Abstract WMP99: Circulating Plasma Biomarkers Associated with Familial Cerebral Cavernous Malformation, Hereditary Hemorrhagic Telangiectasia and Sturge-Weber Syndrome
Bibliographic record
Abstract
Introduction: Circulating plasma protein profiling in individuals with brain vascular disorders may aid in the identification of robust diagnostic biomarkers, stratification of high-risk patients for treatment, and monitoring of disease progression or treatment response. This Brain Vascular Malformation Consortium (BVMC) study aimed to identify circulating inflammatory and angiogenic proteins that associate with familial Cerebral Cavernous Malformation (FCCM), Hereditary Hemorrhagic Telangiectasia (HHT), or Sturge-Weber Syndrome (SWS). Methods: We used the Angiome multiplex ELISA biomarker panel to assess the circulating plasma levels of 22 proteins related to inflammation and angiogenesis in 234 individuals enrolled in the BVMC, including 114 FCCM, 101 HHT and 19 SWS cases. Protein levels were measured in duplicate and absolute measurements obtained. We tested for biomarker associations between disease states using linear regression models adjusting for age at blood collection and sex. We calculated the intraclass correlation coefficient (ICC) to describe the similarity in replicates, and report proportional increase (PI) for protein levels for statistically significant results with Bonferroni-corrected P-values <0.05 (adjusted for 22 markers). Results: All 22 proteins were successfully measured in the 114 FCCM (39% male, median age 52 years), 101 HHT (49% male, median age 42 years, 36% with brain arteriovenous malformation), and 19 SWS (53% male, median age 15 years). The ICC was high for all markers (range 0.904-0.990). As expected, HHT cases with endoglin mutations had ~50% lower endoglin. We observed increased levels of endoglin (PI=1.38, 95% CI:1.27-1.50, P=6.50E-13) and IL10 (PI=1.48, 95% CI:1.16-1.91, P=0.04) in CCM compared to HHT cases, and decreased IL8 (PI=0.60, 95% CI:0.43-0.83, P=0.04) and OPN (PI=0.63, 95% CI:0.47-0.84, P=0.04) in CCM compared to SWS cases. We also observed higher levels of endoglin (PI=1.45, 95% CI:1.24-1.71, P=1.47E-04), GP130 (PI=1.24, 95% CI:1.11-1.38, P=2.91E-03), and OPN (PI=1.80, 95% CI:1.36-2.40, P=1.13E-03), and lower levels of IL1β (PI=0.30, 95% CI:0.15-0.59, P=0.01) in SWS compared to HHT cases. Conclusions: We identified circulating plasma biomarkers associated with FCCM, HHT and SWS. Larger ongoing work will confirm whether these inflammatory/angiogenic markers are relevant as potential diagnostic or therapeutic targets or have broader implications as clinical biomarkers in other brain vascular diseases.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".