Gut microbiome and healthy aging in HIV: data from the correlates of healthy aging in geriatric HIV (CHANGE HIV) cohort
Bibliographic record
Abstract
OBJECTIVES: Despite longer life expectancies, those aging with HIV experience increased comorbidity and other health challenges relative to the general population. Alterations in the composition of the gut microbiome are associated with increased immune activation and aging, but few studies have explored the association of the gut microbiome with adverse age-related outcomes in people living with HIV. We assessed the relationship between gut microbiome composition and healthy aging in HIV. DESIGN/METHODS: The CHANGE HIV study is a Canadian cohort of people aged 65 and older, which aims to investigate correlates of healthy aging in HIV. Rectal swabs were collected at enrolment from a subset of 158 consenting participants, which we analyzed with 16S rRNA gene sequencing to characterize the gut microbiome. Healthy aging was quantified using the Rotterdam Healthy Aging Score (HAS) and categorized as healthy (13-14), intermediate (11-12), and poor (0-10). We collected other markers of healthy aging including cognition, frailty, and demographics. RESULTS: Gut microbiome diversity did not differ based on HAS category, although some disease-associated bacteria were enriched in participants with lower HAS. Gut microbiome diversity did not differ based on age or frailty status. Lower HAS score group was associated with lower household income, poorer nutrition and cognition, and earlier year of HIV infection. CONCLUSION: Gut microbiome composition was not associated with healthy aging as defined by the HAS, although there were weak associations between HAS and disease-associated bacterial genera. Interventions that target social circumstances may provide greater improvements in health among aging persons with HIV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".