Metabolomics: a review of liquid chromatography mass spectrometry-based methods and clinical applications

Targeted metabolomics

Mass spectrometry-based metabolomics analyses can be divided into two main headings: targeted or untargeted [8]. In targeted metabolomics analysis, quantitative measurement of previously biochemically identified metabolites is performed using low-resolution tandem mass spectrometers and multiple reaction monitoring mode (MRM) [9], [10], [11]. In this approach, a precise quantitation can be achieved with the calibration curves obtained using standard reference materials [12]. Targeted metabolomics focuses on specific metabolites and related pathways, offering high sensitivity and specificity for accurate detection and quantification. This technique provides a concentrated analysis, yielding a deeper understanding of specific metabolic pathways in biological processes. Notably fast and cost-effective, targeted metabolomics requires less sample preparation and data processing compared to untargeted approaches. Its value extends to unraveling the role of metabolites in disease and contributing to the development of targeted therapies [10, 11, 13].

Untargeted metabolomics

Untargeted metabolomic methods are global in scope and aim to simultaneously measure as many metabolites as possible from biological samples without bias. This approach allows identifying many metabolites, previously biochemically defined or not. However, in contrast to targeted metabolomics analysis, it is not possible to achieve precise quantitation of these metabolites [14, 15]. Typically, high‐resolution mass spectrometry (HRMS), such as quadrupole time-of-flight (QTOF) [9] and nuclear magnetic resonance spectroscopy (NMR) [16] can be used for untargeted metabolomics. One of the advantages of untargeted analysis is that hundreds of metabolomes can be detected simultaneously; thus, new targets can be identified to guide diagnosis or treatment. In this approach, compound identification (annotation) relies on the use of different libraries, as it is not cost-effective to employ standard reference material for the identification of each metabolite. Additionally, the datasets obtained through this approach are complex, posing significant barriers to data interpretation [17], [18], [19], [20], [21]. Despite the introduction of various metabolomics databases and software in the past decade to tackle this issue, further progress is still necessary. Moreover, the identification of unknown features and the subsequent validation phases demand additional time and effort [17].

Analytical platforms of untargeted metabolomics

Different analytical platforms can be used in untargeted metabolomics. They require a complex and detailed optimization process [22], [23], [24]. Due to factors such as complex sample structure, the dynamic range of intensities, biological variation, and structural heterogeneity of metabolites, specific analytical platforms with high resolution and performance are needed to obtain reliable results. NMR and LC-MS, gas chromatograph mass spectrometry (GC-MS), ion mobility coupled with mass spectrometry (IM-MS) or capillary electrophoresis (CE) have been used in metabolomics. Due to methodological variances, the number of detectable metabolites varies across analytical platforms. Ideally, researchers should employ three analytical platforms within the same study to maximize the detection of metabolites. However, there are limited studies where all three platforms are used simultaneously due to financial limitations. The advantages and disadvantages of the analytical platforms were given in Table 1.

LC-MS

Liquid chromatography-mass spectrometry is a highly sensitive and specific separation and identification technique using a mobile and stationary phase. LC-MS is one of the most applied chromatographic techniques for analyzing polar and non-polar metabolites. Different tandem and hybrid configurations such as Q‐TOF, triple quadrupole (QqQ), Fourier Transform-Ion Cyclotron Resonance mass spectrometry (Qq‐FT‐ICR), quadrupole‐linear ion trap (QqLIT) and Orbitrap can be used in MS/MS systems. Each system has its advantages and disadvantages [10]. Q-TOF or Orbitrap platforms are mainly used for untargeted metabolomics analyses due to their high resolution and acquisition frequency properties compared to other systems [9]. Different ionization techniques, including atmospheric-pressure chemical ionization (APCI), electrospray ionization (ESI) and atmospheric-pressure photoionization (APPI), can be used for ion suppression or enhancement of the molecules in LC-MS based untargeted metabolomics [25]. More detailed information on ionization techniques will be provided in the following sections. LC-MS is the most preferred analytical platform in metabolomic analyzes due to its higher selectivity, sensitivity and identified metabolite number [26, 27]. Compared to NMR, LC-MS can detect metabolites at the picomolar and nanomolar levels [28]. Amino acids and derivatives, fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and other lipid classes, carbohydrates, sugars, nucleotides, nucleosides, can be detected in biological samples by using LC-MS based untargeted metabolomics [29].

