Prevalence of Mutations in Mendelian Stroke Genes in Early Onset Stroke Patients
Bibliographic record
Abstract
OBJECTIVE: Heritability of stroke is assumed not to be low, especially in the young stroke population. However, most genetic studies have been performed in highly selected patients with typical clinical or neuroimaging characteristics. We investigated the prevalence of 15 Mendelian stroke genes and explored the relationships between variants and the clinical and neuroimaging characteristics in a large, unselected, young stroke population. METHODS: We enrolled patients aged ≤55 years with stroke or transient ischemic attack from a prospective, nationwide, multicenter stroke registry. We identified clinically relevant genetic variants (CRGVs) in 15 Mendelian stroke genes (GLA, NOTCH3, HTRA1, RNF213, ACVRL1, ENG, CBS, TREX1, ABCC6, COL4A1, FBN1, NF1, COL3A1, MT-TL1, and APP) using a customized, targeted next generation sequencing panel. RESULTS: Among 1,033 patients, 131 (12.7%) had 28 CRGVs, most frequently in RNF213 (n = 59), followed by ABCC6 (n = 53) and NOTCH3 (n = 15). The frequency of CRGVs differed by ischemic stroke subtypes (p < 0.01): the highest in other determined etiology (20.1%), followed by large artery atherosclerosis (13.6%). It also differed between patients aged ≤35 years and those aged 51 to 55 years (17.1% vs 9.3%, p = 0.02). Only 27.1% and 26.7% of patients with RNF213 and NOTCH3 variants had typical neuroimaging features of the corresponding disorders, respectively. Variants of uncertain significance (VUSs) were found in 15.4% patients. INTERPRETATION: CRGVs in 15 Mendelian stroke genes may not be uncommon in the young stroke population. The majority of patients with CRGVs did not have typical features of the corresponding monogenic disorders. Clinical implications of having CRGVs or VUSs should be explored. ANN NEUROL 2023;93:768-782.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".