Dataset Information

Serum Metabolomic Analysis of Chronic Drug-Induced Liver Injury With or Without Cirrhosis.

ABSTRACT: Background: Chronic drug-induced liver injury (DILI) occurs in up to 20% of all DILI patients. It presents a chronic pattern with persistent or relapsed episodes and may even progress to cirrhosis. However, its underlying development mechanism is poorly understood. Aims: To find serum metabolite signatures of chronic DILI with or without cirrhosis, and to elucidate the underlying mechanism. Methods: Untargeted metabolomics coupled with pattern recognition approaches were used to profile and extract metabolite signatures from 83 chronic DILI patients, including 58 non-cirrhosis (NC) cases, 14 compensated cirrhosis (CC) cases, and 11 decompensated cirrhosis (DC) cases. Results: Of the 269 annotated metabolites associated with chronic DILI, metabolic fingerprints associated with cirrhosis (including 30 metabolites) and decompensation (including 25 metabolites), were identified. There was a significantly positive correlation between cirrhosis-associated fingerprint (eigenmetabolite) and the aspartate aminotransferase-to-platelet ratio index (APRI) (r = 0.315, P = 0.003). The efficacy of cirrhosis-associated eigenmetabolite coupled with APRI to identify cirrhosis from non-cirrhosis patients was significantly better than APRI alone [area under the curve (AUC) value 0.914 vs. 0.573]. The decompensation-associated fingerprint (eigenmetabolite) can effectively identify the compensation and decompensation periods (AUC value 0.954). The results of the metabolic fingerprint pathway analysis suggest that the blocked tricarboxylic acid cycle (TCA cycle) and intermediary metabolism, excessive accumulation of bile acids, and perturbed amino acid metabolism are potential mechanisms in the occurrence and development of chronic DILI-associated cirrhosis. Conclusions: The metabolomic fingerprints characterize different stages of chronic DILI progression and deepen the understanding of the metabolic reprogramming mechanism of chronic DILI progression to cirrhosis.

SUBMITTER: Chen SS

PROVIDER: S-EPMC8039323 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Anti-tuberculosis drug-induced liver injury (ATB-DILI) is an adverse reaction with a high incidence and the greatest impact on tuberculosis treatment. However, there is a lack of effective biomarkers for the early prediction of ATB-DILI. Herein, this study uses UPLC‒MS/MS to reveal the plasma metabolic profile and lipid profile of ATB-DILI patients before drug administration and screen new biomarkers for predicting ATB-DILI. Methods: A total of 60 TB patients were enrolled, and plasma was collected before antituberculosis drug administration. The untargeted metabolomics and lipidomics analyses were performed using UPLC‒MS/MS, and the high-resolution mass spectrometer Q Exactive was used for data acquisition in both positive and negative ion modes. The random forest package of R software was used for data screening and model building. Results: A total of 60 TB patients, including 30 ATB-DILI patients and 30 non-ATB-DILI subjects, were enrolled. There were no significant differences between the ATB-DILI and control groups in age, sex, smoking, drinking or body mass index (p > 0.05). Twenty-two differential metabolites were selected. According to KEGG pathway analysis, 9 significantly enriched metabolic pathways were found, and both drug metabolism-other enzymes and niacin and nicotinamide metabolic pathways were found in both positive and negative ion models. A total of 7 differential lipid molecules were identified between the two groups. Ferroptosis and biosynthesis of unsaturated fatty acids were involved in the occurrence of ATB-DILI. Random forest analysis showed that the model built with the top 30 important variables had an area under the ROC curve of 0.79 (0.65-0.93) for the training set and 0.79 (0.55-1.00) for the validation set. Conclusion: This study demonstrated that potential markers for the early prediction of ATB-DILI can be found through plasma metabolomics and lipidomics. The random forest model showed good clinical predictive value for ATB-DILI.

Project description:Chronic hepatitis B virus (CHB) infection is one of the primary risk factors associated with the development of hepatocellular carcinoma (HCC). Despite having been extensively studied, diagnosing early-stage HCC remains challenging, and diagnosed patients have a poor (3-5%) survival rate. Identifying new approaches to detect changes in the serum metabolic profiles of patients with CHB and liver cirrhosis (LC) may provide a valuable approach to better detect HCC at an early stage when it is still amenable to treatment, thereby improving patient prognosis and survival. In the present study, we, therefore, employed a liquid chromatography-mass spectrometry (LC-MS)-based approach to evaluate the serum metabolic profiles of 30 CHB patients, 29 LC patients, and 30 HCC patients. We then employed appropriate statistical methods to identify those metabolites that were best able to distinguish HCC cases from LC and CHB controls. A mass-based database was then used to putatively identify these metabolites. We then confirmed the identities of a subset of these metabolites through comparisons with the MS/MS fragmentation patterns and retention times of reference standards. The serum samples were then reanalyzed to quantify the levels of these selected metabolites and of other metabolites that have previously been identified as potential HCC biomarkers. Through this approach, we observed clear differences in the metabolite profiles of the CHB, LC, and HCC patient groups in both positive- and negative-ion modes. We found that the levels of taurodeoxy cholic acid (TCA) and 1,2-diacyl-3-β-d-galactosyl-sn-glycerol rose with the progression from CHB to LC to HCC, whereas levels of 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid were gradually reduced with liver disease progression in these groups. The ROC analysis showed that taurodeoxy cholic acid (TCA), 1,2-diacyl-3-β-d-galactosyl-sn-glycerol, 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid had a diagnosis performance with liver disease progression. These four metabolites have a significant correlation with alpha fetal protein (AFP) level and age. Our results highlight novel metabolic biomarkers that have the potential to be used for differentiating between CHB, LC, and HCC patients, thereby facilitating the identification and treatment of patients with early-stage HCC.

Dataset Information

Serum Metabolomic Analysis of Chronic Drug-Induced Liver Injury With or Without Cirrhosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets