Overview of metabolomics
Metabolomics is an established discipline in the post-genomic era that integrates genomics, transcriptomics, and proteomics, forming the core of systems biology. It is considered one of the most dynamic fields in life sciences research.10 The term “metabolomics” was coined by Nicholson at the University of London in 1999.11 This field involves the study of metabolic networks within biological systems, focusing on dynamic changes in metabolites in response to stimuli or perturbations, such as specific gene mutations or environmental factors.12 Metabolomics targets endogenous small molecules with a relative molecular mass smaller than 1,000.10 Compared to genomics, transcriptomics, and proteomics, the number of metabolites is significantly lower, eliminating the need for whole-genome sequencing or extensive expressed sequence tag analysis.13 Minor variations in gene and protein expression can be reflected and amplified through metabolites.13 Analyzing metabolites in biological samples can provide insights into physiological or pathological conditions in the human body.14 The identification of common metabolites across diverse biological samples enhances the applicability of metabolomics, enabling its use in a wide range of clinical and research contexts.14
However, metabolomics research faces several challenges and limitations. The complexity of the metabolome, which involves a vast array of metabolites with different chemical properties, presents significant difficulties in comprehensive analysis. Unlike genomics or proteomics, metabolites are highly dynamic and influenced by factors such as environmental conditions, diet, and disease states.15 This dynamic nature complicates the establishment of standard profiles or biomarkers across individuals or populations. Moreover, the sensitivity and resolution of analytical instruments are crucial; detecting low-abundance metabolites can be challenging. Issues such as metabolite overlap, isomerism, and ion suppression in mass spectrometry further complicate accurate quantification and identification.16 Additionally, the complexity of metabolic networks and the lack of comprehensive databases for all potential metabolites in diverse biological contexts complicate data interpretation.17
To address these challenges, recent advancements have focused on improving the sensitivity and accuracy of analytical techniques. Enhanced sample preparation protocols, such as solid-phase microextraction and derivatization techniques, improve extraction efficiency and reduce matrix interference.18 Furthermore, combining analytical techniques, such as gas chromatography-mass spectrometry (GC-MS) with liquid chromatography-mass spectrometry (LC-MS), provides complementary information, enabling more comprehensive profiling of metabolites.19 Advanced data analysis algorithms, including machine learning and artificial intelligence, have also shown promise in enhancing metabolite identification, quantification, and interpretation of complex datasets.20
Common analytical techniques in metabolomics include nuclear magnetic resonance (NMR), GC-MS, and LC-MS.21 Among these, NMR is one of the earliest and most widely used techniques. It relies on nuclear spin properties, which absorb radiation to generate nuclear energy conversion within an external magnetic field.22 NMR offers advantages such as minimal sample volume requirements and the ability to analyze samples with little or no complex preprocessing, in contrast to techniques like GC-MS or LC-MS that often require extensive sample preparation such as derivatization or extraction.23 Additionally, NMR enables the non-invasive acquisition of spectra from biofluids (e.g., blood, urine, or serum), reflecting the metabolic state of the body and making it suitable for in vivo analysis without requiring tissue biopsy.24 Currently, GC-MS and LC-MS are among the most widely used and effective metabolomics techniques.25 GC-MS is ideal for analyzing small, thermally stable, volatile, and easily vaporized compounds, while LC-MS is suitable for polar compounds with larger molecular masses and lower thermal stability.26 In clinical research, blood, urine, and tissue samples are commonly used. These samples contain various polar, non-volatile, and non-gaseous substances, making LC-MS a more versatile tool compared to GC-MS.27 In recent years, as metabolomics research has advanced, its role in diagnosing CLDs and exploring their mechanisms has become increasingly significant.28,29
Applications of metabolomics in the diagnosis and mechanism exploration of CLDs
Viral hepatitis
Viral hepatitis, particularly hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, is a leading cause of liver disease in China.30 With the widespread adoption of metabolomics technologies, the metabolic profiling of viral hepatitis (including HBV and HCV infections) has been extensively studied, uncovering numerous biomarkers associated with disease progression and severity.31 For example, in a study comparing serum metabolites from patients at different stages of chronic hepatitis B (CHB) and healthy individuals, researchers identified several metabolic disruptions linked to disease progression. Disturbances in ammonia metabolism, glutamine and glutamate metabolism, alanine metabolism, branched-chain amino acid imbalance, and tricarboxylic acid cycle disruption were found to be major contributors. Aspartate, glutamate, glutamine, and alanine fluctuations were particularly characteristic of disease progression.32 Similarly, a study on serum metabolites in CHB revealed that alanine, malic acid, and 5-methoxytryptamine could effectively differentiate CHB patients from healthy controls. These metabolites are involved in energy metabolism, macromolecule synthesis, and redox maintenance pathways, suggesting their role in the disease.33 In another investigation, metabolomic analysis of urine samples from CHB patients uncovered significant disruptions in three key metabolic pathways: cytochrome P450 metabolism of xenobiotics, phenylalanine metabolism, and amino sugar and nucleotide sugar metabolism. Key metabolites, including biotin sulfoxide, 5-oxo-eicosanoic acid, D-glucosaminyl glucoside, and 2-methylhypoxanthine, were significantly altered in CHB patients compared to healthy controls.34 Further supporting these findings, a study of saliva metabolites confirmed differences in various metabolites such as succinic acid, putrescine, acetic acid, tyrosine, and lactic acid in CHB patients compared to healthy individuals. These changes affected nine metabolic pathways, including glycolysis, gluconeogenesis, pyruvate metabolism, and the citric acid cycle, providing a broader understanding of metabolic alterations in CHB.35
