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 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.
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.
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.
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)).
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.
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.