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Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Corresponding author. Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria P: +43 1 40400 47440, F: +43 1 40400 47350 M.
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, AustriaVienna Hepatic Hemodynamic Lab, Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
Diabetes mellitus was independently linked to increased venous ammonia levels.
•
The prognostic performance of ammonia for liver-related outcomes is comparable to UNOS MELD (2016) score and HVPG.
•
Ammonia predicts liver-related outcomes, independently of established prognostic indicators including CRP and HVPG.
•
The prognostic value of ammonia is linked with several key disease-driving mechanisms.
•
However, it is not explained by hepatic dysfunction, systemic inflammation, or portal hypertension severity, suggesting direct toxicity.
Abstract
Background&aims
Ammonia levels predicted hospitalisation in a recent landmark study not accounting for portal hypertension and systemic inflammation severity.
We investigated (i) the prognostic value of venous ammonia levels (outcome cohort) for liver-related outcomes while accounting for these factors and (ii) its correlation with key disease-driving mechanisms (biomarker cohort).
Methods
(i) The outcome cohort included 549 clinically stable outpatients with evidence of advanced chronic liver disease (ACLD). (ii) The partly overlapping biomarker cohort comprised 193 patients, recruited from the prospective Vienna Cirrhosis Study (VICIS: NCT03267615).
Results
(i) In the outcome cohort, ammonia increased across clinical stages as well as hepatic venous pressure gradient (HVPG) and UNOS model for end-stage liver disease (2016) strata and were independently linked with diabetes. Ammonia was associated with liver-related death, even after multivariable adjustment (adjusted hazard ratio [aHR]: 1.05[95%CI:1.00-1.10];p=0.044). The recently proposed cut-off (≥1.4 x upper limit of normal) was independently predictive of hepatic decompensation (aHR: 2.08[95%CI:1.35-3.22];p<0.001), non-elective liver-related hospitalisation (aHR: 1.86[95%CI:1.17-2.95];p=0.008), and – in those with decompensated ACLD – acute-on-chronic liver failure (ACLF; aHR: 1.71[95%CI:1.05-2.80];p=0.031).
(ii) Besides HVPG, venous ammonia was correlated with markers of endothelial dysfunction and liver fibrogenesis/matrix remodelling in the biomarker cohort.
Conclusion
Venous ammonia predicts hepatic decompensation, non-elective liver-related hospitalisation, ACLF, and liver-related death, independently of established prognostic indicators including C-reactive protein and hepatic venous pressure gradient.
While venous ammonia is linked with several key disease-driving mechanisms, its prognostic value is not explained by associated hepatic dysfunction, systemic inflammation, or portal hypertension severity, suggesting direct toxicity.
Lay Summary
A recent landmark study linked ammonia levels (a simple blood test) with hospitalisation/death in patients with clinically stable cirrhosis.
Our study extends the prognostic value of venous ammonia to other important liver-related complications. While venous ammonia is linked with several key disease-driving mechanisms, they do not fully explain its prognostic value. This supports the concept of direct ammonia toxicity and ammonia-lowering drugs as disease-modifying treatment.
Advanced chronic liver disease (ACLD) is a major source of morbidity and mortality world-wide, with non-alcoholic fatty liver disease (NAFLD) emerging as the predominant cause of ACLD in several regions (
). The first development of hepatic decompensation – most commonly ascites, although hepatic encephalopathy (HE) is the predominant first event in NAFLD (
). Those with decompensated ACLD are at risk of acute-on-chronic liver failure (ACLF), which is defined by extrahepatic organ dysfunction (including acute encephalopathy) and high short-term mortality (
). Portal hypertension, which is accompanied by portosystemic shunting, and bacterial translocation-induced systemic inflammation are considered as the main drivers of clinical deterioration (
Bridging the gap between metabolic liver processes and functional tissue structure by integrated spatiotemporal modeling applied to hepatic ammonia detoxification.
). The diagnostic utility of ammonia testing for HE is controversially discussed, since hyperammonaemia is not the only mechanism for HE development, and thus, patients with HE may present with normal ammonia levels (
) evaluated the impact of ammonia on liver-related outcomes in clinically stable patients with ACLD and values ≥1.4 x upper limit of normal predicted liver-related events in stable outpatients with ACLD (
). However, it remains unclear whether this association is independent of portal hypertension/systemic inflammation, i.e., well-established disease-driving mechanisms. Moreover, the findings of experimental studies, i.e., the link between hyperammonaemia and liver fibrogenesis, remains to be confirmed in humans to support the potential role of ammonia-lowering drugs as a disease-modifying treatment.
The objective of our study was (i) to externally validate and extend previous findings on the prognostic value of venous plasma ammonia levels in a large, well-characterized cohort, while accounting for portal hypertension and systemic inflammation severity and (ii) to investigate the relationship between venous plasma ammonia and biomarkers of other disease-driving mechanisms.