Metabolomics has also been instrumental in diagnosing and predicting the progression of hepatitis B cirrhosis (HBC). Researchers found that seven omega-6-derived oxidized lipids, such as 9,10-dihydroxyoctadecenoic acid and thromboxane B2, were altered in patients with HBC. These lipids, combined with traditional markers like alpha-fetoprotein, age, and gender, significantly enhanced the prediction accuracy of HBC.36 Another study showed that six metabolites (α-hydroxyhippuric acid, tyrosyl-β-glucuronide, 3-hydroxyisovaleric acid, uracil riboside, estrone, and glycochenodeoxycholate) were linked to different stages of HBC as classified by the Child-Pugh score, reflecting abnormalities in amino acid, bile acid, and hormone metabolism.37
In the case of HCV infection, metabolomics has also provided valuable insights into disease progression and treatment outcomes. For example, choline and histidine levels were found to be higher in patients with late-stage (F2-F4) fibrosis due to chronic HCV infection, with the choline-to-uric acid ratio being a particularly effective marker for distinguishing disease severity.38 Metabolic profiling of patients with decompensated cirrhosis from HCV infection revealed increased levels of plasma fatty acids, bile acids, and aromatic amino acids, as well as decreased levels of branched-chain amino acids and tricarboxylic acid cycle metabolites. These findings highlight important metabolic shifts associated with disease progression. Lysophosphatidylcholine, taurocholic acid, and ethyl succinate were identified as key metabolites that accurately differentiate compensated and decompensated stages of HCV-related liver cirrhosis.39 Additionally, studies on HCV patients undergoing liver transplantation have shown that certain metabolites, such as sphingomyelins and phosphatidylcholines, can predict the progression of fibrosis post-transplant.40 Furthermore, metabolomics has been shown to predict the virological response to antiviral therapy in HCV-infected patients. High serum tryptophan levels prior to treatment were associated with a better virological response, indicating that metabolomic profiling could help guide treatment decisions.41
ALD
ALD is characterized by impaired metabolic processes, and metabolomics has become an invaluable tool for uncovering the metabolic disruptions linked to ethanol consumption.42 Studies employing this approach have provided deep insights into the molecular changes underlying ALD, particularly in distinguishing the stages of the disease. For example, capillary electrophoresis-mass spectrometry-based metabolomics revealed 19 metabolites associated with alcohol intake, with serine, guanidinosuccinic acid, and glutamine identified as promising biomarkers for early-stage ALD and potential predictors of progression to more severe liver injury, such as alcoholic hepatitis and alcoholic cirrhosis.43
Metabolic dysregulation is particularly evident in the caffeine and tryptophan pathways in ALD. Untargeted and targeted metabolomics approaches have demonstrated that specific caffeine metabolites—such as 1-methylxanthine, paraxanthine, and 5-acetylamino-6-amino-3-methyluracil—can distinguish the severity of ALD, particularly in differentiating alcoholic hepatitis from milder liver conditions.44 In addition, the abundance of 10 tryptophan metabolites was strongly correlated with Model for End-Stage Liver Disease scores, commonly used for assessing liver dysfunction severity in patients with alcoholic cirrhosis.45 Notably, nine of these metabolites showed significant differences between healthy individuals and ALD patients, with two—quinaldic acid and indole sulfate—specifically linked to advanced stages of ALD, such as cirrhosis and liver failure.45
Comprehensive serum metabolite profiling has further illuminated key alterations in ALD. Elevated levels of methylated nucleotides, γ-glutamyl amino acids, bile acids, uracil, and campesterol were observed in patients with alcoholic cirrhosis, while branched-chain amino acids, serotonin, and urate salts were found to be reduced in both alcoholic hepatitis and cirrhosis. These findings suggest that a broad range of metabolites could serve as biomarkers for assessing ALD progression, with 3-ureidopropionic acid, cis-3,3-dimethylglutaryl glycine, retinol, and valine highlighted as particularly promising biomarkers for distinguishing early-stage alcohol-related liver injury from more advanced conditions such as alcoholic cirrhosis.46
Another critical metabolic pathway in ALD is bile acid metabolism. Alterations in bile acid profiles have been identified as a key metabolic signature for the onset and progression of ALD. The serum ratio of glycoursodeoxycholic acid to glycochenodeoxycholic acid has been suggested as a non-invasive biomarker for predicting ALD severity, particularly for differentiating alcoholic hepatitis from cirrhosis and potentially predicting the risk of liver failure in patients with advanced disease.47
NAFLD
The spectrum of NAFLD ranges from simple non-alcoholic fatty liver to non-alcoholic steatohepatitis (NASH) and NAFLD-related cirrhosis.48 While non-alcoholic fatty liver is generally considered a benign condition that can often be reversed through diet and exercise, it carries the risk of progressing to NASH and eventually cirrhosis, which may lead to HCC.49 One of the greatest challenges in managing NAFLD is the non-invasive diagnosis of NASH, as no reliable biomarkers currently exist to detect or predict the inflammation characteristic of the disease.50
Recent advancements in metabolomics have provided significant insights into NAFLD, offering powerful tools for early detection and risk prediction. For instance, a predictive model called the NASH ClinLipMet score has been developed. This model incorporates a combination of biomarkers, such as aspartate aminotransferase, fasting insulin, the patatin-like phospholipase domain-containing protein-3 genotype, and specific amino acids (e.g., glutamate, glycine, isoleucine), to assess the likelihood of progression to NASH.51 Another innovative approach, proposed by Mayo et al.,52 uses a diagnostic algorithm based on 20 serum triglycerides to differentiate NAFLD histological phenotypes, from simple hepatic steatosis to advanced NASH.
