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ORIGINAL ARTICLE
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Serum metabolic disturbances associated with acute-on-chronic liver failure in patients with underlying alcoholic liver diseases: An elaborative NMR-based metabolomics study


1 Department of Clinical Metabolomics, Centre of Biomedical Research (CBMR), Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, India; Department of Zoology, Babasaheb Bhimrao Ambedkar University (BBAU), Lucknow, Uttar Pradesh, India
2 Department of Gastrosurgery, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, India
3 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, India
4 Department of Clinical Metabolomics, Centre of Biomedical Research (CBMR), Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, India

Date of Submission28-May-2020
Date of Decision03-Jun-2020
Date of Acceptance18-Jun-2020
Date of Web Publication17-Dec-2020

Correspondence Address:
Dinesh Kumar,
Centre of Biomedical Research (CBMR), SGPGIMS Campus, Lucknow 226014, Uttar Pradesh, ORCID:0000-0001-8079-6739
India
Gaurav Pande,
Department of Gastroeneterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow 226014, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/JPBS.JPBS_333_20

   Abstract 

Objectives: Acute-on-chronic liver failure (ACLF), which develops in patients with underlying alcoholic liver disease (ALD), is characterized by acute deterioration of liver function and organ failures are secondary to that. The clear understanding of metabolic pathways perturbed in ALD-ACLF patients can greatly decrease the mortality and morbidity of patients through predicting outcome, guiding treatment, and monitoring response to treatment. The purpose of this study was to investigate the metabolic disturbances associated with ACLF using nuclear magnetic resonance (NMR)-based serum metabolomics approach and further to assess if the serum metabolic alterations are affected by the severity of hepatic impairment. Materials and Methods: The serum-metabolic profiles of 40 ALD-ACLF patients were compared to those of 49 age and sex-matched normal-control (NC) subjects making composite use of both multivariate and univariate statistical tests. Results: Compared to NC, the sera of ACLF patients were characterized by significantly decreased serum levels of several amino acids (except methionine and tyrosine), lipid, and membrane metabolites suggesting a kind of nutritional deficiency and disturbed metabolic homeostasis in ACLF. The markedly decreased serum levels of branched-chain-amino-acids (valine, leucine, and isoleucine) and elevated levels of trimethylamine N-oxide (TMAO) and methionine were found significantly correlated with clinical scores used to assess the severity of hepatic impairment in ACLF. Twelve serum metabolic entities (including BCAA, histidine, alanine, threonine, and glutamine) were found with AUROC (i.e., area under ROC curve) value >0.9 suggesting their potential in clinical diagnosis and surveillance. Conclusion: Overall, the study revealed important metabolic changes underlying the pathophysiology of ACLF and those related to disease progression would add value to standard clinical scores of severity to predict outcome and may serve as surrogate endpoints for evaluating treatment response.

Keywords: 1H NMR, acute-on chronic liver failure, alcoholic liver disease, diagnostic panel of biomarkers, multivariate analysis, serum metabolomics



How to cite this URL:
Kumar U, Sharma S, Durgappa M, Gupta N, Raj R, Kumar A, Sharma PN, Krishna V P, Kumar R V, Guleria A, Saraswat VA, Pande G, Kumar D. Serum metabolic disturbances associated with acute-on-chronic liver failure in patients with underlying alcoholic liver diseases: An elaborative NMR-based metabolomics study. J Pharm Bioall Sci [Epub ahead of print] [cited 2021 Apr 21]. Available from: https://www.jpbsonline.org/preprintarticle.asp?id=303530




   Introduction Top


The spectrum of alcoholic liver diseases (ALD) includes steatosis (fatty liver), steatohepatitis (fatty liver with inflammation, also called alcoholic hepatitis), progressive liver fibrosis, and cirrhosis.[1] Acute-on-chronic liver-failure (ACLF) is a new clinical syndrome characterized by intense systemic inflammation and acute deterioration of liver function.[2],[3] ACLF differs from acute decompensation of cirrhosis as it occurs on a background of pre-existing chronic liver dysfunction in patients with compensated or decompensated, though stable, cirrhosis, and extra-hepatic organ failures are secondary to that.[1],[4-6] Despite the best possible treatments, ACLF has poor prognostic outcomes and mortality mimics that of acute liver failure (ALF), therefore, nearly all definitions of ACLF consider increased short-term mortality (estimated between 45% and 90%) as a hallmark of ACLF.[3],[7-10] The burden of ACLF among ALD patients with hepatitis and cirrhosis remains significant with an estimated prevalence of more than 40–50%, the condition classified as ALD-ACLF.[1] Statistically, ALD patients with underlying cirrhosis constitute 24%–40% of ACLF burden and management of these patients is 3–4 times more resource intensive.[3],[4],[7],[11]