Methods
Study design and patients
We performed a retrospective, single-centre cohort study in patients with ACLD who underwent hepatic venous pressure gradient (HVPG) measurement at the Vienna Hepatic Hemodynamic Lab (outcome cohort; Supplementary Fig. 1). Patients from the outcome cohort were included between Q2/04 and Q4/20. Inclusion criteria were (i) liver stiffness measurement ≥10 kPa and/or HVPG ≥6 mmHg, and (ii) availability of venous plasma ammonia levels. Furthermore, patients were excluded if any of the following criteria were present: Patients with a history of orthotopic liver transplantation, any active extrahepatic malignancy, non-parenchymal liver disease, non-elective hospitalisation due to a liver-related complication at HVPG-measurement or within 28 days prior to HVPG-measurement, unsuccessful/unreliable HVPG-measurement, bacterial infection, or missing information on important laboratory parameters and/or clinical follow-up. Recruitment and follow-up over the study period were depicted in Supplementary Fig. 2.
In addition, we assessed biomarkers in a partly overlapping cohort of patients (n=193) from the prospective VIenna CIrrhosis Study (VICIS; NCT03267615; pathophysiology cohort) who were recruited between Q1/2017 and Q3/2022 (Supplementary Fig. 1), applying similar in- and exclusion criteria (biomarker cohort). Overall, n=82 patients are included in both cohorts (15% [82/549] vs. 42% [82/193]).
HVPG-measurement
Under local anaesthesia and ultrasound guidance, a catheter introducer sheath was inserted into the right internal jugular vein (
). Subsequently, a hepatic vein was cannulated and the free and wedged hepatic venous pressures were obtained at least as triplicate measurements by a balloon catheter (
Reiberger T, Schwabl P, Trauner M, Peck-Radosavljevic M, Mandorfer M. Measurement of the Hepatic Venous Pressure Gradient and Transjugular Liver Biopsy. JoVE. 2020(160):e58819.
Routine laboratory tests, venous plasma ammonia, and biomarkers (von Willebrand factor [VWF], procalcitonin [PCT], interleukin 6 [IL-6], enhanced liver fibrosis [ELF] test, copeptin, renin, and bile acids [BA]) were performed by the ISO-certified Department of Laboratory Medicine of the Medical University of Vienna using commercially available methods that are applied in clinical routine and blood samples obtained via a central venous line (i.e., the side port of the catheter introducer sheath) at the time of HVPG-measurement. Venous plasma ammonia was sampled and rapidly transported on ice to the central laboratory. In line with the previous landmark study (
), venous plasma ammonia levels were divided by the sex-specific upper limit of normal of our local laboratory (i.e., 60 mmol x L-1 for males and 51 mmol x L-1 for females).
Clinical stages of ACLD, definition of hepatic decompensation and of ACLF
Patients were classified according to recently defined prognostic/clinical stages. The definition was adapted from D’Amico et al (
). Decompensated ACLD (dACLD) was defined by the presence or history of at least one decompensating event, i.e., ascites, variceal bleeding, or HE. ACLF was defined according to EF-CLIF criteria (
All statistical analyses were performed using IBM SPSS Statistics 27 (IBM, New York, NY, USA), R 4.1.2 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria), or GraphPad Prism 8 (GraphPad Software, CA, USA). Categorical variables were reported as absolute (n) and relative frequencies (%), whereas continuous variables as mean ± SD or median (interquartile range [IQR]), as appropriate. Student’s t-test was used for group comparisons of normally distributed variables and Mann-Whitney-U-test for non-normally distributed variables. Group comparisons of categorical variables were performed using either Chi-squared or Fisher’s exact test, as appropriate.
Univariable and multivariable linear regression analyses were applied to evaluate factors associated with ammonia.
Follow-up time was calculated as the time from HVPG measurement to the date of liver transplantation, death, or last follow-up at one of the hospitals of the Vienna hospital association by the reverse Kaplan-Meier method. Impact of venous plasma ammonia levels on liver-related outcomes was assessed using Cox regression and competing risk analyses considering the removal/suppression of the primary aetiological factor (as defined by Baveno VII (
), i.e., initiation of antiviral therapy/reported alcohol abstinence), liver transplantation, or non-liver-related death, as competing risks. Analyses were performed for hepatic decompensation/liver-related death, liver-related death, development of ACLF/requirement of liver transplantation/liver-related death, and non-elective liver-related hospitalisation/liver-related death as outcomes of interest. For the outcome development of ACLF/requirement of liver transplantation/liver-related death – in patients who had already experienced hepatic decompensation at baseline (i.e., the main at-risk population) – removal/suppression of the primary aetiological factor or non-liver-related death were considered as competing risks. For competing risk regression analyses, Fine and Gray competing risks regression models (cmprsk: subdistribution analysis of competing risks, https://CRAN.R-project.org/package=cmprsk) (
) were calculated. Uni- and multivariable Cox regression analyses were performed to evaluate parameters independently associated with the events of interest. In a first step, we included all parameters into univariable Cox regression models. Baseline characteristics which we considered of particular importance for the endpoint of interest (i.e., age, indicators of hepatic dysfunction, HVPG, and CRP) were further included into two separate multivariable models. The Child-Turcotte-Pugh (CTP) and United Network for Organ Sharing (UNOS) model for end-stage liver disease (MELD) (2016) scores have significant overlap in terms of included variables. Therefore, we generated separate models with either CTP or UNOS MELD (2016) scores.
Time-dependent area under the receiver operating characteristic curve (AUROC) analyses were performed and the R-package ‘timeROC’ was used to compare the prognostic performances for hepatic decompensation/liver-related death and liver-related death between established prognostic indicators (UNOS MELD [2016] score and HVPG) and ammonia (multiplicity-adjusted p-values) over time.
