Kidney dysfunction: prevalence and associated risk factors in a community-based study from the North West Province of South Africa

doi:10.21203/rs.3.rs-1567386/v1

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Research Article

Kidney dysfunction: prevalence and associated risk factors in a community-based study from the North West Province of South Africa

https://doi.org/10.21203/rs.3.rs-1567386/v1

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Background

Chronic kidney disease (CKD) is in the top 10 leading causes of mortality globally. In South Africa where non-communicable diseases are the main cause of mortality the true prevalence of CKD is unknown, and its associated risk factors remain understudied in the general population. In this study we investigate the prevalence and identify the main risk factors involved in kidney dysfunction that will be helpful in guiding health care professionals in the planning of intervention studies.

Methods

This cross-sectional study included 1999 participants older than 30 years. Kidney dysfunction was defined as (i) estimated glomerular filtration rate (eGFR) < 90 ml/min/1.73m², or (ii) urine albuminuria-to-creatinine ration (uACR) ≥ 3.0 mg/mmol, or a combination (i and ii). The risk factors included are age, sex, locality, body mass index (BMI), blood pressure (BP), Lipids, hemoglobin A1c (HbA1C), C-reactive protein (CRP), gamma-glutamyl transferase (GGT), tobacco use and human immunodeficiency virus (HIV).

Results

The mean age of all participants was 48.0 (42.0;56.0) years, and 656/1999 (33%) had kidney dysfunction. Those with kidney dysfunction were older, majority women, had higher measures of adiposity, systolic, diastolic, and mean arterial BP, higher lipids as well as higher CRP (all p ≤ 0.024). In multiple regression analyses, eGFR was associated with systolic BP (β = 0.11) and HIV (β=-0.09), while albuminuria was associated with elevated CRP (β = 0.12) and HIV infection (β = 0.11) (all p < 0.026). In both groups (individuals with and without kidney dysfunction) eGFR associated with age (β=-0.29, β=-0.49, respectively), male sex (β = 0.35, β0.28, respectively), body mass index (BMI) (β=-0.12, β=-0.09, respectively), low density lipoprotein cholesterol/ high density lipoprotein cholesterol ratio (β=-0.17, β=-0.09, respectively) and CRP (β = 0.10, β = 0.09, respectively) (all p < 0.005); while uACR associated with female sex (β = 0.10, β=-0.14, respectively), urban locality (β=-0.11, β=-0.08, respectively), BMI (β=-0.11, β-0.11, respectively), and systolic BP (β = 0.27, β = 0.14) (all p < 0.017).

Conclusions

In this relatively young population kidney dysfunction prevalence is high. When compared to individuals without kidney dysfunction intervention strategies should be focussed on controlling blood pressure, inflammation, and HIV.

kidney dysfunction

prevalence

risk factors

HIV infection

systolic blood pressure

C-reactive protein

Chronic Kidney Disease (CKD) is a growing health problem with an estimated global prevalence of 13% (1, 2). In sub-Saharan Africa (SSA), CKD affects approximately 14% of the adult population (3). The prevalence of CKD varies by region and most of the global burden of the disease occurs in low- and middle-income countries (4). A population-based study that included four SSA countries namely Burkina Faso, Ghana, Kenya and South Africa reported an age standardised prevalence of 10.7% for CKD in this population (5). Available data on the burden of CKD in South Africa has reported prevalence ranging from 2–23.9% but part of this variation pertains to study design and various definitions used for CKD (6, 7). In these studies, CKD was associated with older age, high body mass index (BMI), sex, cholesterol, diabetes and hypertension. While the true prevalence of CKD in SA is speculative, its risk is thought to be high because of the growing prevalence of non-communicable diseases such as obesity, hypertension, diabetes, and persistent HIV infection in the country (8). Many rural communities are undergoing rapid sociodemographic and epidemiological transition that exacerbate the risk for infectious and non-communicable diseases (9).

There are limited studies of the epidemiology of CKD in South Africa, and majority of the available studies on kidney disease and related risk factors are carried out in high-risk groups with advanced CKD stages. Few population-based studies have been conducted in South Africa to determine CKD prevalence and associated risk factors at the early stages of kidney disease. By identifying the main contributors (both modifiable and non-modifiable risk factors) of kidney disease in South Africa, more specific or targeted interventions can be developed to help curb the prevalence of CKD in our population. The aim of this study is to investigate the prevalence of kidney dysfunction and associated risk factors in a mixed urban and rural community-based study from the North West province of South Africa.