Sample type

Determining sample type and sampling method is essential for standardizing the preanalytical phase. Different factors such as target metabolites, study objectives, transfer time, hemolysis risk, transfer conditions, targeted system or organ and turnaround time should be considered in determining sample type. Many different sample types have been used in the metabolomics analyses, including serum [30], plasma [31], amniotic fluid, urine [32], cell [33], tissue [32], saliva [34], cerebrospinal fluid [35] and milk [36]. Among them, plasma and serum are the most commonly used biofluids in metabolomics analyses [37]. Although serum and plasma are obtained from whole blood, different tubes are needed and undergo different processes after collection. The liquid component of blood that remains after clotting and removal of blood cells is known as serum. It is obtained by allowing a blood sample to clot in a tube, followed by centrifugation to remove the fibrin, blood cells and any remaining clotting factors [38]. Plasma and serum can be used interchangeably for many metabolites. However, it is important to keep in mind that there may be differences between the two sample types regarding the levels of some metabolites [37]. The most important reasons for the differences between the two sample types are the use of anticoagulants, the difference in the process of the centrifuge and sampling tube. Liu et al. discovered notable variations in 216 metabolites between serum and plasma samples [38]. UPLC-MS analyses have identified potential variations between plasma and serum samples in relation to specific metabolites. For instance, lysophosphatidylethanolamine (18:0) and lysophosphatidic acid (20:0) have been observed to be higher in serum samples compared to plasma. On the other hand, certain phosphatidylcholines such as (16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC (16:0), sphingomyelin (18:0/22:0), and linoleic acid were found to be lower in serum samples [39]. Some differences were also detected in amino acids between serum and plasma [40]. Yu and colleagues noted that while the reproducibility of serum and plasma samples was good, the plasma measurements were generally higher than serum [37].

The choice of anticoagulants significantly influences the reproducibility of metabolomics analyses, with different tubes containing individual chemical structures. Common anticoagulants for obtaining plasma include heparin, EDTA, and citrate. In a study comparing the effects of EDTA and citrate in LC-MS-based metabolomics analyses, the type of anticoagulant was found to significantly impact the expressions of various lipids and amino acids. These included glycerophospholipids, acylcarnitine, sphingolipids, diacylglycerols, triacylglycerols, cholesteryl esters, as well as aspartate, histidine, and glutamine [41]. In metabolomic studies, choosing the right anticoagulant is essential because it can greatly impact the quality of the findings. Proper selection and handling of the anticoagulant will help ensure the accuracy and reliability of metabolomic data generated.

Spot urine is a commonly chosen sample for metabolomics analysis due to its ease of collection and non-invasiveness, whereas the collection and storage of a 24-h urine sample pose challenges, making it less preferred. A key issue in urine-based metabolomics is the substantial concentration variation in metabolome levels among individuals. To address this, before analysis, creatinine and urine osmolality values in the urine sample should be determined, and metabolite intensities must be normalized based on creatinine or osmolality values [42]. Other problems in urine sampling are storage and sample stability. Samples should be stored at 4 °C as soon as possible after collection to decrease contamination and metabolite degradation [43]. Several preservatives such as sodium azide, hydrochloric acid, thymol and boric acid [44, 45] are used in the chemical analyses of urine samples to prevent bacterial overgrowth and metabolite degradation. However, preservatives should be used with caution as they may cause interference.

Given the presence of cells, crystals, or cylinders in urine, it is crucial to remove these structures to prevent issues in analysis and sample extraction. Filtration or centrifugation is commonly employed for this purpose, but care must be taken to prevent the release of metabolites from cells into the urine during these processes. Metabolite stability can be significantly impacted by freeze-thaw cycles; hence, it is recommended to limit cycles to a maximum of two to maintain sample integrity [46]. Roux et al. reported that storage of urine samples at 4 °C inhibited bacterial overgrowth for at least a 72-h period and slowed the chemical degradation process [43].

