Bibliographic record
Abstract
Fontana et al. (2008) recently reported in Aging Cell their interesting findings, based on studies of 46 subjects, of the impact of chronic severe caloric restriction on insulin-like growth factor 1 (IGF-1) and insulin-like growth factor binding protein 3 (IGFBP-3) levels. They concluded that protein intake may be a more important determinant of the circulating levels than energy restriction, and speculated that this may have implications with respect to longevity. We previously reported (Holmes et al., 2002; Giovannucci et al., 2003) studies of 1037 women and 753 men that explored dietary determinants of IGF-1 and IGFBP-3 levels in a cross-section of subjects on typical North American diets. We observed, in females, a positive association between protein intake with circulating IGF-1 concentration (174, 188, 201, 192, and 196 ng mL−1 across quintiles of protein intake; p = 0.002), which was stronger than the relationship between energy intake and IGF-1 concentration. Similar findings were seen in men, where the relationship of energy intake to IGF-1 levels was seen only in men with body mass index < 25 kg m−2. Thus, the intervention study involving a small number of subjects by Fontana et al. yielded data consistent with the larger observational studies we previously reported. Caution is required in extrapolating data concerning dietary-induced changes in IGF-1 levels to changes in health outcomes, such as longevity or cancer risk. While there is clear evidence that reduced IGF signalling is associated with increased longevity in model organisms, there is evidence that lower IGF-1 levels are associated with increased cardiovascular disease in humans (Brugts et al., 2008). In addition, recent data (e.g. Ma et al., 2008) suggest that with respect to cancer endpoints, it is important to consider insulin levels as well as IGF-1 levels. The relationships between diet, insulin and IGF-1 levels, and clinical health outcomes is an important topic, but one that appears to be more complex than might have been implied by animal models.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".