Spearman’s correlation analyses were conducted to investigate potential associations between ammonia and biomarkers in the biomarker cohort. A heatmap plot was used for graphical illustration of associations between ammonia and biomarkers.
The level of significance was set at a 2-sided p-value <0.05.
Ethics
The study has been conducted in accordance with the principles of the Declaration of Helsinki and its amendments and has been approved by the local ethics committee (EK1531/2022 and EK1262/2017), which waived the requirement of written informed consent for the retrospective analysis of the outcome cohort. All patients included in the prospective biomarker cohort (i.e., VICIS study) provided written informed consent for study participation.
Results
Study population of the outcome cohort
Overall, 2550 patients underwent HVPG-measurement within the study period (Supplementary Fig. 1). After applying in- and exclusion criteria, 549 patients were finally included into the outcome cohort. Mean age at HVPG-measurement was 54±12 years and most patients were male (n=370, 67%; Table 1). Viral hepatitis was the most common aetiology of liver disease (n=207, 38%), followed by alcohol-related liver disease (ARLD; n=196, 36%), other aetiologies of ACLD (n=89, 16%) and NAFLD (n=57, 10%). Regarding portal hypertension severity, mean HVPG was 16±7 mmHg and 65% of patients (n=319) had varices, of whom 67 (12%) had a history of variceal bleeding. Mean UNOS MELD (2016) was 12±5, and mean CTP score was 7±2 points. Most patients were classified as CTP-A (n=342, 62%), whereas 31% of patients were classified as CTP-B (n=168) and 7% as CTP-C (n=39). Two hundred and fifty-two patients (46%) had already experienced hepatic decompensation at study inclusion. Eight percent (n=42) of patients had a history of overt hepatic encephalopathy. Accordingly, 7% (n=38) were on lactulose, 5% (n=27) on rifaximin, and 10% (n=54) on oral L-ornithine O-aspartate at baseline. The median baseline ammonia level was 37.3 (IQR: 28.2-51.6) mmol x L-1 and ammonia adjusted for the upper limit of normal (NH3-ULN) was 0.66 (IQR: 0.49-0.91).
Table 1Detailed patient characteristics at the time of HVPG-measurement of the outcome and the pathophysiology cohort.
Patient characteristics
Outcome cohort, n=549
Pathophysiology cohort, n=193
p-value
Age, years, mean ± SD
54.4±11.5
55.6±12.4
0.208
Sex, n (%)
Male
370 (67%)
131 (68%)
0.902
Female
179 (33%)
62 (32%)
Etiology, n (%)
Viral
207 (38%)
36 (19%)
<0.001
ARLD
196 (36%)
82 (43%)
NAFLD
57 (10%)
20 (10%)
Other
89 (16%)
55 (29%)
Varices, n (%)
319 (65%)
106 (65%)
1.000
History of decompensation, n (%)
252 (46%)
119 (62%)
<0.001
History of variceal bleeding, n (%)
67 (12%)
21 (11%)
0.625
Ascites, n (%)
None
354 (65%)
105 (54%)
0.036
Mild
160 (29%)
75 (39%)
Severe
35 (6%)
13 (7%)
History of hepatic encephalopathy, n (%)
42 (8%)
30 (16%)
0.545
HVPG, mmHg, mean ± SD
16±7
15±6
0.190
HVPG 0-5 mmHg, n (%)
42 (8%)
-
<0.001
HVPG 6-9 mmHg, n (%)
83 (15%)
44 (23%)
HVPG 10-15 mmHg, n (%)
137 (25%)
61 (32%)
HVPG ≥16 mmHg, n (%)
287 (52%)
88 (46%)
UNOS MELD (2016), points, mean ± SD
11.8±4.5
11.4±4.6
0.326
CTP score, points, mean ± SD
6.5±1.7
6.6±1.9
0.541
A, n (%)
342 (62%)
117 (61%)
0.475
B, n (%)
168 (31%)
57 (30%)
C, n (%)
39 (7%)
19 (9%)
Laboratory parameters, median (IQR) or mean ± SD
Platelet count, G x L-1
107 (73-152)
103 (70-142)
0.248
Sodium, mmol x L-1
138.0±3.7
138.1±3.7
0.788
Creatinine, mg x dL-1
0.7 (0.6-0.9)
0.8 (0.6-0.9)
0.401
Albumin, g x L-1
36.5±5.7
37.2±5.5
0.136
Bilirubin, mg x dL-1
1.0 (0.7-1.9)
1.0 (0.6-1.8)
0.638
INR
1.3±0.3
1.4±0.3
0.438
AST, U x L-1
48 (34-67)
40 (29-55)
<0.001
ALT, U x L-1
35 (23-60)
30 (22-42)
<0.001
CRP, mg x L-1
0.2 (0.1-0.6)
0.2 (0.1-0.5)
0.730
Ammonia, mmol x L-1
37.3 (28.2-51.6)
34.5 (26.0-47.4)
0.061
NH3-ULN
0.66 (0.49-0.91)
0.58 (0.43-0.79)
0.061
Categorical variables were reported as absolute (n) and relative frequencies (%), whereas continuous variables as mean ± SD or median (interquartile range [IQR]), as appropriate. Student’s t-test was used for group comparisons of normally distributed variables and Mann-Whitney-U-test for non-normally distributed variables. Group comparisons of categorical variables were performed using either Chi-squared or Fisher’s exact test, as appropriate. P-values in bold denote p<0.05.
