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Record W2111014150 · doi:10.1158/1055-9965.epi-130-3

Association between Insulin-Like Growth Factor-I

2004· article· en· W2111014150 on OpenAlexaff
Manjinder S. Sandhu, Martin Gibson, Adrian Heald, David B. Dunger, Nicholas J. Wareham

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2004
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsLMC Diabetes & Endocrinology (Canada)
Fundersnot available
KeywordsEndocrinologyInternal medicineBody mass indexWaist–hip ratioInsulinAnthropometryWaistObesityMedicineInsulin-like growth factorRisk factorPopulationGrowth factor

Abstract

fetched live from OpenAlex

Several prospective observational studies have suggested that elevated circulating IGF-I levels are associated with an increased risk of cancer. These observations may provide a potential mechanism through which previously identified metabolic and anthropometric factors, such as obesity and elevated insulin and glucose levels, may operate. We therefore examined metabolic and anthropometric influences on circulating levels of insulin-like growth factor-I (IGF-I), insulin-like growth factor-binding protein-1 (IGFBP-1), and the IGF-I:IGFBP-1 ratio in a middle-aged population of 349 men and 492 women. IGF-I showed only modest inverse associations with indices of adiposity. However, we found that low IGFBP-I levels and an increased IGF-I:IGFBP-1 ratio were strongly associated with increased levels of insulin and glucose in men and women. Body mass index was also positively related to the IGF-I:IGFBP-1 ratio in men (P < 0.001) and women (P < 0.001), independent of metabolic correlates of IGFBP-1 and IGF-I. Similarly, waist:hip ratio and waist circumference were also associated with an increased IGF-I:IGFBP-1 ratio and low circulating IGFBP-1 levels. These findings suggest that individuals with greater fat mass and upper body obesity may have elevated levels of bioavailable or free IGF-I, which could, in part, mediate the reported associations among metabolic and anthropometric factors and 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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.056
GPT teacher head0.346
Teacher spread0.290 · 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 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

Citations55
Published2004
Admission routes1
Has abstractyes

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