Physical activity and telomere length in early stage breast cancer survivors
Bibliographic record
Abstract
INTRODUCTION: Telomere length (TL) is a biomarker of accumulated cellular damage and human aging. Evidence in healthy populations suggests that TL is impacted by a host of psychosocial and lifestyle factors, including physical activity. This is the first study to evaluate the relationship between self-reported physical activity and telomere length in early stage breast cancer survivors. METHODS: A cross-sectional sample of 392 postmenopausal women with stage I-III breast cancer at an outpatient oncology clinic of a large university hospital completed questionnaires and provided a blood sample. TL was determined using terminal restriction fragment length analysis of genomic DNA isolated from peripheral blood mononuclear cells. Physical activity was dichotomized into two groups (none versus moderate to vigorous) using the International Physical Activity Questionnaire. Multivariate linear and logistic regression analyses were performed to identify factors associated with mean TL and physical activity. RESULTS: Among participants, 66 (17%) did not participate in any physical activity. In multivariate model adjusted for age, compared to those who participated in moderate to vigorous physical activity, women who participated in no physical activity had significantly shorter TL (adjusted coefficient β=-0.22; 95% confidence interval (CI), -0.41 to -0.03; P=.03). Non-white race, lower education and depressive symptoms were associated with lack of self-reported physical activity (P<0.05 for all) but not TL. CONCLUSION: Lack of physical activity is associated with shortened TL, warranting prospective investigation of the potential role of physical activity on cellular aging in breast cancer survivors.
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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 | 0.001 |
| 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) | 0.002 | 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".