Heart failure promotes multimorbidity through innate immune memory
Bibliographic record
Abstract
Patients with heart failure (HF) often experience repeated acute decompensation and develop comorbidities such as chronic kidney disease and frailty syndrome. Although this suggests pathological interaction among comorbidities, the mechanisms linking them are poorly understood. Here, we identified alterations in hematopoietic stem cells (HSCs) as a critical driver of recurrent HF and associated comorbidities. Bone marrow transplantation from HF-experienced mice resulted in spontaneous cardiac dysfunction and fibrosis in recipient mice, as well as increased vulnerability to kidney and skeletal muscle insults. HF enhanced the capacity of HSCs to generate proinflammatory macrophages. In HF mice, global chromatin accessibility analysis and single-cell RNA-seq showed that transforming growth factor-β (TGF-β) signaling was suppressed in HSCs, which corresponded with repressed sympathetic nervous activity in bone marrow. Transplantation of bone marrow from mice in which TGF-β signaling was inhibited similarly exacerbated cardiac dysfunction. Collectively, these results suggest that cardiac stress modulates the epigenome of HSCs, which in turn alters their capacity to generate cardiac macrophage subpopulations. This change in HSCs may be a common driver of repeated HF events and comorbidity by serving as a key carrier of "stress memory."
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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 | 0.000 |
| 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 | 0.001 |
| 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".