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. 2024 Mar 1;13(5):1440.
doi: 10.3390/jcm13051440.

Cardio-Hepatic Interaction in Cardiac Amyloidosis

Affiliations

Cardio-Hepatic Interaction in Cardiac Amyloidosis

Sandra Michaela Ihne-Schubert et al. J Clin Med. .

Abstract

Background: Congestion is associated with poor prognosis in cardiac amyloidosis (CA). The cardio-hepatic interaction and the prognostic impact of secondary liver affection by cardiac congestion in CA are poorly understood and require further characterisation. Methods: Participants of the amyloidosis cohort study AmyKoS at the Interdisciplinary Amyloidosis Centre of Northern Bavaria with proven transthyretin (ATTR-CA) and light chain CA (AL-CA) underwent serial work-up including laboratory tests, echocardiography, and in-depth hepatic assessment by vibration-controlled transient elastography (VCTE) and 13C-methacetin breath test. Results: In total, 74 patients with AL-CA (n = 17), ATTR-CA (n = 26) and the controls (n = 31) were analysed. ATTR-CA patients showed decreased microsomal liver function expressed by maximal percentage of dose rate (PDRpeak) related to hepatic congestion. Reduced PDRpeak in AL-CA could result from altered pharmacokinetics due to changed hepatic blood flow. Liver stiffness as a combined surrogate of chronic liver damage and congestion was identified as a predictor of all-cause mortality. Statistical modelling of the cardio-hepatic interaction revealed septum thickness, NT-proBNP and PDRpeak as predictors of liver stiffness in both CA subtypes; dilatation of liver veins and the fibrosis score FIB-4 were only significant for ATTR-CA. Conclusions: Non-invasive methods allow us to characterise CA-associated hepatic pathophysiology. Liver stiffness might be promising for risk stratification in CA.

Keywords: 13C-methacetin breath test (MBT); PDRpeak; cardiac amyloidosis; congestion; liver stiffness; vibration-controlled transient elastography (VTCE).

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Conflict of interest statement

Sandra Ihne-Schubert: received financial reimbursement for consulting, advisory board activities, speaker honoraries and/or travel support to attend scientific meetings by Akcea Therapeutics, Alnylam, Pfizer, Janssen-Cilag and Takeda, and further research funding from Pfizer and Akcea Therapeutics. An internship was supported by ONLUS. She was a fellow of the local Clinician Scientist program of the IZKF Würzburg. Oliver Goetze: no conflicts of interest. Felix Gerstendörfer: no conflicts of interest. Floran Sahiti: no conflicts of interest. Ina Schade: no conflicts of interest. Aikaterini Papagianni: received research funding grand from Pfizer, consulting fees from Pfizer, Alnylam, Akcea and Swedish Orphan Biovitrum GmbH-Sobi, financial support for attending meetings and/or travel from Alnylam, Akcea and Pfizer. Aikaterini Papagianni is also PI in ION-682884-CS3 study. She is supported by the IZKF Würzburg (habilitation scholarship). Caroline Morbach: research cooperation with the University of Würzburg and Tomtec Imaging Systems, funded by a research grant from the Bavarian Ministry of Economic Affairs, Regional Development and Energy, Germany; she is supported by the German Research Foundation (DFG) within the Comprehensive Research Center 1525 ‘Cardio-immune Interfaces’ (453989101, project C5) and received financial support from the Interdisciplinary Center for Clinical Research—IZKF, Würzburg (advanced clinician-scientist program; AdvCSP 3). She further received advisory and speakers honoraria as well as travel grants from Tomtec, Alnylam, AKCEA, Pfizer, Boehringer Ingelheim, SOBI, AstraZeneca, NovoNordisk, Alexion, Janssen and EBR Systems, and acted as principal investigator in trials sponsored by Alnylam, Bayer, NovoNordisk and AstraZeneca. Stefan Frantz: received consultancy and lecture fees as well as travel expenses from AMGEN Europe, AstraZeneca, Bayer Vital, Boehringer Ingelheim, Bristol-Meyers Squibb GmbH, Daiichi Sankyo, MSD, Novartis, Pfizer, Sanofi, Servier and Vifor. Hermann Einsele: no conflicts of interest. Stefan Knop: no conflicts of interest. Claudia Sommer: received speaker honoraria from Alnylam. Beat Müllhaupt: no conflicts of interest. Torben Schubert: no conflicts of interest. Stefan Störk: received research support from the German Federal Ministry of Education and Research (BMBF). He has received consultancy and lecture fees from Akcea, AstraZeneca, Bayer, Boehringer Ingelheim, Novartis, Novo Nordisk and Pfizer. His department received case payments for study participation from Akcea Therapeutics, Alnylam and IONIS. Andreas Geier: served as steering committee member or advisor for AbbVie, Alexion, Bayer, BMS, Eisai, Falk, Gilead, Heel, Intercept, Ipsen, Merz, MSD, Novartis, Pfizer, Roche, Sanofi-Aventis and as speaker for AbbVie, Advanz, Alexion, BMS, Burgerstein, CSL Behring, Falk, Gilead, Intercept, Merz, MSD, Novartis, Roche. He received research support from Intercept, Falk (both NAFLD CSG) and Novartis.

Figures

Figure 1
Figure 1
Theoretical model of the cardio-hepatic crosstalk in cardiac amyloidosis (based on the model of Müller et al., 2010 [30] and specifically adapted to cardiac amyloidosis). VCTE is usually applied in patients with a high a priori probability of liver fibrosis due to chronic liver disease and, therefore, stiffness primarily reflects in these patients the grade of liver fibrosis. In contrast, in cardiac amyloidosis, the proposed application of VCTE occurs early in the development of possible fibrosis (so-called cirrhosis cardiaque) and increased stiffness may also result from chronic cardiac congestion. Subtype-spanning main influencing factors for liver stiffness in cardiac amyloidosis are supposed to be the severity of cardiac involvement with resulting impairment of cardiac function, cardiac congestion, inflammation and chronic liver cell damage including fibrosis. Potential clinical, laboratory and instrumental surrogate parameters were assigned based on the literature, their clinical value and availability.
Figure 2
Figure 2
Visualisation of the coefficients of the post-cluster LASSO referring to model 2 (blue dot = regression coefficient; blue lines = 95% confidence intervals).
Figure 3
Figure 3
The Kaplan–Meier curves illustrate the overall survival among the three groups (ATTR-CA blue line; AL-CA red line; controls green line). The X-axis reflects the observation time in years; the Y-axis indicates the proportion of patients still alive.
Figure 4
Figure 4
Forest plot for the visualisation of the association between influencing factors and all-cause mortality (blue dot = regression coefficient; blue lines = 95% confidence intervals).

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