Aging Is Associated With Decreased <i>Lactobacillus</i> and Increased Cervicovaginal Inflammation in Canadian Women
Bibliographic record
Abstract
PROBLEM: Aging is characterized by a general dysregulation of systemic immune responses that increases susceptibility to infections and malignancies. Immune cells in the female genital tract (FGT) are regulated by sex hormones, but little is known about the impact of aging and menopause on immunology in the FGT. METHOD OF STUDY: This study conducted an age-focused sub-analysis of cervicovaginal samples collected from 47 women enrolled in the Vaginal Mucosal Systems study in Winnipeg, Canada. Paired cervicovaginal lavage and cervical cytobrush were collected and analyzed by Luminex cytokine array, mass spectrometry based metaproteomics, metabolomics, and high dimensional flow cytometry. RESULTS: The median age of study participants was 38 (range 19-88), with 12 over the age of 50. Increasing age was significantly correlated with increased cervicovaginal inflammation, including inflammatory cytokine MIP-1β (r = 0.335, p = 0.023), and activated T cells (CD4+HLA-DR+ r = 0406, p = 0.009; CD8+HLA-DR+ r = 0.399, p = 0.010; CD8+CD38+HLA-DR+ r = 0.386, p = 0.013). Proteomic analysis of cervicovaginal mucus identified 925 human proteins, with 108 (11.7%) significantly correlated with age. Pathway analysis indicated biofunctions related to immune response, migration, and myeloid cell phagocytosis increased with age. Interestingly, neutrophil related pathways decreased with age, including G-CSF (r = -0.396, p = 0.006) and reactive oxygen species (z-score = -2.607, p = 2.31E-4). Vaginal Lactobacillus crispatus, a species associated with mucosal health, significantly decreased with age (r = -0.340, p = 0.022), with participants over the age of 50 more likely to have non-Lactobacillus dominant microbiomes compared to those under 40. CONCLUSIONS: Together, our data suggests that there is an increase in cervicovaginal inflammation and a decrease in L. crispatus that occurs with aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".