Global Incidence Trend of Early-Onset Obesity-Related and Non-Obesity-Related Cancers
Bibliographic record
Abstract
The global rise in obesity prevalence and the incidence of early-onset cancer (diagnosed between 20 and 49 years of age) is a serious public health concern. We, therefore, evaluated the recent global trends in the incidence of early-onset obesity-related cancers and compared them to those of non-obesity-related cancers. We obtained age-standardized incidence rates of early-onset cancers diagnosed between 2000 and 2012 in 44 countries from the Cancer Incidence in Five Continents database. Using joinpoint regression models, we calculated the average annual percentage changes (AAPCs) and their corresponding 95% confidence intervals (95% CIs) for combined and individual categories of obesity-related cancers (11 and 9 cancer types in females and males, respectively) and non-obesity-related cancers (12 cancer types in both females and males). Differences in the AAPC were assessed by comparing 95% CIs, where nonoverlapping 95% CIs were considered statistically significantly different. We observed statistically significant positive AAPCs for early-onset obesity-related cancers in all available countries combined among females (global AAPC, 4.3%; 95% CI, 4.1-4.6%) and males (global AAPC, 1.4%; 95% CI, 1.2-1.7%). When analyzed by countries, we observed statistically significant positive AAPCs in 26 countries among females and 11 countries among males. AAPCs for early-onset obesity-related cancers were statistically significantly higher than those of non-obesity-related cancers in several regions, especially North America and Oceania. In conclusion, this study indicates that the incidence of early-onset obesity-related cancers exhibited a more pronounced increasing trend than non-obesity-related cancers among both sexes in many countries and regions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".