Currently, the biggest challenge in the clinical management of ACLF is the lack of reliable methods for rapid evaluation of disease severity, predicting therapeutic outcomes and survival. The Model for End-Stage Liver Disease (MELD)––which is commonly applied method for outcome prediction in patients with stable cirrhosis[12],[13]––has several limitations with regard to outcome prediction in ACLF.[14] Therefore, recently a new organ failure based CLIF-SOFA (i.e., chronic liver failure sequential organ failure assessment) score has been developed.[15] Even though, the clinical decisions related to both selecting the appropriate treatment options as well as liver transplantation in ACLF patients are still driven by liver biopsy. Therefore, there is paucity and felt need of noninvasive surrogate markers to improve clinical diagnosis and prognosis of ACLF, monitoring treatment response and moreover to assess the severity of hepatic function. Metabolomics, because it allows rapid identification of metabolic perturbations in biological systems in response to a disease or therapeutic intervention, is increasingly being applied for identification of metabolic markers in body fluids (such as blood plasma/serum, and urine) for improving the diagnosis and prognosis of human diseases.[16] Starting our efforts in this direction, we used nuclear magnetic resonance (NMR)-based metabolomics approach to investigate altered metabolic profiles in the sera of ALD-ACLF patients and sought to identify perturbed metabolic pathways associated with severity of hepatic impairment. Recent past has witnessed plethora of metabolomics studies carried out in the context of ALD on both human subjects and animal models.[8],[17-21] Recently, Richard Moreau with more than 49 coworkers[22] performed untargeted blood-metabolomics study (a multicenter study) using liquid-chromatography coupled to high-resolution mass-spectrometry and uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism underlying ACLF. The NMR-based serum metabolomics-fingerprints of ACLF in patients with alcoholic cirrhosis have also been reported previously by Amathieu et al.[8] The study compared the serum metabolic profiles of 93 patients with compensated or decompensated cirrhosis (CLF group, but stable liver function) and 30 cirrhotic patients hospitalized for the management of ACLF. Compared to CLF group, the sera of ACLF patients were characterized by increased signals of lactate, pyruvate, ketone bodies, glutamine, phenylalanine, tyrosine, and creatinine, whereas the NMR signals of high-density lipids were lower in the ALCF group. Based on this, authors suggested that serum-based metabolic profiling may aid clinical evaluation of patients with cirrhosis, and have the potential to provide useful insights into metabolic pathways affected in acute impairment of hepatic function.[8] However, the metabolic disturbances associated with ALD-ACLF have not been characterized systematically, hitherto by NMR-based metabolomics approach. Further, metabolomics on humans is influenced by many confounding factors such as age, sex, diet, and ethnicity.[23] and thus, large validation studies with suitable control cohorts must be used to remove any potential bias.[24] To the best of our knowledge, this is the first NMR-based serum metabolomics study performed on the north Indian population. As described here,[25] the traditional NMR-based metabolomics analysis involves a comparison of normalized spectral features, that is, spectral bins integrated and then normalized w.r.t. the total spectral intensity. However, this study additionally entailed the discriminatory analysis based exclusively on concentration profiles of about 34 circulatory metabolites and 4 metabolic ratios. The reason to perform additional exercise lies in the fact that the levels of lipid and membrane metabolites differ significantly in the sera of ACLF patients owing to acute deterioration of liver function, as reported previously.[26],[27] As these metabolites contribute significantly in the total spectral intensity of NMR spectrum; therefore, comparison of spectral features normalized w.r.t total spectral intensity may lead to unrealistic or contradictory results. Further, normalization understates the metabolic differences which are relatively very small compared to the total spectral intensity. To rule out such confounding effects due to spectral normalization and to efficiently retrieve more meaningful profiles of metabolic variations related to disease pathology, the concentrations of 38 circulatory metabolites were explicitly measured with respect to formate as an internal reference; this is like creatinine used in urine-based metabolomics analysis to minimize analytical variations.[28] Other specific advantages of using formate as an internal reference are as follows:

  1. The singlet NMR signal of formate appears most downfield in the NMR spectrum of serum and does not overlap with NMR signals of other metabolites.
  2. Formate does not show any binding interaction with serum proteins as shown in a recent study.[29]
  3. On top of this, the commercial software CHENOMX[30] provides the option to use formate as calibration standard; therefore, the analysis can be performed with much ease.


The NMR spectroscopy combined with multivariate statistical analysis is currently the technique of choice for clinical metabolomics studies being noninvasive, simple, cost-effective and fast, and on top of this, it provides high sensitivity and good specificity.[25] The technique when applied to human liver diseases has shown a close relationship between metabolic abnormalities and the severity of the disease in sera and tissues.[18-21],[31-33] Considering this and moreover the need to establish metabolic patterns related to severity of hepatic impairment and outcome prediction in ACLF derived our interest to pursue such an elaborative NMR-based serum metabolomics study so that to identify metabolic disturbances associated with ACLF and further to hunt for metabolic profiles correlated with clinical scores of disease severity.


   Materials and Methods Top


Recruitment of subjects

The study protocol was approved by the institutional research and ethical committee, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India (IEC Code: 2017-186-DM-99(B); File Number: PGI/BE/804/2017; Approval Date: October 30, 2017). The enrollment of subjects was carried out according to the norms of the World Medical Association (WMA) declaration of Helsinki. An informed written consent was obtained from the guardians/kin of the patients after informing them of the purpose of study. Relevant clinical and demographic details were collected for all the subjects in a custom-designed questionnaire. Serum samples were obtained from patients with ALD (n = 40, 100% male with ascites) admitted in a critical intensive care unit (ICU) for the management of ACLF (with Grade ≥II based on CLIF-SOFA score[3]). Only patients with alcoholic acute hepatitis and alcoholic cirrhosis were considered and ACLF was diagnosed as per APASL[34] and ACLF grades as per CLIF–SOFA criteria.[15] Exclusion criteria were age >65 year, severe cardiopulmonary disease, chronic kidney disease (CKD) on dialysis, evidence of hepatocarcinoma or hepatic malignancy, infection with the human immunodeficiency virus, hepatitis B or C viruses, and a past history of acute decompensation during the previous 6 months. Blood samples were obtained within 1–3 days after the patient is stabilized in the ICU. Gender, age, the presence of ascites or hepatic encephalopathy (HE), serum albumin, bilirubin, the international normalized ratio, serum glutamic-oxaloacetic transaminase (SGOT) activity, serum glutamic pyruvic transaminase (SGPT) activity, and serum urea and creatinine levels were recorded at inclusion. For comparative analysis, the serum samples from 49 age-matched normal control (NC) male subjects were collected after taking an informed consent. In each case, it was confirmed that the NC subjects are normotensive with no cardiovascular abnormalities and satisfying the above exclusion criteria. Each subject, either patient or healthy volunteer, provided a blood sample after overnight fasting for serum extraction. The serum was extracted as per the established protocol,[35] transferred into a sterile 1.5-mL microcentrifuge tube (MCT) immediately after the processing and stored at −80°C until the NMR experiments were performed.

