NCOG-27. HIGHER LEVELS OF SERUM ICAM-1 AND IL-10 ARE ASSOCIATED WITH COGNITIVE DYSFUNCTION IN BOTH IDH-WILDTYPE AND IDH-MUTANT GLIOMA
Bibliographic record
Abstract
Abstract BACKGROUND Cognitive dysfunction is common among glioma patients, but the contribution of specific mechanisms to the development of cognitive symptoms is unclear. Circulating levels of proteins linked to neurodegeneration, inflammation, and vascular damage have been associated with cognitive symptoms in neurological diseases and aging, but their prognostic value in glioma is unknown. Here, we examined associations between cognitive symptoms and serum levels of protein biomarkers in glioma patients. METHODS Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA, scores ≤ 25 = cognitive dysfunction). Cytokines (interleukin-1β [IL-1β], IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, interferon-γ [IFN-γ], tumor necrosis factor-α [TNF-α]), and neurodegeneration (neurofilament light chain [NfL], tau, glial fibrillary acidic protein [GFAP]), and vascular damage markers (Serum amyloid A [SAA], C-reactive protein [CRP], vascular cell adhesion molecule 1 [VCAM-1], and intercellular adhesion molecule 1 [ICAM-1]) were measured using ultrasensitive assays (Meso Scale Discovery). RESULTS Patients (n=73) were predominantly male (58%), white (74%), with a median age of 44 (range=24,74). Cognitive dysfunction was found in 53% of the patients. Levels of NfL (p=0.035), IL-6 (p=0.006), IL-10 (p=0.007), TNF-α (p=0.035), IFN-γ (p=0.011), CRP (p=0.016), VCAM-1 (p=0.012), and ICAM-1 (p<0.001) were higher in patients with cognitive dysfunction when compared to those with normal cognition. After adjusting for isocitrate dehydrogenase (IDH) tumor mutation status, age, tumor grade, and number of surgeries, higher levels of ICAM-1 and IL-10, but not other markers, remained associated with cognitive dysfunction. MoCA scores were negatively correlated with GFAP (r=-0.24, p=0.038), IFN-γ (r=-0.31, p=0.008), IL-6 (r=-0.29, p=0.014), IL-10 (r=-0.24, p=0.039), and ICAM-1 (r=-0.39, p<0.001). CONCLUSIONS ICAM-1 and IL-10 levels were higher in patients with MOCA determined cognitive dysfunction, suggesting an association between cognitive symptoms and inflammation and vascular impairment in glioma patients. Future analyses in larger longitudinal cohorts are warranted to assess the predictive value of protein biomarkers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".