Abbreviations: ARLD alcohol-related liver disease; ALT alanine transaminase; NH3-ULN ammonia level corrected to the upper limit of normal; AST aspartate transaminase; CRP C-reactive protein; CTP Child-Turcotte-Pugh; HVPG hepatic venous pressure gradient; INR international normalized ratio; NAFLD non-alcoholic fatty liver disease; UNOS MELD (2016) score United Network for Organ Sharing model for end-stage liver disease (2016).
Clinical events during follow-up in the outcome cohort
Patients were followed for a median of 41.0 (95%CI: 37.3-44.7) months. One hundred and four deaths (19%) were considered liver-related, while one hundred seventy-five events (32%) were captured for the combined endpoint hepatic decompensation/liver-related death. For the endpoint liver-related death, one hundred and ninety-nine competing risks (36%) occurred, whereas for the combined endpoint (hepatic decompensation/liver-related death), one hundred and seventy-six competing risks (32%) were captured. Among decompensated patients, forty-five patients (18%) developed ACLF.
Ammonia levels increase with liver disease and portal hypertension severity in the outcome cohort
NH3-ULN/NH3 consistently increased with liver disease/portal hypertension severity, as evaluated by CTP score (p<0.001) and UNOS MELD (2016) score (p<0.001), as well as severity of portal hypertension (p<0.001; Figure 1; Supplementary Table 1). Additionally, NH3-ULN also increased across clinical stages (p<0.001).
Figure 1Comparison of NH3 corrected to the upper limit of normal according to (A) CTP, (B) UNOS MELD (2016) score and (C) HVPG strata as well as (D) clinical stages in the outcome cohort. NH3-ULN levels were reported as median (IQR) and compared by Mann-Whitney-U-test.
Univariable and multivariable analyses of factors associated with ammonia in the outcome cohort
In univariable analyses, NH3-ULN was directly associated with severity of liver disease (CTP score: unstandardized regression coefficient [B]: 0.095 [95%CI: 0.070, 0.117]; p<0.001, UNOS MELD [2016] score: B: 0.032 [95%CI: 0.023, 0.041]; p<0.001), and portal hypertension severity (HVPG: B: 0.020 [95%CI: 0.014, 0.026]; p<0.001; Table 2). Additionally, there was a positive association with systemic inflammation (CRP: B: 0.109 [95%CI: 0.026, 0.192]; p=0.011), and with body mass index (BMI: B: 0.012 [95%CI: 0.004, 0.020]; p=0.005), presence of varices (B: 0.128 [95%CI: 0.073, 0.183]; p<0.001), diabetes (B: 0.130 [95%CI: 0.016, 0.243]; p=0.025), as well as dACLD (B: 0.276 [95%CI: 0.194, 0.358]; p<0.001). Finally, NH3-ULN was negatively associated with male sex (B: -0.142 [95%CI: -0.232, -0.052]; p=0.002), arterial hypertension (B: -0.099 [95%CI: -0.185, -0.013]; p=0.024), as well as serum sodium (B: -0.017 [95%CI: -0.028, -0.005]; p=0.004) and albumin levels (B: -0.021 [95%CI: -0.029, -0.014]; p<0.001).
Table 2Simple and multiple linear regression analysis of factors associated with NH3 corrected for the upper limit of normal including – among other parameters – either CTP score, as well as serum sodium and creatinine (model 1), or UNOS MELD (2016) score, clinical stage, and serum albumin (model 2) in the outcome cohort.
Biopsy-proven, controlled attenuation parameter >248dB x m-1, or diagnosed by ultrasound.
0.064
-0.030, 0.158
0.183
-
-
-
-
-
-
ARLD, vs. other etiologies
0.074
-0.015, 0.163
0.102
-
-
-
-
-
-
Varices
0.128
0.073, 0.183
<0.001
0.087
0.020, 0.154
0.011
0.069
0.001, 0.138
0.048
CTP score, point
0.095
0.070, 0.117
<0.001
0.087
0.050, 0.124
<0.001
-
-
-
UNOS MELD (2016), point
0.032
0.023, 0.041
<0.001
-
-
-
0.022
0.008, 0.036
0.002
HVPG, mmHg
0.020
0.014, 0.026
<0.001
0.006
-0.004, 0.015
0.234
0.004
-0.006, 0.013
0.417
Decompensated vs. compensated ACLD
0.276
0.194, 0.358
<0.001
-
-
-
0.117
0.001, 0.234
0.048
Sodium, mmol x L-1
-0.017
-0.028, -0.005
0.004
0.009
-0.007, 0.024
0.266
-
-
-
Creatinine, mg x dL-1
0.107
-0.054, 0.268
0.192
0.231
0.032, 0.430
0.023
-
-
-
Albumin, g x L-1
-0.021
-0.029, -0.014
<0.001
-
-
-
-0.004
-0.015, 0.007
0.519
ALT, U x L-1
0.000
0.000, 0.000
0.314
-
-
-
-
-
-
CRP, mg x L-1
0.109
0.026, 0.192
0.011
-0.037
-0.150, 0.077
0.526
-0.020
-0.133, 0.093
0.727
P-values in bold denote p<0.05.
