Serum Insulin-Like Growth Factor 1 and the Risk of Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose— Low insulin-like growth factor 1 (IGF-1) has been associated with increased risk of atherosclerosis and atrial fibrillation in cross-sectional studies. Yet, prospective data linking IGF-1 levels to the development of ischemic stroke remain inconclusive. We examined prospectively the association between serum IGF-1 levels and incident ischemic stroke. Methods— We measured serum IGF-1 levels in 757 elderly individuals (mean age 79±5, 62% women), free of prevalent stroke, from the Framingham original cohort participants at the 22nd examination cycle (1990–1994) and were followed up for the development of ischemic stroke. Cox models were used to relate IGF-1 levels to the risk for incident ischemic stroke, adjusted for potential confounders. Results— During a mean follow-up of 10.2 years, 99 individuals developed ischemic stroke. After adjustment for age, sex, and potential confounders, higher IGF-1 levels were associated with a lower risk of incident ischemic stroke, with subjects in the lowest quintile of IGF-1 levels having a 2.3-fold higher risk of incident ischemic stroke (95% confidence interval, 1.09–5.06; P =0.03) as compared with those in the top quintile. We observed an effect modification by diabetes mellitus and waist–hip ratio for the association between IGF-1 and ischemic stroke ( P <0.1). In subgroup analyses, the effects were restricted to subjects with diabetics and those in top waist–hip ratio quartile, in whom each standard deviation increase in IGF-1 was associated with a 61% (hazard ratio, 0.39; 95% confidence interval, 0.20–0.78; P =0.007) and 41% (hazard ratio, 0.59; 95% confidence interval, 0.37–0.95; P =0.031) lower risk of incident ischemic stroke, respectively. Conclusions— IGF-1 levels were inversely associated with ischemic stroke, especially among persons with insulin resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".