Tissues have sophisticated compositions containing various morphological cell types and extracellular matrix material. Therefore, obtaining a representative and homogeneous tissue sample requires careful consideration [47]. In animal studies, complete tissue can be harvested; however, limited tissue samples by using biopsy techniques or surgery can be obtained as representative tissue samples in humans. After the harvesting procedure specimen should be rinsed to remove the remaining blood and then frozen as soon as possible to overcome continuing metabolic changes and enzymatic degradation of tissue samples [48, 49].

The cell culture technique is frequently used to establish many disease models, such as cancer and neurological pathologies. It has many advantages, such as working with different cell types, getting results faster than in-vivo studies, and obtaining genetically modified lines. Footprint or fingerprint approaches are used in cell culture metabolomics [50]. While the footprint approach aims to detect extracellular metabolites, the fingerprint approach seeks to detect intracellular metabolites [51]. These approaches can be used together or separately. Cell culture metabolomics poses challenges such as the impact of growth medium, standardizing cell number and density, and cell quenching difficulties. To address these issues, a novel and powerful tool called single-cell metabolomics has been introduced. This technique allows for the determination of metabolic profiles in individual cells, eliminating metabolites from different tissues or cell groups. This enhances the probability of identifying primary disease-contributing metabolites [52], [53], [54].

Sample storage

Maintaining the quality of samples is of utmost importance in metabolomics since the metabolites under analysis are often vulnerable to decay and instability. Various factors such as temperature, pH, exposure to light and air can all play a significant role in affecting the stability of metabolites in biological samples [55]. To mitigate the impact of storage, it is crucial to exercise caution when handling and storing samples. The temperature at which samples are stored is a critical factor to be considered. To maintain the integrity of metabolites, it is generally recommended to store samples at extremely low temperatures, typically −80 °C or below. Such low temperatures can help impede metabolic processes that may otherwise cause deterioration of the samples. Samples can be stored at −80 °C for extended periods, often for years, without significant degradation. Another important factor to consider is the type of storage container [56], [57], [58], [59], [60], [61]. It is advisable to store samples in containers that are airtight and chemically inert, as well as resistant to potential leaching or contamination. While glass or plastic vials are commonly utilized for storing samples, Ensuring the compatibility of the material used with the specific type of sample being stored is crucial [62, 63]. The quality of stored samples is influenced not only by temperature and storage containers but also by the duration of storage. Ideally, samples should be analyzed promptly after collection to minimize degradation. However, circumstances may necessitate longer storage times, making regular quality checks crucial to ensure stability for subsequent analysis. Adhering to standardized methods for sample collection, processing, and storage is essential to maintain optimal quality. Guidelines from organizations like the Metabolomics Standards Initiative (MSI) provide valuable benchmarks for proper sample handling in metabolomics [64]. These guidelines provide recommendations for sample collection, processing, and storage, as well as quality control and assurance measures to ensure the reliability and reproducibility of metabolomics data. Generally, multiple aliquots should be composed for each sample types, which are stored at −80 °C to slow or prevent enzymatic changes of metabolites. Multiple freeze/thaw cycles should not be used.

Sample preparation

Determining the appropriate sample volume is an essential part of the extraction step. The sample volume obtained after the extraction process should exceed the minimum sample volume required for analysis. Generally, 1−100 mg of tissue, 10⁶, and 10−250 µL of biofluids are acceptable for the extraction phase. On the other hand, these values may vary between devices. Thus, the minimum sample volume must be determined before the extraction step.