Study population and participants

This current study forms part of the multinational Prospective Urban and Rural Epidemiological (PURE) stud y that recruited urban and rural participants who are 30 years and older from 20 high-, middle-, and low-income countries. Briefly, the PURE study aims to examine the impact of social, economic, and demographic factors and lifestyle behaviour on cardiovascular risk profiles and the incidence of non-communicable diseases (10). The PURE study is a cohort study with longitudinal follow-up at five and ten years after recruitment. In this sub-study, the baseline cross-sectional data collected in 2005 are presented. Community-based recruitment took place in 2005 and a total of 2010 adults of self-identified black African ethnicity older than 30 years were recruited from two rural (Ganyesa and Tlakgameng) and two urban (Ikageng and Sonderwater extension 7 and 11) areas in the North West province, South Africa. A total of 4000 individuals who met the inclusion criteria were identified in the communities and 2010 individuals consented to participate and were included in the study. The inclusion criteria were age, self-identified black African ethnicity and permanent residency of the above-mentioned areas. Participants with missing data regarding kidney function (estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (uACR)) were excluded (n=11). Thus, the final sample size for this analysis was n=1999. All participants gave written informed consent prior to their participation. This sub-study was approved by the Health Research Ethics Committee (NWU-00486-20-A1) of the North-West University and all procedures were done according to the institutional guidelines and the declaration of Helsinki.

Questionnaires

Questionnaires that were used in this study were validated for the South African population. With the help of trained field workers, participants completed questionnaires in which they reported information on demographic, health and lifestyle related information including age, sex, alcohol consumption, tobacco use, and medical history.

Anthropometric measurements

Body height (Invicta Stadiometer, IP 1465, Invicta, London, UK), weight (Electronic scale, Precision Health Scale, A&D Company, Tokyo, Japan) and waist circumferences (WC) (Holtain unstretchable metal tape) were measured using standardised methods according to guidelines of the International Society for the Advancement of Kinanthropometry (ISAK) (11). We calculated BMI using the standard weight (kg)/height (m²) equation.

Cardiovascular measurements

Clinic brachial blood pressure measurements were determined following standardised guidelines (12) using the Omron HEM-757 device (Omron Healthcare, Kyoto, Japan). Prior to measurements being taken, participants were required to be in a seated resting position for 10 minutes. Two measurements were taken on the left arm with 5-minute intervals between the measurements, and in this study, we reported the second measurement values. Pulse pressure (PP) was calculated as the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), and the mean arterial pressure (MAP) was calculated as DBP added to a third of the PP. Hypertension was defined as clinic SBP of ≥140 mmHg and/or DBP of >90 mmHg and/or the use of hypertension medication. Hypertensive individuals who were on any class of hypertensive medication were defined as using hypertensive medication.

Biochemical measurements

Participants were requested to be in a fasted state for approximately 10 hours prior to measurements. Blood samples were taken by a registered nurse and participants were also asked to give morning spot urine. The blood and urine samples were immediately placed in ice filled insulated containers, they were then centrifuged and aliquoted and stored at -20°C within 2 hours of collection. Samples were then transferred to centralized long-term storage in –80°C bio-freezers.

Albumin and creatinine were analysed from serum and spot urine samples using Konelab20i™ auto-analyser (Thermo Fischer Scientific Oy, Vantaa, Finland) and Cobas Integra 400 plus (Roche Diagnostics, Basel, Switzerland), respectively. Urine albumin was analysed using immunoturbidimetric assay and urine creatinine was determined by the kinetic colorimetric assay. uACR was calculated by dividing urine albumin with urine creatinine. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation 2009 for creatinine (13) without the adjustment for African ethnicity(14).

