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Record W4393092514 · doi:10.1158/1538-7445.am2024-2232

Abstract 2232: Association between sleep characteristics and cancer survival: Findings from the UK Biobank study

2024· article· en· W4393092514 on OpenAlexaff
Jiajing Che, Jiali Lv, Keyu Pan, Tiantian Sun, Chao Cao, Shengxu Li, Tao Zhang, Fuzhong Xue, Lin Yang

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsBiobankMedicineCancerAssociation (psychology)GerontologySleep (system call)Internal medicineOncologyBioinformaticsPsychologyBiology

Abstract

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Abstract Background Sleep characteristics have been associated with patient outcomes after cancer. We aimed to appraise the causal relationship of chronotype, sleep duration, insomnia, and snoring with cancer survival. Methods This large prospective cohort study enrolled 38350 cancer survivors aged ≥40 years from the UK Biobank between 2004 and 2022. Habitual sleep characteristics were obtained from the self-reported baseline questionnaire, including chronotype, sleep duration, insomnia, and snoring. Cox proportional hazards regression models were applied to evaluate the association between sleep characteristics and cancer survival, then one sample Mendelian randomization (MR) analyses were further used to validate these causal relationships. In particular, restricted cubic splines (RCS) and non-linear MR analyses were applied to assess the possible nonlinear association between sleep duration and cancer survival. Results Among 38350 study participants (mean [SD] age, 59.8 [7.3] years; 24150 [63.0%] males; 35534 [92.7%] White individuals), 12671 (33.1%) reported evening preference chronotype; 9050 (23.6%) reported short and 3773 (9.8%) reported long sleep duration;12274 (32.0%) reported snoring; and 30426 (79.4%) reported experiencing insomnia sometimes or usually. During up to 18 years of follow-up, 6274 deaths occurred. Multivariable regression models showed that evening preference (HR = 1.09, 95% CI: 1.03 to 1.15), short (HR = 1.14, 95% CI: 1.07 to 1.21) and long sleep duration (HR = 1.37, 95% CI: 1.28 to 1.48), insomnia (HR = 1.14, 95% CI: 1.09 to 1.21) and snoring (HR = 0.83, 95% CI: 0.78 to 0.87) were associated with death among pan to cancer survivors. Restricted cubic splines suggested nonlinear associations between sleep duration and death after cancer (nonlinear P < 0.001). In the one-sample MR, genetically predicted sleep duration (HR = 0.71, 95% CI: 0.56 to 0.90) and snoring (HR = 1.18, 95% CI: 1.05 to 1.34) demonstrated a reversed cancer survival pattern compared to multivariable regressions. A nonlinear U-shape association between sleep duration and cancer survival was observed in restricted cubic spline analyses (P < 0.001) but not evident when evaluating by nonlinear MR (P > 0.05). Conclusions Evening preference chronotype, insomnia, snoring, and short sleep duration are likely to be causally associated with a higher risk of death among cancer survivors, whilst long sleep duration does not appear to be a causal factor. Citation Format: Jiajing Che, Jiali Lv, Keyu Pan, Tiantian Sun, Chao Cao, Shengxu Li, Tao Zhang, Fuzhong Xue, Lin Yang. Association between sleep characteristics and cancer survival: Findings from the UK Biobank study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2232.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.124
GPT teacher head0.445
Teacher spread0.322 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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