The sample preparation should be fast and involve a minimum number of steps. It should also be non-selective for untargeted metabolomics analysis. Multiple sample types and analytic techniques can be used in metabolomics analyses; therefore, each sample type and analytical platform need different sample preparation procedures. Direct injection of samples is not possible due to the use of sensitive column and detector technology in mass spectrometric techniques [65]. Protein, salt, and some lipids in the sample must be removed [66, 67]. In untargeted metabolomics with LC-MS, a common approach for sample preparation is solvent protein precipitation, often using a one-step extraction method with one or more solvents simultaneously. The choice of solvent is crucial, considering the polarity index, which determines the range of extracted metabolites. Solvents, listed in order of increasing polarity index, include water, dichloromethane, acetone, methanol, acetonitrile, acetone, and hexane. Highly polar solvents pose challenges for effective extraction of lipid-derived metabolites. Researchers must carefully select the solvent based on the desired metabolite profile [27].

Dilute and shoot methods are commonly used in urine and targeted metabolomics as they are fast, simple, and require minimal sample preparation. In this approach, a small volume of urine is diluted with a suitable solvent to reduce matrix effects, and then directly injected into the analytical instrument for analysis [68, 69]. This method is advantageous for compounds with high abundance and low variability in urine, where sample dilution does not significantly affect the analytical signal. It is also useful approach when limited sample volume is available, or the metabolites of interest are not retained by the sample preparation sorbent [70]. However, the dilute and shoot method should be used with caution. The high complexity of urine matrix and the large dynamic range of metabolite concentrations can affect the detection of low abundance metabolites.

Equipment such as needle tip filters is critical for the success of the extraction phase. Syringe filters are commonly used in untargeted metabolomics sample preparation to remove unwanted particles and impurities from biological samples, including urine, blood, and plasma. Nylon syringe filters with a pore size of 0.22 µm are commonly used in metabolomics. The use of 0.22 µm nylon syringe filters is especially important for LC-MS-based metabolomics analysis, where the presence of particulates or impurities can interfere with chromatographic separation and reduce the sensitivity and accuracy of the analytical method. All solvents should be evaporated after filtration stage to normalize and optimize the samples. Different techniques can be used, including vacuum concentrators and lyophilization for this purpose [7, 71], [72], [73]. Sample extraction procedures for untargeted metabolomics for each sample type were given in Figure 1.

Figure 1:

Sample preparation strategies for LC-MS based untargeted metabolomics analysis of diverse sample types.

Quality assurance of metabolomics

The validity of measurements in the laboratory is ensured through quality control (QC) procedures, which aim to reduce precision errors and analytic bias. Various internal and external programs can be utilized for this purpose. Quality assurance and control procedures for targeted LC-MS analyses are highly standardized, and guidance on these procedures can be obtained from the Clinical and Laboratory Standards Institute (CLSI) C62-A document [74]. The CLSI C62-A document provides comprehensive information on various aspects related to quality control procedures in LC-MS analyses. This includes guidelines for the selection of appropriate quality control materials, frequency of analysis, and instructions on evaluating corrective actions to be taken in case of failed QC. Additionally, the document provides guidance on ensuring accessibility to QC procedures.

The application of QC procedures in untargeted metabolomics is crucial to identify and rectify potential sources of bias or errors that may impact the results when attempting to identify as many metabolites as possible in a sample. While some alternative approaches to quality control (QC) procedures have been reported for LC-MS based untargeted metabolomics, there is currently no widely accepted or standardized procedure for routine use [75], [76], [77], [78]. In untargeted metabolomics, QC procedures can be classified into three main categories: device-related, sample-related, and analysis-related procedures. The device QC procedure involves ensuring the proper functioning of the injector, column, ionization source, and mass spectrometer performance. To carry out these procedures, it is important to obtain detailed information from the producer on the control procedure and frequency of these device parts. Several standard sample and analyze related QC procedures are used in untargeted metabolomics to achieve this goal, including the use of blank, pooled samples, internal standards, replicates, and reference samples.

