Salivary metabolites are promising noninvasive biomarkers of drug-induced liver injury

Abstract

BACKGROUND

Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.

AIM

To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.

METHODS

Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.

RESULTS

We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1).

CONCLUSION

Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.

Key Words: Drug-Induced liver injury, Salivary, Metabolomics, Biomarker, Weighted metabolite Coexpression network analysis, Machine learning, Noninvasive, Diagnostic method, Metabolites

Core Tip: Drug-induced liver injury (DILI) is one of the most common and serious adverse reactions to drugs. Conventional biomarkers are not specific and there is an urgent need for a non-invasive DILI marker. Our study has revealed a significant difference in salivary metabolites between patients with DILI and healthy individuals, and identified five metabolites that can distinguish DILI from healthy control, namely 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine. Our study may provide a potential feasible non-invasive diagnostic method for DILI.

INTRODUCTION

Drug-induced liver injury (DILI) refers to liver damage caused by the consumption of prescription or nonprescription drugs, health products, natural remedies, biological agents, and herbal and dietary supplements (HDSs)[1]. According to recent epidemiological studies, the incidence of DILI in Western countries is approximately 14-19 cases per 100000 people, with an increasing trend[2,3]. While liver injury can improve after the discontinuation of the suspected drugs in most patients with DILI, more than 10% of patients with DILI may progress to chronic hepatitis, acute liver failure, or even death[4]. It has been reported that DILI is the most common cause of acute liver failure, with a 180-day case fatality rate of 8% in the United States[5,6]. Unlike metabolic liver diseases, which have a well-defined histology, a liver biopsy from a patient with DILI can reveal a variety of histological features, such as inflammation, fibrosis, vascular injury, cholestasis, necrosis, nodular regeneration, and ductal destruction[7]. Thus, establishing a diagnosis of DILI can be challenging, as it relies mainly on excluding other common causes of liver injury. As there are currently no diagnostic tests or biomarkers available for idiosyncratic DILI, diagnosis typically depends on a thorough medical history that includes a detailed medication history, exclusion of other potential causes of liver disease, and a series of liver biochemical tests before and after cessation of the medication[8]. Several clinical tools have been developed to assess the causality of DILI, with the Roussel Uclaf Causality Assessment Method (RUCAM) being the most widely used. However, importantly, RUCAM is far from perfect in terms of its accuracy and effectiveness[9]. It is therefore extremely important to develop noninvasive biomarkers with high sensitivity and specificity for the early detection and therapeutic evaluation of DILI.

Metabolomics involves profiling small-molecule metabolites and offers the potential to identify and characterize specific metabolic phenotypes associated with a given disease[10]. To date, metabolite biomarkers for DILI have been identified in plasma, serum and urine[11-14]. Saliva is a highly promising biological fluid for the detection of disease biomarkers owing to its noninvasive and painless collection procedures as well as its simple storage and handling requirements, which require minimal training[15]. Several recent studies have highlighted the potential use of metabolite biomarkers in saliva for identifying patients with oral cancer[16], breast cancer[17], periodontitis[18], diabetes[19], schizophrenia[20], Alzheimer’s disease[21], hepatocellular carcinoma and chronic liver disease[22], but there are no related reports in patients with DILI. This study aimed to identify novel biomarkers for DILI and represents the first demonstration of the utility of salivary metabolites for discriminating patients with DILI from healthy individuals.

MATERIALS AND METHODS

Subject recruitment

This study included patients with DILI who were hospitalized at the Fifth Medical Center of PLA General Hospital between July 2020 and June 2021. The study was approved by the Ethics Committee of the Fifth Medical Center of PLA General Hospital (2020050D). Prior to their inclusion in the study, all research subjects or their representatives were required to sign a written informed consent form. Patient demographic information and laboratory data were obtained through electronic medical records and questionnaires.

The inclusion criteria for this study were as follows: (1) Aged between 18 and 70 years; (2) newly diagnosed with DILI; and (3) voluntary participation in the study and signing of the informed consent form upon inclusion.

The exclusion criteria for this study were as follows: (1) Patients with other concomitant causes of liver injury (such as viral, alcoholism, autoimmune, metabolic, tumor, genetic, or biliary diseases); and (2) patients with autoimmune diseases, malignant tumors, or severe heart, lung, or renal insufficiency.

The diagnostic criteria utilized in this study to identify DILI were as follows[8]: (1) Recent liver biochemistry indices displaying clinically significant abnormalities; (2) a comprehensive history of medication and HDS use within the 180 d prior to presentation; and (3) exclusion of alternative causes of liver injury. Clinically significant abnormalities in liver biochemistry commonly met at least one of the following criteria[8]: (1) Serum aspartate aminotransferase (AST) or alanine aminotransferase (ALT) levels greater than 5 times the upper limit of normal (ULN) or alkaline phosphatase (ALP) levels greater than 2 times the ULN on at least two separate occasions, each 24 hours apart; (2) total serum bilirubin (TBIL) levels greater than 2.5 mg/dL in conjunction with elevated serum AST, ALT, or ALP levels; or (3) international normalized ratio (INR) levels greater than 1.5 with elevated serum AST, ALT, or ALP levels. According to the updated RUCAM[23], patients with definite or probable DILI (score 6 or higher) were included in this study.

Saliva collection

Saliva was collected in Salivette tubes (Sarstedt AG and Co., Numbrecht, Germany) as described previously[24]. All samples were collected between 9 a.m. and 12 a.m. Each subject was asked to refrain from smoking, eating, drinking and tooth brush procedures for at least 1 h before saliva collection. Additionally, the subjects were required to gently gargle with water prior to saliva collection to remove food debris. To stimulate salivation, subjects were instructed to gently chew and roll the cotton swab for 60-90 seconds and then to spit the cotton swab back into the collection tube of the kit. The tubes were immediately kept on ice and centrifuged within 1 h at 10000 × g for 10 min at 4 °C. The collected supernatants were aliquoted (100 μL) without any further processing and stored at -80 °C until sample analysis.

Metabolite extraction and ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry

This study was conducted according to previously described methods[25,26]. The saliva samples (100 μL) were placed in EP tubes and resuspended in prechilled 80% methanol by thorough vortexing. The samples were then incubated on ice for 5 minutes and centrifuged at 15000 g and 4 °C for 20 min. A portion of the supernatant was diluted with liquid chromatography (LC)/mass spectrometry (MS)-grade water to obtain a final concentration of 53% methanol. The resulting mixture was transferred to a fresh Eppendorf tube and centrifuged at 15000 × g and 4 °C for 20 min. Finally, the supernatant was injected into the LC-MS/MS system for analysis.

Statistical analysis

Statistical analyses were conducted using R software (version R-4.2.2) and Python (version 3.11.1). In cases where the data were not normally distributed, the area normalization method was employed to attempt normal transformations. Principal component analysis (PCA) was carried out in an unsupervised manner to provide a broad view of the data distribution. Partial least squares-discriminant analysis (PLS-DA) was performed as a supervised model to evaluate metabolic alterations among groups, and a permutation test was carried out 200 times to check for overfitting risks in the PLS-DA model. Pathway enrichment analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods were employed for optimization and modeling. Weighted metabolite coexpression network analysis (WMCNA) was used to identify sets of metabolites with similar expression profiles across samples. The Pearson correlation coefficient was used to determine the correlation between metabolites and liver function indices. The χ² test or Fisher’s exact test was used for categorical parameters, while student’s t test or the Mann-Whitney U test was used for two-group comparisons, as appropriate. A P value < 0.05 was considered to indicate statistical significance.

RESULTS

Baseline characteristics

A total of 31 patients diagnosed with DILI and 35 healthy controls (HCs) were included in this study. Table 1 presents the clinical characteristics of both groups. Compared with the HCs, the patients in the DILI group exhibited significantly greater ALT, AST, TBIL, ALP, INR, gamma-glutamyl transpeptidase (GGT), and total bile acid levels and significantly lower levels of albumin (P < 0.001). There were no significant differences in other baseline characteristics, including age, sex, weight, or body mass index, between the two groups (P > 0.05).

Table 1 inical characteristics of 66 subjects.

Clinical characteristic	DILI (n = 31)	HC (n = 35)	P value
Age, yr, median (IQR)	50.0 (46.0-60.0)	52.0 (45.0-61.0)	0.585
Female	19 (61.3)	19 (54.3)	0.566
Weight, kg, median (IQR)	60.0 (53.0-74.0)	63.0 (58.0-70.0)	0.310
BMI, kg/m2, median (IQR)	21.5 (20.2-25.0)	22.9 (20.8-25.4)	0.676
Laboratory examination
ALT, U/L, median (IQR)	238.0 (91.0-526.0)	22.0 (12.0-27.0)	< 0.001
AST, U/L, median (IQR)	182.0 (74.0-270.0)	25.0 (16.0-30.0)	< 0.001
TBIL, μmol/L, median (IQR)	72.7 (32.1-117.4)	8.5 (5.7-13.1)	< 0.001
ALP, U/L, median (IQR)	137.0 (92.0-215.0)	86.0 (77.0-114.0)	< 0.001
GGT, U/L, median (IQR)	140.0 (82.0-213.0)	32.0 (17.0-41.0)	< 0.001
ALB, g/L, median (IQR)	36.0 (32.0-39.0)	41.0 (39.0-46.0)	< 0.001
TBA, μmol/L, median (IQR)	42.0 (11.0-73.0)	9.0 (5.0-17.0)	< 0.001
LDH, U/L, median (IQR)	186.0 (158.0-202.0)	188.0 (146.0-243.0)	0.550
INR, median (IQR)	1.0 (1.0-1.1)	0.9 (0.9-1.0)	< 0.001

