Meta-Analysis of Serum Insulin-Like Growth Factor 1 in Alzheimer’s Disease
Bibliographic record
Abstract
Insulin-like growth factor 1 (IGF-1) serum levels have been reported to be altered in Alzheimer's disease patients, and it was suggested that the changes in IGF-1 serum level may play a role in disease pathology and progression. However, this notion remained controversial due to conflicting findings. We conducted a meta-analysis to determine the relationship between IGF-1 serum levels and Alzheimer's disease. We searched the databases PUBMED, Ovid SP, and Cochrane library for relevant studies. The primary data analyzed was serum IGF-1 from Alzheimer's disease subjects and controls. Pooled weighted mean difference using a random effects model was used to determine the relationship between serum levels and disease state. Nine studies were included in the meta-analysis compromising a total of 1639 subjects. The pooled weighted mean difference was -2.27ng/ml (95% CI: [-22.221, 17.66]) with a P value of 0.82. Thus our finding did not show clear relationship between low IGF-1 and Alzheimer's disease subjects. We did not find evidence of publication bias by analyzing a funnel plot as well as Egger's and Begg's tests. While eight out of the nine studies included in this meta-analysis detected a statistically significant increase or decrease in serum levels of IGF-1 in Alzheimer's disease subjects, the analysis as a whole did not show a significant trend in either direction. Thus, IGF-1 level is likely a critical personalized factor. A large database of clinical trials is required for better understanding the relationship between IGF-1 levels and Alzheimer's disease.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".