The Association between Obesity and Cancer Risk: A Meta-Analysis of Observational Studies from 1985 to 2011
Bibliographic record
Abstract
Background. Cancer and cardiovascular diseases are the leading causes of mortality and morbidity worldwide. The purpose of this meta-analysis is to synthesize the evidence evaluating the association between obesity and 13 cancers shown previously to be significantly associated with obesity. Methods. Relevant papers from a previously conducted review were included in this paper. In addition, database searches of Medline and Embase identified studies published from the date of the search conducted for the previous review (January, 2007) until May, 2011. The reference lists of relevant studies and systematic reviews were screened to identify additional studies. Relevance assessment, quality assessment, and data extraction for each study were conducted by two reviewers independently. Meta-analysis was performed for men and women separately using DerSimonian and Laird's random effects model. Results. A total of 98 studies conducted in 18 countries from 1985 to 2011 were included. Data extraction was completed on the 57 studies judged to be of strong and moderate methodological quality. Results illustrated that obese men were at higher risk for developing colon (Risk Ratio (RR), 1.57), renal (1.57), gallbladder (1.47), pancreatic (1.36), and malignant melanoma cancers (1.26). Obese women were at higher risk for esophageal adenocarcinoma (2.04), endometrial (1.85), gallbladder (1.82), renal (1.72), pancreatic (1.34), leukemia (1.32), postmenopausal breast (1.25), and colon cancers (1.19). Conclusions. The results of this meta-analysis illustrate a significant, positive, and, for some cancers, strong association between obesity and cancer incidence. Given that approximately 23% of Canadians are obese, a significant proportion of cancer in Canada could be avoided if obesity was eliminated or significantly reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.055 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".