Markers of inflammation and activation of coagulation are associated with anaemia in antiretroviral-treated HIV disease
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to determine the relationship between inflammatory interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hsCRP)] and coagulation (D-dimer) biomarkers and the presence and type of anaemia among HIV-positive individuals. DESIGN: A cross-sectional study. METHODS: Combination antiretroviral therapy (cART)-treated adults participating in an international HIV trial with haemoglobin and mean corpuscular volume (MCV) measurements at entry were categorized by presence of anaemia (haemoglobin ≤14 g/dl in men and ≤12 g/dl in women) and, for those with anaemia, by type [microcytic (MCV < 80 fl), normocytic (80-100 fl), macrocytic (>100 fl)]. We analysed the association between inflammation (IL-6 and hsCRP) and coagulation (D-dimer) and haemoglobin, controlling for demographics (age, race and sex), BMI, HIV plasma RNA levels, CD4⁺ T-cell counts (nadir and baseline), Karnofsky score, previous AIDS diagnosis, hepatitis B/C coinfection and use of zidovudine. RESULTS: Among 1410 participants, 313 (22.2%) had anaemia. Of these, 4.1, 27.2 and 68.7% had microcytic, normocytic and macrocytic anaemia, respectively. When compared with participants with normal haemoglobin values, those with anaemia were more likely to be older, black, male and on zidovudine. They also had lower baseline CD4⁺ T-cell counts and lower Karnofsky scores. Adjusted relative odds of anaemia per two-fold higher biomarker levels were 1.22 (P = 0.007) for IL-6, 0.99 for hsCRP (P = 0.86) and 1.35 (P < 0.001) for D-dimer. Similar associations were seen in those with normal and high MCV values. CONCLUSION: Persistent inflammation and hypercoagulation appear to be associated with anaemia. Routine measurements of haemoglobin might provide insights into the inflammatory state of treated HIV infection.
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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 | 0.000 |
| 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".