Effect of Menstrual Cycle and Menopause on Human Gastric Electrophysiology
Bibliographic record
Abstract
Abstract Chronic gastroduodenal symptoms disproportionately affect females of childbearing age; however, the effect of menstrual cycling on gastric electrophysiology is poorly defined. To establish the effect of the menstrual cycle on gastric electrophysiology, healthy subjects underwent non-invasive Body Surface Gastric Mapping (BSGM; 8×8 array), with validated symptom logging App (Gastric Alimetry Ⓡ , New Zealand). Participants were premenopausal females in follicular (n=26) and luteal phases (n=18). Postmenopausal females (n=30) and males (n=51) were controls. Principal gastric frequency (PGF), BMI-adjusted amplitude, Gastric Alimetry Rhythm Index (GA-RI), fasted-fed amplitude ratio (ff-AR), meal response curves, and symptom burden were analysed. Menstrual cycle-related electrophysiological changes were then transferred to an established anatomically-accurate computational gastric fluid dynamics model (meal viscosity 0.1 Pas), to predict the impact on gastric mixing and emptying. PGF was significantly higher in the luteal vs. follicular phase (mean 3.21 cpm, SD (0.17) vs. 2.94 cpm, SD (0.17), p<0.001) and vs. males (3.01 cpm, SD (0.2), p<0.001). In the computational model, this translated to 8.1% higher gastric mixing strength and 5.3% faster gastric emptying for luteal versus follicular phases. Postmenopausal females also exhibited higher PGF than females in the follicular phase (3.10 cpm, SD (0.24) vs. 2.94 cpm, SD (0.17), p=0.01), and higher BMI-adjusted amplitude (40.7 µV (33.02-52.58) vs. 29.6 µV (26.15-39.65), p<0.001), GA-RI (0.60 (0.48-0.73) vs. 0.43 (0.30-0.60), p=0.005), and ff-AR (2.51 (1.79-3.47) vs. 1.48 (1.21-2.17), p=0.001) than males. There were no differences in symptoms. These results define variations in gastric electrophysiology with regard to human menstrual cycling and menopause. New and Noteworthy This study evaluates gastric electrophysiology in relation to the menstrual cycle using a novel non-invasive high-resolution methodology, revealing substantial variations in gastric activity with menstrual cycling and menopause. Gastric slow wave frequency is significantly higher in the luteal versus follicular menstrual phase. Computational modelling predicts that this difference translates to higher rates of gastric mixing and emptying in the luteal phase, which is consistent with previous experimental data evaluating menstrual cycling effects on gastric emptying.
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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 | 0.000 |
| 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".