Alterations in circulating amino acids, such as branched-chain amino acids (leucine, isoleucine, valine) and aromatic amino acids (tryptophan, tyrosine, phenylalanine), are commonly observed in NAFLD patients.53–55 Research has shown that increased plasma levels of branched-chain amino acids correlate with hepatocyte ballooning and inflammation, while other amino acids like glutamate, serine, and glycine are linked to insulin resistance and can differentiate fibrosis stages in NAFLD.54
Metabolomic profiling has also proven useful in distinguishing NAFLD subtypes. For example, Masarone et al.56 employed untargeted plasma metabolomics combined with machine learning to classify biopsy-confirmed NAFLD patients into distinct groups: pure steatosis, NASH, and NAFLD-related cirrhosis. Their model, which included biomarkers like glycine, taurine-conjugated bile acids, phenylalanine, and branched-chain amino acids, demonstrated high accuracy (94.0%), sensitivity (94.1%), and specificity (93.8%) in classifying these subtypes.56
Additionally, bile acid profiles are closely associated with the severity of NASH. Significant changes in bile acid composition have been observed, with NASH patients showing elevated levels of primary bile acids and reduced levels of secondary bile acids.57 These bile acid alterations are not only indicative of disease severity but also linked to specific pathological features. For instance, steatosis is primarily associated with taurine-conjugated bile acids, while lobular inflammation and hepatocyte ballooning are more closely tied to glycine-conjugated bile acids.57
AILDs
AILDs, including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC), are chronic inflammatory conditions that significantly impact liver function.58 While these diseases are increasingly recognized, their complex etiology remains poorly understood, making diagnosis and treatment particularly challenging. Liver biopsy, although the gold standard for diagnosing AILDs, is invasive, painful, costly, and prone to sampling errors and interpretational variability.59
Metabolomics has gained considerable attention in recent years as a promising tool for identifying non-invasive biomarkers to more accurately diagnose and assess the severity of AILDs. For instance, a study using NMR spectroscopy analyzed plasma samples from patients with AIH, PBC, metabolic dysfunction-associated liver disease, chronic viral hepatitis, and healthy controls.60 This research identified 15 metabolites that could distinguish AIH from other liver diseases, with AIH patients showing elevated levels of alanine, aspartate, glutamate, choline, betaine, dimethylamine, and several branched-chain amino acids (valine, leucine, isoleucine).60
Additionally, a shift in energy metabolism has been suggested in AIH patients. Elevated plasma levels of acetate, lactate, acetone, acetoacetate, and glucose point to the activation of aerobic glycolysis—a metabolic change typically associated with immune activation.61
For PBC, a serum metabolomics study identified significant alterations in glucose, fatty acid, and amino acid metabolism.62 A diagnostic model was developed using metabolites such as 4-hydroxyproline, 3-hydroxyisovalerate, citrate, and ketone esters, achieving excellent diagnostic performance with a 95% confidence interval (0.868–0.976).62
Bile acid metabolism has also proven to be a crucial factor in distinguishing different AILDs. Targeted serum bile acid analysis has shown that PBC patients have significantly higher levels of chenodeoxycholic acid, lithocholic acid, and tauroursodeoxycholic acid compared to those with AIH.63 These bile acids not only help differentiate the two conditions but also correlate with disease severity, as reflected in the Child-Pugh classification.63
Furthermore, unique metabolic changes in PSC were observed during liver transplantation studies. In bile from PSC patients, a notable enrichment of dipeptides and polyamines was detected.64 This finding suggests that such metabolic alterations may play a role in disrupting liver homeostasis and influencing gut microbiota composition, both of which are critical in the pathogenesis of PSC.64
DILI