Abbreviations: ARLD alcohol-related liver disease; ALT alanine transaminase; BMI body mass index; CRP C-reactive protein; CTP Child-Turcotte-Pugh score; HVPG hepatic venous pressure gradient; UNOS MELD (2016) score United Network for Organ Sharing model for end-stage liver disease (2016) score.
a BMI ≥25 kg x m-2.
b BMI ≥30 kg x m-2.
c Fasting blood glucose 100-125mg x dL-1; HbA1c 5.7-6.4%.
d Fasting blood glucose >125mg x dL-1, HbA1c ≥6.5%, or antidiabetic medication.
e Blood pressure >140/90mmHg, or antihypertensive medication.
f Triglycerides >150 mg x dL-1.
g Total cholesterol >200 mg x dL-1.
h <35mg x dL-1 for males and <39mg x dL-1 for females.
i Biopsy-proven, controlled attenuation parameter >248dB x m-1, or diagnosed by ultrasound.
After multivariable adjustment for either CTP score, sodium, and creatinine (model 1) or UNOS MELD (2016) score, serum albumin, and dACLD (model 2), severity of liver disease (model 1: CTP score: B: 0.087 [95%CI: 0.050, 0.124]; p<0.001; model 2: UNOS MELD [2016] score: B: 0.022 [95%CI: 0.008, 0.036]; p=0.002), presence of dACLD (model 2: B: 0.117 [95%CI: 0.001, 0.234]; p=0.048), BMI (model 1: B: 0.013 [95%CI: 0.002, 0.023]; p=0.019; model 2: B: 0.014 [95%CI: 0.003, 0.024]; p=0.010), male sex (model 1: B: -0.174 [95%CI: -0.285, -0.064]; p=0.002; model 2: B: -0.164 [95%CI: -0.272, -0.055]; p=0.003), diabetes (model 1: B: 0.123 [95%CI: 0.004, 0.241]; p=0.043; model 2: B: 0.134 [95%CI: 0.016, 0.252]; p=0.026), presence of varices (model 1: B: 0.087 [95%CI: 0.020, 0.154]; p=0.011; model 2: B: 0.069 [95%CI: 0.001, 0.138]; p=0.048), and creatinine levels (model 1: B: 0.231 [95%CI: 0.032, 0.430]; p=0.023) were the only parameters with independent positive associations (Table 2).
Impact of ammonia on liver-related outcomes in the outcome cohort
NH3 not only increased with liver disease severity in cross-sectional analyses but was also longitudinally associated with liver-related death (HR: 1.08 [95% confidence interval (95%CI): 1.05-1.12]; p<0.001). Its independent prognostic value was confirmed in two multivariable models (model 1: adjusted HR [aHR]: 1.05 [95%CI: 1.00-1.12]; p=0.044; model 2: aHR: 1.04 [95%CI: 1.00-1.08]; p=0.049), which were adjusted for age, HVPG, and CRP as well as additionally CTP-score, sodium, and serum creatinine levels in model 1 and UNOS MELD (2016)-score, decompensation status and serum albumin levels in model 2 (Table 3).
Table 3Uni- and multivariable Cox regression analyses of factors associated with liver-related death including – among other parameters – CTP score, serum sodium, and creatinine (model 1) or UNOS MELD (2016) score, clinical stage, and serum albumin (model 2) in the outcome cohort.
Patient characteristics
Univariable
Model 1 (incl. CTP score, sodium, and creatinine)
Model 2 (incl. MELD, and albumin)
HR (95%CI)
p-value
aHR (95%CI)
p-value
aHR (95%CI)
p-value
Age, year
1.05 (1.03-1.07)
<0.001
1.05 (1.03-1.08)
<0.001
1.05 (1.03-1.07)
<0.001
HVPG, mmHg
1.09 (1.06-1.13)
<0.001
1.03 (0.99-1.07)
0.124
1.03 (0.99-1.07)
0.119
CTP score
A
1
1
-
-
B
3.05 (1.99-4.69)
<0.001
2.04 (1.23-3.38)
0.006
-
-
C
5.89 (3.40-10.21)
<0.001
3.55 (1.76-7.17)
<0.001
-
-
UNOS MELD (2016) score, point
1.11 (1.07-1.15)
<0.001
-
-
1.04 (0.99-1.09)
0.080
Decompensated vs. compensated ACLD
2.38 (1.60-3.54)
<0.001
-
-
1.02 (0.63-1.66)
0.934
Sodium, mmol x L-1
0.92 (0.88-0.96)
<0.001
0.99 (0.94-1.05)
0.812
-
-
Creatinine, mg x dL-1
1.91 (0.99-3.71)
0.055
0.72 (0.35-1.49)
0.371
-
-
Albumin, g x L-1
0.92 (0.89-0.94)
<0.001
-
-
0.95 (0.92-0.99)
0.003
CRP, mg x L-1
2.08 (1.59-2.71)
<0.001
1.36 (0.98-1.90)
0.070
1.25 (0.87-1.78)
0.227
NH3, μmol x L-1, per 10
1.08 (1.05-1.12)
<0.001
1.05 (1.00-1.10)
0.044
1.04 (1.00-1.08)
0.049
Concordance ± SE
0.764 ± 0.024
0.776 ± 0.023
AIC
1084.940
1084.510
P-values in bold denote p<0.05.
