Association between Insulin-Like Growth Factor-I
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".