Serum samples were used to determine total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides, C-reactive protein (CRP) and gamma-glutamyl transferase (GGT) using Konelab20i™ auto-analyser (Thermo Fischer Scientific, Vantaa, Finland). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula (LDL-C=HDL-C – VLDL-C where VLDL-C=0.456*TG) (15). Glucose was analysed in fluoride samples using Vitros DT6011 Chemistry Analyzer (Ortho-Clinical Diagnostics, Rochester, New York, USA). Haemoglobin A1c (HbA1c) was analysed in ethylenediaminetetraacetic acid (EDTA) plasma samples using D10 Haemoglobin testing system from Bio-Rad (#220-0101) that uses ion-exchange high-performance lipid chromatography. Human Immuno-deficiency Virus (HIV) status was determined in whole blood according to the protocol of the South African Department of Health, using First Response rapid HIV card test (Premier Medical Corporation Ltd, Daman, India). If the first test results were positive, this was confirmed with a second test using a test kit from an alternative supplier (Pareeshak card test (BHA T Biotech, India)).

Definitions

Using the kidney disease improving global outcomes (KDIGO) 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease guidelines the criteria used to assess kidney function were (i) eGFR (stages G1-5); and (ii) uACR (stages A1-3) (16). For eGFR (ml/min/1.73m²) stage G1: eGFR≥90; stage G2: eGFR 60-89; stage G3a: eGFR 45-59; stage G3b: eGFR 30-44; stage 4: eGFR 15-29; and stage 5: eGFR <15. For uACR, stage A1: uACR<3mg/mmol; stage 2: uACR 3 to 30 mg/mmol; stage A3: uACR >30 mg/mmol (16).For this study, we defined kidney dysfunction as eGFR <90 mL/min/1.73 m² and/or uACR >3.0 mg/mmol.

Statistical analyses

We used IBM® SPSS® Statistics, Version 27 software (IBM Corporation, Armonk, New York) for all statistical analyses. Sample size estimation was performed for this study, and the study was able to detect an effect size of 0.33 with a power of 95% using a minimum of 84 participants per group and significance level set at 0.05 for a multiple linear regression with a maximum of 10 covariates. Variables were tested for normality by visual inspection (histograms and q-q plots) as well as skewness and kurtosis coefficients. Continuous data were expressed as median (interquartile range) and categorical data as proportions. Most of our variables were not normally distributed and therefore non-parametric analysis were performed. We used Mann-Whitney U test to compare means and proportions between those with and without kidney dysfunction (eGFR<90 vs >90 mL/min/1.73m2 and/or ACR <3.0 vs >3.0 mg/mmol). Spearman’s rank correlations were used to determine associations between eGFR and uACR with lifestyle and cardiometabolic risk factors. We used multiple regression to identify independent associations between various risk factors (age, sex, locality, BMI, SBP, HbA1c, LDLC/HDLC ratio, CRP, GGT, tobacco use and HIV status) and measures of kidney function in individuals with and without kidney dysfunction. We used z-scores for the continuous variables and actual values for categorical variables.

The characteristics of the study population overall and stratified by kidney function are presented in Table 1. The median age of the total study population (n=1999) was 48.0 years, 37.2% were men and 50.2% were from rural areas. Individuals with kidney dysfunction were older and included a higher percentage of women (both p<0.001); and had higher BMI, WC, blood pressure measures (SBP, DBP and MAP); HbA1c, lipid measures (total cholesterol, LDLC, HDLC and triglycerides) as well as CRP (all p<0.024). Compared to those with normal kidney function, those with kidney dysfunction remained hypertensive despite a relatively higher proportion of participants taking antihypertensive medication, and they also had a lower percentage of alcohol consumption and tobacco use (all p<0.023).

