Jenča, Dominik

Introduction: Heart failure (HF) affects interstitial lung structure and function. AI-assisted quantitative computed tomography (qCT) offers possibility of objective assesment of lung structure, allowing to detect the presence and the severity of HF. Hypothesis: Can automated qCT analysis discriminate HF from controls and identify tissue structures most linked to degree of congestion ? Methods: Subjects underwent right heart catheterization (RHC), native CT chest imaging (full inspiration/expiration) by Naeotom-α photon-counting CT scanner (Siemens), lab testing, echocardiography. Automated AI-based analysis (VIDA Diagnostics) was employed to evaluate lung volumes, densities and tissue texture. Image series were used in mediastinal and lung window reconstruction with slice thickness 0,8 mm and increments 0,5 mm. Results: 186 HF patients (85% HFrEF, NYHA 2.8±0.5, 58 y, 80% males, NTproBNP 4491 pg/ml, LVEF 26%) and 43 controls of similar sex and BMI were analyzed; both groups free of primary lung disease. In HF, 85 % had PH (mPAP>20mmHg). HF patients had comparable inspiratory lung volume, reduced air volume and increased lung tissue volume, compared to controls. Mean lung density (-782±50 vs -832±25 HU, p<0.0001), density variability and estimated lung fluid content (21.3 vs 16.7%, p<0.0001) were all higher in HF compared to controls. Across various density cut-offs, the most notable difference was at lung tissue density < -856 HU at inspiration. Lung tissue density corelated with mean PA pressure (r=0.49, p<0.0001), and less with pulmonary vascular resistance (r=0.21, p=0.006). Insp/exp scan analysis showed no difference in air trapping, but reduced lung tissue compliance (Jacobian strain: 1.8±0.4 vs 1.6±0.3, p=0.0005) in HF. Lung texture analysis showed increased ground-glass opacity (GGO) mass (48.8 g vs 9.1 g; p < 0.001), reticulation (42.2 g vs. 8.4 g; p < 0.001) and consolidation (0.5 g vs 0.2 g; p=0.0011) in HF compared to controls, with no difference in honeycombing. GGO mass correlated strongly with pulmonary capillary wedge pressure (PCWP) (r=0.48, p<0.001) and mitral regurgitation grade (r=0.28,<0.0001), but not with inflammation (hsCRP, IL6, WBC), smoking exposure or oncotic pressure. Conclusions: qCT distinguished HF patients from controls by increased lung tissue mass, reduced air volume, increased lung density and increased mass of GGO and reticulation. GGO mass in HF strongly reflects increased hydrostatic pressure, but not inflammation.

BACKGROUND: Some, but not all, patients with heart failure (HF) develop pulmonary vascular disease (PVD), which contributes to poor prognosis. Mechanisms leading to PVD in HF are poorly understood. We aimed to analyze transpulmonary gradients of proteins consumed or elaborated across the lungs to identify mediators of PVD by unbiased proteomics. METHODS: Overall, 21 controls and 160 patients with HF with reduced ejection fraction underwent pulmonary artery catheterization with blood sampling from postcapillary (wedged balloon) and precapillary (unwedged) position to obtain transpulmonary gradients. The samples from controls and HF from the highest (Q4, n=40) and lowest quartile (Q1, n=40) of pulmonary vascular resistance (PVR) were analyzed using the proteomic proximity extension assay (Olink) of 275 proteins. Venous blood concentrations or transpulmonary gradients were analyzed to identify biomarkers or potential mediators of PVD. RESULTS: Comparison of Q1 and Q4 of PVR identified PSP-D (pulmonary surfactant-associated protein D) as a marker of PVD. Examination of gradients across the lungs in high PVR HF revealed significant uptake of 18 proteins, mostly associated with inflammation (chemokines, oncostatin-M, MMP9 [matrix metalloproteinase 9]) or with TGF (transforming growth factor)/activin pathway (GDF2 [growth differentiation factor 2]/BMP9 [bone morphogenic protein 9]), and release of 5 proteins, notably IL (interleukin) 6 and IL33. In contrast, these protein gradients were negligible in controls and low PVR patients with HF. Active pulmonary release of IL6 contributed to systemic elevation of IL6 and correlated with right ventricular function. CONCLUSIONS: The lungs of patients with HF with high PVR display abnormal uptake and release of proinflammatory cytokines from IL6/gp130 family (IL6, IL33, oncostatin-M), along with increased transpulmonary uptake of GDF2/BMP9. The study shows that proteins orchestrating inflammation or pulmonary vessel remodeling in group 1 pulmonary hypertension, are also operating in patients with PVD due to HF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06331208.

Abstract Background Some, but not all, patients with heart failure (HF) develop pulmonary vascular disease (PVD), which contributes to poor prognosis. Mechanisms leading to PVD in HF are poorly understood. Unbiased analysis of transpulmonary gradients may identify not only markers but also mediators of PVD by identifying proteins that may be consumed or elaborated across the lungs. Purpose Examine hemodynamics and transpulmonary proteome gradients in HFrEF and controls. Methods 21 non-HF controls and 160 patients with advanced HFrEF underwent pulmonary artery (PA) catheterization with blood sampling from the PA catheter in the wedged position (02sat &gt; 90%, indicating pulmonary venous blood) and the un-wedged position to obtain transpulmonary proteome gradients. Samples from controls and HF from the highest quartile (Q4, n=40) and lowest quartile (Q1, n=40) of pulmonary vascular resistance (PVR) were analyzed using proteomic PEA assay of 275 proteins (Olink Target 96, panels CVII, CVIII, Inflammation). Pulmonary artery concentrations ("markers") or transpulmonary fold-change gradients (i.e. putative "mediators") of normalized protein expression (NPX) were plotted against significance; Benjamini–Hochberg FDR&lt;0.05 considered as significant. Results Patients displayed typical characteristics of HFrEF (age 56±8y, NYHA 3.0 ±0.6, 87% males, LVEF 24±9.6%), Controls had similar anthropometrics, gender and age. Comparing all HF to Con, 38 markers of HF were identified (NPX fold-change&gt;2), including 33 up (top 5: NTproBNP, BNP, GDF15, FGF23, IL8) and 5 down. HF patients in Q1 had median PVR 1.5 (IQR: 0.3-1.7) WU and those in Q4 had PVR 5.4 (4.3-6.6) WU. Comparison of Q1 and Q4 identified 3 protein markers of high PVR in PA blood from HF: chemokine CCL3, TNFRSF13B and surfactant protein-D (PSP-D). Examination of protein gradients from HF lungs (Figure) showed significant uptake of 14 protein mediators, most of which were associated with inflammatory responses (top 5: OSM, MMP9, CCL19, BNP, TR), and release of 8 mediators (top 4: IL-6, IL33, CCL4, CXCL10). In contrast, protein gradients were negligible in controls. Patients in Q4 PVR group were characterized by the highest pulmonary uptake of OSM (Oncostatin-M), lymphatic chemokine CCL19, BNP, MMP9, and had more significant transpulmonary release of IL6 and IL33. Across all patients, OSM lung uptake was the strongest correlate of transpulmonary pressure gradient (r=-0.3, p&lt;0.001). Conclusions Lungs of patients with HF, and particularly those with high PVR, display abnormal uptake and release of proinflammatory CC and CXC chemokines and cytokines from IL1 and IL6 family, along with increased pulmonary uptake of IL6-type cytokine Oncostatin-M, a known mediator of lung inflammation and fibrosis, that acts via gp130/JAK/STAT, suggesting a novel role in PVD due to HF.Transpulmonary proteome in HF patients