In China, DILI accounts for approximately 10% of hospitalized patients with liver diseases.65 While mild DILI is primarily characterized by elevated transaminase levels, severe cases can progress to liver failure and even death.66 The absence of specific and sensitive biomarkers for DILI complicates early diagnosis and intervention, often resulting in poor patient prognosis.67 Recent studies employing metabolomics have identified serum biomarkers associated with DILI and explored potential indicators for predicting its severity and progression.68 Notable findings include significantly elevated levels of palmitic acid, taurochenodeoxycholic acid, glycochenodeoxycholate, and taurochenodeoxycholate in the serum of DILI patients compared to healthy controls, alongside a marked reduction in serum lysophosphatidylethanolamine levels.68 These metabolites, involved in bile acid biosynthesis, alpha-linolenic acid metabolism, and glycerophospholipid metabolism, have emerged as promising clinical biomarkers for DILI.69 Furthermore, metabolites such as deoxycholic acid, glycochenodeoxycholate, taurochenodeoxycholate, chenodeoxycholate, deoxycholate, and lithocholic acid have been identified as independent risk factors for distinguishing severe DILI from milder forms, suggesting their potential as predictive biomarkers for liver injury severity.70 Plasma metabolomic and lipidomic profiling in anti-tuberculosis DILI has revealed potential early predictive markers and highlighted the involvement of iron deposition and unsaturated fatty acid biosynthesis pathways in liver injury pathogenesis.71 In addition, urinary metabolomic analysis of 14 patients with anti-tuberculosis DILI and 30 age- and gender-matched non-DILI controls identified 28 key differential metabolites associated with bile secretion, niacin and niacinamide metabolism, tryptophan metabolism, and other pathways. These metabolites demonstrate potential for predicting DILI risk in patients undergoing anti-tuberculosis treatment.72
HCC
HCC often progresses silently, without noticeable symptoms, until it reaches an advanced stage.73 This delayed onset significantly contributes to its high mortality rate, making early detection, diagnosis, and treatment crucial for improving patient outcomes. Metabolomics has emerged as a powerful tool in oncology, offering the potential to identify metabolic disruptions associated with cancer onset and progression. By detecting these changes early, metabolomics can play a pivotal role in diagnosing and assessing the prognosis of HCC.74
In a study of Bangladeshi patients with HBV-related HCC, urine metabolites were analyzed using NMR spectroscopy. Higher levels of carnitine were observed in HCC patients compared to those with chronic HBV infection, cirrhosis, or healthy controls, while metabolites such as creatinine, hippurate, and trimethylamine-N-oxide were significantly lower in the HCC group.75 These findings suggest that HCC impacts specific metabolic pathways involved in energy production and detoxification. Another study conducted in Egypt explored urine metabolomics in HCC patients, identifying several metabolites that distinguished tumor patients from both healthy controls and cirrhosis patients. Increased concentrations of glycine, trimethylamine-N-oxide, hippurate, citrate, creatinine, and carnitine were observed in the urine of HCC patients.76 These changes indicate alterations in energy metabolism and may be linked to abnormal chromosomal methylation pathways. A more in-depth analysis of portal vein blood from HCC patients revealed significant alterations in key metabolic processes, including primary bile acid biosynthesis, taurine metabolism, and phenylalanine and tryptophan metabolism. Elevated levels of metabolites such as DL-3-phenyllactic acid, L-tryptophan, glycodeoxycholic acid, and 1-methylnicotinamide were found, correlating with liver dysfunction and poorer survival outcomes.77 Conversely, lower levels of arachidonic acid and phenol were detected, suggesting a potential protective role against tumor development.77 A large-scale prospective study analyzed serum samples from over 520,000 participants before HCC diagnosis. The study identified several metabolites positively correlated with HCC risk, including N1-acetylspermidine, indole red, hydroxyphenyllactic acid, and sphingosine. In contrast, metabolites such as retinol, dehydroepiandrosterone sulfate, glycerophosphocholine, and creatine were negatively correlated with HCC risk.78 These findings highlight the involvement of metabolic pathways related to bile acids, amino acids, phospholipids, and steroids, as well as the influence of environmental and dietary factors in HCC development.
In summary, metabolomics provides a comprehensive understanding of the biochemical alterations in CLDs, enabling the identification of biomarkers with diagnostic and prognostic value. Metabolites directly reflect disruptions in critical biological pathways associated with liver disease. For instance, amino acids such as alanine, glutamine, and branched-chain amino acids indicate hepatocellular function and nitrogen metabolism—processes central to the liver’s metabolic roles.79 Similarly, bile acids provide insights into hepatic synthesis, transport, and detoxification pathways, often impaired during disease progression.80 Lipid-derived metabolites and fatty acids highlight disruptions in energy storage and membrane dynamics, particularly in cirrhosis and fibrosis.81 These metabolites serve as indicators of systemic metabolic changes caused by liver dysfunction, making them valuable for non-invasive diagnosis, disease staging, and prognostic assessment. However, inconsistencies in sample collection protocols (e.g., fasting status, storage conditions) and the use of different analytical platforms (e.g., NMR, GC-MS, LC-MS) can lead to variations in metabolite detection. These challenges emphasize the need for standardized methodologies and multi-center collaborations to enhance the reproducibility and comparability of findings. Future research should focus on standardizing methodologies and validating findings across diverse populations to improve clinical applicability. A summary of metabolomics applications in diagnosing CLD and HCC is provided in Table 1.32-41,43–47,51,52,54,56,57,60–64,68,70–72,75–78
Table 1Application of metabolomics in the diagnosis of chronic liver diseases
| Disease | Sample | Metabolites | Metabolic pathways | Applications | References |
|---|
| CHB | Serum | Aspartate, glutamate, glutamine, and methionine | Amino acid metabolism | Prognostic assessment | 32 |
| CHB | Serum | Alanine, malic acid, and 5-methoxytryptamine | Energy metabolism and redox reactions | Diagnosis | 33 |
| CHB | Urine | Biotin sulfoxide, 5-oxodocosanoic acid, D-glucosamine, and 2-methylhippuric acid | Phenylalanine metabolism, amino acid and nucleotide metabolism | Diagnosis | 34 |
| CHB | Saliva | Acetic acid, putrescine, acetic acid, succinic acid, tyrosine, lactic acid, butyric acid, pyruvic acid, pyridoxal, and p-hydroxybenzoic acid | Glycolysis and amino acid metabolism | Diagnosis | 35 |
| HBC | Serum | 9,10-dihydroxyoctadecenoic acid, 14,15-dihydroxyeicosatrienoic acid, 13-hydroxyoctadecadienoic acid, 12-hydroxyeicosatetraenoic acid, 11-HETE, and thromboxane B2 | Lipid metabolism | Prognostic assessment | 36 |
| HBC | Urine | Alpha-hydroxyhippurate, tyrosine-beta-glucuronide, 3-hydroxyisovalerate, indoleacetic acid succinate, estrone, and glycochenodeoxycholate | Amino acid metabolism, bile acid metabolism, hormone, and intestinal microbiota metabolism | Diagnosis | 37 |
| HCV infection | Serum | Choline and histidine | Glutathione metabolism and amino acid metabolism | Diagnosis | 38 |
| HCV infection | Plasma | Lysophosphatidylcholine, bovine bile acid, and acetic acid | Fatty acid metabolism, bile acid metabolism, and amino acid metabolism | Diagnosis | 39 |
| HCV infection | Serum | Metabolites of two phosphatidylcholines and two phospholipids | Metabolism of sphingomyelin and phosphatidylcholine | Prognostic assessment | 40 |
| HCV infection | Serum | Tryptophan | Tryptophan metabolism | Guide treatment decisions | 41 |
| ALD | Plasma | Succinic acid, guanidinosuccinic acid, glutamine | Glutamate and glutamine metabolism | Prognostic assessment | 43 |
| ALD | Urine | 1-methylxanthine, hypoxanthine, and 5-acetamido-6-amino-3-methyluracil | Caffeine metabolism | Diagnosis | 44 |
| ALD | Urine | Quinaldic acid, indole-3-carboxylic acid | Tryptophan metabolism | Diagnosis | 45 |
| ALD | Serum | 3-Ureidopropionate, (3R)-3,3-dimethylglutaryl-glycine, retinol, and methionine | Amino acid metabolism | Diagnosis | 46 |
| ALD | Serum | Taurine, glycine-conjugated bile acids | Bile acid metabolism | Prognostic assessment | 47 |
| NAFLD | Serum | Glutamate, glycine, lsoleucine, phosphatidylcholine (16:0), and phosphatidylethanolamine (40:6) | Lipid metabolism | Prognostic assessment | 51 |
| NAFLD | Serum | Triglycerides | Lipid metabolism | Diagnosis | 52 |
| NAFLD | Serum | Glutamate, serine, and glycine | Amino acid metabolism | Diagnosis | 54 |
| NAFLD | Plasma | Glycocholic acid, taurocholic acid, phenylalanine, and branched-chain amino acids | Amino acid metabolism and bile acid metabolism | Diagnosis | 56 |
| NAFLD | Plasma | Taurocholate and glycocholate | Bile acid metabolism | Prognostic assessment | 57 |
| AIH | Plasma | Alanine, aspartate, glutamate, choline, betaine, dimethylamine, valine, leucine, and isoleucine | Amino acid metabolism | Diagnosis | 60 |
| AIH | Plasma | Pyruvate, lactic acid, acetic acid, acetoacetate, and glucose | Aerobic glycolysis | Diagnosis | 61 |
| PBC | Serum | 4-Hydroxyproline, 3-Hydroxyisobutyrate, citrate, and acetoacetate | Glucose metabolism, fatty acid metabolism, and amino acid metabolism | Diagnosis | 62 |
| PBC | Serum | Goose deoxycholic acid, lithocholic acid, and taurine-conjugated bile acid | Bile acid metabolism | Prognostic assessment | 63 |
| PSC | Bile | Dipeptides and polyamine metabolites | Bile acid metabolism and lipid metabolism | Diagnosis | 64 |
| DILI | Serum | Cholic acid, deoxycholic acid, and lithocholic acid | Bile acid metabolism | Diagnosis | 68 |
| DILI | Serum | Deoxycholic acid, glycoursodeoxycholic acid, and taurodeoxycholic acid | Bile acid metabolism | Prognostic assessment | 70 |
| DILI | Plasma | Niacin, glycyl-L-leucine, kudzu alkaloid, and 18-β-glycyrrhetinic acid, among 22 metabolites | Iron deficiency, and unsaturated fatty acid biosynthesis | Predict disease onset | 71 |
| DILI | Urine | Choline, kudzu alkaloid, N-acetylputrescine, uric acid, among 28 metabolites | Bile secretion, niacin and niacinamide metabolism, tryptophan metabolism | Predict disease onset | 72 |
| HCC | Urine | Carnitine, creatinine, urate salts, and trimethylamine-N-oxide | Choline metabolism and intestinal microbiota metabolism | Diagnosis | 75 |
| HCC | Urine | Glycine, trimethylamine-N-oxide, urate salts, citrate salts, creatinine, creatine, and carnitine | Energy metabolism and aberrant chromosomal methylation metabolism | Diagnosis | 76 |
| HCC | Serum | DL-3-phenyllactic acid, L-tryptophan, glycoursodeoxycholic acid and 1-methylnicotinamide, linoleic acid, and phenol | Amino acid metabolism and bile acid metabolism | Prognostic assessment | 77 |
| HCC | Serum | N1-acetylputrescine, indigo, hydroxyphenyllactic acid, tyrosine, sphingosine, leucylprolyl, glycoursodeoxycholate, glycodeoxycholate, 7-methylxanthine, retinol, dehydroepiandrosterone sulfate, glycerophosphocholine, gamma-carboxyethyl hydroxybenzopyran, and creatine | Bile acid metabolism, amino acid metabolism, phospholipid metabolism, and steroid metabolism | Prognostic assessment | 78 |
The application of metabolomics in studying microbial-derived metabolites in CLDs
The human gut microbiota comprises a number of microbial cells approximately equal to the number of human cells in the body. However, the collective genome of the gut microbiota contains 450 times more genes than the human genome.82 The gut microbiota plays a central role in regulating host metabolism and immune responses by producing metabolites such as short-chain fatty acids, bile acids, and amino acid derivatives.83–85 These microbial metabolites influence liver function through the gut-liver axis, with metabolomics providing critical insights into their roles in the pathogenesis of CLDs.86
Research has demonstrated a clear association between gut dysbiosis and various liver diseases, underscoring the importance of the gut-liver axis.87 Dysbiosis, characterized by reduced beneficial bacterial populations and altered metabolite profiles, impacts liver health and disease through multiple mechanisms. For instance, secondary bile acids and lipopolysaccharides (pathogen-associated molecular patterns) can activate Toll-like receptors in the liver, triggering pro-inflammatory cytokine release and exacerbating liver damage.88 Studies on NAFLD have revealed that elevated levels of deoxycholic acid-derived bile acids and diminished Lactobacilli populations are linked to advanced disease stages.89 In experimental models, a high-cholesterol diet induced gut microbiota changes, such as an increase in Proteobacteria and Desulfobulbus, which were associated with elevated taurine-conjugated bile acids and reduced indole derivatives. These changes drove the progression of steatosis, steatohepatitis, fibrosis, and hepatocarcinogenesis.90
Specific microbial metabolites have been identified as both biomarkers and therapeutic targets in various liver diseases. For example, reduced Blautia abundance and elevated succinate levels are associated with immune globulin G4-related sclerosing cholangitis. Decreased Ruminococcus abundance and reduced microbial-derived secondary bile acids are linked to PSC.91 Alterations in tryptophan and glutamate metabolism pathways are connected to the progression of ALD.92 In CHB, gut microbiota-derived metabolites such as phenylalanine and tyrosine strongly correlate with liver function markers, providing valuable insights into disease mechanisms.93 For HCC, metabolites such as acetate, glutamate, and arachidonic acid are associated with early disease recurrence.94 Microbial species like Veillonella and Gemella, along with metabolites like indoleacetic acid and taurochenodeoxycholic acid, show promise as non-invasive diagnostic biomarkers for HCC.95 Additionally, microbial metabolites have been implicated in liver toxicity. For instance, 1-phenyl-1,2-propanedione, a gut-derived metabolite, exacerbates acetaminophen-induced liver injury by depleting hepatic glutathione levels.96
In summary, the gut microbiota produces a diverse array of metabolites that play significant roles in liver health and disease. These metabolites—including secondary bile acids, short-chain fatty acids, and indole derivatives—not only influence the gut but also affect liver function via the portal circulation, contributing to CLD progression. The integration of metabolomics and microbiome research has provided valuable insights into these mechanisms, uncovering novel diagnostic and therapeutic opportunities for CLDs. The application of metabolomics in studying microbial-derived metabolites in CLDs is summarized in Table 2.89–96
Table 2The application of metabolomics in microbial-derived metabolites of chronic liver disease
| Disease | Microbiota | Metabolites | Metabolic pathways | Applications | References |
|---|
| NAFLD | Lachnospiraceae and lactobacillaceae | Deoxycholic acid | Bile acid metabolism | Diagnosis | 89 |
| NAFLD | Genus Helicobacter, genus desulfurobacterium, genus anaerobiospirillum, family desulfurobacteriaceae | Tauroursodeoxycholic acid and 3-indolepropionic acid | Cholesterol metabolism and bile acid metabolism | Prognostic assessment | 90 |
| IgG4-related sclerosing cholangitis | Brucella bacterium | Succinic acid | Succinic acid metabolism | Diagnosis | 91 |
| PSC | Lactobacillus genus | Secondary bile acids | Bile acid metabolism | Diagnosis | 91 |
| ALD | Pseudomonas and agrobacterium | Short-chain fatty acids, bile acids, indole compounds | Linoleic acid metabolism, histidine metabolism, fatty acid degradation, and glutamate metabolism | Prognostic assessment | 92 |
| CHB | Streptococcus, vibrio, and haemophilus | Phenylalanine and tyrosine | Amino acid metabolism | Diagnosis | 93 |
| HCC | Vibrio, streptococcus pneumoniae, and bifidobacterium species | Twenty-three metabolites including acetic acid, glutamate, and arachidonic acid | Lipid metabolism, glucose metabolism, and amino acid metabolism | Prognostic assessment | 94 |
| HCC | Enterobacter cloacae and Lactococcus lactis | Waardenburg, taurocholic deoxycholic acid, glycocholic deoxycholic acid, theophylline, and xanthine | Bile acid metabolism and purine metabolism | Diagnosis | 95 |
| DILI | Brewer’s yeast | 1-Phenyl-1,2-propanedione | Glutathione metabolism | Diagnosis | 96 |
Application of metabolomics in the treatment of CLDs with TCM
TCM is increasingly recognized worldwide for its multi-component formulations targeting various biological pathways, offering novel avenues for drug discovery and supplemental therapies.97 Metabolomics, a powerful approach analyzing extensive metabolic responses of biological systems to pathological stimuli and therapeutic interventions, provides crucial insights into disease mechanisms by mapping systemic metabolic changes in complex biological matrices.98 This approach aligns well with the holistic philosophy of TCM, enhancing its potential to assess biological activities, elucidate mechanisms of action, and drive drug development.99,100
Recent studies have applied metabolomics to evaluate the efficacy and underlying biochemical mechanisms of TCM in treating CLDs. For instance, plasma metabolomics revealed that Tripterygium wilfordii could reverse metabolic disruptions in bile acid and fatty acid metabolism caused by concanavalin A, demonstrating its protective effect against AIH, primarily attributed to its active compound, Triptolide.101 Likewise, Schisandra chinensis has shown potential in modulating the arachidonic acid metabolism pathway, offering protective effects against acetaminophen-induced DILI, as revealed by plasma metabolomics studies.102
Further research has highlighted the therapeutic potential of other herbs. For example, Gardenia jasminoides modulates amino acid metabolism and exerts beneficial effects on ALD through ultra-performance liquid chromatography-mass spectrometry-based metabolomics.103 Likewise, Artemisia capillaris has been shown to influence the tricarboxylic acid cycle, sphingolipid metabolism, and taurine-hypotaurine pathways, contributing to its protective role in alcoholic liver injury.104 Additionally, studies on inulin polysaccharides have demonstrated their capacity to improve dysregulated blood lipid and amino acid metabolism in NAFLD rats, further emphasizing the broad therapeutic potential of TCM for liver diseases.105 Additionally, metabolomics analyses reveal that curcumin extract may exert therapeutic effects on NAFLD by modulating lipid metabolism, particularly pathways involving steroid hormone biosynthesis, glycerophospholipid metabolism, and glycerolipid metabolism.106 Untargeted metabolomics has also identified the regulation of amino acid metabolism as a potential therapeutic mechanism of verbascoside in treating CHB.107
In addition to single-ingredient herbs, Chinese herbal compound formulas have proven effective for treating CLDs, especially for complex diseases with multiple symptoms. The increasing application of metabolomics to study these formulas has revealed their multifaceted mechanisms of action. For instance, Sancao Granule, composed of Herba qiancao, Herba xianxiancao, Radix scutellariae, Rhizoma coptidis, Forsythiae Fructus, and Radix glycyrrhizae, regulates multiple metabolic pathways, including fatty acid biosynthesis and arachidonic acid metabolism. This comprehensive metabolic modulation significantly improved concanavalin A-induced AIH in mice by reducing serum biochemical markers, alleviating histological damage, and inhibiting neutrophil infiltration in liver tissue.108
Similarly, Dachaihu Formula has shown promise in improving metabolic disturbances in NAFLD by regulating pathways related to glycine/serine/threonine metabolism, glucose-6-phosphate metabolism, and arachidonic acid metabolism.109 Another study demonstrated that Shenling Baizhu Formula, a combination of herbs including Lotus plumule and Coix seed, modulates sphingolipid metabolism, providing a potential therapeutic strategy for improving liver lipid profiles in NAFLD rats.110