BMI: Body mass index; ALT: Alanine transaminase; AST: Aspartate transaminase; GGT: γ-glutamyl transferase; TBIL: Total serum bilirubin; ALP: Alkaline phosphatase; GGT: Gamma-glutamyl transpeptidase; ALB: Aalbumin; TBA: Total bile acid; LDH: Lactate dehydrogenase; INR: International normalized ratio; DILI: Drug-induced liver injury; HC: Healthy control.

Differences in salivary metabolite composition between the two groups

In total, 853 metabolites were detected by ultrahigh-pressure LC coupled with tandem MS. The HC and DILI groups were not well separated according to unsupervised PCA (Figure 1A and B). A PLS-DA was performed for both the positive and negative modes of MS, which clearly differentiated between the HC and DILI groups (Figure 1C and D). The 200-permutation test further validated the reliability and validity of the PLS-DA model (Figure 1E and F), indicating that there was no overfitting in the PLS-DA model. Interestingly, the salivary metabolite composition significantly differed between the control and DILI groups. The differentially expressed metabolites (DEMs) between the two groups were screened using an independent sample t test (variable importance in the projection ≥ 1, FC ≥ 1.5 or ≤ 0.67, and P < 0.05). We found 188 salivary DEMs screened in positive mode and 59 in negative mode (Figure 2) (Supplementary Table 1).

Figure 1 Multivariate statistical analysis. A: Principal component analysis (PCA) scatter plots of the two groups in positive mode; B: PCA scatter plots of the two groups in negative mode; C: Partial least squares-discriminant analysis (PLS-DA) scatter plots of the two groups in positive mode; D: PLS-DA scatter plots of the two groups in negative mode; E: Cross-validation plot of the two groups with a permutation test repeated 200 times in positive mode; F: Cross-validation plot of the two groups with a permutation test repeated 200 times in negative mode. Healthy controls group (n = 35, blue circles), drug-induced liver injury group (n = 31, red circles). PCA: Principal component analysis; PLS-DA: Partial least squares-discriminant analysis.

Figure 2 Differentially abundant metabolite analysis. A: Volcanograms of differentially abundant metabolites between drug-induced liver injury (DILI) and healthy controls (HC) in positive mode; B: Volcanograms of differentially abundant metabolites between DILI and HC in negative mode; C: Heat plots of the differentially abundant metabolites between DILI and HC in positive mode; D: Heat plots of the differentially abundant metabolites between DILI and HC in negative mode.

WMCNA and identification of hub metabolites

WMCNA is a powerful tool for identifying sets of metabolites that exhibit highly synergistic changes. Rather than focusing solely on differentially abundant metabolites, WMCNA leverages information from thousands or even tens of thousands of relevant metabolites and conducts significant association analyses of the metabolites with phenotypes. Therefore, we used WMCNA and constructed a network to further investigate the relationships between metabolites and DILI. The results of the WMCNA classified metabolites into five modules of closely associated metabolites, among which the turquoise consensus module was the most relevant to DILI (r = 0.77, P < 0.001) (Figure 3A and B) (Supplementary Table 2). Consequently, the turquoise module was selected as a clinically significant module for further analysis. The results of KEGG enrichment analysis showed that metabolites in the turquoise module were mainly involved in purine metabolism and beta-oxidation of very long-chain fatty acids (Figure 3C). Additionally, eight metabolites in the turquoise module with a metabolite significance of > 0.2 and a module membership of > 0.8 were selected (Figure 3D). Remarkably, these eight metabolites in the turquoise module overlapped with those previously identified in the PLS-DA.

Figure 3 Identification of the hub metabolites using weighted metabolite coexpression network analysis. A: The branches of the cluster dendrogram for metabolites related to drug-induced liver injury (DILI) traits; B: Heatmap depicting correlations between modules and DILI traits; C: Results of Kyoto Encyclopaedia of Genes and Genomes enrichment analysis for the turquoise module; D: Scatter plot of the turquoise module.

