The Dietary Inflammatory Index Is Not Associated With Gut Permeability or Biomarkers of Systemic Inflammation in HIV Immunologic Non-responders
Bibliographic record
Abstract
Immunologic non-responders (INRs) are a subset of individuals living with HIV who have suboptimal blood CD4+ T cell recovery despite effective antiretroviral therapy (ART). They are at an increased risk of serious non-AIDS co-morbidities and death, and demonstrate enhanced systemic immune activation. In other populations diet has been correlated with markers of systemic inflammation through the Diet Inflammatory Index (DII), but this association has not been studied in persons living with HIV (PLWH). Blood was collected from 28 INR PLWH with a blood CD4+ T cell count <350/μL despite ≥2 years of effective ART. Participants completed a Canadian Diet History Questionnaire, and their responses were used to calculate the DII. Plasma inflammatory markers (IFNγ, TNF, IL-6, sVCAM, D-dimer, sCD14 and CRP) were assayed by ELISA, cellular immune activation (HLA-DR and CD38 on CD4+ and CD8+ T cells) was quantified using flow cytometry, and small bowel permeability assessed by calculation of the urine LacMan ratio after drinking a mix of lactulose and mannitol. Participants were a median age of 57 years, had been on effective ART for 15 years, and the median DII was -1.91 (range of -3.78 to +2.23). No correlation was observed between DII and plasma markers of inflammation, levels of T cell activation, gut permeability, or the biomarker of bacterial translocation sCD14. Self-reported alcohol intake, a potential confounder of the relationship between diet and inflammatory biomarkers, was also not associated with systemic inflammation or gut permeability. Our findings suggest that other mechanisms, rather than diet, are likely to be the major driver of systemic inflammation in INR individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".