Interleukin 6 Is a Stronger Predictor of Clinical Events Than High-Sensitivity C-Reactive Protein or D-Dimer During HIV Infection
Bibliographic record
Abstract
BACKGROUND: Interleukin 6 (IL-6), high-sensitivity C-reactive protein (hsCRP), and D-dimer levels are linked to adverse outcomes in human immunodeficiency virus (HIV) infection, but the strength of their associations with different clinical end points warrants investigation. METHODS: Participants receiving standard of care in 2 HIV trials with measured biomarker levels were followed to ascertain all-cause death, non-AIDS-related death, AIDS, cardiovascular disease (CVD), and non-AIDS-defining malignancies. Hazard ratios (HRs) and 95% confidence intervals (CIs) of each end point for quartiles and log2-transformed IL-6, hsCRP, and D-dimer levels were calculated using Cox models. Marginal models modelling multiple events tested for equal effects of biomarker levels on different end points. RESULTS: Among 4304 participants, there were 157 all-cause deaths, 117 non-AIDS-related deaths, 101 AIDS cases, 121 CVD cases, and 99 non-AIDS-defining malignancies. IL-6 was more strongly associated with most end points, compared with hsCRP. IL-6 appeared to be a stronger predictor than D-dimer for CVD and non-AIDS-defining malignancies, but 95% CIs overlapped. Independent associations of IL-6 were stronger for non-AIDS-related death (HR, 1.71; 95% CI, 1.43-2.04) and all-cause death (HR, 1.56; 95% CI, 1.33-1.84) and similar for CVD (HR, 1.35; 95% CI, 1.12-1.62) and non-AIDS-defining malignancies (HR, 1.30; 95% CI, 1.06-1.61). There was heterogeneity of IL-6 (P < .001) but not hsCRP (P = .15) or D-dimer (P = .20) as a predictor for different end points. CONCLUSIONS: IL-6 is a stronger predictor of fatal events than of CVD and non-AIDS-defining malignancies. Adjuvant antiinflammatory and antithrombotic therapies should be tested in HIV-infected individuals.
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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) | 0.000 | 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".