Hypoxia‐inducible factor 1‐alpha (HIF‐1α) as a factor mediating the relationship between obesity and heart failure with preserved ejection fraction
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HFpEF), a common condition with an increased mortality, is strongly associated with obesity and the metabolic syndrome. The latter two conditions are associated with increased epicardial fat that can extend into the heart. This review advances the proposition that hypoxia-inhibitory factor-1α (HIF-1α) maybe a key factor producing HFpEF. HIF-1α, a highly conserved transcription factor that plays a key role in tissue response to hypoxia, is increased in adipose tissue in obesity. Increased HIF-1α expression leads to expression of a potent profibrotic transcriptional programme involving collagen I, III, IV, TIMP, and lysyl oxidase. The net effect is the formation of collagen fibres leading to fibrosis. HIF-1α is also responsible for recruiting M1 macrophages that mediate obesity-associated inflammation, releasing IL-6, MCP-1, TNF-α, and IL-1β with increased expression of thrombospondin, pro α2 (I) collagen, transforming growth factor β, NADPH oxidase, and connective tissue growth factor. These factors can accelerate cardiac fibrosis and impair cardiac diastolic function. Inhibition of HIF-1α expression in adipose tissue of mice fed a high-fat diet suppressed fibrosis and reduces inflammation in adipose tissue. Delineation of the role played by HIF-1α in obesity-associated HFpEF may lead to new potential therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".