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Record W4403340981 · doi:10.7189/jogh.14.04205

Global obesity epidemic and rising incidence of early-onset cancers

2024· article· en· W4403340981 on OpenAlexaboutno aff
Jianjiu Chen, Piero Dalerba, Mary Beth Terry, Wan Yang

Bibliographic record

VenueJournal of Global Health · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsIncidence (geometry)ObesityMedicineMEDLINEDemographyPediatricsInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: Incidence of early-onset cancers at multiple organ sites has increased worldwide in recent decades. We investigated whether such increasing trends could be explained by trends in obesity. Methods: We obtained incidence data for 21 common cancers among 25-49-year-olds during 2000-2012 in 42 countries from the Cancer Incidence in Five Continents database. Nine cancers we examined have been classified as obesity-related by the International Agency for Research on Cancer. Estimates of overweight and obesity prevalence came from the Non-communicable Disease Risk Factor Collaboration. Using country-level data, we examined whether changes in the prevalence of overweight and obesity combined were correlated with changes in cancer incidence, after accounting for various time lags (0-15 years) between exposure and cancer diagnosis. To test the validity of our approach, we conducted negative control analyses (using non-obesity-related cancers as the outcome variable, and per-capita gross national income as the exposure variable), and sensitivity and supplemental analyses using alternative data streams or processing. Results: We found increased incidence for six of nine obesity-related and seven of twelve non-obesity-related cancers in 25-49-year-olds. These increases were more predominant in Western countries (particularly Australia, the USA, Canada, Norway, the Netherlands, and Lithuania). For four obesity-related cancers displaying increased incidence (colon, rectum, pancreas, kidney), changes in cancer incidence were positively correlated with changes in overweight and obesity prevalence. When accounting for a 15-year lag, the estimated correlation was 0.27 (95% confidence interval (CI) = -0.04, 0.53; P = 0.090) for colon cancer, 0.33 (95% CI = 0.02, 0.58; P = 0.036) for rectal cancer, 0.39 (95% CI = 0.08, 0.64; P = 0.018) for pancreatic cancer, and 0.22 (95% CI = -0.10, 0.50; P = 0.173) for kidney cancer. Similar correlations were found in the sensitivity and supplemental analyses. We did not find similar correlations with excess body weight for the non-obesity-related early-onset cancers, nor correlations with per-capita gross national income for any cancer types, in the negative control analyses. Conclusions: Worldwide increases in early-onset colon, rectal, pancreatic, and kidney cancers may have been partly driven by increases in excess body weight. The increases in other early-onset cancers, however, were likely driven by other factors deserving of further investigation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.397
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2024
Admission routes1
Has abstractyes

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