Abbreviations: ACLD advanced chronic liver disease; aHR adjusted hazard ratio; AIC Akaike information criterion; NH3-ULN ammonia adjusted for the upper limit of normal; ARLD alcohol-related liver disease; CI confidence interval; CRP C-reactive protein; CTP Child-Turcotte-Pugh score; HVPG hepatic venous pressure gradient; NAFLD non-alcoholic fatty liver disease; SE standard error; UNOS MELD (2016) United Network for Organ Sharing Model of End-stage Liver Disease (2016).
Next, we evaluated the prognostic performance of the previously provided cut-off of 1.4 in our outcome cohort. Importantly, this cut-off was not only associated with liver-related death in competing risk regression analysis (Supplementary Table 2), but also hepatic decompensation (Cox regression Table 4 and Supplementary Table 3; competing risk regression Supplementary Table 4), as well as liver-related hospitalisation (Cox regression Supplementary Table 5; competing risk regression Supplementary Table 6), and ACLF in dACLD (Cox regression Supplementary Table 7; competing risk regression Supplementary Table 8).
Table 4Uni- and multivariable Cox regression analyses of factors associated with hepatic decompensation/liver-related death including – among other parameters – CTP score, serum sodium, and creatinine (model 1) or serum UNOS MELD (2016) score, clinical stage, and serum albumin (model 2) in the outcome cohort.
Patient characteristics
Univariable
Model 1 (incl. CTP score, sodium, and creatinine)
Model 2 (incl. MELD, and albumin)
HR (95%CI)
p-value
aHR (95%CI)
p-value
aHR (95%CI)
p-value
Age, year
1.03 (1.02-1.05)
<0.001
1.02 (1.01-1.04)
0.007
1.02 (1.01-1.04)
0.003
HVPG, mmHg
1.15 (1.12-1.17)
<0.001
1.10 (1.07-1.13)
<0.001
1.09 (1.06-1.12)
<0.001
CTP score
A
1
1
-
-
B
4.62 (3.33-6.43)
<0.001
2.22 (1.52-3.25)
<0.001
-
-
C
6.91 (4.33-11.04)
<0.001
2.47 (1.38-4.40)
0.002
-
-
UNOS MELD (2016) score, point
1.12 (1.09-1.15)
<0.001
-
-
0.99 (0.96-1.03)
0.713
Decompensated vs. compensated ACLD
5.63 (3.96-8.00)
<0.001
-
-
2.35 (1.58-3.49)
<0.001
Sodium, mmol x L-1
0.90 (0.87-0.93)
<0.001
0.98 (0.94-1.03)
0.443
-
-
Creatinine, mg x dL-1
2.49 (1.52-4.06)
<0.001
1.38 (0.84-2.26)
0.199
-
-
Albumin, g x L-1
0.89 (0.86-0.91)
<0.001
-
-
0.95 (0.92-0.98)
<0.001
CRP, mg x L-1
2.47 (1.98-3.08)
<0.001
1.69 (1.28-2.23)
<0.001
1.64 (1.24-2.16)
<0.001
NH3-ULN ≥1.4 vs. <1.4
3.84 (2.58-5.71)
<0.001
2.08 (1.35-3.22)
<0.001
1.99 (1.31-3.02)
0.001
Concordance ± SE
0.819 ± 0.015
0.825 ± 0.014
AIC
1819.569
1807.099
P-values in bold denote p<0.05.
Abbreviations: ACLD advanced chronic liver disease; aHR adjusted hazard ratio; AIC Akaike information criterion; NH3-ULN ammonia adjusted for the upper limit of normal; ARLD alcohol-related liver disease; CI confidence interval; CRP C-reactive protein; CTP Child-Turcotte-Pugh score; HVPG hepatic venous pressure gradient; NAFLD non-alcoholic fatty liver disease; SE standard error; UNOS MELD (2016) United Network for Organ Sharing Model of End-stage Liver Disease (2016).
In addition, we compared the prognostic performance of NH3-ULN for hepatic decompensation/liver-related death and liver-related death to UNOS MELD (2016)-score and HVPG in time-dependent AUROC analyses for the following time points: 12, 24, 36, 48, and 60 months. Regarding hepatic decompensation/liver-related death, HVPG showed significantly better discriminatory ability compared to NH3-ULN at 24 months (NH3-ULN vs. HVPG: p=0.044 after accounting for multiplicity). In contrast, NH3-ULN was comparable to time-dependent AUROC of the UNOS MELD (2016) score (Figure 2A). Importantly, time-dependent AUROC of NH3-ULN for liver-related death was comparable to those of UNOS MELD (2016)-score and HVPG at all tested time points (Figure 2B).
Figure 2Univariable time-dependent area under the receiver operating curve (AUROC) analyses comparing the prognostic performances of UNOS MELD (2016), HVPG, and NH3-ULN for prognostication of (A) hepatic decompensation/liver-related death and (B) liver-related death in the outcome cohort.
Finally, stratifying the cohort according to NH3-ULN quartiles (q1: <0.49, q2: 0.49-0.66, q3: 0.66-0.91, q4: ≥0.91) identified patient groups with a distinct prognosis (subdistribution HR [SHR] p<0.001; Figure 3A/B). In line, the previously proposed cut-off identified patients with particular poor outcomes in regard to liver-related death (SHR: 3.39 [95%CI: 2.08-5.53]; p<0.001; Figure 3C) as well as hepatic decompensation/liver-related death (SHR: 4.11 [95%CI: 2.74-6.16]; p<0.001; Figure 3D).
