Insulinlike Growth Factor Binding Protein-1 and Ghrelin Predict Health Outcomes Among Older Adults: Cardiovascular Health Study Cohort
Bibliographic record
Abstract
Context: Multiple diseases may explain the association of the growth hormone/insulinlike growth factor-I (GH/IGF-I) axis with longevity. Objective: To relate circulating GH/IGF-I system protein levels with major health events. Design and Setting: This is a cohort study set in 4 US communities. Participants: Adults (N = 2268) 65 years and older free of diabetes and cardiovascular disease. Measurements: We assessed insulinlike growth factor binding protein-1 (IGFBP-1) and ghrelin in fasting and 2-hour oral glucose tolerance test (OGTT) blood samples, as well as fasting IGF-I and IGFBP-3. Hazard ratios for mortality and a composite outcome for first incident myocardial infarction, stroke, heart failure, hip fracture, or death were adjusted for sociodemographic, behavioral, and physiological covariates. Results: During 13,930 person-years of follow-up, 48.1% of individuals sustained one or more components of the composite outcome and 31.8% died. Versus the lowest quartiles, the highest quartiles of fasting and 2-hour ghrelin were associated with 27% higher (95% confidence interval [CI]: 6%, 53%) and 39% higher (95% CI: 14%, 71%) risks of the composite outcome, respectively. The highest quartile of 2-hour IGFBP-1 was associated with 35% higher (95% CI: 1%, 52%) risk of the composite end point. Similarly, higher mortality was significantly associated with higher fasting and 2-hour ghrelin levels and with 2-hour IGFBP-1 level. When examined together, 2-hour post-OGTT levels of IGFBP-1 and ghrelin tended to predict outcomes better than fasting levels. Conclusions: Circulating IGFBP-1 and ghrelin measured during an OGTT predicted major health events and death in older adults, which may explain the influence of the GH/IGF-I axis on lifespan and health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".