n = 1970). After applying the inclusion and exclusion criteria, 300 patients with advanced CKD (stages G4–G5) were included in the final analysis.
Abbreviations: CKD, chronic kidney disease; CVD, cardiovascular disease; AKI, acute kidney injury; n, number of observations.
Ethics Approval
Ethical clearance for the study was obtained from the University of the Witwatersrand Human Research Ethics Committee, Medical (HREC approval no: M2010101). Written permissions were secured from the unit heads in the Division of Nephrology and Internal Medicine and from the Chief Executive Officer of the hospital. Informed consent was waived as the study entailed a retrospective review of medical records. All data were de-identified and handled with strict confidentiality. The study was performed in accordance with the ethical principles outlined in the Declaration of Helsinki.
Statistical Analysis
Continuous variables are reported as means and standard deviations when the data are normally distributed, or as medians with interquartile ranges when the distribution was non-normal. Data normality was determined using the Shapiro–Wilk test. Categorical variables are summarized as frequencies and percentages. The Student’s t-test was used to compare normally distributed variables, and the Mann–Whitney U-test was used for non-normally distributed data. The Pearson’s chi-squared (χ2) was used to compare categorical variables. To identify factors independently associated with CVD, multivariable logistic regression analysis was performed. Variables that were clinically relevant or demonstrated an association with CVD in univariable analysis (p < 0.10) were included in the multivariable model. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were calculated. Missing data were handled using a complete-case approach. Variables with incomplete data were analyzed using the available observations, and the number of participants included in each analysis reported in the respective tables. A p-value < 0.05 was considered as a threshold for statistical significance. STATA version 18.1SE (Stata Corp LLC, College Station, TX, USA) was used for the data analysis.
Definition of Variables
Chronic kidney disease was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) 2024 guidelines as abnormalities of kidney structure or function that have been present for a minimum of three months and have health implications.13 CKD was classified based on the glomerular filtration rate (GFR) as category (G1–G5), with advanced CKD being (G4–G5).13 CVD was defined as the presence of at least one documented cardiovascular condition in the patients’ medical records, including heart failure, ischemic heart disease, arrhythmias, non-rheumatic valvular heart disease, pericardial disease, stroke, or peripheral vascular disease. These diagnoses were based on documented clinical assessments and supported by available electrocardiographic (ECG) and/or echocardiographic findings recorded during routine clinical care at the study institution. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Diastolic dysfunction was defined as an E/A ratio of less than 0.8 (grade 1), 0.8–2.0 (grade 2), or greater than 2 (grade 3).14
Results
Among 300 patients with advanced CKD, 58.7% (95% CI 52.9–64.3) had CVD (Figure 1). These included ischemic ECG changes (95.1%), diastolic dysfunction (45.5%), LVH (42.6%), arrhythmias (26.2%), valvular heart disease (18.1%), and pulmonary hypertension (8.5%). The overall mean age of the cohort was 55.3 ± 15.0 years, and 54.3% of patients were males. Patients with CVD were younger than those without (52.8 ± 13.8 vs 58.8 ± 16.0 years; p<0.001) (Table 1). The mean age at CVD diagnosis was 46.6 ± 13.7 years, with a median interval of 8.3 months (interquartile ranges [IQR] 2.5–27.6 months) between CKD onset and CVD diagnosis. Most participants were of African ancestry (92.0%).
Table 1.