Figure 3Cumulative incidence plots of remaining free of liver-related death (A/C) and hepatic decompensation/liver-related death (B/D) according to NH3-ULN quartiles (A/B) and previously published cut-off of 1.4 (C/D) in the outcome cohort. For these cumulative incidence plots, competing risk curves were depicted. In Figures A and B, the subdistribution hazard ratios (SHR) were calculated. For Figures C and D, patients with < 1.4 vs. ≥ 1.4 NH3-ULN were compared.
Associations between ammonia and disease-driving mechanisms in the biomarker cohort
Baseline characteristics of the biomarker cohort are provided in Table 1. Patients included in the biomarker cohort had less viral and more ARLD as underlying aetiology and were more often decompensated at baseline (62% vs. 46%; p<0.001).
Finally, we evaluated the correlation of ammonia with the severity of liver disease (UNOS MELD [2016] score and HVPG), serum BA levels, endothelial dysfunction (VWF), markers of systemic inflammation (CRP, PCT, and IL-6) as well as liver fibrogenesis/matrix remodelling (ELF-test), and markers of hyperdynamic circulation/systemic hemodynamic impairment (mean arterial pressure [MAP], copeptin, renin, and serum sodium).
As demonstrated in Figure 4, ammonia showed low correlations with UNOS MELD (2016)-score, BA, HVPG, VWF, and ELF-test, as well as associations with CRP, IL-6, and PCT, MAP, renin, and serum sodium in the biomarker cohort. Further results on the correlations of ammonia with these biomarkers among compensated and decompensated patients are reported in the Supplementary materials.
Figure 4Heatmaps of correlations of NH3 and pathophysiological biomarkers in the biomarker cohort. Abbreviations: BA bile acids; CRP C-reactive protein; ELF enhanced liver fibrosis; HVPG hepatic venous pressure gradient; IL-6 interleucine-6; MAP mean arterial pressure; NH3 ammonia; PCT procalcitonin; UNOS MELD United Network for Organ Sharing Model for Liver Disease (2016); VWF von Willebrand factor antigen. * denotes p<0.05; ** denotes p<0.001; Spearman’s correlation analyses were conducted.
While routinely measured ammonia is of limited value for diagnosing HE, a recent landmark study highlighted its prognostic implications. In our study, venous ammonia increased across clinical stages of ACLD as well as with more severe hepatic dysfunction and portal hypertension. Importantly, time-dependent AUROC values of ammonia for liver-related death were similar to the laboratory-based composite score UNOS MELD (2016) and HVPG, which can only be measured invasively. Ammonia was not only independently associated with liver-related death – as shown previously – but also with other liver-related outcomes including ACLF, even after adjusting for liver disease, systemic inflammation, and portal hypertension severity. Notably, our findings support the use of the previously proposed cut-off of NH3-ULN of ≥1.4 for risk stratification. Finally, we have provided information on associated pathophysiologic mechanisms that may explain the prognostic value of ammonia, i.e., liver fibrogenesis/matrix remodelling and endothelial dysfunction.
Interestingly, diabetes was independently associated with venous plasma ammonia levels, even after adjusting for various co-factors (adjusted B: 0.134 [95%CI: 0.016, 0.252]; p=0.026; Table 2). Diabetes may increase venous plasma ammonia levels by autonomic dysfunction, extended gastrointestinal transit times, and bacterial overgrowth as well as increased protein catabolism and accelerated muscle-breakdown (
). Even in earlier stages of fibrosis in patients with NAFLD/metabolic-associated fatty liver disease (MAFLD), deficiencies in urea synthesis (in part due to impaired liver-α-cell axis with glucagon resistance and impaired ureagenesis (
)), microglial activation, astrocyte swelling, as well as possibly even neurodegenerative changes and brain atrophy – all due to elevated ammonia levels – have been reported (
) have found that in patients with cirrhosis hospitalised with HE, ammonia was often normal and did not impact treatment decisions, thereby arguing against the routine use of ammonia as either an initial diagnostic test or for guiding medical therapy (
) indicated that ammonia is predictive of hospitalisation/liver-related complications and mortality, showing a better prognostic performance than traditional scores (
). To corroborate the main finding of this study, we have externally validated the cut-off of ≥1.4 NH3-ULN. However, the authors did neither adjust their multivariable models for systemic inflammation nor portal hypertension severity, which has been accounted for in our work. Notably, ammonia levels varied significantly throughout the study centres, with only one centre reporting ammonia levels that were comparable to our cohort, which may be explained by differences in patient selection and characteristics. Interestingly, ammonia levels reported by Gairing et al (
). Our study extends these findings, as it demonstrated that stratifying patients according to the proposed cut-off of ≥1.4 NH3-ULN identifies decompensated patients at risk for ACLF, even if they are still outpatients/clinically stable. Therefore, it may provide the opportunity for the timely initiation of disease-modifying interventions that are currently under investigation (e.g., LIVERHOPE, NCT03150459).
Hyperammonaemia in ACLD is driven by ammonia overproduction/altered microbiome in the intestinal tract (
Bridging the gap between metabolic liver processes and functional tissue structure by integrated spatiotemporal modeling applied to hepatic ammonia detoxification.