Blank sample handling is a method used to address background signals, carryover or contaminants that may be present in the instrument or reagents. This is necessary to provide a baseline measurement and ensure accurate results [75]. Another QC procedure commonly used in untargeted metabolomics is the pooled sample. The pooled sample is created by combining equal amounts of the analyzed samples. Adding a pooled sample to the QC procedure in untargeted metabolomics is a useful way to increase the validity and reliability of the results and to enhance confidence in the conclusions drawn from the data. The incorporation of a pooled sample enables researchers to evaluate the performance of their analytical method and detect any potential sources of error or bias that could impact the results [79]. If the results obtained from the pooled sample are significantly different from those obtained from the individual samples, it suggests a problem with the analytical method or sample preparation that needs to be addressed [75, 79, 80].

To correct for potential variations in the sample preparation or measurement process in untargeted metabolomics, internal standards of known amounts are added to the samples. These standards can be deuterated and labeled with stable isotopes. They are particularly useful to normalize the data and account for differences in extraction efficiency, sample preparation, and measurement conditions. Deuterated internal standards have several advantages over other internal standards, and their use can significantly improve the accuracy and reliability of the results [75, 77]. Repeat analysis of a subset of samples, also known as replicates, and the use of reference samples are another useful tools to detect any technical or analytical variation and assess the results’ reliability [77, 81].

Column strategies

The choice of column strategy will depend on several factors, including the nature of the sample, the size and complexity of the metabolites, and the study’s goals. Some commonly used column strategies in LC-MS based untargeted metabolomics include reverse phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) [82].

Reverse phase chromatography uses a hydrophobic stationary phase, such as C18, to retain hydrophobic metabolites in the sample. This column strategy is often used to analyze small, polar, and medium polar metabolites, such as amino acids and organic acids [83]. Using RPC in LC-MS based untargeted metabolomics can provide high-resolution separations of hydrophobic metabolites, improve the sensitivity of the analysis, and increase the selectivity of the results.

The HILIC strategy uses a hydrophilic stationary phase such as an amide to retain hydrophilic metabolites in the sample. This column strategy is often used to analyze polar and polarizable metabolites. The retention mechanism of HILIC and RPC is different. HILIC uses hydrogen bonding and dipole-dipole interactions to retain hydrophilic metabolites. It is particularly useful for the analysis of very polar metabolites such as sugars and glycerophospholipids [83], [84], [85], [86]. Both HILIC and RPC are valuable column strategies for untargeted metabolomics. A combination of both HILIC and RPC may be necessary to fully characterize the metabolic profile of a complex biological sample [27, 87].

MS acquisition strategies

Acquisition strategies refer to the methods used to obtain mass spectrometric data from a sample. Different acquisition techniques, including full scan MS, selected ion monitoring (SIM), multiple reaction monitoring (MRM), data-dependent acquisition (DDA) and data-independent acquisition (DIA), can be used in LC-MS based metabolomics [88].

Full scan MS is the most basic acquisition strategy and involves acquiring a complete mass spectrum of all the ions present in the sample. In this method, the MS scans over a specified mass range and detects all the ions in the sample. It is often used as a screening method to identify the presence of specific ions [10]. To monitor the intensity of one or more specific ions, SIM mode can be used. In this method, the MS is set to detect only the specified ions, and the intensities of these ions are recorded over time. SIM is often used to increase the sensitivity and specificity of the analysis, as it reduces the background noise and improves the signal-to-noise ratio [89]. MRM acquisition strategies are being used to detect specific transitions between precursor and product ions. In this method, the MS is set to detect only specific precursor-product ion pairs, and the intensities of these pairs are recorded over time. MRM is often used to increase the specificity of the analysis, as it allows for the detection and quantification of specific metabolites in a sample [90], [91], [92], [93], [94], [95]. DDA and DIA are the common acquisition strategies used in untargeted metabolomics. To speed up the process of collecting data in untargeted metabolomics, a new mode called DDA has been developed. In this mode, the mass spectrometry (MS) instrument performs a full-scan followed by MS2 analysis on a list of precursor ions chosen from the full-scan spectrum. This allows for both quantitative and structural information to be acquired at the same time, making it easier to identify metabolites. However, DDA has some limitations, such as the potential to miss low abundance metabolites that are not selected for fragmentation and reduced signal intensity for MS1 features. To overcome these limitations, DIA has been recently implemented in metabolomics studies. DIA allows for the generation of MS2 spectra for all precursor ions, which can detect and identify more metabolites, especially those at lower concentrations. In DIA mode, the MS instrument cycles through the precursor ion m/z range with a large precursor ion mass width to fragment multiple precursor ions at once. AIF and MS^All/MS^E are common DIA methods that enable continuous and unbiased acquisition of MS2 information for all metabolites [10, 96].