Demographics
| Variable | All Patients n=300 | Patients with CVD n=176 | Patients Without CVD n=124 | P-value |
|---|---|---|---|---|
| Age (years) | 55.3 ± 15.0 | 52.8 ± 13.8 | 58.8 ± 16.0 | <0.001 |
| Age at time of diagnosis of CKD (years) | 49.1 ± 15.0 | 46.5 ± 13.8 | 52.8 ± 15.8 | <0.001 |
| Age at the diagnosis of CVD (years) | 46.6 ± 13.7 | |||
| Duration between CVD and CKD diagnosis (months) | 8.3 (2.5−27.6) | |||
| Male, n (%) | 163 (54.3) | 99 (56.3) | 64 (51.6) | 0.427 |
| African ancestry, n (%) | 276 (92.0) | 160 (90.9) | 116 (93.6) | 0.559 |
| BMI (kg/m2), n=226 | 28.4 ± 6.8 | 27.9 ± 6.5 | 29.4 ± 7.2 | 0.119 |
| Obesity | 144 (48.0) | 96 (54.6) | 48 (38.7) | <0.001 |
| Obesity class 1 | 52 (17.3) | 34 (19.3) | 18 (14.5) | |
| Obesity class 2 | 28 (9.3) | 16 (9.1) | 12 (9.7) | |
| Obesity class 3 | 10 (3.3) | 7 (4.0) | 3 (2.4) | |
| Stage 4 CKD, n (%) | 129 (43.0) | 70 (39.8) | 59 (47.6) | 0.179 |
| Stage 5 CKD, n (%) | 171 (57.0) | 106 (60.2) | 65 (52.4) | |
| Dialysis, n (%) | 49 (16.3) | 49 (27.8) | 0.0 | <0.001 |
| PD, n = (%) | 31 (10.3) | 31 (17.6) | 0.0 | |
| HD, n = (%) | 18 (6.0) | 18 (10.2) | 0.0 | <0.001 |
Notes: Variables are presented as mean ± standard deviation (SD) or median (interquartile range, IQR) for continuous variables, and as percentages (%) for categorical variables. n represents the number of participants with available data, and the P-value is used to compare CVD vs those without CVD.
Abbreviations: BMI, body mass index; CKD, chronic kidney diseases; PD, peritoneal dialysis; HD, hemodialysis.
Hypertension was the most prevalent cardiovascular risk factor (86.7%), followed by obesity (48.0%), HIV infection (29.3%), diabetes mellitus (27.0%), and dyslipidemia (3.3%) (Table 2). The HIV-infected group was younger than the non-HIV-infected group, although the difference did not reach statistical significance (53.4 ± 10.3 vs 56.0 ± 16.5 years; p = 0.165).
Table 2.
Clinical Data and Comorbidities
| Variable | All Patients n=300 |
Patients with CVD n=176 |
Patients Without CVD n=124 |
P-value |
|---|---|---|---|---|
| Systolic BP, mmHg | 145 (127–165) | 145 (129–166) | 144 (126–164) | 0.411 |
| Diastolic BP, mmHg | 85.5 ± 19.7 | 88.5 ± 20.6 | 81.3 ± 17.6 | 0.002 |
| MAP, mmHg | 105 (90–119) | 108 (90.5–124.5) | 101 (89.5–114.5) | 0.032 |
| Hypertension, n (%) | 260 (86.7) | 161 (91.5) | 99 (79.8) | 0.003 |
| Diabetes, n (%) | 81 (27.0) | 42 (23.9) | 39 (31.5) | 0.145 |
| Male, n (%) | 39 (13.0) | 23 (13.1) | 16 (12.9) | 0.296 |
| Dyslipidemia, n (%) | 10 (3.3) | 5 (2.8) | 5 (4.0) | 0.571 |
| HIV, n (%) | 88 (29.3) | 42 (23.9) | 46 (37.1) | 0.013 |
| Smoking, n (%) | 9 (3.0) | 5 (2.8) | 4 (3.2) | 0.165 |
Notes: Variables are expressed in mean ± standard deviation or frequency and percentages.
Abbreviations: BP, blood pressure; mmHg, millimeters of mercury; MAP, mean arterial pressure; HIV, human immunodeficiency virus.
Diabetes mellitus was numerically higher in patients without CVD (p = 0.145). There was no statistically significant difference in the prevalence of diabetes by sex category (p = 0.296). Among the 16.3% of patients receiving dialysis, hemodialysis and peritoneal dialysis were the modalities employed. All had established CVD, with peritoneal dialysis accounting for 10.3% of cases. The mean estimated glomerular filtration rate (eGFR) was 13.3 ± 8.3 mL/min/1.73 m2, and lower eGFR values were associated with a higher CVD burden (11.8 ± 7.9 vs 15.3 ± 8.5; p<0.001). More than half (57%) of the cohort had stage 5 CKD, representing a greater proportion of those with CVD (60.2% vs 39.8%); however, this association was not statistically significant. Patients with CVD had higher mean arterial pressure,108 (90.5–124.5) vs 101 (89.5–114.5 mmHg); p = 0.032, and a higher diastolic blood pressure (88.5 ± 20.6 vs 81.3 ± 17.6 mmHg; p = 0.002) than those without CVD. The mean hemoglobin concentration was 10.6 ± 2.7 g/dL and was slightly lower in patients with CVD (10.4 ± 2.8 vs 10.9 ± 2.6 g/dL; p = 0.129). Urea, 22.6 (16.6–32.7) vs 17.5 (13.0–26.1) mmol/L; p<0.001, creatinine, 481 (306–1032) vs 320 (227–596) µmol/L; p<0.001, and urine protein-to-creatinine ratio, 0.8 (0.00–2.2) vs 0.3 (0.00–1.6) g/mmol; p = 0.022, were significantly higher in those with CVD (Table 3). The use of triple antihypertensive therapy was more frequent among patients with CVD (67.2%) compared to those on dual (26.1%) or monotherapy (6.3%) (Table 4). Conversely, HIV infection and antiretroviral therapy in CKD patients were associated with a lower prevalence of CVD (23.9% vs 37.1%; p = 0.013).
