Unfavorable transcriptome profiles and social disadvantage in hematopoietic cell transplantation: a CIBMTR analysis
Bibliographic record
Abstract
Patient-reported outcomes (PROs) capture subjective social determinants of health (SDOHs), which can affect health outcomes through the stress response pathway. The conserved transcriptional response to adversity (CTRA) is a stress-mediated proinflammatory transcriptomic pattern that has been linked to adverse hematopoietic cell transplant (HCT) outcomes. This study examined the association of pretransplant CTRA with patient-reported SDOHs in allogeneic HCT recipients. In this cross-sectional study, pre-HCT SDOH-related PROs included the 36-Item Short Form Health Survey and the Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT). CTRA was assessed by RNA sequencing of whole blood specimens, with mixed effects linear regression models relating CTRA expression to PRO scores while controlling for age, sex, race, disease, and performance status. Among 121 patients, the median age was 54 years, 42% were female, and 91% were White. CTRA was elevated in participants reporting lower scores on the FACT-BMT (P = .003), including the general (P = .003) and BMT-specific (P = .014) components. Effects were driven by the social well-being domain (P = .0001). This corresponded to an 8% to 15% difference in CTRA RNA expression across a 4 standard deviation range in patient-reported SDOHs. Ancillary bioinformatics analyses confirmed the association of well-being with reduced proinflammatory transcription pathway activity [cyclic AMP response element-binding protein, (CREB), NF-κB, and activating protein-1 (AP-1)]. In conclusion, HCT-treated patients who experience unfavorable social conditions show elevated CTRA expression in pretransplant blood samples. These data highlight the biologic sequelae of social well-being and community context and suggest a potential molecular mechanism for the impact of social gradients in HCT outcomes. Targeting this pathway could optimize outcomes in this high-risk population.
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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 | 0.001 |
| 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".