Early detection of metabolic changes in drug-induced steatosis using metabolomics approaches

Diagnostic value of triglyceride-glucose index and related parameters in metabolism-associated fatty liver disease in a Chinese population: a cross-sectional study

OBJECTIVE

Our study aimed to explore the diagnostic value of triglyceride-glucose (TyG) and its related parameters in metabolism-associated fatty liver disease (MAFLD).

DESIGN

A cross-sectional study of residents who attended medical checkups at the First Hospital of Nanping City, Fujian Medical University, between 2015 and 2017.

SETTING

One participation centre.

PARTICIPANTS

2605 subjects met the inclusion-exclusion criteria and were grouped according to whether they had MAFLD.

RESULTS

The TyG index and its associated parameters are positively associated with the risk of developing MAFLD (p<0.001). Restriction cube spline analysis showed a significant dose-response relationship between the TyG index and MAFLD. The risk of developing MAFLD increases significantly with a higher TyG index. After adjusting for confounders, this relationship remains (OR: 4.89, 95% CI 3.98 to 6.00). The areas under the receiver operating characteristic curves of the TyG index for MAFLD detection were 0.793 (0.774 to 0.812). The areas under the curve (AUC) of TyG-related parameters were improved, among which TyG-waist circumference (TyG-WC) showed the largest AUC for MAFLD detection (0.873, 95% CI 0.860 to 0.887). In addition, the best cut-off value of the TyG-WC was 716.743, with a sensitivity and specificity of 88.7% and 71.4%, respectively.

CONCLUSION

The TyG index effectively identifies MAFLD, and the TyG-related parameters improved the identification and diagnosis of MAFLD, suggesting that TyG-related parameters, especially TyG-WC, may be a useful marker for diagnosing MAFLD.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.