Table 3.
Biochemistry
| Variable | All Patients n=300 |
Patients with CVD n=176 |
CKD Without CVD n=124 |
P-value |
|---|---|---|---|---|
| Hemoglobin, g/dL | 10.6 ± 2.7 | 10.4 ± 2.8 | 10.9 ± 2.6 | 0.129 |
| Sodium, mmol/L | 140 (137–143) | 140 (137–143) | 141 (138−143) | 0.142 |
| Potassium, mmol/L | 5.2 ± 1.0 | 5.2 ± 1.1 | 5.1 ± 0.9 | 0.352 |
| Bicarbonate, mmol/L | 15 (11–19) | 15 (11–19) | 15 (12–19) | 0.459 |
| Urea, mmol/L | 20.5 (15.1–30.4) | 22.6 (16.6–32.7) | 17.5 (13.0–26.1) | <0.001 |
| Creatinine, µmol/L | 406 (262–762) | 481 (306–1032) | 320 (227–596) | <0.001 |
| eGFR, mL/min/1.73m2 | 13.3 ± 8.3 | 11.8 ± 7.9 | 15.3 ± 8.5 | <0.001 |
| Albumin, g/L, n= 299 | 42 (37–45) | 41 (36–45) | 42 (39–45) | 0.077 |
| Calcium, mmol/L, n = 299 | 2.2 ± 0.3 | 2.2 ± 0.3 | 2.2 ± 0.3 | 0.519 |
| Phosphate, mmol/L, n = 296 | 1.5 (1.1–2.26) | 1.6 (1.2–2.4) | 1.4 (1.0–2.1) | 0.056 |
| Cholesterol, mmol/L n = 295 | 4.3 (3.7–5.1) | 4.3 (3.7–5.1) | 4.1 (3.7–5.0) | 0.226 |
| HDL, mmol/L n = 234 | 1.1 (0.9–1.4) | 1.1 (0.9–1.4) | 1.2 (0.9–1.5) | 0.361 |
| LDL, mmol/L n = 231 | 2.4 (1.8–2.9) | 2.4 (1.9–2.9) | 2.3 (1.7–2.8) | 0.299 |
| CD4, cells/µL, n = 88 | 424 (271–646) | 486 (309–592) | 414 (236–664) | 0.616 |
| Viral load, copies/mL, n = 88 | 20 (20–54) | 20 (20–46) | 20 (20–68) | 0.538 |
| UPCR, g/mmol, n = 297 | 0.5 (0.0–2.0) | 0.8 (0.0–2.2) | 0.3 (0.0–1.6) | 0.022 |
Notes: n represents the number of participants with available data.
Abbreviations: g/L, grams per liter; g/dL, grams per deciliter; mmol/L, milli mol per liter; µmol/L, micro mol per liter; eGFR, estimated glomerular filtration rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein; copies/mL, copies per milliliter; UPCR, urine protein creatinine ratio.
Table 4.
