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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".