Table 1. Baseline characteristics of individuals with and without kidney dysfunction
	Total group (n=1999)	Normal kidney function (n=1343)	Kidney dysfunction (n=656)	P*
Age, years	48.0 (42.0;56.0)	47.0 (41.0;55.0)	51.0 (44.0;60.0)	<0.001
Sex, men	743/1999 (37.2)	604/1343 (45.0)	139/656 (21.2)	<0.001
Locality, rural	1004/1999 (50.2)	680/1343 (50.6)	324/656 (49.4)	0.60
Body composition
BMI, kg/m²	24.7 (19.3;28.8)	22.2 (19.0;27.9)	24.5 (20.4;30.2)	<0.001
WC, cm	77.5 (70.2;87.6)	76.3 (69.9;86.6)	79.6 (70.9;89.8)	0.001
Cardiovascular measurements
SBP, mmHg	129 (116;146)	128 (115;143)	133 (117;152)	<0.001
DBP, mmHg	87.0 (78.0;97.0)	86.0 (77.0;95.0)	89.0 (80.0;100)	<0.001
MAP, mmHg	101 (91.0;113)	100 (90.0;111)	104 (93.0;118)	<0.001
HT status, n/total (%)	944/1984 (47.6)	587/1334 (56.0)	293/650 (45.1)	<0.001
Biochemical measurements
eGFR, mL/min/1.73m²	101 (86.7;111)	107 (100;114)	81.3 (70.2;89.5)	<0.001
uACR, mg/mmol	0.59 (0.29;1.46)	0.50 (0.27;1.02)	1.07 (0.36;4.65)	<0.001
UMOD, µg/ml	36.9 (17.0;66.9)	37.7 (18.0;68.1)	35.3 (14.7;62.7)	0.035
Glucose, mmol/L	4.80 (4.30;5.30)	4.80 (4.30;5.30)	4.80 (4.38;5.30)	0.14
HbA1c, %	5.50 (5.20;5.80)	5.50 (5.20;5.80)	5.60 (5.30;5.90)	0.002
Diabetes status, n/total (%)	62/1826 (3.2)	36/1216 (3.0)	26/610 (4.3)	0.15
Total cholesterol, mmol/L	4.82 (4.00;5.87)	4.71 (3.89;5.73)	5.06 (4.22;6.23)	<0.001
LDLC, mmol/L	2.78 (2.07;3.64)	2.71 (2.02;3.50)	2.92 (2.21;3.93)	<0.001
HDLC, mmol/L	1.42 (1.06;1.87)	1.38 (1.03;1.86)	1.45 (1.12;1.89)	<0.001
Triglycerides, mmol/L	1.08 (0.81;1.55)	1.05 (0.80;1.49)	1.14 (0.84;1.66)	0.022
C-reactive protein, mg/L	3.38 (1.01;9.35)	3.04 (0.86;8.74)	4.13 (1.33;12.0)	0.024
GGT, U/L	46.0 (29.7;88.0)	46.4 (29.6;88.0)	45.1 (29.9;85.1)	0.61
HIV status, n/total (%)	318/1988 (16.0)	202/1336 (15.1)	116/652 (17.8)	0.13
Lifestyle factors
Alcohol intake, n/total (%)	873/1986 (44.0)	614/1338 (45.9)	259/648 (40.0)	0.013
Tobacco use, n/total (%)	1111/1989 (55.9)	771/1338 (57.6)	340/651 (52.2)	0.023
Medication use
HT medication, n/total (%)	335/1999 (16.8)	186/1343 (13.8)	149/656 (22.7)	<0.001
Abbreviations: BMI – body mass index; WC – waist circumference; SBP – systolic blood pressure; DBP – diastolic blood pressure; MAP – mean arterial pressure; HT – hypertension; eGFR – estimated glomerular filtration rate; uACR – urine albumin-to-creatinine ratio; HbA1c – hemoglobin A1c; LDLC – low density lipoprotein cholesterol; HDLC – high density lipoprotein; GGT – gamma-glutamyl transferase; HIV – human immunodeficiency virus; HT – hypertension. P value for comparing between the normal and kidney dysfunction group. Values are expressed as median (25th to 75th percentile) or number of participants and percentages (%). P<0.05 was considered significant and the values are written in bold. eGFR is estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without adjustment for African American ethnicity.

The stages of kidney dysfunction by eGFR and ACR for the total population and stratified by sex are summarised in Table 2. We found that 29.2% of the study population had kidney dysfunction according to eGFR of which 13.5% were men and 86.5% were women; and according to uACR 13% had kidney dysfunction and the percentage was proximately the same for men and women. 3.7% of the population had a single screening of eGFR of <60 mL/min/1.73m². Based on CKD classification 3.7% of the population had CKD.

