Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification
Bibliographic record
Abstract
Abstract Acute chest syndrome (ACS) is a severe complication of sickle cell disease (SCD), but we lack tool to identify patients at high risk of ACS. Epidemiological studies have found an association between asthma and ACS but whether this link is causal is unclear. We used polygenic score (PGS) to analyze whether the genetic susceptibility to asthma was associated with ACS and could be used to stratify the risk of ACS. We identified that a PGS for asthma (PGS asthma ) was associated with ACS rate, but not ACS occurrence, in both the CSSCD and the GEN-MOD cohorts, independently of fetal hemoglobin (HbF) (β=0.17, standard error=0.06, p=0.006). This effect was mainly found in patients with HbF <5%. Combining PGS asthma and HbF allowed to identify a population at high risk of ACS recurrence: individuals within the highest PGS asthma quintile and the lowest HbF quintile. Partitioned PGS suggested that lymphocytes were the main driver of the genetically mediated risk of ACS by asthma. Finally, we assessed asthma and ACS overall genetic correlation. We found that these two conditions only partially overlap distinct, suggesting that asthma is not the main determinant of genetic propensity to ACS. In sum, our result suggests that patients with high genetic propensity to asthma are prone to recurrent ACS if not protected by high HbF levels. Combining PGS asthma and HbF may allow identifying high risk patients for personalize ACS management. Apart from this population at high risk of ACS, additional genetic determinants independent from asthma contribute to ACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".