Abstract A35: Associations between time spent sitting and cancer-related biomarkers: An exploration of effect modifiers
Bibliographic record
Abstract
Abstract Purpose: Despite convincing evidence that prolonged periods of sitting may influence critical biological mediators of cancer development, few studies assessed the relationship between time spent sitting and cancer-related biomarkers. Methods: This cross-sectional study included 825 postmenopausal women who were enrolled in an ancillary study of the Women's Health Initiative Observational Study between February 1995 and July 1998. Plasma levels of biomarkers were measured at the third annual visit. The time spent sitting per day was categorized as quartiles (Qs) and analysis of covariance was used to assess the relationships between sedentary time and cancer biomarkers. Results: No clear linear patterns were observed between the time spent sitting and levels of biomarkers; however, these relationships were modified by race, physical activity level, and exogenous estrogen use. Insulin-like growth factor-I (IGF-I) levels among black women were higher than those of white women across the Qs of time spent sitting. Likewise, IL-6 levels in black women were higher than those in white women at Q3 and Q4 of sedentary time. IGF binding protein-3 levels were higher and insulin levels were lower among women meeting guidelines for physical activity than women who were not across the Qs of sedentary time. Additionally, C-reactive protein levels were higher among estrogen users than nonusers at Q1, Q2, and Q4 of sedentary time. Conclusions: Few meaningful associations were observed between the time spent sitting and the cancer-related biomarkers. More research is needed to characterize the relationship between time spent sitting and cancer-related biomarkers in the context of pertinent effect modifiers. Citation Format: Su Yon Jung, Raheem J. Paxton, Jennifer Hays-Grudo, Electra Paskett, Stephen D. Hursting, Jenifer Fenton, Michael Pollak, Mara Vitolins, Shine Chang. Associations between time spent sitting and cancer-related biomarkers: An exploration of effect modifiers. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr A35.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".