Nuclear magnetic resonance measurements

Before starting NMR data collection, all the serum samples (0.5mL in each case) were thawed and centrifuged at 10,000rpm for 5min to remove precipitates. For NMR measurements, the samples were prepared following the procedure as described previously.[25],[36] The serum metabolic profiles were measured on 800-MHz NMR spectrometer using transverse relaxation-edited 1D 1H CPMG (Carr–Purcell–Meiboom–Gill) NMR spectra.[37] The recorded CPMG NMR spectra were manually corrected for phase and baseline distortions using PROCESSOR module of commercial software CHENOMX (NMR Suite, v8.2, Chenomx Inc., Edmonton, Canada). All spectra were calibrated w.r.t. formate δ(8.43) ppm (used here as an internal reference) and the concentration of formate was set to 10 micromolar, that is, nearly close to the detection limit of 800-MHz NMR spectrometer (the circulatory concentration of formate may vary from 10 to 100 μM depending on the health or diseased condition of the subject[38],[39]). The phase and baseline corrected one-dimensional 1H CPMG NMR spectra were imported into PROFILER-Module of CHENOMX for concentration profiling of circulatory metabolites. The data matrix containing concentrations of all samples were grouped according to their respective class information and analyzed using multivariate and univariate statistical tests to identify disease-specific metabolic disturbances. As the study also involves discriminatory analysis based on normalized spectral features, for this, we divided the spectrum ranging from 0.6 to 8.6ppm into equal sized bins of 0.02ppm. The variability due to residual water signal was removed by discarding the spectral region δ(4.56 to 5.1)ppm before binning. The total intensity sum of the spectral bins was used to normalize the integral value of each spectral bin. The resulted data matrix containing normalized spectral bins (or features) was then subjected to multivariate and univariate statistical analysis using different modules of MetaboAnalyst (v4.0, a freely available, user-friendly, web-based analytical platform for metabolomics data analysis from the University of Alberta, Canada: www.metaboanalyst.ca).[40],[41] The analysis was performed following procedure as described previously.[42]

Multivariate analysis

Multivariate analysis is used to transform the complex multivariate data into easily understandable graphical representations and identifying discriminatory variables. We used multivariate analysis to both (a) CPMG data matrix containing normalized spectral bins and (b) concentration profiles of 38 circulatory metabolic entities. Before multivariate analysis, the CPMG data matrix containing normalized spectral bins was pareto-scaled and subsequently, subjected to unsupervised principal component analysis (PCA) for an initial overview of the grouping trend (i.e., intrinsic clustering) and outliers within the data set. After initial overview and identifying the outliers, the supervised partial least-squares discriminant analysis (PLS-DA) was used as a diagnostic model to identify the distinguishing features and further to identify the marker metabolites that can differentiate the ALD-ACLF group from NC group. The data matrix containing concentration profiles was directly subjected to PCA analysis to detect intrinsic clusters and outliers within the data set. PCA was then followed by PLS-DA to maximize class discrimination. As PLS-DA inclines to over fit the data and, therefore, the model robustness was assessed by, 10-fold cross-validation algorithm –which helps to evaluate 100% classification accuracy using the top 5 latent variables.[40] The resulted cross-validation parameters R2 and Q2 were used to assess the quality of the PLS-DA models, that is, the goodness-of discrimination model by R2 and the predictive ability of the model by Q2.[43] The PLS-DA model was further used to identify the metabolites of discriminatory potential based on their higher ranking in the VIP (variable importance on projection) score plot.[44] The metabolic changes of discriminatory potential were further tested for statistical significance using student t-test and those showing P ≤ 0.05 were considered as statistically significant. The boxplot representations (evaluated through univariate t tests) were used to visualize the variation in the levels of significantly altered metabolites. The key metabolic changes were further evaluated for diagnostic potential using receiver operating characteristic (ROC) curve analysis (performed using Biomarker module of Metaboanalyst) and the area under the ROC (AUROC) curve more than 0.9 were considered the criterion for diagnostic performance. Continuous variables were expressed as the mean ± standard deviation (SD) and categorical variables as percentage.


   Results Top


Clinical and demographic details

The clinical and demographic characteristics of the subjects are summarized in [Table 1]. Based on the inclusion and exclusion criteria (as described in “Materials and Method” section), a total 89 subjects were involved in this study, 40 forming the disease group (ALD-ACLF) and 49 forming the NC group. The mean age of the ALD-ACLF and NC groups was 41.7 ± 7.54 and 45 ± 6.73 years, respectively. The main diagnoses for ACLF patients included in this study were sepsis (20%), HE, ascites (100%), and jaundice, whereas the main etiologic precipitant for ACLF was alcohol intake (within 4 weeks) with or without bacterial infection.
Table 1: Biochemical, clinical, and demographic characteristics of patients with ALD-ACLF and control cohorts recorded at inclusion

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Metabolic disturbances associated with acute-on-chronic liver failure in patients with alcohol-related liver disease

