Lung and Plasma Metabolome in HIV-Associated Obstructive Lung Disease
Bibliographic record
Abstract
BACKGROUND: HIV is a risk factor for obstructive lung disease (OLD), independent of smoking. We used mass spectrometry (MS) approaches to identify metabolomic biomarkers that inform mechanistic pathogenesis of OLD in persons with HIV (PWH). METHODS: We obtained bronchoalveolar lavage fluid (BALF) samples from 52 PWH, in case:control (+OLD/-OLD) pairs matched on age, smoking status, and antiretroviral treatment. Four hundred nine metabolites from 8 families were measured on BALF and plasma samples using a MS-based Biocrates platform. After filtering metabolites with a high proportion of missing values and values below the level of detection, we performed univariate testing using paired t tests followed by false discovery rate corrections. We used distance-weighted discrimination (DWD) to test for an overall difference in the metabolite profile between cases and controls. RESULTS: After filtering, there were 252 BALF metabolites for analysis from 8 metabolite families. DWD testing found that collectively, BALF metabolites differentiated cases from controls, whereas plasma metabolites did not. In BALF samples, we identified 3 metabolites that correlated with OLD at the false discovery rate of 10%; all were in the phosphatidylcholine family. We identified additional BALF metabolites when analyzing lung function as a continuous variable, and these included acylcarnitines, triglycerides, and a cholesterol ester. CONCLUSIONS: Collectively, BALF metabolites differentiate PWH with and without OLD. These included several BALF lipid metabolites. These findings were limited to BALF and were not found in plasma from the same individuals. Phosphatidylcholine, the most common lipid component of surfactant, was the predominant lipid metabolite differentially expressed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".