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Effect Of Menstrual Cycle Phase And Hormonal Contraceptives On Resting Metabolic Rate And Body Composition

2023· article· en· W4387062467 on OpenAlexaff
Megan A. Kuikman, Alannah K. A. McKay, Rachel Harris, Kirsty J. Elliott‐Sale, Trent Stellingwerff, Ella S. Smith, Rachel McCormick, Nicolin Tee, Clare Minahan, Jessica Skinner, Kathryn E. Ackerman, Louise M. Burke

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsCanadian Sport Centre Pacific
Fundersnot available
KeywordsBasal metabolic rateMenstrual cycleEstrogenEndocrinologyInternal medicineMedicineHormoneResting energy expenditureFollicular phasePhysiologyEnergy metabolism

Abstract

fetched live from OpenAlex

The cyclical changes in sex hormones across the menstrual cycle (MC) are associated with various biological changes that may alter resting metabolic rate (RMR) and body composition estimates. Hormonal contraceptive (HC) use must also be considered given their impact on endogenous sex hormone levels. Understanding if MC phase or HC use affect RMR may have implications for the energy needs of female athletes, as well as the accurate measurement and interpretation of RMR and body composition. PURPOSE: To determine if RMR and body composition change across the MC and with HC usage. METHODS: During a 5-week training camp involving the National Rugby League Indigenous Women’s Academy, RMR was measured using indirect calorimetry and body composition using dual-energy X-ray absorptiometry. Measurements occurred during Phase 1 (low estrogen and progesterone) and Phase 4 (medium estrogen and high progesterone) of naturally menstruating athletes (n = 8; 21 ± 3 yr). HC users (n = 12; 22 ± 4 yr; n = 7 implants, n = 1 injection, n = 4 oral contraceptive pill) were tested on two occasions, avoiding the withdrawal bleed. Naturally menstruating athletes tracked their MC for 8 weeks prior to the study and MC phase was confirmed retrospectively by assessing serum estrogen and progesterone. Results were analyzed using general linear mixed models. RESULTS: Relative RMR did not differ between MC phase (Phase 1: 33.8 ± 2.6 kcal/kg fat free mass (FFM)/day vs. Phase 4: 33.9 ± 2.8 kcal/kg FFM/day; p = 0.723) or within HC users (32.8 ± 3.0 kcal/kg FFM/day vs. 32.1 ± 2.4 kcal/kg FFM/day; p = 0.185). Similarly, no change in absolute RMR across MC phase (p = 0.577) or between groups was observed (p = 0.075). Neither FFM nor fat mass changed across MC phase (p > 0.05). While both groups had reductions in fat mass during the training camp (p < 0.001), FFM increased in HC users (+0.9 kg; p < 0.001), but not in naturally menstruating athletes (+0.3 kg; p = 0.431). CONCLUSION: Our findings suggest that RMR and body composition do not significantly differ between Phase 1 and Phase 4 of the menstrual cycle, when estrogen and progesterone are significantly higher, or with HC use. This project was co-funded by the Australian Catholic University, Wu Tsai Human Performance Alliance and the Australian Institute of Sport Female Athlete Performance and Health Initiative.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.294
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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