Medication Used
| Variable | All Patients n=300 |
Patients with CVD n=176 |
Patients Without CVD n=124 |
P-value |
|---|---|---|---|---|
| Anti-diabetics, n (%) | 81 (27.0) | 43 (24.4) | 38 (30.7) | 0.233 |
| Anti-lipids, n (%) | 174 (58.0) | 105 (59.7) | 69 (55.7) | 0.488 |
| Anti-retroviral, n (%) | 88 (29.3) | 42 (23.9) | 46 (37.1) | 0.013 |
| Anti-hypertensives, n (%) | 2.6 ± 1.1 | 2.9 ± 0.9 | 2.3 ± 1.2 | <0.001 |
| Monotherapy, n (%) | 28 (9.3) | 11 (6.3) | 17 (13.7) | |
| Dual therapy, n (%) | 82 (27.3) | 46 (26.1) | 36 (29.0) | |
| Triple therapy, n (%) | 176 (58.7) | 118 (67.2) | 58 (46.8) |
The most frequent pre-existing cardiovascular comorbidities on history were hypertensive heart disease, heart failure, coronary artery disease, and cardiomyopathy (Figure 2). Echocardiographic data were available for 165 patients. The most common findings were diastolic dysfunction (45.5%) and left ventricular hypertrophy (42.6%) (Table 5). Electrocardiographic abnormalities were dominated by ischemic changes (95.1%), followed by LVH (39.3%) (Table 6). On univariable logistic regression analysis, age, diastolic blood pressure, mean arterial pressure, glomerular filtration rate, hypertension, and HIV were significantly associated with CVD. However, in the age and sex adjusted multivariable analysis, higher diastolic blood pressure (adjusted odds ratio [aOR] 1.04; 95% CI 1.00–1.08; p = 0.029) was associated with increased odds of CVD, whereas higher estimated GFR (aOR 0.96; 95% CI 0.94–0.99; p = 0.022) was associated with a lower likelihood of CVD (Table 7).
Figure 2.
Distribution of pre-existing cardiovascular comorbidities among patients with cardiovascular disease in the study cohort, identified from documented medical history.
Notes: frequencies are used to express pre-existing comorbidities from history.
Abbreviations: HHD, hypertensive heart disease; CAD, coronary artery disease; CMO, cardiomyopathy; HF, heart failure; CVA, cerebrovascular accident; VHD, valvular heart disease; PVD, peripheral vascular disease.
Table 5.
Echocardiogram
| Variable | n = 165 |
|---|---|
| LVIDD (mm) | 45 (42.8–50.0) |
| LV ejection fraction (%) | 63 (58–68) |
| LVH (%) | 75 (42.6%) |
| Diastolic dysfunction (%) | 80 (45.5%) |
| Non-rheumatic-valvular heart diseases (%) | 32 (18.1%) |
| Pulmonary hypertension (%) | 15 (8.5%) |
Notes: Variables are expressed as medians and interquartile ranges for continuous variables (non-normal distribution), and frequencies and percentages for categorical data.
Abbreviations: LVIDD, left ventricular internal diameter in diastole; LV, left ventricle; LVH, left ventricular hypertrophy.
Table 6.
The Electrocardiogram
| Variable | n = 122 |
|---|---|
| Sinus rhythm | 100 (82.0%) |
| Left axis deviation | 7 (5.7%) |
| Arrhythmias | 32 (26.2%) |
| Ischemic changes | 116 (95.1%) |
| Left ventricular hypertrophy | 48 (39.3%) |
Notes: All the above variables are expressed as frequency and percentages.
Table 7.
Univariable and Multivariable Logistic Regression Analysis for Predictors of CVD
| Variable | Univariable OR (95% CI) | P-value | Multivariable aOR (95% CI) | P-value |
|---|---|---|---|---|
| Age (years) | 0.97 (0.96–0.99) | 0.001 | 1.01 (0.92–1.12) | 0.741 |
| Age at diagnosis of CKD | 0.97 (0.96–0.99) | <0.001 | 0.96 (0.87–1.06) | 0.427 |
| Obesity yes/no | 0.53 (0.10–2.87) | 0.470 | ||
| Diastolic BP <86 ≥86 | 1.02 (1.01–1.03) | 0.002 | 1.04 (1.00–1.08) | 0.029 |
| MAP <105 ≥105 | 1.01 (1.00–1.02) | 0.021 | 0.97 (0.94–1.01) | 0.096 |
| eGFR <30 or ≥30 | 0.95 (0.92–0.98) | <0.001 | 0.96 (0.94–0.99) | 0.022 |
| Phosphate | 1.00 (1.00–1.01) | 0.666 | ||
| Albumin | 0.97 (0.93–1.01) | 0.105 | ||
| UPCR | 1.03 (0.44–2.38) | 0.947 | ||
| Hypertension, yes/no | 2.71 (1.36–5.39) | 0.004 | 2.17 (0.98–4.81) | 0.057 |
| HIV, yes/no | 0.53 (0.32–0.88) | 0.014 | 0.62 (0.35–1.13) | 0.119 |
Note: Adjusted for age and sex.