Table 2. Kidney Dysfunction: Stages defined according to eGFR and uACR in men and women
Stages	eGFR categories (mL/min/1.73m²)	Number (percentages)
		Total group	Men	Women
G1	>90	1154 (70.8)	548 (90.4)	606 (59.2)
G2	60-89	415 (25.5)	56 (9.2)	359 (35.1)
G3a	45-59	47 (2.9)	2 (0.3)	45 (4.4)
G3b	30-44	12 (0.7)	-	12 (1.2)
G4	15-29	1 (0.1)	-	1 (0.1)
G5	<15	0	-	-
Stages	uACR categories (mg/mmol)	Number (percentages)
		Total group	Men	Women
A1	<3	1657 (87.0)	619 (87.1)	1038 (87.0)
A2	3-30	234 (12.3)	89 (12.5)	145 (12.2)
A3	>30	13 (0.7)	3 (0.4)	10 (0.8)
Abbreviations: CKD – chronic kidney disease; eGFR – estimated glomerular filtration rate; uACR – urinary albumin-to-creatinine ratio

Spearman’s rank correlation was used to test for the association between eGFR and uACR with lifestyle and cardiometabolic factors (Table S1) to determine the required covariates to use in the multiple regression model.

In multivariate regression analyses (Table 3), risk factors including age; male sex; BMI, SBP, LDLC/HDLC ratio; CRP and HIV naive associated with eGFR (all p<0.026) in individuals with kidney dysfunction. These associations were also observed in individuals with normal kidney function except the associations with SBP and HIV. The risk factors that associated with uACR in those with kidney dysfunction are female sex; urban locality; BMI; SBP, CRP and HIV infection (all p<0.017), and the same association were observed in individuals with normal kidney function excluding the associations with CRP and HIV infection.

Table 3. Multiple-regression analysis of eGFR, uACR and risk factors in individuals with and without kidney dysfunction
	Estimated glomerular filtration rate (mL/min/1.73m²)
	Normal kidney function (n=1343)		Kidney dysfunction (n=656)
	β (95% CI)	P	β (95% CI)	P
Adjusted R²	0.324; p<0.001		0.272; p<0.001
Age, years	-0.49 (-0.33;-0.26)	<0.001	-0.29 (-0.32;-0.18)	<0.001
Sex, men	0.28 (0.24;0.37)	<0.001	0.35 (0.67;1.04)	<0.001
Locality, rural	0.04 (-0.01;0.10)	0.11	0.03 (-0.09;0.20)	0.42
BMI, kg/m²	-0.09 (-0.09;-0.02)	0.004	-0.12 (-0.20;-0.04)	0.005
SBP, mmHg	0.04 (-0.01;0.05)	0.18	0.11 (0.03;0.17)	0.005
HbA1c, %	0.01 (-0.03;0.03)	0.87	-0.01 (-0.08;0.07)	0.85
LDLC/HDLC ratio	-0.09 (-0.08;-0.02)	<0.001	-0.17 (-0.28;-0.12)	<0.001
C-reactive protein, mg/L	0.09 (0.02;0.08)	0.001	0.10 (0.03;0.17)	0.005
GGT, U/L	0.03 (-0.02;0.06)	0.30	-0.01 (-0.07;0.05)	0.75
HIV infection, (%)	-0.02 (-0.10;0.06)	0.58	-0.09 (-0.42;-0.03)	0.026
Tobacco use, (%)	0.01 (-0.05;0.07)	0.73	0.05 (-0.04;0.25)	0.15
	Urine albumin-to-creatinine ratio (mg/mmol)
	Normal kidney function (n=1343)		Kidney dysfunction (n=656)
	β (95% CI)	P	β (95% CI)	P
Adjusted R²	0.037; p<0.001		0.117; p<0.001
Age, years	0.002 (-0.01;0.01)	0.95	-0.06 (-0.21;0.03)	0.15
Sex, women	-0.14 (-0.06;-0.02)	<0.001	0.10 (0.07;0.73)	0.017
Locality, urban	-0.08 (-0.04;-0.01)	0.007	-0.11 (-0.61;-0.11)	0.006
BMI, kg/m²	-0.11 (-0.03;-0.01)	0.002	-0.11 (-0.32;-0.04)	0.013
SBP, mmHg	0.14 (0.01;0.03)	<0.001	0.27 (0.28;0.53)	<0.001
HbA1c, %	0.003 (-0.01;0.01)	0.92	-0.01 (-0.14;0.12)	0.91
LDLC/HDLC ratio	-0.04 (-0.01;0.002)	0.16	-0.03 (-0.20;0.09)	0.43
C-reactive protein, mg/L	0.05 (-0.002;0.02)	0.12	0.12 (0.07;0.31)	0.002
GGT, U/L	0.04 (-0.003;0.02)	0.15	0.01 (-0.08;0.11)	0.80
HIV infection, (%)	0.02 (-0.02;0.03)	0.51	0.11 (0.11;0.73)	0.011
Tobacco use, (%)	0.01 (-0.02;0.02)	0.73	0.05 (-0.09;0.41)	0.22
Abbreviations: eGFR – estimated glomerular filtration rate; uACR – urine albumin-to-creatinine ratio; BMI – body mass index; SBP – systolic blood pressure; HbA1c – hemoglobin A1c; LDLC/HDLC ratio – low density lipoprotein cholesterol – to – high density lipoprotein cholesterol ratio; GGT - gamma-glutamyl transferase; HIV – human immunodeficiency virus. P<0.05 was considered significant and the values are written in bold.