This study aimed to compare the NMR-based serum metabolic profiles of 40 ALD-ACLF patients and NC subjects. [Figure 1] compares the cumulative 1D 1H CPMG NMR spectra of serum samples obtained from ALD-ACLF patients (n = 40) and NC subjects (n = 49) and provides an overview of metabolic variations. The majority of metabolites in the NMR spectra were identified and assigned by comparing the chemical shift and peak patterns with the 800-MHz database library of CHENOMX NMR suite.[30] These metabolite assignments were further validated using previously reported NMR assignments of serum/plasma metabolites in the literature in tandem with BMRB database (Biological Magnetic Resonance Data Bank) and HMDB (The Human Metabolome Database: http://www.hmdb.ca).[45],[46],[47],[48] The exercise resulted into the unambiguous identification of 34 circulatory metabolites for concentration profiling in CHENOMX: 3-Hydroxybutyrate (3HB), acetate, acetone, alanine, aspartate, choline, citrate, creatine, creatinine, fumarate, glucose, lactate, glutamate, glutamine, glycerol, glycine, isoleucine, leucine, lysine, mannose, methionine, phenylalanine, proline, propylene-glycol, pyruvate, succinate, threonine, trimethylamine, trimethylamine N-oxide (TMAO), tyrosine, urea, valine, sn-Glycero-3-phosphocholine (GPC), and histidine. Database chemical shifts and literature reports additionally provided identification of NMR signals of (a) N-acetyl-glycoproteins (NAG), (b) lipid and membrane metabolites, for example, choline, GPC, polyunsaturated fatty acids (PUFAs) and (c) lipoproteins, for example, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL) [Figure 1].
Figure 1: Stack plot of cumulative 1D 1H CPMG NMR spectra (left ranging from δ0.6 to δ4.65ppm and right ranging from δ5.0 to δ9.0ppm) recorded on sera of ALD-ACLF patients (in blue) and normal controls (NC, in red). The spectral peaks are labeled according to metabolic assignment. HDL = high-density lipoprotein; LDL = low-density lipoproteins; VLDL = very-low density lipoproteins; DMA = dimethylamine; TMA = trimethylamine; NAG = N-acetyl glycoproteins; 3HB = 3-hydroxybutyrate; GPC = glycerophosphocholine; TMAO = trimethyl-amine N-oxide

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Consistent with previous reports,[8],[26],[27] the visual comparison of 1D 1H CPMG NMR spectra revealed altered serum levels for lipid and membrane metabolites in addition to evidently decreased serum levels of acute-phase proteins as inferred from low intensity NMR signal of N-acetyl glycoproteins at 2.01ppm. However, the subtle metabolic differences in the sera of patients and NC subjects were not visually evident. Therefore, the NMR spectral features were subjected to multivariate data analysis to identify serum metabolic disturbances associated with ALD-ACLF. First, the CPMG data matrix containing normalized spectral features was analyzed using unsupervised PCA method, for evaluating initial grouping trends and class separation. Further, we used supervised clustering method PLS-DA to reveal subtle metabolic differences among the study groups. The two-dimensional (2D) score-plot derived PLS-DA model based analysis of normalized CPMG spectral features is shown in [Figure 2]A. Clearly evident that the serum samples of patients and control groups are well clustered and separated from each other indicating that the serum metabolic profiles of ACLD-ACLF patients are distinctively different from NC subjects. The PLS-DA model cross-validation parameters, R2 (explained variation) and Q2 (predictive capability) were significantly higher (R2 > 0.95, Q2 > 0.88) [Figure 2]B, suggesting that discriminatory model possesses a satisfactory fit with good predictive power. The metabolic features responsible for separating the study cohorts were identified using VIP score indexing. The VIP score plot highlighting top 25 metabolic changes with highest VIP score (VIP score ≥1.5) is shown in [Figure 2]C and the corresponding Student’s t test results are shown in electronic supplementary material ESM, [Figure S1 [Additional file 1]]. Compared to NC group, the serum levels of glucose, branched chain amino acids (valine, leucine), NAG, lipoproteins (HDL, LDL, and VLDL), and other lipid and membrane metabolites (such as choline, GPC, and PUFA) were decreased in ALD-ACLF patients, whereas those of glucose, lactate, acetate, TMAO, betaine, creatinine, and methionine were increased in the patient cohort [Table 2].
Figure 2: (A,D) 2D score plots derived from PLS-DA model analysis involving normalized spectral features (A) and explicit concentration profiles (D) obtained for serum samples of ALD-ACLF patients (in red) and normal control subjects (in blue). The shaded or semi-transparent areas represent the 95% confidence regions of each group as depicted by their respective colors. (B,E) are barplots showing the three performance measures (prediction accuracy, multiple correlation coefficient R2 and the explained variance in prediction Q2) obtained after10 fold Cross Validation analysis. The validation parameters (R2 and Q2) obtained for PLS-DA model based on five and three components are displayed in the respective score plots in (A) and (D). (C,F) The VIP score plots derived from PLS-DA model based on five components in (C) and three components in (F). The symbol asterisk “*” represents the metabolic change is statistically significant as well

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Table 2: Key serum metabolic profiles of discriminatory relevance evaluated for diagnostic potential in differentiating ALD-ACLF from NC using the receiver operating characteristic (ROC) curve analysis

