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Record W4415257671 · doi:10.1016/j.eclinm.2025.103569

Association between adherence to cancer prevention guidelines and cancer risk: a comprehensive systematic review and dose-response meta-analysis of cohort studies

2025· article· en· W4415257671 on OpenAlexaboutno aff
Jialei Fu, Li‐Juan Tan, Shang Ling Lou, Woo‐Kyoung Shin, Daehee Kang, Sangah Shin

Bibliographic record

VenueEClinicalMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersWorld Cancer Research FundMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsChristian ministryCancer preventionCancerCohort studyGovernment (linguistics)MEDLINEAlternative medicine

Abstract

fetched live from OpenAlex

Background No systematic review and meta-analysis has comprehensively assessed the association between adherence to major cancer prevention guidelines and risks of cancers in cohort studies. We aimed to evaluate the associations of adherence to the 2018 and the 2007 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) and the 2012 American Cancer Society (ACS) guidelines with risks of multiple cancer outcomes. Methods This systematic review and meta-analysis was conducted by searching cohort studies across PubMed, Web of Science, Embase, and Scopus, from database inception to July 31, 2025. We assessed associations between adherence to the three major global cancer prevention guidelines: the 2018 WCRF/AICR guidelines, the 2007 WCRF/AICR guidelines, and the 2012 ACS guidelines, and the risk of total cancer, obesity-related cancer, and 13 site-specific cancers (Colorectal, Breast, Lung, Prostate, Bladder, Kidney, Stomach, Pancreas, Esophagus, Liver, Ovary, Uterus, Gallbladder). Hazard ratios (HRs) with 95% confidence intervals (CIs) were extracted to evaluate risks of cancers. DerSimonian and Laird random-effects models were used to pool effect sizes, and the I 2 statistic was employed to quantify heterogeneity. The strength of associations was quantified by the magnitude of HRs and 95% CIs. A qualitative synthesis was undertaken to assess additional cancers. Study quality was evaluated using the Newcastle-Ottawa Scale. Publication bias was examined by funnel plots, Egger's test, and Begg's test. Findings This study comprised 28 cohort articles, examining 15 cancer types for quantitative analysis and 15 additional cancer types for qualitative analysis. It revealed that adherence to cancer prevention guidelines was associated with a significant 14% reduction in total cancer risk (HR = 0.86, 95% CI: 0.83–0.88, I 2 = 68.1%). Significant risk reductions were consistently observed across three guidelines for the following cancers: colorectal cancer (HR = 0.69, 95% CI: 0.63–0.74, I 2 = 65.3%), breast cancer (HR = 0.80, 95% CI: 0.74–0.85, I 2 = 64.5%), lung cancer (HR = 0.80, 95% CI: 0.69–0.91, I 2 = 83.6%), kidney cancer (HR = 0.62, 95% CI: 0.56–0.69, I 2 = 22.7%), esophageal cancer (HR = 0.61, 95% CI: 0.52–0.70, I 2 = 0.0%), obesity-related cancers (HR = 0.78, 95% CI: 0.71–0.85, I 2 = 59.3%), uterus cancer (HR = 0.65, 95% CI: 0.47–0.83, I 2 = 89.9%). In contrast, no significant associations were found for prostate cancer (HR = 1.00, 95% CI: 0.93–1.06, I 2 = 52.6%) or ovarian cancer (HR = 0.87, 95% CI: 0.67–1.06, I 2 = 66.4%), findings that were consistent across all three guidelines. Interpretation Adherence to cancer prevention guidelines was associated with reduced risks of total cancer and multiple major cancers, while no significant associations were observed for prostate cancer or ovarian cancer. These findings reinforce the importance of integrating cancer prevention guidelines into public health strategies for cancer prevention. Further research is warranted to elucidate associations of 2018 WCRF/AICR with risk of a broader spectrum of cancers, particularly in Asian populations. Funding This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government through the Ministry of Science and ICT (MSIT) (grant number: RS-2025-00556573).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.279
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.425
GPT teacher head0.556
Teacher spread0.131 · 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 teacher head, not a consensus.

Study designMeta-analysis
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

Citations2
Published2025
Admission routes1
Has abstractyes

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