Annotation

Annotation in untargeted metabolomics refers to the process of identifying the chemical structure and name of metabolites in a sample. It aims to match the mass spectral data obtained from the sample with reference spectral data in a database, to identify the metabolites [18, 97]. Several steps are involved in the annotation process, including data preprocessing, feature detection, metabolite identification and confirmation of identification [98]. Data preprocessing involves cleaning and processing the raw data obtained from the mass spectrometer to ensure the data is high quality and free from any artefacts. This step may include removing background noise, baseline correction, and data normalization [99]. The feature detection step involves detecting and extracting peaks from the processed data corresponding to the sample’s metabolites. The peaks are then compared to a database of reference spectral data to determine the most likely candidate metabolites [100, 101]. This may involve different algorithms and techniques, such as database searching, spectral matching, and fragment pattern analysis. Identified metabolites should be verified using additional information, such as retention time, mass accuracy, and additional MS/MS data. This step aims to confirm the identification of the metabolites and reduce the risk of false positive identifications [18, 97, 98, 102, 103].

Database searching technique searches the mass spectral data obtained from the sample against a reference database of known metabolites to determine the most likely candidate metabolites. This technique is based on comparing the mass-to-charge ratio (m/z) and the retention time of the metabolites in the sample with those in the reference database. Some commonly used databases include the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [104, 105]. In fragment pattern analysis, the chemical structure of the metabolites is ascertained by examining the fragments generated from the metabolites during the mass spectrometric analysis. To identify the most likely potential metabolites, the fragments are matched to a database of known fragments [106, 107].

Bioinformatics

Bioinformatics plays a crucial role by providing tools for analyzing, interpreting, and visualizing large and complex data sets. Using bioinformatics can significantly enhance metabolomics studies’ speed, accuracy, and reproducibility and provide new insights into the biology and biochemistry of cells and tissues. These are some of the key applications and bioinformatic tools, including commercial and open-source software for analyzing LC-MS based untargeted metabolomics data (Figure 2).

Figure 2:

Some commercial and open-source software for analyzing LC-MS based untargeted metabolomics data.

Clinical applications of untargeted metabolomics

The clinical application of untargeted metabolomics has the potential to revolutionize the diagnosis and treatment of many diseases, including cancer, metabolic disorders, and cardiovascular diseases. Some specific examples of the clinical applications of untargeted metabolomics include cancer, infectious disease, diabetes, and cardiovascular disease. Overall, the clinical application of untargeted metabolomics holds great promise for improving disease diagnosis, personalized medicine, drug development, and lifestyle interventions [108, 109].

