Changes in Metabolic, Inflammatory and Coagulation Biomarkers after HIV Seroconversion – the Health in Men (Him) Biomarker Substudy
Bibliographic record
Abstract
BACKGROUND: Biomarkers of inflammation, coagulation, lipids and vitamin D have been associated with cardiovascular and mortality risk in HIV-infected individuals. Scarce data exist on changes in these markers from pre- to post-HIV seroconversion. METHODS: The study participants were drawn from the Health in Men Study, which recruited HIV-negative homosexual men. Participants with incident HIV infection (n=26) were compared with HIV-negative controls (n=52) matched on age at enrolment, date of visit and reported intravenous drug use. Levels of metabolic (lipids and vitamin D), inflammatory (C-reactive protein and interleukin-6) and coagulation (D-dimer and fibrinogen) biomarkers were measured at pre- and post-HIV seroconversion visits and corresponding visits for controls. Random-effect models were used to compare changes in markers between cases and controls. RESULTS: The median gap between pre- and post-seroconversion or matched first and second visits in controls was 12 months. HIV seroconversion was associated with decline in high density lipoprotein (HDL-C; difference in mean change between cases and controls -0.14 mmol/l; 95% CI -0.22, -0.01; P=0.035). There were no significant differences in changes in other lipids, markers of inflammation, coagulation or vitamin D. CONCLUSIONS: Decline in HDL-C seems to be the main proatherogenic change within 1-1.5 years after HIV seroconversion. HIV seroconversion was not associated with profound changes in other lipids, or markers of inflammation, coagulation and vitamin D. Longitudinal assessment of these markers in comparable population needs further assessment.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".