Association of Telomere Length with Breast Cancer Prognostic Factors
Bibliographic record
Abstract
INTRODUCTION: Telomere length, a marker of cell aging, seems to be affected by the same factors thought to be associated with breast cancer prognosis. OBJECTIVE: To examine associations of peripheral blood cell-measured telomere length with traditional and potential prognostic factors in breast cancer patients. METHODS: We conducted a cross-sectional analysis of data collected before surgery from 162 breast cancer patients recruited consecutively between 01/2011 and 05/2012, at a breast cancer reference center. Data on the main lifestyle factors (smoking, alcohol consumption, physical activity) were collected using standardized questionnaires. Anthropometric factors were measured. Tumor biological characteristics were extracted from pathology reports. Telomere length was measured using a highly reproducible quantitative PCR method in peripheral white blood cells. Spearman partial rank-order correlations and multivariate general linear models were used to evaluate relationships between telomere length and prognostic factors. RESULTS: Telomere length was positively associated with total physical activity (rs = 0.17, P = 0.033; Ptrend = 0.069), occupational physical activity (rs = 0.15, P = 0.054; Ptrend = 0.054) and transportation-related physical activity (rs = 0.19, P = 0.019; P = 0.005). Among post-menopausal women, telomere length remained positively associated with total physical activity (rs = 0.27, P = 0.016; Ptrend = 0.054) and occupational physical activity (rs = 0.26, P = 0.021; Ptrend = 0.056) and was only associated with transportation-related physical activity among pre-menopausal women (rs = 0.27, P = 0.015; P = 0.004). No association was observed between telomere length and recreational or household activities, other lifestyle factors or traditional prognostic factors. CONCLUSIONS: Telomeres are longer in more active breast cancer patients. Since white blood cells are involved in anticancer immune responses, these findings suggest that even regular low-intensity physical activity, such as that related to transportation or occupation, could be recommended to breast cancer patients.
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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 | 0.001 | 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".