Abbreviations: OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; CKD, chronic kidney disease; BP, blood pressure; MAP, mean arterial pressure; eGFR, estimated glomerular filtration rate; UPCR, urine protein creatinine ratio; HIV, human immunodeficiency virus; CVD, cardiovascular disease.
Discussion
In this single-center retrospective study of patients with advanced CKD (stages G4–G5) in Johannesburg, South Africa, the prevalence of CVD was notably high at 58.7%. All the patients in our cohort were relatively young. 16.3% of participants were receiving hemodialysis or peritoneal dialysis, and 67.1% had resistant hypertension. Although similar descriptive studies have been reported in other low- and middle-income countries, data from South Africa remain limited. The present study, therefore, provides important region-specific evidence from a large public-sector tertiary hospital serving a predominantly African population with a high burden of hypertension, HIV infection, and other cardiometabolic risk factors. Understanding the spectrum of CVD in this context is essential for informing local, relevant screening and management strategies in resource-constrained health systems.
In patients with CKD, cardiovascular complications are the predominant cause of death.2,15 CVDs have been shown to account for approximately 40–50% of all deaths in patients with advanced CKD (stage 4–5).16 Therefore, early identification of CVD risk factors and their treatment are paramount.
We also demonstrated that the patients with CVD were significantly younger than those without CVD. This may reflect South Africa’s renal replacement therapy (RRT) selection criteria, under which patients older than 60 years attending public health facilities are ineligible for chronic dialysis or transplantation and are managed conservatively.17 Consequently, younger patients with CKD are overrepresented in state dialysis centers. This observation aligns with the 2022 South African Renal Registry, which showed that patients receiving RRT in the public sector were considerably younger than those in the private sector (44.7 vs. 56.8 years).18 In our cohort, two-thirds of patients had CVD. This is congruent with findings from other low- and middle-income countries: Saudi Arabia (56.3%), Uganda (54.4%), and South India (50.1%).6,8 However, our prevalence was higher than that in the developed world.3,4,19,20 This might be due to the fact that our patients were generally younger with earlier onset of atherosclerosis in the context of advanced CKD and the absence of institutionalized CVD screening programs for patients with CKD.
Obesity was associated with a higher prevalence of CVD in our study. Among patients with CKD, obesity, especially when accompanied by metabolic syndrome, acts synergistically to amplify CVD risk through mechanisms involving inflammation and neurohormonal dysregulation.21 This burden is further compounded by the rising prevalence of metabolic syndrome in South Africa, driven by rapid epidemiological transition, including lifestyle changes and urbanization.22 Consequently, there is an urgent need for the emergence of cardio-kidney-metabolic specialists and a multidisciplinary team-based approach to effectively manage this growing public health challenge.
All patients undergoing dialysis in our cohort presented with CVD. As GFR declines, the metabolic disturbances that underpin CVD pathogenesis become increasingly pronounced.23,24 These include dysregulation of glucose and lipid metabolism, anemia, inflammation, and vascular calcification.24 Although dialysis is a life sustaining therapy, it imposes significant hemodynamic and metabolic stress on the myocardium through rapid shifts in fluid and electrolyte balance, thereby predisposing patients to myocardial stunning, arrhythmias, and heart failure.25 Furthermore, repeated exposure to indwelling dialysis catheters increases susceptibility to bloodstream infections, endocarditis, and valvular damage.26–28 Therefore, clinicians managing CKD patients on dialysis should maintain a high index of suspicion for cardiovascular complications and implement vigilant, multidisciplinary monitoring strategies.
