HIV, the gut microbiome and clinical outcomes, a systematic review
Bibliographic record
Abstract
BACKGROUND: Effective antiretroviral therapy (ART) has improved the life expectancy of people with HIV (PWH). However, this population is now experiencing accelerated age-related comorbidities, contributed to by chronic immune activation and inflammation, with dysbiosis of the gut microbiome also implicated. METHOD: We conducted a systematic literature search of PubMed, Embase, Scopus, Cochrane reviews and international conference abstracts for articles that examined for the following non-communicable diseases (NCDs); cardiovascular disease, cancer, frailty, metabolic, bone, renal and neurocognitive disease, in PWH aged >18 years. Studies were included that measured gut microbiome diversity and composition, microbial translocation markers or microbial metabolite markers. RESULTS: In all, 567 articles were identified and screened of which 87 full-text articles were assessed for eligibility and 56 were included in the final review. The data suggest a high burden NCD, in particular cardiovascular and metabolic disease in PWH. Alterations in bacterial diversity and structure varied by NCD type, but a general trend in reduced diversity was seen together with alterations in bacterial abundances between different NCD. Lipopolysaccharide was the most commonly investigated marker of microbial translocation across NCD followed by soluble CD14. Short-chain fatty acids, tryptophan and choline metabolites were associated with cardiovascular outcomes and also associated with chronic liver disease (CLD). CONCLUSIONS: This systematic review is the first to summarise the evidence for the association between gut microbiome dysbiosis and NCDs in PWH. Understanding this interaction will provide insights into the pathogenesis of many NCD and help develop novel diagnostic and therapeutic strategies for PWH.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".