Abstract TMP107: Microbiome Signature of Cerebral Cavernous Malformation Patients
Bibliographic record
Abstract
Background: Cerebral cavernous malformation (CCM) patients have lesions comprised of dilated capillaries in the brain. Despite the knowledge that mutations of three CCM genes can cause this disease, other genetic and environmental factors that contribute to lesion formation are likely given the highly variable disease expression. Genome-wide association studies in CCM cohorts and mouse models of CCM, both point to a role of lipopolysaccharide. Mouse studies have shown that the microbiome, particularly Gram-negative bacteria, drive CCM lesion development. These data point to a possibility that CCM disease is affected by the gut microbiome. In this study, we investigated if human CCM disease could be linked to the gut microbiome. Methods: Fecal samples from 88 CCM patients, from four sites, were assayed using 16S rRNA gene sequencing. Following taxonomic classification by exact sequence variant analysis (ESV) using DeBlur, microbiome composition was compared with those of a reference non-CCM population (n=348), or between subgroups of CCM patients based on clinical data elements. Results: Analyses of microbiome composition statistics identified bacterial ESVs belonging to Clostridiales , Lachnospiraceae, Ruminococcaceae , and the genus Bacteroides that were significantly enriched in CCM patients compared to healthy controls. Within our CCM cohort, patients with germline CCM mutations had stool-associated enrichment of ESVs annotated to Clostridiales, and the genera Bacteroides and Prevotella , when compared to sporadic CCM patients, while patients with CCM1 and CCM2 mutations had different proportions of Clostridiales, Lachnospiraceae, and the genus Ruminococcus . Furthermore, Lachnospiraceae and Bacteroides proportions differentiated disease aggressiveness (All p<0.05, false discovery rate corrected). Conclusions: These data are the first to show that CCM patients have a distinct microbiome signature. Germline mutation and disease aggressiveness can correlate with further unique microbiome composition. This study supports further investigation into the mechanistic link between CCM disease and the microbiome. This will enhance our understanding of the brain-gut axis in CCM disease and the use of microbiome as a therapeutic target.
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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 | 0.001 |
| 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) | 0.002 | 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".