Increased Serum Fibroblast Growth Factor 23 Predicts Mortality in People With HIV/HCV Coinfection
Bibliographic record
Abstract
BACKGROUND: People with HIV and hepatitis C virus (HCV) coinfection experience excess mortality because of multiple causes. Identification of biomarkers associated with mortality beyond that attributable to liver fibrosis may be relevant for prognostication. Fibroblast growth factor 23 (FGF23), a phosphotropic hormone, predicts adverse outcomes in several chronic conditions. We aimed to investigate whether elevated FGF23 predicts all-cause mortality in patients with HIV/HCV coinfection. METHODS: We included patients with HIV/HCV coinfection from the Canadian Coinfection Cohort with available serum FGF23, fibrosis biomarker fibrosis-4 (FIB-4), and at least 1-year follow-up. Elevated FGF23 and advanced liver fibrosis were defined as FGF23 > 241 reference unit/mL and FIB-4 > 3.25, respectively. All-cause mortality was analyzed using survival analysis. The effect of advanced liver fibrosis as a mediator on mortality was estimated by mediation analysis. RESULTS: Three hundred twenty-one patients were included (24% with elevated FGF23, 19% with advanced liver fibrosis). During a mean follow-up period of 8.4 years, 34% of the cohort died. The incidence rate of all-cause mortality was higher in patients with elevated FGF23 (66.1 per 1000 person-years, 95% confidence interval 45.8 to 92.3) relative to patients without elevated FGF23 (37.5 per 1000 person-years, 95% confidence interval 29.6 to 46.9). After adjusting for potential confounders, elevated FGF23 was associated with significant direct and indirect effects (mediated through advanced liver fibrosis) on all-cause mortality, with 57% of deaths not mediated through advanced fibrosis. CONCLUSIONS: In patients with HIV/HCV coinfection, FGF23 may be used as prognostic biomarker for risk stratification accounting also for death causes other than those attributable to liver fibrosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".