Rare genetic variant risks in patients with sepsis-associated acute respiratory distress syndrome
Bibliographic record
Abstract
Abstract Background Acute respiratory distress syndrome (ARDS) is a complex, heterogeneous, and deadly condition often resulting from pulmonary lesions due to sepsis, among other causes. There is a lack of targeted therapies to specifically treat the patients. Common genetic factors in the population (frequency >1%) have been associated with ARDS susceptibility, but systematic genetic screens of the role of rare genetic variants are lacking. We used the network of known molecular interactions to identify ARDS risks from clusters of biologically related genes containing qualifying variants (QVs) with frequency <1% likely affecting function. Methods We conducted whole-exome sequencing in sepsis patients from the GEN-SEP cohort (n=822, of which 272 developed ARDS). A network-based heterogeneity clustering algorithm was used to identify significant gene clusters ( p <1×10 -5 ). Gene-set enrichment analysis and logistic regression models aggregating QVs were used for characterization of gene clusters and findings validation. Results We identified 19 significant clusters ( p lowest =3.29×10 -10 ), each containing an average of 102 genes (11.6% mean similarity). QVs in eight gene clusters were associated with sepsis-associated ARDS ( p lowest =2.35×10 -4 ) but were not associated with 28-day survival. Clusters were enriched in several biological pathways, notably the Interferon signaling and Toll-like receptor cascades . Conclusions These results support a marked genetic heterogeneity underlying ARDS susceptibility and the presence of risk variants involving multiple biological processes that are associated with sepsis outcomes. This evidence paves the way for future development of preventive and therapeutic approaches targeting those pathways to reduce the risk for sepsis-associated ARDS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".