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Point to be mentioned here is that the metabolic differences derived from normalized spectral features may provide ambiguous information when the corresponding bin contains signals from multiple metabolites. For example, the spectral bin at 3.23ppm, which mainly represents glucose, does have contribution of NMR signals from GPC and trimethylamine-N-oxide (TMAO). Other than this, the dominant signals of lipoproteins (LDL, VLDL), lipid/membrane metabolites, glucose, and lactate––which contribute to multiple spectral bins–-does not render the subtle but significant metabolic changes to show off in the VIP score plot [Figure 2]C. Therefore, to extract further relevant information about metabolic changes, the concentration profiles of 34 circulatory metabolites were estimated from CPMG NMR spectra using CHENOMX NMR suite. The concentrations were further used to estimate circulatory ratios relevant in this study context such as glutamate-to-glutamine ratio (EQR); phenylalanine-to-tyrosine ratio (PTR); histidine-to-tyrosine ratio (HTR); and branched-chain amino-acid-to-tyrosine ratio (BTR, estimated as (leucine+isoleucine+valine)/tyrosine; this is also referred as Fischer ratio).[49],[50] The resulted 38 serum metabolic entities [Table 2] were compared using supervised PLS-DA model-based discriminatory analysis and the results are shown in [Figure 2]D–[F]. The 2D score plot derived from PLS-DA model is shown in [Figure 2]D. Clearly evident that the samples are well clustered within their respective groups and the samples of two study groups are well separated suggesting that the serum metabolic profiles of patients with ALD-ACLF are distinctively different from NC subjects [Figure 2]D. Further, the higher values of cross-validation parameters (R2 ~0.75, Q2 ~0.66) clearly established the goodness of separation between classes, and the statistical significance of the class-separating metabolic features [Figure 2]E. [Figure 2]F showing the VIP score plots highlights top 15 metabolic changes ranked according to their increasing discriminatory potential. Compared to NC, the sera of patients with ACLF were characterized by decreased serum levels of various amino acids (valine, leucine, isoleucine, alanine, glycine, proline, threonine, glutamine, glutamate, aspartate, histidine, and phenylalanine), and other circulatory metabolic entities such as glucose, acetate, acetone, citrate, fumarate, mannose, glycerol, trimethylamine, lactate, HTR, and branched-chain-amino-acid-to-tyrosine ratio (BTR), whereas circulatory levels of methionine, TMAO and EQR were found significantly elevated in ALD-ACLF patients.

Following the identification of discriminatory serum metabolic entities (NMR variables), the ROC analysis was performed as a quantitative measure to evaluate their potential (i.e., specificity and sensitivity) for differentiating ACLF from NC cohort [Figure 3]. First, we computed ROC curves for all the top 25 normalized spectral features and the results are summarized in [Table 2]. Of various discriminatory metabolic entities, 14 metabolic entities (HDL, NAG+Lipid, LDL, lipid, choline, GPC, NAG, betaine, glucose, PUFA, TMAO, and glucose, VLDL, and creatinine) were found with AUROC value >0.8, suggesting these NMR-based serum signals show significant potential for differentiating ACLF patients from NC cohort. The representative ROC curve plots of 12 serum metabolic entities with the highest diagnostic potential are shown in [Figure 3] in tandem with corresponding box plots to highlight the degree of metabolic alterations in ACLF compared to NC group.
Figure 3: Top 12 key marker metabolites identified based on ROC curve analysis performed with all 25 normalized spectral features as tabulated in [Table 2]. The computed 95% confidence interval (CI) for individual marker metabolites is highlighted in the faint blue background over the ROC curve. The area under the receiver operating characteristic curve (AUROC) is shown in red to highlight the diagnostic potential of corresponding circulatory metabolite. The box-cum-whisker plots shown in the right side of each ROC curve plot clearly reveal metabolic change in patients with ALD-ACLF compared to NC. For each box plot showing quantitative variations of relative NMR signal integrals, the boxes denote interquartile ranges, the horizontal red line inside the box denotes the median, and the bottom and top boundaries of the boxes are the 25th and 75th percentiles, respectively. Lower and upper whiskers are the 5th and 95th percentiles, respectively

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Next, the concentration profiles of circulatory metabolites were evaluated for their diagnostic potential and the results of ROC curve analysis are summarized in [Table 3]. Of 38, 12 circulatory metabolic entities (valine, isoleucine, leucine, HTR, BTR, histidine, alanine, trimethylamine, GPC, fumarate, threonine, and glutamine) were found with AUROC value >0.9 suggesting these could be potential biomarkers for clinical evaluation and surveillance of patients with ACLF in critical care. The representative ROC curve plots of 12 serum metabolic entities with the highest diagnostic potential are shown in [Figure 4]A in tandem with their respective box plots in [Figure 4]B to highlight the metabolic aberrations in ACLF compared to NC group. The implications of these metabolic changes (i.e., similarities or differences) in the pathogenesis of ACLF patients with underlying ALD have been discussed in detail below.
Table 3: Biomarker analysis performed on circulatory metabolites for discriminating patients with ALD-ACLF from normal control subjects

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Figure 4: (A) Receiver operating characteristic (ROC) curve analysis performed for evaluating the diagnostic potential of various circulatory metabolites and their specific ratios for differentiating ALD-ACLF from NC group. The ROC plots of 12 circulatory metabolites identified with highest value of area under the ROC curve are shown here. (B) The box-cum-whisker plots showing quantitative variations for key circulatory metabolites of discriminatory potential (identified based on their top ranking in the VIP score plot and high AUROC values). In the box plots, the boxes denote interquartile ranges, horizontal line inside the box denote the median, and bottom and top boundaries of boxes are 25th and 75th percentiles, respectively. Lower and upper whiskers are 5th and 95th percentiles, respectively

