Prediagnosis biomarkers of insulin-like growth factor-1, insulin, and interleukin-6 dysregulation and multiple myeloma risk in the Multiple Myeloma Cohort Consortium
Bibliographic record
Abstract
Insulin-like growth factor-1 (IGF-1), insulin, and IL-6 are dysregulated in multiple myeloma pathogenesis and may also contribute to multiple myeloma etiology. To examine their etiologic role, we prospectively analyzed concentrations of serologic markers in 493 multiple myeloma cases and 978 controls from 8 cohorts in the Multiple Myeloma Cohort Consortium. We computed odds ratios (ORs) and 95% confidence intervals (CIs) for multiple myeloma per 1-SD increase in biomarker concentration using conditional logistic regression. We examined heterogeneity by time since blood collection (≤ 3, 4- ≤ 6, and > 6 years) in stratified models. Fasting IGF binding protein-1 concentration was associated with multiple myeloma risk within 3 years (OR, 95% CI per 1-SD increase: 2.3, 1.4-3.8, P = .001) and soluble IL-6 receptor level was associated within 6 years after blood draw (OR (≤ 3 years), 95% CI, 1.4, 1.1-1.9, P = .01; OR(4- ≤ 6 years), 95% CI, 1.4, 1.1-1.7, P = .002). No biomarker was associated with longer-term multiple myeloma risk (ie, > 6 years). Interactions with time were statistically significant (IGF binding protein-1, P-heterogeneity = .0016; sIL6R, P-heterogeneity = .016). The time-restricted associations probably reflect the bioactivity of tumor and microenvironment cells in transformation from monoclonal gammopathy of undetermined significance or smoldering multiple myeloma to clinically manifest multiple myeloma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".