In this study, hypertension was strongly associated with CVD (p = 0.003). The higher prevalence of CVD among patients receiving triple anti-hypertensive therapy likely reflects an increased burden of resistant hypertension in this cohort. Hypertension is by far the most common risk factor linking CKD and CVD, with both conditions reinforcing each other in a bidirectional and self-perpetuating cycle.29,30 As kidney function declines, neurohormonal activation, fluid overload, and vascular dysfunction further exacerbate hypertension, rendering it increasingly resistant to therapy.29,30 These maladaptive responses contribute to accelerated atherosclerosis and adverse cardiac remodeling, including left ventricular hypertrophy, diastolic dysfunction, heart failure, and ischemic heart disease, consistent with the ECG and echocardiographic findings in our cohort.29,30 This observation is consistent with the growing evidence that SSA faces an increasing burden of undiagnosed and poorly controlled hypertension. This is largely due to limited screening programs and restricted access to effective antihypertensive therapy.31–33 Therefore, implementing strategies aimed at early detection and optimal blood pressure control remains crucial for mitigating CVD risk in this population. The concerning ECG ischemic changes observed in our cohort included ST-segment abnormalities, T-wave inversion, and LVH with or without a strain pattern. These are likely reflecting hypertensive structural remodeling, microvascular dysfunction, and subendocardial ischemia. However, occlusive ischemic heart disease was not formally excluded, and definitive testing is necessary for confirmation.
In our cohort, HIV infection and ART use in patients with CKD were associated with a lower prevalence of CVD. This finding contrasts with the previous studies, which demonstrated that HIV is associated with more than a two-fold increase in the risk of CVD.34,35 Several factors may explain the unexpected paradoxically lower prevalence of CVD among HIV-positive patients in our cohort. HIV-positive participants in our study were generally younger, which may reduce the likelihood of age-related cardiovascular complications. In addition, the differences in the distribution of traditional CVD risk factors between HIV positive and HIV-negative patients may influence the observed associations. Furthermore, survival bias may have contributed to this finding, as patients with more severe CVD may have been less likely to survive long enough to be captured in this retrospective cohort. Given the cross-sectional design of the study, causal relationships cannot be established, and these findings should therefore be interpreted cautiously. Further prospective studies involving large multi-center populations are needed to better understand the relationship between HIV infection, CKD, and CVD in SSA.
Proteinuria, as indicated by an elevated urine protein creatinine ratio (UPCR), was significantly associated with CVD in our cohort. UPCR is primarily used to assess early kidney injury and to monitor the response to treatment. Elevated UPCR levels reflect glomerular damage and systemic endothelial dysfunction, key mechanisms that drive CKD progression and heighten cardiovascular risk. However, in this study, an increased UPCR did not function as an early marker or precursor but rather signified the severity of already established disease.
These findings have important implications for cardiovascular screening policies and the integration of cardio-renal care pathways within resource-constrained health systems in SSA. Our results, therefore, provide important baseline data for clinicians and policymakers seeking to improve integrated cardio-renal care in resource-limited settings. In addition, the relatively young age at which CVD occurred in our cohort highlights the need for earlier cardiovascular risk stratification among patients with CKD in SSA.
Study Limitations
This study has several limitations. First, it was conducted at a single center, which may limit the generalizability of the findings to the broader South African CKD population. However, as the study site is a public tertiary hospital that primarily manages referred patients eligible for RRT, the cohort may reasonably reflect the characteristics of the national CKD population requiring advanced care. Second, the retrospective design may have constrained the ability to confirm CVD diagnoses, which relied on existing clinical records and were potentially subject to confounding factors. Moreover, a formal assessment of interobserver variability in ECG and echocardiographic interpretation was not possible. Third, the limited availability of advanced imaging modalities, such as echocardiography and coronary angiography, may have resulted in underdiagnosis of subclinical CVD. Fourthly, the exclusion of patients aged ≥60 years from RRT eligibility in the public sector may have introduced selection bias. We had initially planned to conduct our study at two tertiary institutions (Charlotte Maxeke Johannesburg Hospital (CMJAH) and CHBAH). However, due to a fire incident during data collection, the study was ultimately conducted at a single institution. Collectively, these limitations should be considered when interpreting the study findings.
Conclusion
In this cross-sectional study of patients attending a tertiary hospital in South Africa, the prevalence of CVD among individuals with advanced CKD was notably high, occurred at a relatively younger age. These findings underscore the substantial cardiovascular burden in this population and highlight the importance of incorporating early routine cardiovascular screening into CKD management. However, the findings should be interpreted with caution, as this is a cross-sectional, single-center study that may limit causal inference and generalizability. Large-scale, multicenter, prospective studies in SSA are warranted to validate these results and to inform the development of targeted preventive and therapeutic cardio-renal strategies for this high-risk group.
Acknowledgments
I sincerely thank the patients and nephrology staff at Chris Hani Baragwanath Academic Hospital for their support of this project.
Disclosure
The authors declare no conflicts of interest.
References
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