Scientific Rationale
Metabolic dysfunction drives fibroinflammatory damage across multiple organs-HFpEF (heart), MASH (liver), and CKD (kidney). Adipose dysfunction releases pro-inflammatory adipokines (leptin, resistin) while reducing adiponectin. Endotrophin (ETP), a collagen VI-derived peptide, links adipose expansion to multi-organ fibrosis and represents a key therapeutic target. Identifying ETP-elevated patients using clinical variables could enable precision medicine approaches.
Research Questions:
What adipokine/proteomic signatures (especially ETP) characterize metabolic phenotypes across HFpEF, MASH, and CKD?
How do these correlate with organ-specific fibroinflammation (cardiac/liver/kidney imaging)?
Can simple clinical criteria identify high-ETP patients with multi-organ risk?
Objectives
Obj1: Analyze ETP, adipokines, inflammatory proteins in UK Biobank (n!50,000) stratified by HFpEF (NT-proBNP, diastolic dysfunction), MASH (FIB-4, steatosis), CKD (eGFR<60, albuminuria)
Obj2: Correlate proteomics with imaging: cardiac T1/ECV (fibrosis), liver MRI-PDFF/elastography, kidney morphology
Obj3: Iteratively test clinical criteria:
BMI!30 alone vs BMI+comorbidities
Identify high-ETP subtypes with multi-organ involvement
Optimize criteria for therapy selection
Obj4: Estimate prevalence of ETP-elevated phenotypes
Methodology
Stratify participants by metabolic criteria (BMI!BMI+diabetes/dyslipidemia!clinical obesity). Integrate Olink proteomics with multi-organ imaging. Use machine learning to identify ETP-associated pathways. Validate findings through cross-validation.
Expected Impact
Establish ETP as pan-organ fibroinflammatory biomarker, enable clinical identification of therapy-responsive patients, quantify multi-organ disease burden, and guide development of ETP-targeted interventions for metabolic complications.
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