In this community-based cross-sectional study in a mixed urban and rural population from the North West province of South Africa, we show that kidney dysfunction is prevalent and associated with HIV infection, elevated SBP, and elevated CRP (a non-specific marker of systemic inflammation). Overall, the crude prevalence of albuminuria was 13% and that of eGFR < 60 ml/min/1.73m² was 3.7%. Between sex differences were not evident for those with albuminuria, however, at all stages of GFR (1–5), more women were affected than men.

The association with HIV and albuminuria is well-described (17, 18). HIV infection can either directly affect the kidneys by changing the cellular structure of their parenchymal cells causing kidney damage that may lead to albuminuria or indirectly by causing an immune inflammatory respond (19). The absence of an association with declining eGFR in our study is notable as several studies have reported a decline in eGFR in HIV-infected populations (20–22). One potential explanation is that our participants were young with relatively preserved kidney function, and most were ART naive so the association with albuminuria might have been a consequence of untreated HIV infection or the presence of metabolic syndrome. In the absence of confirmatory testing for albuminuria or longitudinal follow-up, whether kidney dysfunction persisted as CKD, progressed, or resolved after initiation of ART remains speculative.

Persistent, low-grade inflammation plays an important role in the pathophysiology of CKD (23). There are many factors that contribute to the inflammatory state in CKD, and they include the increased production of proinflammatory cytokines and oxidative stress, acidosis, chronic and recurrent infection to name a few (24). While this study is one of a few in SA to describe the association between albuminuria and markers of inflammation (elevated CRP) this finding is well-described (25, 26). One proposed mechanism to explain the association is that CRP promotes inflammatory cytokines which leads to mesangial cell proliferation, matrix overproduction and increased vascular permeability resulting in albuminuria (27). The findings support the notion that inflammation may play a role in the development of microalbuminuria

High blood pressure is closely linked with kidney disease, and it can either be a cause or a consequence of CKD (28). High blood pressure has been associated with the progression of CKD, the development of CVD and cardiovascular mortality (29–31). Hypertension has been shown to be a player in kidney disease in South African populations (8, 32). There’s a high prevalence of hypertension in South Africa (33) and since CKD increases with blood pressure it is not surprising that we found eGFR to associate with SBP in our population.

Obesity is one of the common causes of CKD that lead to the deterioration of structure and function of the kidney (34). BMI had been shown to be a risk factor associated with CKD in SSA (35). Our finding confirms that eGFR declines with an increase in obesity (36). There are, however, conflicting findings on the association of obesity and CKD. Some studies have found counterintuitive associations between obesity and more favourable clinical outcomes in individuals with CKD (37). The underlying mechanisms of this reverse epidemiology are still unknown.

Dyslipidaemia is another complication of CKD characterised by high triglycerides, high LDLC, low HDLC and altered lipoprotein composition (38). Disturbances in lipoprotein metabolism is seen in individuals with kidney disease and individuals with hypercholesterolemia were found to have a high prevalence of albuminuria and reduced eGFR (39). We found eGFR to associate with LDLC/HDLC ratio, and according to our knowledge, this is the first study to look at LDLC/HDLC ratio in a South African population.

