Genetic basis of intracranial aneurysm formation and rupture: clinical implications in the postgenomic era
Bibliographic record
Abstract
OBJECTIVE: Despite the prevalence and impact of intracranial aneurysms (IAs), the molecular basis of their pathogenesis remains largely unknown. Moreover, there is a dearth of clinically validated biomarkers to efficiently screen patients with IAs and prognosticate risk for rupture. The aim of this study was to survey the literature to systematically identify the spectrum of genetic aberrations that have been identified in IA formation and risk of rupture. METHODS: A literature search was performed using the Medical Subject Headings (MeSH) system of databases including PubMed, EMBASE, and Google Scholar. Relevant studies that reported on genetic analyses of IAs, rupture risk, and long-term outcomes were included in the qualitative analysis. RESULTS: A total of 114 studies were reviewed and 65 were included in the qualitative synthesis. There are several well-established mendelian syndromes that confer risk to IAs, with variable frequency. Linkage analyses, genome-wide association studies, candidate gene studies, and exome sequencing identify several recurrent polymorphic variants at candidate loci, and genes associated with the risk of aneurysm formation and rupture, including ANRIL (CDKN2B-AS1, 9p21), ARGHEF17 (11q13), ELN (7q11), SERPINA3 (14q32), and SOX17 (8q11). In addition, polymorphisms in eNOS/NOS3 (7q36) may serve as predictive markers for outcomes following intracranial aneurysm rupture. Genetic aberrations identified to date converge on posited molecular mechanisms involved in vascular remodeling, with strong implications for an associated immune-mediated inflammatory response. CONCLUSIONS: Comprehensive studies of IA formation and rupture have identified candidate risk variants and loci; however, further genome-wide analyses are needed to identify high-confidence genetic aberrations. The literature supports a role for several risk loci in aneurysm formation and rupture with putative candidate genes. A thorough understanding of the genetic basis governing risk of IA development and the resultant aneurysmal subarachnoid hemorrhage may aid in screening, clinical management, and risk stratification of these patients, and it may also enable identification of putative mechanisms for future drug development.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".