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The metabolic alterations contributing to the development and progression of a disease are often correlated. [Figure S2 [Additional file 2]] and [Figure S3 [Additional file 3]] (in ESM) show the statistical correlation heat maps generated using Pearson r method for concentration profiles of patients with ACLF and NC subjects, respectively. Clearly evident that the concentration profiles of circulatory metabolites depleted in the sera of patients with ACLF are positively correlated. The various metabolic alterations were found grouped into four clusters. Cluster I included acetate, glucose, leucine, succinate, aspartate, mannose, GPC, TMA, choline, glycerol, lysine, isoleucine, valine, glutamate, fumarate, lactate, alanine, threonine, glutamine, glycine, histidine, phenylalanine, proline, citrate and tyrosine. Cluster II included 3-hydroxybutyrate and acetone, Cluster-III included creatinine, creatine, and urea, and Cluster IV included HTR, BTR, and PTR. Similarly, the concentration profiles of circulatory metabolites elevated in the sera of ACLF patients (i.e., TMAO and methionine) are positively correlated. The correlation analysis thus established that the observed metabolic changes are well relevant in the context of ACLF pathology and may show correlation with clinical parameters or scores computed empirically to assess the severity of the disease. The Model for End-Stage Liver Disease (MELD), Child–Turcotte–Pugh (CTP), and CLIF-SOFA (i.e., chronic liver failure sequential organ failure assessment[15]) scores are three common clinical parameters used in this study for predicting outcomes in ACLF. Therefore, the Pearson’s correlation analysis was performed to evaluate if the marker metabolites discriminating ACLF from NC are correlated with these clinical parameters and the results are summarized in [Figure 5]. The bar plots in [Figure 5A]–[C] show the Pearson correlation coefficient (r) estimated for normalized spectral features [Table 2] with different clinical scores. Clearly evident that MELD score correlated positively with spectral bins corresponding to betaine and TMAO (r = 0.56 and 0.46, respectively, with P < 0.05), whereas MELD showed negative correlation with spectral bins corresponding to valine (r = –0.32, P < 0.05), glucose (r = –0.32, P < 0.05), choline (r = –0.28, P < 0.1) and GPC (r = –0.27, P < 0.1). Although CTP score showed no significant correlation with spectral features, a positive correlation is evident with betaine and negative correlation with LDL, HDL, choline, and GPC [Figure 5B]. As higher HDL and LDL levels seem to reflect good hepatic function, the observed negative correlation seems to be pathologically relevant. Like MELD, CLIF-SOFA scores were also found positively correlated with spectral bins corresponding to betaine and TMAO (r = 0.37 and 0.32, respectively, with P < 0.05). The bar plots in [Figure 5D]–[F] show the Pearson correlation coefficient (r) estimated for serum metabolic concentrations of ACLF patients [Table 3] with different clinical scores. Like in case of normalized spectral features, the clinical MELD score correlated positively with TMAO (r = 0.57, P < 0.0001) and correlated negatively with valine, isoleucine, and cumulative concentration of branched-chain amino acids (i.e., BCAA= valine + leucine + isoleucine) with P-value nearly close to statistical significance (i.e., 0.1, 0.14, and 0.16, respectively, for valine, isoleucine, and BCAA). The clinical CTP score showed statistically significant (with P < 0.05), but, negative correlation with choline (r = –0.36) and glutamate (r = –0.32) [Figure 5E]. Like MELD, the CLIF-SOFA score was also found positively correlated with TMAO (r = 0.37, P < 0.05); however, unlike MELD, the CLIF-SOFA showed statistically significant (with P < 0.05), but negative correlations with aspartate (r = –0.37), N,N-dimethylglycine (r = –0.36), BTR (r = –0.31), and leucine (r = –0.30) [Figure 5]F.
Figure 5: The metabolic profiles subjected to Pearson r based correlation analysis. (A, B and C) are the correlation plots for normalized spectral bins with MELD, CTP and CLIF-SOFA, respectively. (D, E and F) are the correlation plots for metabolic concentration profiles with MELD, CTP and CLIF-SOFA, respectively. The circulatory metabolites showing P value nearly close to statistical significance (i.e., P < 0.1) have been highlighted with ^

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   Discussion Top


This study involves NMR-based serum metabolomics analysis to identify the diagnostic panel of biomarkers for predicting outcomes, evaluating the severity of hepatic impairment, and monitoring treatment response in ACLF. The observed metabolic disturbances will further improve our understanding about biological pathways affected in ACLF. First, we were able to clearly show distinct differences in the NMR spectra acquired on the serum samples of ACLF patients compared to those acquired on normal healthy controls [Figure 1]. In conjunction with multivariate statistical models, then we revealed aberrant serum metabolic disturbances associated with ACLF in patients with underlying ALD [Figure 2]. Next, NMR-based serum metabolic profiles were evaluated for their diagnostic potential using univariate statistical tools [Figure 3] and [Figure 4]. Finally, we used statistical correlation methods to establish the clinical utility of NMR-based serum metabolic profiles for predicting clinical outcomes and disease severity in ACLF [Figure 5].

The discriminant analysis based on normalized spectral features revealed significantly decreased NMR signals of lipid and membrane metabolites (including HDL, LDL, VLDL, PUFA, choline, and GPC) [Table 2] and [Figure 3]. According to a recent NMR-based plasma metabolomics study involving surviving and nonsurviving patients with decompensated cirrhosis,[51] the NMR plasma profiles of phosphatidylcholines and lipid metabolites were significantly reduced in nonsurvivors. Rachakonda et al.[17] compared the serum metabolic profiles of acute alcoholic hepatitis (AAH) and cirrhotic patients and reported that compared to cirrhotic patients, the patients with severe AAH showed significant reductions in three major phospholipid metabolites––choline, GPC, and glycerol-3-phosphate.[17] Considering this and the observed statistically significant correlation of circulatory choline with CTP score [Figure 5E] and MELD score [Figure 5A], we believe that choline may serve as a prognostic marker for predicting hepatic severity in ACLF for patients with ALD.