Selection of biomarkers for DILI

To establish a diagnostic model of DILI, LASSO and RF were performed on the above eight metabolites. There were five hub metabolites in LASSO area under the curve (AUC) = 0.998 and seven hub metabolites in RF (AUC = 0.969) (Figure 4). We further intersected the hub metabolites of the two models to obtain five common metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine. These results suggest that the five hub metabolites may serve as potential diagnostic biomarkers for DILI. The AUC values of 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine were 1.000, 0.999, 0.966, 0.834, and 0.733, respectively (Figure 5A). In particular, the levels of the five hub metabolites were significantly greater than those in HCs (Figure 5B). Additionally, distance correlation matrix plots revealed correlations between the five hub metabolites and crucial clinical indicators of patients with DILI, including the levels of ALT, AST, TBIL, ALP, and GGT (Figure 5C).

Figure 4 Area under the receiver operating characteristic curves and hub metabolite screening of machine learning algorithms for significantly different metabolites. A: Area under the receiver operating characteristic (AUROC) curve of the least absolute shrinkage and selection operator (LASSO) model; B: AUROC curve of the random forest (RF) model; C: Hub metabolite screening of LASSO; D: Hub metabolite screening of RF. LASSO: Least absolute shrinkage and selection operator; RF: Random forest; AUC: Area under the curve; ROC: Receiver operating characteristic.

Figure 5 Diagnostic value of the hub metabolites. A: Area under the receiver operating characteristic curve of hub metabolites; B: Expression of hub metabolites in both groups; C: Distance correlation matrix plots displaying the partial Spearman’s correlation among the five hub metabolites and 12 clinical indicators of drug-induced liver injury. AUC: Area under the curve.

DISCUSSION

DILI is one of the most serious and common adverse reactions to drugs and is a major cause of clinically acute liver injury or failure[27,28]. It is crucial to detect DILI signal events early before they become symptomatic or severe to prevent clinically significant liver injury. The currently available biomarkers for DILI, namely, ALT, AST, and ALP, lack both specificity and sensitivity for detecting DILI at an early stage and are not reliable for predicting clinical outcomes[8]. Accordingly, a noninvasive marker for DILI and elucidation of the pathogenic mechanisms underlying DILI in humans are urgently needed. Using saliva for biomarker discovery is appealing because it can be collected noninvasively and is stable for long periods at room temperature[29]. To the best of our knowledge, this is the first study to investigate salivary metabolites in patients with DILI. Our study revealed a significant difference in salivary metabolites between patients with DILI and healthy individuals. Subsequently, we employed machine learning to identify combinations of salivary metabolites with predictive power to serve as biomarkers for DILI diagnosis.

In this study, WMCNA yielded five modules, of which the turquoise module was deemed clinically relevant. Metabolite pathway analysis of the turquoise module suggested that metabolic disorders associated with DILI might be linked to purine metabolism and beta-oxidation of very long-chain fatty acids. Additionally, eight hub metabolites in the turquoise module showed significant associations with DILI and were also found to exhibit changes in the PLS-DA model. Notably, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine were identified as having diagnostic value.

Based on research, disruptions in purine metabolism can cause the liver to release ATP, which can modulate immune responses and lead to the death of hepatocytes[30-32]. Purine metabolism may also affect DILI by interfering with drug metabolism or cell death processes[33-35]. Thus, purine metabolism is considered an important metabolic change in DILI, with hypoxanthine and inosine identified as key metabolites that impact this pathway[36,37]. Hypoxanthine and inosine are intermediates in the purine degradation pathway, and their levels increase during hypoxia, which is when adenine nucleotides are rapidly degraded[38-40]. When oxygen becomes available during tissue reperfusion, xanthine oxidase oxidizes hypoxanthine and inosine, generating reactive oxygen species that can trigger liver injury[41,42]. Our findings suggest that hypoxanthine and inosine may serve as potential indicators for diagnosing DILI, and efforts to repair impaired purine metabolism could benefit patients with this condition. Further research is needed to confirm these results.

Furthermore, we observed a significant increase in the levels of certain lipid metabolism metabolites, including 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, and tetradecanedioic acid, in DILI patients compared to HCs. Fatty acids, which are synthesized and metabolized primarily in the liver, are heavily impacted by liver injury[43]. The excessive buildup of medium- and long-chain fatty acids, such as 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, and tetradecanedioic acid, may hinder beta-oxidation in DILI patients, leading to anomalous energy metabolism and fat storage[44-46]. Treating DILI may involve preventing the accumulation of these toxic fatty acids in the liver, but very little research has been conducted in this field, which makes it an interesting area of study. Taken together, these findings indicate that abnormal fatty acid accumulation could act as a biomarker for DILI and contribute to its development, which is consistent with the literature[47].

CONCLUSION

To the best of our knowledge, this study provides initial evidence showing the ability of salivary metabolites to distinguish patients with DILI from healthy individuals. Our approach to sampling DILI biomarkers was not only less invasive but also less expensive than prior diagnostic methods. Moreover, additional investigations are currently underway to substantiate these findings across separate cohorts and ascertain the role of these metabolites in DILI patient outcomes.