Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms
Bibliographic record
Abstract
An estimated 30-40% of cancers can be prevented through changes in modifiable lifestyle and environmental risk factors known to be associated with cancer incidence. Despite this knowledge, there remains limited awareness that these associations exist. The purpose of this review article was to summarize the epidemiologic evidence concerning the contribution of physical activity, sedentary behavior, and obesity to cancer etiology and to provide an overview of the biologic mechanisms that may be operative between these factors and cancer incidence. Strong and consistent evidence exists that higher levels of physical activity reduce the risk of six different cancer sites (bladder, breast, colon, endometrial, esophageal adenocarcinoma, gastric cardia), whereas moderate evidence inversely associates physical activity with lung, ovarian, pancreatic and renal cancer, and limited evidence inversely correlates physical activity with prostate cancer. Sedentary behavior, independent of physical activity, has been shown to increase the risk of colon, endometrial, and lung cancers. Obesity is an established risk factor for 13 different cancer sites (endometrial, postmenopausal breast, colorectal, esophageal, renal/kidneys, meningioma, pancreatic, gastric cardia, liver, multiple myeloma, ovarian, gallbladder, and thyroid). The main biologic mechanisms whereby physical activity, sedentary behavior, and obesity are related to cancer incidence include an effect on endogenous sex steroids and metabolic hormones, insulin sensitivity, and chronic inflammation. Several emerging pathways related to oxidative stress, DNA methylation, telomere length, immune function, and gut microbiome are presented. Key recommendations for future research in both the epidemiology and biology of the associations between physical activity, sedentary behavior, obesity, and cancer risk are also provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".