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Record W2102113127 · doi:10.1093/jnci/djt123

Hyperglycemia, Insulin Resistance, Impaired Pancreatic β-Cell Function, and Risk of Pancreatic Cancer

2013· article· en· W2102113127 on OpenAlexaff
Brian M. Wolpin, Ying Bao, Zhi Rong Qian, Chen Wu, Peter Kraft, Shuji Ogino, Meir J. Stampfer, Kaori Sato, Jing Ma, Julie E. Buring, Howard D. Sesso, I‐Min Lee, John Michael Gaziano, Anne McTiernan, Lawrence S. Phillips, Barbara B. Cochrane, Michaël Pollak, JoAnn E. Manson, Edward L. Giovannucci, Charles S. Fuchs

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2013
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsMcGill University
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteU.S. Public Health ServiceNational Institutes of HealthHoward Hughes Medical Institute
KeywordsProinsulinInsulin resistanceInternal medicineMedicinePancreatic cancerDiabetes mellitusEndocrinologyInsulinImpaired glucose tolerancePeripheralCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity and diabetes mellitus are associated with an increased risk of pancreatic cancer. These associations may be secondary to consequences of peripheral insulin resistance, pancreatic β-cell dysfunction, or hyperglycemia itself. Hemoglobin A1c (HbA1c) is a measure of hyperglycemia, whereas plasma insulin and proinsulin are markers of peripheral insulin resistance, and the proinsulin to insulin ratio marks pancreatic β-cell dysfunction. METHODS: This was a prospective, nested case-control study of 449 case patients and 982 control subjects with prediagnostic blood samples and no diabetes history from five prospective US cohorts followed through 2008. Two or three control subjects were matched to each case patient by year of birth, cohort, smoking, and fasting status. Pancreatic cancer risk was assessed by prediagnostic HbA1c, insulin, proinsulin, and proinsulin to insulin ratio with multivariable-adjusted logistic regression. All P values were two-sided. RESULTS: The highest vs lowest quintiles of HbA1c, insulin, and proinsulin were associated with with an increased risk for pancreatic cancer (odds ratio [OR] = 1.79; 95% confidence interval [CI] = 1.17 to 2.72, P trend = .04 for HbA1c; OR = 1.57; 95% CI = 1.08 to 2.30; Ptrend = .002 for insulin; and OR = 2.22; 95% CI = 1.50 to 3.29; P trend < .001 for proinsulin). Proinsulin to insulin ratio was not associated with pancreatic cancer risk. Results were similar across studies (all P heterogeneity > .29). In cancers developing 10 or more years after blood collection, the associations with insulin and proinsulin became stronger (highest vs lowest quintile, OR = 2.77; 95% CI = 1.28 to 5.99 for insulin and OR = 3.60; 95% CI = 1.68 to 7.72 for proinsulin). In mutually adjusted models including HbA1c, insulin, and proinsulin, only proinsulin remained statistically significant ( highest vs lowest quintile, OR = 2.55; 95% CI = 1.54 to 4.21; Ptrend < .001). CONCLUSIONS: Among participants from five large prospective cohorts, circulating markers of peripheral insulin resistance, rather than hyperglycemia or pancreatic β-cell dysfunction, were independently associated with pancreatic cancer risk.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.034
GPT teacher head0.321
Teacher spread0.288 · 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

Citations177
Published2013
Admission routes1
Has abstractyes

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