Kidney dysfunction: prevalence and associated risk factors in a community-based study from the North West Province of South Africa
Bibliographic record
Abstract
Abstract Background Globally, the World Health Organization ranks chronic kidney disease (CKD) as one of the top 10 causes of mortality. In South Africa, where noncommunicable diseases have become leading causes of mortality, the true population prevalence of CKD is unknown and associated risk factors remain understudied. This study aimed to describe the prevalence of kidney dysfunction and associated risk factors in a community from the North West province of South Africa. 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/1.73m 2 , or (ii) urine albuminuria-to-creatinine ratio (uACR) ≥ 3.0 mg/mmol, or a combination (i and ii). Risk factors included age, sex, urban/rural locality, body mass index (BMI), blood pressure (BP), lipid profile, haemoglobin A1c (HbA1C), C-reactive protein (CRP), gamma-glutamyl transferase (GGT), tobacco use, and HIV status. Results Mean age of participants was 48 (42;56) years, and 655/1999 (33%) had eGFR < 90 ml/min/1.73m 2 and/or uACR ≥ 3.0 mg/mmol. Compared to those with normal kidney function, participants with eGFR < 90 ml/min/1.73m 2 and/or uACR ≥ 3.0 mg/mmol were older, female, had higher measures of adiposity, systolic, diastolic, and mean arterial blood pressure, serum lipids and C-reactive protein (CRP) (all p ≤ 0.024). In multiple regression analyses eGFR was associated with systolic BP (β = 0.11) and HIV infection (β = -0.09), and 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 respectively), eGFR was associated with age (β = -0.29, β = -0.49), male sex (β = 0.35, β = 0.28), BMI (β = -0.12, β = -0.09), low-density/high-density lipoprotein cholesterol ratio (β = -0.17, β = -0.09) and CRP (β = 0.10, β = 0.09) (all p < 0.005); and uACR was associated with female sex (β = 0.10, β = -0.14), urban locality (β = -0.11, β = -0.08), BMI (β = -0.11, β-0.11), and systolic BP (β = 0.27, β = 0.14) (all p < 0.017). Conclusion In this study from the North West province, South Africa, eGFR < 90 ml/min/1.73m 2 and/or uACR ≥ 3.0 mg/mmol was prevalent and associated with modifiable risk factors. The findings may inform screening strategies for kidney disease prevention, focusing on women, obesity, blood pressure control, dyslipidaemia, identifying and treating inflammation, and HIV diagnosis and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".