Distinct structural and dynamic components of portal hypertension in different animal models and human liver disease etiologies

Abstract

Background and aims: Liver fibrosis is the static and main (70%-80%) component of portal hypertension (PH). We investigated dynamic components of PH by a three-dimensional analysis based on correlation of hepatic collagen proportionate area (CPA) with portal pressure (PP) in animals or HVPG in patients.

Approach and results: Different animal models (bile duct ligation: n = 31, carbon tetrachloride: n = 12, thioacetamide: n = 12, choline-deficient high-fat diet: n = 12) and patients with a confirmed single etiology of cholestatic (primary biliary cholangitis/primary sclerosing cholangitis: n = 16), alcohol-associated (n = 22), and metabolic (NASH: n = 19) liver disease underwent CPA quantification on liver specimens/biopsies. Based on CPA-to-PP/HVPG correlation, potential dynamic components were identified in subgroups of animals/patients with lower-than-expected and higher-than-expected PP/HVPG. Dynamic PH components were validated in a patient cohort (n = 245) using liver stiffness measurement (LSM) instead of CPA. CPA significantly correlated with PP in animal models (Rho = 0.531; p < 0.001) and HVPG in patients (Rho = 0.439; p < 0.001). Correlation of CPA with PP/HVPG varied across different animal models and etiologies in patients. In models, severity of hyperdynamic circulation and specific fibrosis pattern (portal fibrosis: p = 0.02; septa width: p = 0.03) were associated with PH severity. In patients, hyperdynamic circulation (p = 0.04), vascular dysfunction/angiogenesis (VWF-Ag: p = 0.03; soluble vascular endothelial growth factor receptor 1: p = 0.03), and bile acids (p = 0.04) were dynamic modulators of PH. The LSM-HVPG validation cohort confirmed these and also indicated IL-6 (p = 0.008) and hyaluronic acid (HA: p < 0.001) as dynamic PH components.

Conclusions: The relative contribution of "static" fibrosis on PH severity varies by type of liver injury. Next to hyperdynamic circulation, increased bile acids, VWF-Ag, IL-6, and HA seem to indicate a pronounced dynamic component of PH in patients.

FIGURE 1

Experimental liver disease animal models and corresponding liver disease etiologies in patients. (A) BDL was performed to induce biliary cirrhosis for 4 weeks. The related control group underwent a sham operation (SHAM). Toxic liver fibrosis was induced by intraperitoneal injections of hepatotoxic CCl₄ for 8 weeks or TAA for 12 weeks, while control groups received only the related vehicle substances as injections: olive oil (OO) or isotonic saline (NaCl). NASH was induced by a combination of CDHFD for 12 weeks, including 7 weeks of intraperitoneal sodium nitrite (NaNO₂) injections. The control group was fed standard chow food (CHOW) and received injections of the vehicle substance for NaNO₂: PBS. (B) Liver biopsies of 57 patients were classified as definite and single liver disease etiologies by pathologist: biliary liver diseases (n = 16: by PBC, PSC), ALD (n = 22), and NASH (n = 19)

FIGURE 2

Histologic assessments and hemodynamic correlations in different animal models of liver disease. Overall, n = 67 diseased animals were studied (BDL: n = 31, CCl₄: n = 12, TAA: n = 12, CDHFD: n = 12). (A) Correlation of CPA with PP. (B) Correlations of CPA‐to‐PP are shown separately for the different animal models as linear regression lines. (C) Comparison of diseased animal models by severity level of liver fibrosis shown by CPA%. (D) Comprehensive histologic characterization of each model by ISHAK score and different pathologic features including perivenular, periportal, and portal fibrosis, septa width, ductular proliferation, and bile duct damage semiquantitatively scored by 0‐3. (E) Representative histological images of PSR‐stained liver tissue are shown, including the final morphometry analysis performed on whole liver lobe slide scans. (Significant p values are stated within each graph. Statistical tests used: one‐way ANOVA test and Tukey’s multiple comparison correction or Kruskal‐Wallis test and Dunn’s multiple comparison correction)

FIGURE 3

Histologic assessment and hemodynamic correlation in patients with different liver disease etiologies. Overall, n = 57 patients were included: n = 16 with PBC/PSC, n = 22 with ALD, and n = 19 with NASH. (A) Correlation of CPA with HVPG. (B) Correlations of CPA‐to‐HVPG are shown separately for different groups/etiologies of human liver disease as linear regression lines. (C) Comparison of liver fibrosis severity (CPA%) between the different huma liver disease groups. (D) Comprehensive characterization of each etiology group by ISHAK score and histopathologic features: perivenular, periportal, and portal fibrosis, septa width, ductular proliferation, and bile duct damage scored by 0‐3. (E) Representative histological images of PSR‐stained liver biopsies, including the final morphometry analysis performed on the whole biopsy slide scans. (Significant p value stated within each graph. Statistical tests used: one‐way ANOVA test and Tukey’s multiple comparison correction or Kruskal‐Wallis test and Dunn’s multiple comparison correction)

FIGURE 4

Identification of potential modulators of PH: (A) 3D analysis model by stratification of dataset into groups of lower‐than‐expected (−PP/HVPG), expected (~PP/HVPG, inside the IQR), and higher‐than‐expected (+PP/HVPG) PP or HVPG as predicted from the linear regression model based on histological fibrosis area (CPA%). (B) Visualized allocation of study cohort into animals of −/~/+PP (n = 16/37/14) and (C) proportion of different animal models represented in each group. (D) Visualized allocation of the study cohort into patients of −/~/+HVPG (n = 12/30/15) and (E) proportion of human liver disease etiologies represented in each group. (F) Potential modulators of PH were identified by statistical significance or a logic trend across the different groups of animals with −PP vs. ~PP vs. +PP, respectively

FIGURE 5

Identification of potential modulators of PH in patients according to lower vs. higher‐than‐expected HVPG (−/+HVPG, n = 12/15) vs. the IQR (~HVPG, n = 30). (A) HD‐I (HR/MAP), spleen diameter, and the systemic inflammation biomarker IL‐6. (B) VWF‐Ag levels as marker for endothelial dysfunction and the angiogenesis marker sVEGFR1 and its ratio to PlGF. (C) Bile acid level and histopathological bile duct damage and ductular proliferation as indicators of biliary damage. (D) Enhanced Liver Fibrosis (ELF) score and its single components TIMP1, P3NP, and HA. (E) Additional pathohistologic liver fibrosis features as ISHAK score, periportal and portal fibrosis, and septa width. (F) Representative histological images of hematoxylin and eosin (H&E), PSR, α‐SMA, and CD31‐stained liver tissue, including the final morphometry analysis performed on the whole slide scans. (G) Histologic α‐SMA and CD31 expression pattern across −/~/+HVPG and their correlation of each parameter to respective CPA data

FIGURE 6

(A) Visualized allocation of patients with −/~/+HVPG by stratification (n = 65/122/58) based on the correlation of VCTE with HVPG in another patient cohort including various etiologies of liver disease (n = 245). (B) Identification of potential dynamic modulators of PH across patient groups of −/~/+HVPG