Other compound formulas, such as Longchai Formula, regulate phospholipid metabolism, contributing to liver cell repair and energy metabolism, offering potential treatments for CHB.111Jiawei Xiaoyao Formula has also been linked to intervention in HCC in rats by modulating key metabolic pathways such as bile acid biosynthesis and cholesterol metabolism, providing a promising avenue for addressing liver-qi stagnation and spleen deficiency.112
The cumulative application of metabolomics techniques in TCM treatments for CLDs underscores the valuable role of integrative, multi-target therapeutic strategies in modern medicine. The applications of metabolomics in TCM treatments for CLDs are summarized in Table 3.101–112
Table 3Application of metabolomics in traditional Chinese medicine treatment of chronic liver diseases
| Drug | Disease | Sample | Metabolomics platform | Metabolic pathways | References |
|---|
| Chinese herbal monomers | | | | | |
| Tripterygium wilfordii and triptolide | AIH | Plasma | UPLC-Q-TOF-MS | Bile acid metabolism and fatty acid metabolism | 101 |
| Schisandra | DILI | Plasma | UPLC-Q-TOF-MS | Arachidonic acid metabolism | 102 |
| Gardenoside | ALD | Serum | UPLC-Q-TOF-MS | Amino acid metabolism | 103 |
| Wormwood | ALD | Serum | UPLC-Q-TOF-MS | Tricarboxylic acid cycle, sphingolipid metabolism, and bile acid metabolism | 104 |
| Inulin | NAFLD | Serum | GC-MS | Lipid metabolism and amino acid metabolism | 105 |
| Turmeric extract | NAFLD | Serum | UPLC-Q-TOF-MS | Fatty acid metabolism | 106 |
| Syringin | CHB | Serum | | Amino acid metabolism | 107 |
| Traditional Chinese Medicine formula | | | UPLC-MS | | |
| Sancao Granules | AIH | Serum | UPLC-Q-TOF-MS | Fatty acid metabolism | 108 |
| Dachaihu Formula | NAFLD | Serum | UPLC-Q-TOF-MS | Fatty acid metabolism and amino acid metabolism | 109 |
| Shenling Bai zhu formula | NAFLD | Serum | UPLC-MS | Sphingolipid metabolism | 110 |
| Longchai formula | CHB | Serum | UPLC-Q-TOF-MS | Phospholipid metabolism | 111 |
| Jiawei Xiaoyao formula | HCC | Plasma | ESI-QTRAP-MS/MS | Bile acid metabolism, phenylalanine metabolism, and cholesterol metabolism | 112 |
Limitations
Despite its significant promise, the application of metabolomics in the study of CLDs faces several limitations. A primary challenge lies in the variability of sample populations across different geographic regions, introducing confounding factors such as genetic, environmental, and lifestyle differences that may compromise the generalizability of results.113 Additionally, discrepancies in the types of biological samples used, alongside variations in detection platforms and analytical methodologies, often lead to inconsistencies and hinder the reproducibility of findings across studies.114 Furthermore, most current metabolomics research on CLDs remains non-targeted, focusing predominantly on identifying differential metabolites rather than deeply exploring their biological implications. This approach limits our understanding of the underlying pathophysiological mechanisms of these diseases.115
Challenges also arise in applying metabolomics to TCM research. Variations in animal models, the composition of herbal formulas, and differences in processing methods can all influence metabolite profiles and their regulation, complicating the interpretation of experimental results. Quality control of Chinese herbal medicines is critical to ensuring treatment consistency and efficacy, and metabolomics holds promise in identifying factors affecting medicinal quality. However, the inherent chemical complexity of herbal formulas, which often contain numerous bioactive compounds, further complicates their analysis and standardization.
To address these limitations and enhance the utility of metabolomics, future research should focus on several critical areas. First, there is an urgent need to standardize metabolomics platforms, quantification techniques, and sample collection procedures to improve the reproducibility and comparability of results across studies. Expanding sample sizes and ensuring geographic diversity will help mitigate population variability and enhance the generalizability of findings. Second, research should prioritize targeted metabolomics, focusing on specific metabolites and their biological roles, to gain deeper insights into metabolic dysregulation in CLDs and TCM. This approach will help elucidate the mechanisms of action of bioactive compounds in both individual herbs and complex formulations. Third, integration of multi-omics approaches, such as genomics, transcriptomics, proteomics, and network pharmacology, will provide a more comprehensive understanding of the molecular mechanisms underlying CLDs and the pharmacological effects of TCM. This integration will bridge traditional practices with modern molecular biology, strengthening the scientific foundation for TCM research. Finally, metabolomics holds significant potential for quality control in TCM, as it can identify biomarkers for assessing the quality and consistency of medicinal materials, supporting the development of standardized quality assurance systems to ensure the safety and efficacy of herbal treatments.