Distinct pathophysiological pathways in women and men with heart failure
Bibliographic record
Abstract
AIMS: Clinical differences between women and men have been described in heart failure (HF). However, less is known about the underlying pathophysiological mechanisms. In this study, we compared multiple circulating biomarkers to gain better insights into differential HF pathophysiology between women and men. METHODS AND RESULTS: In 537 women and 1485 men with HF, we compared differential expression of a panel of 363 biomarkers. Then, we performed a pathway over-representation analysis to identify differential biological pathways in women and men. Findings were validated in an independent HF cohort (575 women, 1123 men). In both cohorts, women were older and had higher left ventricular ejection fraction (LVEF). In the index and validation cohorts respectively, we found 14/363 and 12/363 biomarkers that were relatively up-regulated in women, while 21/363 and 14/363 were up-regulated in men. In both cohorts, the strongest up-regulated biomarkers in women were leptin and fatty acid binding protein-4, compared to matrix metalloproteinase-3 in men. Similar findings were replicated in a subset of patients from both cohorts matched by age and LVEF. Pathway over-representation analysis revealed increased activity of pathways associated with lipid metabolism in women, and neuro-inflammatory response in men (all p < 0.0001). CONCLUSION: In two independent cohorts of HF patients, biomarkers associated with lipid metabolic pathways were observed in women, while biomarkers associated with neuro-inflammatory response were more active in men. Differences in inflammatory and metabolic pathways may contribute to sex differences in clinical phenotype observed in HF, and provide useful insights towards development of tailored HF therapies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".