Physical Activity, Heart Rate, Metabolic Profile, and Estradiol in Premenopausal Women
Bibliographic record
Abstract
PURPOSE: To study whether physical inactive women with a tendency to develop metabolic syndrome have high levels of 17beta-estradiol (E2) of importance for breast cancer risk. METHODS: Two hundred and four healthy women of reproductive age were assessed for self-reported leisure-time physical activity (LPA), resting heart rate (HR), blood pressure (BP), anthropometry, and serum glucose, lipids, and insulin [Norwegian Energy Balance and Breast Cancer Aspect (EBBA) study]. E2 was measured in daily saliva samples throughout an entire menstrual cycle. A clustered metabolic risk score [z metabolic syndrome (zMS); total cholesterol-high-density lipoprotein-cholesterol (HDL-C) ratio, insulin resistance, total fat tissue, BP, and triglycerides] was defined. Linear regression and linear mixed models were used, and confounding factors were tested. RESULTS: Physically active women had lower fat percentage (Ptrend = 0.003) and HRs (Ptrend = 0.003) than sedentary women. We estimated an increase in E2 of 1.27 pmol.L [95% confidence interval (CI), 0.06-2.47] for each 11.7 beats.min (1 SD) increase in HR, and this corresponds to the 7% change in mean concentration of E2 for the total group. Associations with E2 were also found for fat tissue, total cholesterol-HDL-C ratio, insulin resistance, and triglycerides. A dose-response relationship was observed among the three levels of LPA and HR and zMS (Ptrend = 0.03 for LPA; Ptrend = 0.004 for HR). Women in the highest tertile of the clustered metabolic risk score had average salivary E2profiles that were markedly higher, throughout the cycle, than those of the other groups, with a cycle peak-day difference in E2 of 22-28%. CONCLUSION: LPA and HR were associated with metabolic risk score, and this score was associated with daily level of E2, pointing to important biologic mechanisms operating between a sedentary lifestyle and an increased breast cancer risk.
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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 | 0.001 |
| 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".