Cancer and untargeted metabolomics

Cancer is a disease characterized by uncontrolled cell growth and proliferation. One of the hallmarks of cancer is metabolic reprogramming, which refers to changes in how cancer cells use energy and nutrients to support their growth and survival. Metabolic changes in cancer cells can include increased glucose uptake, altered glucose metabolism, changes in amino acid metabolism, fatty acid uptake and synthesis, and changes in mitochondrial function [110]. Understanding the metabolic changes in cancer cells is essential for developing new diagnostic and therapeutic strategies that target these processes. In recent years, increasing interest has been in targeting cancer metabolism to improve cancer treatment outcomes [110], [111], [112]. Early diagnosis, tumor classification, response to therapy, and tumor metabolism were some specific examples of the clinical applications of untargeted metabolomics [113]. The metabolic profile of cancer cells is often distinct from normal cells, making it possible to identify metabolic biomarkers that can be used for early cancer detection. Zhu and colleagues discovered that certain metabolic pathways, including inositol phosphate metabolism, primary bile acid biosynthesis, phosphatidylinositol signaling system, and linoleic acid metabolism, play a significant role in the development of colorectal cancer. They suggested that by using GC-MS based metabolomics and pattern recognition techniques, it may be possible to identify cancer-specific changes early on, leading to early diagnosis and the development of new treatments [114]. Research suggests that combining untargeted metabolomics with machine learning approaches in female-specific cancers could offer a substantial advantage in developing early detection methods for these types of cancers [115]. According to Liu and colleagues, urine and plasma samples exhibit distinct metabolic signatures in cases of ovarian tumors. As a result, untargeted metabolomics analyses may have the potential to provide valuable insights into the classification of ovarian cancer. By identifying unique metabolic profiles associated with different subtypes of ovarian cancer, this approach may help improve the accuracy of diagnosis and guide the development of personalized treatment strategies for patients with this disease [116]. Untargeted metabolomics analyses can be used to estimate progression and create a personalized treatment. Yang and colleagues linked alterations in glycerophospholipid metabolism to the development and advancement of esophageal squamous cell carcinoma. Additionally, the authors proposed that targeting glycerophospholipid metabolism could be a potential therapeutic strategy for treating esophageal squamous cell carcinoma. These findings suggest that manipulating the metabolic pathways involved in the synthesis and degradation of glycerophospholipids could be a promising approach to developing new treatments for this type of cancer [117]. Hexadecasphinganine, linoleamide, and N-hydroxy arachidonoyl amine have been identified as potential diagnostic markers for gastric cancer. These molecules may be used as biomarkers to detect the presence of gastric cancer, enabling earlier diagnosis and treatment [118]. Research has indicated that untargeted metabolomics analysis using the LC-MS platform can identify a metabolite panel that has the potential to predict the response of locally advanced rectal cancer to neoadjuvant chemo-radiation therapy. This approach may help to personalize treatment strategies for individual patients by identifying those who are likely to benefit from this therapy and those who may require alternative treatment options. These findings suggest that using metabolomics to guide treatment decisions may improve patient outcomes in locally advanced rectal cancer [119]. The use of receiver operating characteristic (ROC) analysis has identified. These biomarkers include Leukotriene A4, PC (24:1 (15Z)/24:1 (15Z)), TG (17:0/17:0/18:0), hypoxanthine, and cis-ACCP has been identified potential biomarkers for early detection of breast cancer in a LC-QTOF-MS based untargeted metabolomics [120].

Untargeted metabolomics can provide valuable insights into cancer metabolism, diagnosis, the discovery of novel biomarkers, identification of therapeutic targets and personalized medicine, but its application requires careful consideration of the limitations such as complex data analysis, technical variability, and limited coverage of metabolites.

Infectious disease and untargeted metabolomics

Untargeted metabolomics has been applied to studying various infectious diseases caused by viruses, bacteria, and parasites. Untargeted metabolomics can contribute to the study of infectious diseases in the fields of host-pathogen interaction, diagnosis, and monitoring response to therapy. Infections can cause significant changes in the metabolic profile of the host. Studies have identified plasma lipids and alterations in their levels as potential biomarkers for detecting Coronavirus disease 2019 (COVID-19) infection [121]. It has been reported that glucose metabolism and the urea cycle may be targets for treating COVID-19 at various disease stages [122]. Deoxycytidine, ureidopropionate, kynurenine, and multiple short-chain acylcarnitines have been determined as candidate biomarkers for predicting COVID-19 severity [123]. Doğan et al. found downregulations in R-S lactoglutathione and glutamine, besides upregulations in hypoxanthine, inosine, and leukotriene D4 in COVID-19 patients. Considering the identified metabolites, purine, glutamine, LTD4, and glutathione metabolisms were associated with the pathogenesis of COVID-19 [30]. Further research in this area could help improve our understanding of the pathogenesis of COVID-19 and facilitate the development of effective diagnostic and therapeutic strategies. Untargeted metabolomic analyses have also been performed on diseases other than COVID-19, leading to significant new findings. Chen et al. indicated that dysregulation of amino acid metabolism might have a role in regulating inflammation and immunity in patients with sepsis [124]. Untargeted metabolomics analysis revealed that Mycobacterium tuberculosis induced tryptophan metabolism in human macrophages. This finding has been associated with a potential therapeutic strategy for pulmonary tuberculosis [125]. In an untargeted serum metabolomics analysis, glycerophospholipid species have been found as potential trichinellosis markers [126]. The study identified dysregulation in the metabolism of amino acids, as well as the biosynthesis of phosphatidylcholines and lysophosphatidylcholines, which have been linked to the development and treatment of hepatitis B virus infection [127]. Untargeted metabolomics provides valuable information about the metabolic changes occurring in the host and pathogen during infectious diseases. This information can be used to develop new diagnostic tools, and therapies, and improve our understanding of the host-pathogen interaction.