Disparities in the risk and prevalence of CKD between men and women have been reported. Women tend to have higher prevalence of CKD whereas men are more likely to suffer kidney failure as kidney function declines faster in men(40). In our study women appeared to have a higher prevalence of CKD and had more advanced stages according to eGFR compared to men. Our study further confirmed the well-established decline in eGFR with normal ageing. This decline is part of a normal physiological process of cellular and organ senescence. We also found albuminuria to associate with urban locality. Due to rapid urbanisation accompanied by changes in diet, lack of physical activity and increased habits such as smoking and alcohol consumption, it is not surprising that high prevalence of CKD is observed in urban areas (41, 42).

We also looked at the relationship between uromodulin and the risk factors and we found uromodulin to associate with GGT, tobacco use, urban locality, CRP and HIV infection. According to our knowledge these findings have never been described before and future studies are needed to investigate further.

The strength of this study is the large sample size; the cohort was well characterised, and the research was conducted in a well-equipped research facility. The main study limitation is that the two measures of kidney function, eGFR and uACR were only measured once and therefore kidney dysfunction was defined on a single measurement. The absence of confirmation of low eGFR and high uACR could have let to the overestimation of kidney dysfunction. Also due to the cross-sectional nature of this study, a causal relationship between the lifestyle and cardiometabolic risk factors and measures of kidney function (eGFR and uACR) could not be established. Another limitation is that we did not have data on all the lifestyle measures such as diet and physical activity to investigate their role in kidney dysfunction. Most of our HIV infected individuals were newly diagnosed and were ART naïve, and those who were not we did not have data on the use of ART, and therefore we could not examine the effect of treatment on the relationship between HIV infection and the measures of kidney function. CRP is a non-specific and this study did not look at other infectious or inflammatory markers to confirm the association of inflammation with uACR. Lastly, our study included participants from specific rural and urban areas in the North West province, and therefore our findings may not be generalisable to the general population.

Based on our findings, initiatives, and strategies to reduce blood pressure, inflammatory markers, new HIV infections should receive priority when planning CKD interventions. Since modifying these risk behaviours should decrease the prevalence of kidney dysfunction in South Africa.

BMI

body mass index

CKD

Chronic kidney disease

CKD-EPI

CKD Epidemiology Collaboration

CVD

Cardiovascular disease

CRP

C-reactive protein

DBP

Diastolic blood pressure

eGFR

Estimated glomerular filtration rate

GGT

Gamma-glutamyl transferase

HbA1c

Hemoglobin A1c

HDLC

High density lipoprotein

HIV

Human immunodeficiency virus

Hypertension

KDIGO

Kidney disease improving global outcomes

LDLC

Low density lipoprotein cholesterol

MAP

Mean arterial pressure

PURE

Prospective Urban and Rural Epidemiological

SBP

Systolic blood pressure

Total cholesterol

uACR

Urine albumin-to-creatinine ratio

UMOD

uromodulin

Waist circumference

Ethics approval and consent to participate

This study was approved by the Health Research Ethics Committee (NWU-00486-20-A1) of the North-West University and all procedures were done according to the institutional guidelines and the declaration of Helsinki. All participants gave and signed an informed consent to participate.

Availability of data and materials

The dataset generated and/or analysed during the current study are not publicly available due to contractual agreements but are available from the corresponding author on reasonable request.

Competing interest

The authors declare that they have no competing interests

Funding

This project was funded by the South African Netherlands Research Programme on Alternatives in Development, North-West University, Population Health Research Institute, Roche Diagnostics (South Africa) and South African Medical Research Council, South African National Research Foundation (NRF) and South African Sugar Association (SASA) for the analyses (grant number: Project 249).

Opinions expressed199and conclusions are those of the authors and are not necessarily to be attributed to the NRF.

Authors’ contribution

NHN was responsible for the planning, writing and composition of the manuscript as well as the statistical analyses. GGM, LFG-M, JF and LL analysed the results of the statistical analyses, gave recommendations for the framework, writing and composition of the manuscript as well as revising it critically for intellectual content. LL further supervised the statistical analyses.

Acknowledgements

The authors would like to thank all the participants who voluntarily took part in the study, the students and supporting staff of the PURE study and in particular: (i) The PURE-SA research team, the field workers and the office staff at the North-West University, South Africa. (ii) The PURE-International: The PURE project office staff at the Population Health Research Institute (PHRI), Hamilton Health Sciences and McMaster University, ON, Canada.

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No competing interests reported.

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Kidney dysfunction: prevalence and associated risk factors in a community-based study from the North West Province of South Africa

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Background

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