Urinary Transforming Growth Factor-Beta 1 (uTGF-β1) and Prevalent CKD Risk in HIV-Positive Patients in West Africa
Bibliographic record
Abstract
IntroductionThis study investigated the association of urinary transforming growth factor-β1 (uTGF-β1) with prevalent chronic kidney disease (CKD) in the HIV-infected population.MethodsHIV-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.ResultsThe 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.ConclusionHIV+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 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 | 0.001 |
| 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) | 0.001 | 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 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".