The menstrual cycle may not be limited to the endometrium but also may impact gut permeability
Bibliographic record
Abstract
OBJECTIVE: To examine associations between IgA responses to Gram-negative gut commensal bacteria and peri-menstrual symptoms and sex hormone levels during the menstrual cycle in women with and without premenstrual symptoms. METHODS: Forty women aged 18-45 years completed the Daily Record of Severity of Problems (DRSP) during all 28 consecutive days of the menstrual cycle. We assayed, in plasma, IgA responses to six Gram-negative bacteria, that is, Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii, Klebsiella pneumoniae, Pseudomonas putida and Citobacter koseri, progesterone and oestradiol at days 7, 14, 21 and 28 of the menstrual cycle. RESULTS: Significant changes in Δ (actual - 1 week earlier) IgA to lipopolysaccharides (LPS) of the six Gram-negative bacteria during the menstrual cycle were observed with peak IgA levels at T4 (day 28) and lows at T1 or T2 (day 7 or 14). The ΔIgA changes in H. alvei, M. Morganii, P. putida during the menstrual cycle were significantly and positively associated with changes in the total DRSP score, and severity of physio-somatic, anxiety and breast-craving, but not depressive, symptoms. The changes in IgA responses to LPS were largely predicted by changes in progesterone and steady-state levels of progesterone averaged over the luteal phase. DISCUSSION: Menstrual cycle-associated changes in IgA directed against LPS and by inference bacterial translocation may be driven by the effects of progesterone on transcellular, paracellular and vascular pathways (leaky gut) thereby contributing to the severity of physio-somatic and anxiety symptoms as well as fatigue, breast swelling and food cravings.
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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 | 0.002 |
| 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) | 0.004 | 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".