Two important metabolites, TMAO and betaine, were found significantly increased in the sera of patients with ACLF [Figures 3] and [4]. Both these metabolites are generated by gut microbiota from dietary nutrients (phosphatidylcholine, choline and L-carnitine). In conjunction with lower circulatory levels of choline, this can also be a representation of impaired bile-acid metabolism in the liver. The elevated circulatory levels of TMAO have been implicated in the pathogenesis of many cardiovascular diseases (CVDs) including atherosclerosis.[52],[53],[54],[55] Recently, the elevated TMAO levels during an earlier period of ischemic brain stroke have been associated with poor prognosis.[56] Further in individuals with type 1 diabetes, higher plasma levels of TMAO have been found to be associated with mortality, CVD events, and poor renal outcome, independent of conventional risk factors.[57] Considering these clinical findings on TMAO which is also positively and most significantly correlated to MELD and CLIF-SOFA score, we believe that the elevated serum levels of TMAO in patients with ACLF might be related to a higher risk of mortality, CVD events, neurological and renal manifestations. The higher serum levels of TMAO further convinced us of the validity of our previous study results where we showed increased gut permeability in patients with ACLF compared to liver cirrhosis patients.[58] The leaky gut, therefore, may allow the passage of gut microbial metabolite of choline and betaine directly into the bloodstream and can cause dysregulated bile acids (BAs) metabolism and other associated complications.[59]

Another common complication associated with ACLF in patients with alcoholic liver cirrhosis is ascites. Approximately 95% of the patients with ACLF involved in this study were manifested with ascites [Table 1]. Yang et al.[60] studied the serum metabolomic alterations in liver cirrhotic patients with ascites. Compared to controls, the serum levels of several amino acids (including alanine, valine, leucine, phenylalanine, proline, threonine, glutamine, and histidine) were significantly decreased in patients with cirrhotic ascites (P < 0.01). Consistent with this report,[60] our results also indicated decreased serum levels of several amino acids except for methionine and tyrosine. The study by Yang et al.[60] showed no significant difference in the serum levels of methionine between patients and controls. However, markedly increased serum levels of methionine (the condition known as hypermethionemia) have been found in fulminant hepatic (acute liver) failure patients and cirrhotic patients manifested with decompensated signs such as ascites, jaundice, hepatomegaly, HE and features of the systemic inflammatory response syndrome (SIRS).[17],[61],[62] The serum methionine levels have also been suggested to be useful in predicting the severity of liver damage in patients with irreversible fulminant hepatic failure[62] and predicting encephalopathy in severe hepatic failure.[61],[63] Hypermethionemia lends aberrant methyl group flux which in turn contributes to the development of hepatic, neurological, and cardiovascular dysfunction in humans.[64] Recently Yang et. al.[20] compared the serum metabolic profiles between well-characterized cohorts of heavy drinking ALD patients (HD-ALD) and alcoholic cirrhosis patients (AC) w.r.t. NCs. The study clearly showed that the serum levels of methionine are elevated in AC cohort compared to HD and NC cohorts and further serum methionine levels were higher in HD compared to NC.[20] Therefore, the significantly elevated levels of methionine in the sera of ACLF patients (which may reflect decreased use of methionine for S-adenosyl methionine (SAMe) generation[17]) might be related to progressive liver damage as inferred from its positive correlation with TMAO (r = 0.54, P < 0.0001) as well as with MELD (r = 0.21, though not statistically significant with P = 0.19). The correlation analysis further revealed an interesting fact that though the serum levels of proline are significantly reduced in patients, it is positively correlated with methionine (r = 0.46, P < 0.01). Elevated serum proline (i.e., hyperprolinemia) is characteristic of ALD and has been related to the hyperlactacidemia found in alcoholic cirrhotics.[65] Hyperprolinemia has also been suggested as a possible marker of liver fibrogenesis in ALD.[65]

Consistent with previous metabolomics reports on alcoholic cirrhotic patients,[20] the serum levels of branched-chain amino acids (BCAAs, valine, isoleucine, and leucine) were found most significantly decreased in patients with ACLF [Table 3] and [Figure 4] and found to be negatively correlated with MELD and CLIF-SOFA (with P-value nearly close to the statistical significance, [Figure 5D], [F]. The discriminatory analysis based on normalized spectral features also mimed this trend; however, the degree of alteration was found to be different [Table 2]. Important to mention here is that branched-chain amino acids (BCAAs) are energy substrates in muscles and previous studies proved that cirrhotic patients had lower levels of BCAAs, but higher levels of aromatic amino acids (AAAs).[66],[67] Based on these metabolic alterations, the branched-chain amino acid (BCAA)/tyrosine (Tyr) ratio (BTR) has been reported to be a good indicator of the severity of hepatic disorders,[68] parenchymal injury in patients with chronic liver diseases,[49],[50] and a reliable marker for monitoring response to nutritional therapy in patients with chronic liver disease.[69] The decreased serum BTR levels are also thought to be a cause of HE.[50] Consistent with these reports, the serum BTR levels were significantly decreased in patients with ACLF and also negatively correlated with CLIF-SOFA score [with r = –0.31 and P < 0.05, [Figure 5F]] suggesting that serum BTR levels could serve to improve the prognostic screening of ACLF. However unlike cirrhotic patients, the serum tyrosine levels were not significantly different between ACLF and NC groups which may limit the utility of BTR in predicting disease severity; therefore future studies are required to confirm its utility in future clinical evaluation.

