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Record W2809472804 · doi:10.1093/jnci/djy098

Dietary Insulin Load and Cancer Recurrence and Survival in Patients With Stage III Colon Cancer: Findings From CALGB 89803 (Alliance)

2018· article· en· W2809472804 on OpenAlexaff
Vicente Morales‐Oyarvide, Chen Yuan, Ana Babić, Sui Zhang, Donna Niedzwiecki, Jennie Brand‐Miller, Laura Sampson-Kent, Xing Ye, Yanping Li, Leonard B. Saltz, Robert J. Mayer, Rex B. Mowat, Renaud Whittom, Alexander Hantel, A.B. Benson, Daniel Atienza, Michael J. Messino, Hedy L. Kindler, Alan P. Venook, Shuji Ogino, Kana Wu, Walter C. Willett, Edward L. Giovannucci, Brian M. Wolpin, Jeffrey A. Meyerhardt, Charles S. Fuchs, Kimmie Ng

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsHôpital du Sacré-Cœur de Montréal
FundersNational Cancer InstituteStand Up To CancerNational Institutes of HealthAlliance for Clinical Trials in Oncology Foundation
KeywordsMedicineInternal medicineColorectal cancerHazard ratioInsulinBody mass indexHyperinsulinemiaCancerProportional hazards modelOncologyEndocrinologyConfidence intervalInsulin resistance

Abstract

fetched live from OpenAlex

Background: Evidence suggests that diets inducing postprandial hyperinsulinemia may be associated with increased cancerrelated mortality.The goal of this study was to assess the influence of postdiagnosis dietary insulin load and dietary insulin index on outcomes of stage III colon cancer patients.Methods: We conducted a prospective observational study of 1023 patients with resected stage III colon cancer enrolled in an adjuvant chemotherapy trial who reported dietary intake halfway through and six months after chemotherapy.We evaluated the association of dietary insulin load and dietary insulin index with cancer recurrence and survival using Cox proportional hazards regression adjusted for potential confounders; statistical tests were two-sided.Results: High dietary insulin load had a statistically significant association with worse disease-free survival (DFS), comparing the highest vs lowest quintile (adjusted hazard ratio [HR] ¼ 2.77, 95% confidence interval [CI] ¼ 1.90 to 4.02, P trend < .001).High dietary insulin index was also associated with worse DFS (highest vs lowest quintile, HR ¼ 1.75, 95% CI ¼ 1.22 to 2.51, P trend ¼ .01).The association between higher dietary insulin load and worse DFS differed by body mass index and was strongest among patients with obesity (HR ¼ 3.66, 95% CI ¼ 1.88 to 7.12, P interaction ¼ .04).The influence of dietary insulin load on cancer outcomes did not differ by mutation status of KRAS, BRAF, PIK3CA, TP53, or microsatellite instability.Conclusions: Patients with resected stage III colon cancer who consumed a high-insulinogenic diet were at increased risk of recurrence and mortality.These findings support the importance of dietary management following resection of colon cancer, and future research into underlying mechanisms of action is warranted.Postdiagnosis lifestyle behaviors, including diet and physical activity, may be important risk factors for cancer recurrence and death in colon cancer patients (1-3).The exact mechanisms through which these factors influence outcomes are unknown, but epidemiological evidence suggests that insulin may play a role (4,5).Moreover, insulin signaling involves downstream pathways that are relevant for colon cancer pathogenesis and response to treatment (eg, KRAS/NRAS, BRAF, PIK3CA) (6,7).Insulin secretion is strongly influenced by diet, with carbohydrate-rich diets inducing postprandial hyperinsulinemia (8,9).Dietary glycemic load-an indicator of the body's plasma glucose response to different foods-has been

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.313
Teacher spread0.277 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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