Sex Hormones, the Stool Microbiome, and Subclinical Atherosclerosis in Women With and Without HIV
Bibliographic record
Abstract
CONTEXT: Cardioprotective roles of endogenous estrogens may be particularly important in women with HIV, who have reduced estrogen exposure and elevated cardiovascular disease risk. The gut microbiome metabolically interacts with sex hormones, but little is known regarding possible impact on cardiovascular risk. OBJECTIVE: To analyze potential interplay of sex hormones and gut microbiome in cardiovascular risk. METHODS: Among 197 postmenopausal women in the Women's Interagency HIV Study, we measured 15 sex hormones in serum and assessed the gut microbiome in stool. Presence of carotid artery plaque was determined (B-mode ultrasound) in a subset (n = 134). We examined associations of (i) sex hormones and stool microbiome, (ii) sex hormones and plaque, and (iii) sex hormone-related stool microbiota and plaque, adjusting for potential confounders. RESULTS: Participant median age was 58 years and the majority were living with HIV (81%). Sex hormones (estrogens, androgens, and adrenal precursors) were associated with stool microbiome diversity and specific species, similarly in women with and without HIV. Estrogens were associated with higher diversity, higher abundance of species from Alistipes, Collinsella, Erysipelotrichia, and Clostridia, and higher abundance of microbial β-glucuronidase and aryl-sulfatase orthologs, which are involved in hormone metabolism. Several hormones were associated with lower odds of carotid artery plaque, including dihydrotestosterone, 3α-diol-17G, estradiol, and estrone. Exploratory mediation analysis suggested that estrone-related species, particularly from Collinsella, may mediate the protective association of estrone with plaque. CONCLUSION: Serum sex hormones are significant predictors of stool microbiome diversity and composition. The gut microbiome may play a role in estrogen-related cardiovascular protection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".