A Prospective Study of Circulating Chemokines and Angiogenesis Markers and Risk of Multiple Myeloma and Its Precursor
Bibliographic record
Abstract
Abstract Background Experimental and clinical studies have implicated certain chemokines and angiogenic cytokines in multiple myeloma (MM) pathogenesis. To investigate whether systemic concentrations of these markers are associated with future MM risk and progression from its precursor, monoclonal gammopathy of undetermined significance (MGUS), we conducted a prospective study within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Methods We measured concentrations of 45 immunologic and pro-angiogenic markers in sera from 241 MM case patients, 441 participants with nonprogressing MGUS, and 258 MGUS-free control participants using Luminex-based multiplex assays and enzyme-linked immunosorbent assays. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression. We also evaluated absolute risk of progression using weighted Kaplan-Meier estimates. All statistical tests were two-sided. Results Prediagnostic levels of six markers were statistically significantly elevated among MM case patients compared with MGUS-free control participants using a false discovery rate of 10% (EGF, HGF, Ang-2, CXCL12, CCL8, and BMP-9). Of these, three angiogenesis markers were associated with future progression from MGUS to MM: EGF (fourth vs first quartile: OR = 3.01, 95% CI = 1.61 to 5.63, Ptrend = .00028), HGF (OR = 2.59, 95% CI = 1.33 to 5.03, Ptrend = .015), and Ang-2 (OR = 2.14, 95% CI = 1.15 to 3.98, Ptrend = .07). A composite angiogenesis biomarker score substantially stratified risk of MGUS progression to MM beyond established risk factors for progression, particularly during the first 5 years of follow-up (areas under the curve of 0.71 and 0.64 with and without the angiogenesis marker score, respectively). Conclusions Our prospective findings provide new insights into mechanisms involved in MM development and suggest that systemic angiogenesis markers could potentially improve risk stratification models for MGUS patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".