683-P: Menstrual Cycle and Glycemic Differences during Prolonged, Fasting Exercise in Females with Type 1 Diabetes
Bibliographic record
Abstract
Glycemic control may differ throughout the menstrual cycle, with increased incidence of hyperglycemia in the luteal phase. However, it is unclear if glycemic differences exist in the follicular vs. luteal phase during prolonged fasted exercise in T1D. The aim of this study was to determine if the early follicular vs. luteal phase impacts blood glucose levels during and post-exercise. Females with T1D (n = 7, age 28±6 years, A1c 6.9±0.6%) completed two 2-hr aerobic exercise sessions (∼45% VO2peak), during the luteal (days 18-24) and early-follicular phases (days 1-6) of menstruation. Carbohydrate (CHO) intake (0.3g/kg/hr) occurred every 30-min if glucose was between 72-180 mg/dL. No differences existed between the two phases for starting glucose, changes in glucose, CHO consumption and CHO oxidation during exercise (Table). Baseline glucagon was elevated in the luteal (28±25pg/mL) vs. follicular phase (13±12pg/mL; P<0.05). Following exercise, overnight time in hyperglycemia (>180 mg/dL) was greater in the luteal (59±44%) vs. follicular phase (21±30%; P<0.05). Overall, glycemic responses during prolonged fasting exercise are similar between luteal and follicular phases in women with T1D, as are CHO intake needs. However, after exercise in the luteal phase, glycemia tends to be elevated and appears to contribute to greater time in hyperglycemia. Disclosure S.M. McGaugh: None. D. Zaharieva: Speaker’s Bureau; Self; Ascensia Diabetes Care, Insulet Corporation, Medtronic. R. Pooni: None. N.C. D’Souza: None. J.E. Yardley: Research Support; Self; Abbott, Dexcom, Inc., LifeScan Canada. Speaker’s Bureau; Self; Dexcom, Inc. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S.
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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) | 0.003 | 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".