Abstract 151: Genetic Factors And Outcomes After Stroke: A Preliminary Analysis Of The Strong Study
Bibliographic record
Abstract
Introduction: Measures of genetic variation have provided insight into underlying biology and treatment approaches in a number of medical sciences. The STRONG (“The Stroke, sTress, RehabilitatiON, and Genetics Study”) Study evaluated genetic polymorphisms in relation to stroke recovery. Methods: The STRONG Study recruited patients with a new stroke from 28 U.S. stroke centers. Entry criteria included age ≥18 years and new ischemic stroke or intracerebral hemorrhage. Visit 1 was during acute admission; Visit 2, 90 days post-stroke; Visit 3, 6 mo post-stroke; and Visit 4, 12 mo post-stroke. Linear regression examined specific genotypes in relation to 2 primary outcomes (change in grip force from V1-V2 and global function (SIS-ADL) at V2) and 2 secondary outcomes (depression (PHQ-8) at V2 and cognitive function (t-MoCA) at V4). No correction for multiple comparisons is made below. Results: There were 763 patients enrolled from 2016-2020, with mean age 63.1±14.9 years, 41.1% female, median NIHSS score 4 [2-9]. Data were available in 510 patients at Visit 2 and 482 at Visit 4. The BDNF val66met polymorphism (rs6265) was related to poorer V4 MoCA score (p=0.01). Presence of ApoE4 was not related to any endpoint. For the dopamine polygene score, a relationship with lower score (reduced dopamine neurotransmission) and greater depression (higher V2 PHQ-8 score) was suggested (p=0.1). A polymorphism (rs1842681) associated with chronic mRS score in a prior GWAS study (Neurology. 2019;92:e1271) was related to STRONG Study d90 mRS score (p=0.05). Three polymorphisms (rs76221407, rs150862264, rs182008837) associated with 3-mo mRS score in a separate GWAS study (Circ Res. 2019;124:114) were not related to STRONG Study d90 mRS score (p=.71-.78). ConclusionS: The STRONG Study collected genotypes and detailed longitudinal behavioral data during the year following stroke. Data do not support several hypotheses. However, presence of the BDNF val66met polymorphism may be related to poorer cognitive outcome. Lower dopamine gene score showed a trend with greater depression, consistent with a prior publication. Current findings support a prior GWAS finding for chronic mRS score. Genetics studies provide insights into inter-subject differences in recovery after stroke.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".