Biomarker Signature and Pathophysiological Pathways in Patients with Chronic Heart Failure and Metabolic Syndrome
Bibliographic record
Abstract
Abstract Aim The comorbidities that collectively define metabolic syndrome are common in patients with heart failure. However, the role of metabolic syndrome in the pathophysiology of heart failure is not well understood. We therefore investigated the clinical and biomarker correlates of metabolic syndrome in patients with heart failure. Methods and results In 1103 patients with heart failure, we compared the biomarker expression using a panel of 363 biomarkers among patients with (n = 468 [42%]) and without (n = 635 [58%]) metabolic syndrome. Subsequently, a pathway overrepresentation analysis was performed to identify key biological pathways. Findings were validated in an independent cohort of 1433 patients with heart failure of whom 615 (43%) had metabolic syndrome. Metabolic syndrome was defined as the presence of three or more of five criteria, including central obesity, elevated serum triglycerides, reduced high-density lipoprotein cholesterol, insulin resistance and hypertension. The most significantly elevated biomarkers in patients with metabolic syndrome were leptin (log2 fold change 0.92, p = 5.85 × 10−21), fatty acid-binding protein 4 (log2 fold change 0.61, p = 1.21 × 10−11), interleukin-1 receptor antagonist (log2 fold change 0.47, p = 1.95 × 10−13), tumour necrosis factor receptor superfamily member 11a (log2 fold change 0.35, p = 4.16 × 10−9), and proto-oncogene tyrosine-protein kinase receptor Ret (log2 fold change 0.31, p = 4.87 × 10−9). Network analysis identified 10 pathways in the index cohort and 6 in the validation cohort, all related to inflammation. The primary overlapping pathway in both the index and validation cohorts was up-regulation of the natural killer cell-mediated cytotoxicity pathway. Conclusion Metabolic syndrome is highly prevalent in heart failure and is associated with biomarkers and pathways relating to obesity, lipid metabolism and immune responses underlying chronic inflammation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".