Are Plasma Biomarkers of Immune Activation Predictive of HIV Progression: A Longitudinal Comparison and Analyses in HIV-1 and HIV-2 Infections?
Bibliographic record
Abstract
BACKGROUND: Chronic immune activation is a hallmark of HIV infection and has been associated with disease progression. Assessment of soluble biomarkers indicating immune activation provide clues into pathogenesis and hold promise for the development of point-of-care monitoring of HIV in resource-poor-settings. Their evaluation in cohort resources is therefore needed to further their development and use in HIV research. METHODOLOGY/PRINCIPAL FINDINGS: Longitudinal evaluation of βeta-2 microglobulin (β-2 m), neopterin and suPAR soluble urokinase-type plasminogen activator receptor (suPAR) was performed with archived plasma samples to predict disease progression and provided the first direct comparison of levels in HIV-1 and HIV-2 infections. At least 2095 samples from 137 HIV-1 and 198 HIV-2 subjects with starting CD4% of ≥ 28 and median follow up of 4 years were analysed. All biomarkers were correlated negatively to CD4% and positively to viral load and to each other. Analyses in subjects living for ≥ 5 years revealed increases in median β-2 m and neopterin and decreases in CD4% over this period and the odds of death within 5 years were positively associated with baseline levels of β-2 m and neopterin. ROC analyses strengthened the evidence of elevation of biomarkers in patients approaching death in both HIV-1 and HIV-2 infections. Regression models showed that rates of biomarker fold change accelerated from 6-8 years before death with no significant differences between biomarker levels in HIV-1 and HIV-2 at equal time points prior to death.An 'immune activation index' analysis indicative of biomarker levels at equivalent viral loads also showed no differences between the two infections. CONCLUSIONS/SIGNIFICANCE: Our results suggest that β-2 m and neopterin are useful tools for disease monitoring in both HIV-1 and HIV-2 infections, whereas sUPAR performed less well. Levels of immune activation per amount of virus were comparable in HIV-1 and HIV-2 infected subjects.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".