ACLF in patients with ALD often develops as a consequence of an acute burst of inflammatory reaction in response to precipitating events (such as sepsis, alcoholic hepatitis, acute liver injury due to ischemic, and others) and the severity of the inflammatory reaction correlates with prognosis.[70] Studies suggest that inflammation and immune activation reduce the turnover of phenylalanine due to reduced activity of phenylalanine hydroxylase (PAH) enzyme.[71],[72],[73],[74],[75] Therefore, circulatory levels of phenylalanine and PTR elevate in patients with inflammatory condition such as sepsis, cancer, viral infections, and post-infection reactive arthritis.[71],[72],[73],[74],[75] Recent data suggest that actually these are the reactive oxygen species which destroy the oxidative labile 5, 6, 7, 8-tetrahydrobiopterin (BH4) which is cofactor for several aromatic amino acid monooxygenases such as PAH and tyrosine hydroxylase (TH).[73] TH is the rate-limiting enzyme of catecholamine biosynthesis, therefore, oxidative stress can reduce the biosynthesis of catecholamines (norepinephrine, epinephrine, and dopamine) lending neurospychiatric symptoms such as mood changes and depression.[71],[73] Contrary to expectation, the circulatory levels of phenylalanine were significantly decreased in patients with ACLF, whereas no significant difference was observed for PTR levels compared to NC suggesting PTR levels are poor indicators of oxidative stress in ACLF. In conjunction with decreased NMR signals of N-acetyl glycoproteins (NAG) for ACLF serum samples [Figure 3], the metabolomics snapshot represented compromised anti-inflammatory response in ACLF.

Histidine––another aromatic amino acid profiled by NMR––is a key precursor for histamine and serves as a principle mediator of many pathological processes necessary for Inflammatory signaling and oxidative stress.[76] The serum levels of histidine and HTR were found significantly decreased in patients with ACLF compared to NC. However, both histidine and HTR levels were not found significantly correlated to clinical scores suggesting these are poor prognostic indicators, but, may have their contribution to ACLF symptomatology. We further assessed the status of activated glutaminolysis in patients with ACLF through comparing the glutamate-to-glutamine ratio (EQR). Glutamine (an important immunonutrient) is reported to improve the clinical outcome in critically ill ICU patients and its marked depletion is shown to be an independent predictor of hospital mortality.[77],[78] This is because majority of nonhepatic cells, particularly, the immune cells prefer glutaminolysis under conditions of systemic inflammation and converts glutamine to glutamate and ammonia. The resulted glutamate is then metabolized to provide intermediates for various important biological processes including Krebs Cycle to replenish the high energy demand in critically ill patients.[29] On the contrary, liver glutamine metabolism (i.e., generation of glutamine from glutamate and ammonia in the presence of glutamine synthase enzyme) serves to control the circulatory levels of glutamate and ammonia.[79] Therefore, it is legitimate to consider that under severe deterioration of hepatic functions, the glutamine turnover will decrease rendering elevated EQR levels. As expected, the serum EQR levels were significantly increased in ACLF compared to NC; however, there was no significant correlation found between EQR levels and clinical scores of hepatic severity. Rather, the serum glutamate levels––which were significantly decreased in ACLF––were found significantly correlated to CTP score suggesting need of future studies to understand whether high EQR or low glutamate levels should be regarded as prognostic biomarkers or as contributors to disease progression.

Concluding remarks

This study represents the first most elaborative high-field NMR-based metabolomics analysis of human sera collected from well-characterized ACLF patients with pre-existing ALDs. The sera of ACLF patients were characterized by significantly decreased serum levels of various amino acids (except methionine and tyrosine) and those of lipid and membrane metabolites suggested a severe nutritional deficiency in ACLF which may be attributed to several factors such as poor oral intake and follow a low-calorie diet, fat malabsorption due to impaired mucosal barrier function,[58] and metabolic response to the stress of critically ill.[80] Malnutrition such as protein-energy malnutrition, muscle, and adipose tissue depletion is common in patients with advanced liver disease[81],[82],[83] and may progress further as liver function crumbles resulting in other critical complications like ascites, HE, infections, and death as evident in case of ACLF.[84] Serum metabolic features such as BCAAs, BTR, methionine, TMAO, and betaine, were found significantly correlated to the severity of hepatic functions as inferred from their statistical correlations with clinical MELD, CTP and CLIF-SOFA scores. The ROC curve analysis confirmed the diagnostic potential of these metabolic signatures and their potential utility as a biomarker panel for predicting prognosis and monitoring therapeutic response. The various results when compared with previous serum/plasma metabolomics studies involving cirrhotic patients with and without ACLF further confirmed the validity of observed metabolic disturbances and their association with the severity of hepatic function impairment underlying the pathogenesis of ACLF in patients with ALDs. However, the clinical use of NMR-based serum metabolomics approach as a severity assessment tool especially of prognostic value for organ failures in ACLF needs further studies and validation studies on larger prospective cohorts of patients with ACLF in a longitudinal manner to confirm these results and their association with clinical outcomes and severity of hepatic and extra-hepatic impairment. Nevertheless, this study will form the basis for future clinical metabolomics studies aiming to identify metabolomics biomarkers for early diagnosis, predicting outcomes, differentiating clinical subtypes, determining the severity of the condition, monitoring treatment response, and guiding clinical trial testings.

Acknowledgement

DK acknowledges the Department of Science and Technology for financial assistance under SERB EMR Scheme (Ref. No.: EMR/2016/001756). AG acknowledges the Department of Science and Technology (DST), Government of India for financial assistance under DST INSPIRE Faculty Award (Ref. No. DST/Inspire Faculty Award 2014/LSBM-120) and SERB Women Excellence Award (Ref. No. SB/WEA-08/2019). We would also like to acknowledge the Department of Medical Education, Govt. of Uttar Pradesh for supporting the High Field NMR Facility at Centre of Biomedical Research, Lucknow, India. UK acknowledges receipt of a SRF fellowship [ICMR sanction no.3/1/3/JRF-2014/HRD-100 (32508)] from The Indian Council of Medical Research (ICMR), New Delhi, India. RR and NG acknowledge receipt of an SRF fellowship from CSIR, India. Dr. Gaurav Pande and Dr. Dinesh Kumar have equally contributed to this article and both of them are corresponding author. Therefore, either of them can be contacted for any future correspondence.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

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