). Recent works on the impact of hyperammonaemia in animal and in vitro studies on fibrosis demonstrated the induction of oxidative stress and apoptosis as well as the activation of hepatic stellate cells (HSC) (
). Intriguingly, we observed consistent (i.e., both in cACLD and dACLD) positive correlations between ammonia and ELF-test, which has been shown to reflect fibrogenesis/extracellular matrix remodelling irrespective of the stage of ACLD and indicate HSC activation (
). Moreover, there were also positive correlations with systemic inflammation, VWF as a marker of endothelial dysfunction, and severity of hepatic dysfunction and portal hypertension. Finally, ammonia also correlated with BA, which may be interpreted as a biomarker for portosystemic shunting (
The main limitation of our study is its retrospective design. However, patients were thoroughly characterized at the time of HVPG measurement. For our multivariable models, we were unable to consider several potentially important prognostic indicators (e.g., sarcopenia/frailty), as they have not been recorded systematically. Nevertheless, patients included in our study were extensively characterized in terms of portal hypertension severity, prognostic scores, and routine laboratory parameters including markers of systemic inflammation – importantly, all of these aspects have been considered in our analyses. Model selection was based on expert opinion/biological relevance. Applying backward elimination for variable selection yielded a ‘slimmer’ model for predictive purposes, that still included NH3. Furthermore, we cannot exclude that some hepatic decompensation events have been missed. However, we have thoroughly reviewed electronic health records of the Vienna hospital association and nation-wide electronic health records. Moreover, we have also performed searches of the liver transplant database of our institution (i.e., the only transplant centre in eastern Austria) and examined the nation-wide death registry. Since complete information on (reason of) death is guaranteed by the latter measure, we included liver-related death in all composite endpoints to ensure the ascertainment of the most severe disease courses. We cannot rule-out selection bias since we only included patients undergoing HVPG measurement. However, hemodynamic evaluations are routinely performed for risk stratification and treatment monitoring purposes at our centre, and thus, we are confident that our study population is quite representative of clinically stable outpatients with ACLD treated at our centre. Finally, ammonia testing has several limitations. There is substantial lab variability (
) and venous sampling substantially increases feasibility and therefore clinical utility in outpatients. We have provided a detailed description of the measurement of ammonia in the Methods section of this study and are confident that our results are reliable, since preanalytical and analytical conditions were highly standardized.
In conclusion, venous ammonia predicts hepatic decompensation, non-elective liver-related hospitalisation, ACLF, and liver-related death, independently of established prognostic indicators including CRP and HVPG.
While venous ammonia is linked with several key disease-driving mechanisms, its prognostic value is not explained by associated hepatic dysfunction, systemic inflammation, or portal hypertension severity, suggesting direct toxicity.
Author’s contribution
Concept of the study (LB, JK, MM), data collection (LB, JK, RP, BSi, BSc, MM), statistical analysis (LB, JK, MM), drafting of the manuscript (LB, JK, MM), revision for important intellectual content and approval of the final manuscript (all authors).
Conflicts of interest
The authors have nothing to disclose regarding the work under consideration for publication. The following authors disclose conflicts of interests outside the submitted work: LB, JK, GS, MJ, LH, AFS, PS, and TS have nothing to disclose.
RP received travel support from AbbVie, Gilead and Takeda.
BSc received travel support from AbbVie, Ipsen, and Gilead.
BSi received travel support from AbbVie and Gilead.
MP served as a speaker and/or consultant and/or advisory board member for Bayer, Bristol-Myers Squibb, Eisai, Ipsen, Lilly, MSD, and Roche, and received travel support from Bayer and Bristol-Myers Squibb.
MT served as a speaker and/or consultant and/or advisory board member for Albireo, BiomX, Falk, Boehringer Ingelheim, Bristol-Myers Squibb, Falk, Genfit, Gilead, Hightide, Intercept, Janssen, MSD, Novartis, Phenex, Pliant, Regulus, Siemens and Shire, and received travel support from AbbVie, Falk, Gilead, and Intercept as well as grants/research support from Albireo, Alnylam, Cymabay, Falk, Gilead, Intercept, MSD, Takeda, and UltraGenyx. He is also co-inventor of patents on the medical use of 24-norursodeoxycholic acid.
TR received grant support from AbbVie, Boehringer-Ingelheim, Gilead, Intercept, MSD, Myr Pharmaceuticals, Philips Healthcare, Pliant, Siemens, and W. L. Gore & Associates; speaking honoraria from AbbVie, Gilead, Gore, Intercept, Roche, and MSD; consulting/advisory board fees from AbbVie, Bayer, Boehringer-Ingelheim, Gilead, Intercept, MSD, and Siemens; and travel support from AbbVie, Boehringer-Ingelheim, Gilead, and Roche.
MM served as a speaker and/or consultant and/or advisory board member for AbbVie, Collective Acumen, Gilead, Takeda, and W. L. Gore & Associates and received travel support from AbbVie and Gilead.
Financial support
No financial support specific to this study was received.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Appendix A. Supplementary data
The following is the supplementary data to this article.
Bridging the gap between metabolic liver processes and functional tissue structure by integrated spatiotemporal modeling applied to hepatic ammonia detoxification.
Reiberger T, Schwabl P, Trauner M, Peck-Radosavljevic M, Mandorfer M. Measurement of the Hepatic Venous Pressure Gradient and Transjugular Liver Biopsy. JoVE. 2020(160):e58819.
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