Circulating Levels of Tumor Necrosis Factor-Alpha Receptor 2 Are Increased in Heart Failure with Preserved Ejection Fraction Relative to Heart Failure with Reduced Ejection Fraction: Evidence for a Divergence in Pathophysiology
Bibliographic record
Abstract
BACKGROUND: Various pathways have been implicated in the pathogenesis of heart failure (HF) with preserved ejection fraction (HFPEF). Inflammation in response to comorbid conditions, such as hypertension and diabetes, may play a proportionally larger role in HFPEF as compared to HF with reduced ejection fraction (HFREF). METHODS AND RESULTS: This study investigated inflammation mediated by the tumor necrosis factor-alpha (TNFα) axis in community-based cohorts of HFPEF patients (n = 100), HFREF patients (n = 100) and healthy controls (n = 50). Enzyme-linked immunosorbent assays were used to investigate levels of TNFα, its two receptors (TNFR1 and TNFR2), and a non-TNFα cytokine, interleukin-6 (IL-6), in plasma derived from peripheral blood samples. Plasma levels of TNFα and TNFR1 were significantly elevated in HFPEF relative to controls, while levels of TNFR2 were significantly higher in HFPEF than both controls and HFREF. TNFα, TNFR1 and TNFR2 were each significantly associated with at least two of the following: age, estimated glomerular filtration rate, hypertension, diabetes, smoking, peripheral vascular disease or history of atrial fibrillation. TNFR2 levels were also significantly associated with increasing grade of diastolic dysfunction and severity of symptoms in HFPEF. CONCLUSIONS: Inflammation mediated through TNFα and its receptors, TNFR1 and TNFR2, may represent an important component of a comorbidity-induced inflammatory response that partially drives the pathophysiology of HFPEF.
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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 | 0.001 |
| 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 | 0.000 |
| 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".