Urinary Transforming Growth Factor-Beta 1 (uTGF-β1) and Prevalent CKD Risk in HIV-Positive Patients in West Africa
Bibliographic record
Abstract
Introduction This study investigated the association of urinary transforming growth factor-β1 (uTGF-β1) with prevalent chronic kidney disease (CKD) in the HIV-infected population. Methods HIV-positive patients without CKD (HIV + CKD − , n = 194) and 114 with CKD (HIV + CKD + ) who did not have hypertension, diabetes mellitus, or hepatitis B or C, had their urinary protein-creatinine ratio (uPCR), serum transforming growth factor (TGF)–β1, and uTGF-β1 measured. uTGF-β1-creatinine ratios (uTGF-β1Cr) were calculated. Spearman correlation was used to determine the association between uTGF-β1Cr and various attributes, and the Cuzick trend test was used to assess the presence of a linear trend in median uTGF-β1Cr levels across the stages of CKD. Multivariable robust linear regression models were used to assess independent association with variability in uTGF-β1Cr and estimated glomerular filtration rate (eGFR) levels. Results The age of the participants was 38.3 ± 10.3 years with 73.4% women. The median uTGF-β1Cr was higher among HIV + CKD + (4.85 ng/mmol [25th–75th percentile 1.96–12.35] vs. 2.95 [1.02–5.84]; P = 0.001]). There was significant correlation between uTGF-β1Cr and age ( P = 0.02), eGFR ( P = 0.001), and uPCR ( P < 0.001) in the HIV + CKD + group. Among the HIV + CKD + patients, there was gradual reduction in the median level of uTGF-β1Cr with CKD severity ( P = 0.04). HIV + CKD + patients had significantly higher levels of uTGF-β1Cr after controlling for potential confounders. Using eGFR as dependent variable, proteinuria explained the changes associated with uTGF-β1Cr levels. Conclusion HIV + CKD + patients express higher levels of uTGF-β1 especially in the early stages of CKD apparently related to proteinuria levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".