Coronary heart disease and untargeted metabolomics

Coronary heart disease (CHD) is a medical condition in which plaque builds up inside the coronary arteries, the blood vessels that supply oxygen-rich blood to the heart muscle. The plaque is made up of cholesterol, fat, calcium, and other substances, and it can narrow or block the arteries, reducing blood flow to the heart. This can lead to chest pain (angina), shortness of breath, heart attack, or other complications. CHD is a common type of heart disease and can be caused by various factors, including high blood pressure, high cholesterol levels, smoking, diabetes, and a family history of heart disease.

A strong relationship exists between metabolic changes and coronary heart disease (CHD). Metabolic changes refer to alterations in the way the body processes nutrients and energy, including glucose, fats, and proteins. Some of the metabolic changes, including insulin resistance, dyslipidemia, obesity, type 2 diabetes, and metabolic syndrome, are associated with an increased risk of CHD. Metabolic changes can contribute to the pathogenesis of CHD through various mechanisms, including damage to the endothelium, inflammation, and plaque formation within the coronary arteries. Management of metabolic changes through lifestyle modifications, medications, and other interventions can help reduce the risk of CHD. There is a growing interest in using metabolomics to understand the pathogenesis of CHD and to identify biomarkers for early detection and personalized treatment.

Several studies have shown that patients with CHD have altered lipid, glucose, and amino acid metabolism, among others. According to Ullah et al., alterations in the metabolism of d-arginine and d-ornithine, glycolysis, oxidation and degradation of branched-chain fatty acids, and sphingolipid metabolism have been linked to coronary heart disease (CHD) [128]. It has been discovered that serum oxyneurine and triglyceride are candidate biomarkers for the early diagnosis of CHD [129]. Nineteen metabolites related to amino acids, lipids, peptides, carbohydrates, nucleotide, and xenobiotics metabolism have been identified as biomarkers in predicting CHD risk [130]. The study observed inverse correlations between creatine, creatinine, and phenylalanine, and positive correlations between mannose, acetaminophen-glucuronide, lactate, and apolipoprotein B, with incidents of cardiovascular disease [131]. Upregulation in metabolites related to the fatty acid oxidation and glucose oxidation pathways, including oleic acid, stearic acid, palmitic acid, linoleic acid, galactose, pyruvic and lactic acids, has been associated with CHD [132]. Branch chain amino acids, l-arginine, linoleic acid, l-serine, l-cysteine, fructose-6-phosphate, glycerol, creatine and 3-phosphoglyceric acid have been identified as candidate biomarkers to explain the pathogenesis of type 2 diabetes mellitus assisted CHD [133].

Biomarkers are measurable indicators that can be used to predict the risk of disease, diagnose the disease, or monitor disease progression. Untargeted metabolomics can help identify biomarkers for CHD. Using untargeted metabolomics, several metabolites have been identified as potential biomarkers for CHD. Furthermore, metabolomics can help identify targets for therapeutic interventions. By identifying specific metabolic pathways dysregulated in CHD, researchers can develop targeted therapies to restore metabolic homeostasis and prevent or treat CHD.