Menopause and Estrogen Associations With Gut Barrier, Microbial Translocation, and Immune Activation Biomarkers in Women With and Without HIV
Bibliographic record
Abstract
OBJECTIVES: Estrogens may protect the gut barrier and reduce microbial translocation and immune activation, which are prevalent in HIV infection. We investigated relationships of the menopausal transition and estrogens with gut barrier, microbial translocation, and immune activation biomarkers in women with and without HIV. DESIGN: Longitudinal and cross-sectional studies nested in the Women's Interagency HIV Study. METHODS: Intestinal fatty acid binding protein, lipopolysaccharide binding protein, and soluble CD14 (sCD14) levels were measured in serum from 77 women (43 with HIV) before, during, and after the menopausal transition (∼6 measures per woman over ∼13 years). A separate cross-sectional analysis was conducted among 72 postmenopausal women with HIV with these biomarkers and serum estrogens. RESULTS: Women in the longitudinal analysis were a median age of 43 years at baseline. In piecewise, linear, mixed-effects models with cutpoints 2 years before and after the final menstrual period to delineate the menopausal transition, sCD14 levels increased over time during the menopausal transition (Beta [95% CI]: 38 [12 to 64] ng/mL/yr, P = 0.004), followed by a decrease posttransition (-46 [-75 to -18], P = 0.001), with the piecewise model providing a better fit than a linear model (P = 0.0006). In stratified analyses, these results were only apparent in women with HIV. In cross-sectional analyses, among women with HIV, free estradiol inversely correlated with sCD14 levels (r = -0.26, P = 0.03). Lipopolysaccharide binding protein and intestinal fatty acid binding protein levels did not appear related to the menopausal transition and estrogen levels. CONCLUSIONS: Women with HIV may experience heightened innate immune activation during menopause, possibly related to the depletion of estrogens.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".