Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Introduction

Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.

Methods

PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.

Results

Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.

Conclusion

Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

     

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid,free fatty acids and amino acid levels

Background

Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn’s disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression.

Objectives

The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples.

Methods

A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis.

Results

Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A₂ and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease.

Conclusions

Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease.

     

Ethnicity influences gut metabolites and microbiota of the tribes of Assam,India

Introduction

The human gut microbes and their metabolites are involved in multiple host metabolic pathways. Dysbiosis in the gut microbiota and altered metabolite profiles were reported in diseased state. In a region like Assam, where 12.4% of the populations are tribal population, evaluating the influence of ethnicity on gut microbiota and metabolites has become important to further differentiate it from the diseased state.

Objective

To study the influence of ethnicity on fecal metabolite profile and their association with the gut microbiota composition.

Methods

In this study, we determined the untargeted fecal metabolites from five ethnic groups of Assam (Tai-Aiton, Bodo, Karbi, Tea-tribe and Tai-Phake) using GC–MS and compared them among the tribes for common and unique metabolites. Metabolites of microbial origin were related with the available metagenomic data on gut bacterial profiles of the same ethnic groups and functional analysis were carried out based on HMDB.

Results

The core fecal metabolite profile of the Tea-tribe contained aniline, benzoate and acetaldehyde. PLS-DA based on the metabolites suggested that the individuals grouped based on their ethnicity. PCA plot of the data on bacterial abundance at the level of genus indicated clustering of individuals based on ethnicity. Positive correlations were observed between propionic acid and the genus Clostridium (R?=?0.43 and p?=?0.03), butyric acid and the genus Lactobacillus (R?=?0.45 and p?=?0.024), acetic acid and the genus Bacteroides (R?=?0.63 and p?=?0.001) and methane and the genus Escherichia (R?=?0.58 and p?=?0.002).

Conclusion

Results of this study indicated that ethnicity influences both gut bacterial profile and their metabolites.

     

Tartrate inhibition of prostatic acid phosphatase improves seminal fluid metabolite stability

Introduction

Human seminal fluid (hSF) has been suggested as a biofluid suitable to characterise male reproductive organ pathology with metabolomics. However, various enzymatic processes, including phosphorylcholine hydrolysis mediated by prostatic acid phosphatase (PAP), cause unwanted metabolite variation that may complicate metabolomic analysis of fresh hSF samples.

Objectives

To investigate the effects of PAP inhibition with tartrate.

Methods

Using NMR spectroscopy, the kinetics of phosphorylcholine to choline hydrolysis was characterized in hSF samples from three subjects at different temperatures and tartrate concentrations. Principal components analysis was used to characterise the effects of tartrate and temperature on personal differences in metabolite profiles. Potential effects of tartrate on RNA quantification were also determined.

Results

Metabolite profiles and the kinetics of phosphorylcholine degradation are reproducible in independent samples from three ostensibly normal subjects. Increasing concentrations of tartrate and refrigerated sample storage (279 K) resulted in greatly reduced reaction rates as judged by apparent rate constants. Multivariate statistical analysis showed that personal differences in metabolite profiles are not overshadowed by tartrate addition, which stabilises phosphorylcholine and choline concentrations. The tartrate signal also served as an internal concentration standard in the samples, allowing the determination of absolute metabolite concentrations in hSF. Furthermore, the presence of tartrate did not affect RNA expression analysis by qPCR.

Conclusion

Based on these results we recommend as standard protocol for the collection of hSF samples, that 10 mM tartrate are added immediately to samples, followed by sample storage/handling at 277 K until clinical processing within 6 h to remove/inactivate enzymes and isolate metabolite supernatant and other cellular fractions.

     

Serum metabolomic study for detecting biomarkers of non-traumatic osteonecrosis of the femoral head

Introduction

Non-traumatic osteonecrosis of the femoral head (NTONFH) is a progressive disease, always leading to hip dysfunction if no early intervention was applied. The difficulty for early diagnosis of NTONFH is due to the slight symptoms at early stages as well as the high cost for screening patients by using magnetic resonance imaging.

Objective

The aim was to detect biomarkers of early-stage NTONFH, which was beneficial to the exploration of a cost-effective approach for the early diagnose of the disease.

Methods

Metabolomic approaches were employed in this study to detect biomarkers of early-stage NTONFH (22 patients, 23 controls), based on the platform of ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the uses of multivariate statistic analysis, putative metabolite identification, metabolic pathway analysis and biomarker analysis.

Results

In total, 33 serum metabolites were found altered between NTONFH group and control group. In addition, glycerophospholipid metabolism and pyruvate metabolism were highly associated with the disease.

Conclusion

The combination of LysoPC (18:3), l-tyrosine and l-leucine proved to have a high diagnostic value for early-stage NTONFH. Our findings may contribute to the protocol for early diagnosis of NTONFH and further elucidate the underlying mechanisms of the disease.

     

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure,correlation of metabolites to cell number,and impact of the cell harvesting method

Introduction

Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.

Objectives

We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.

Methods

We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.

Results

We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.

Conclusion

We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

     

Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population

Introduction

The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish.

Objectives

As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes.

Methods

In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique.

Results

We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development.

Conclusion

Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.

     

A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Introduction

Chromosomal anomalies (CA) are the most frequent fetal anomalies.

Objective

To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester .

Methods

This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment.

Results

Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation.

Conclusion

The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.

     

Identification of a urine metabolite constellation characteristic for kidney allograft rejection

Introduction

Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.

Objectives

We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.

Methods

NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.

Results

We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).

Conclusion

A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

     

Granulocyte colony-stimulating factor alters the systemic metabolomic profile in healthy donors

Introduction

Peripheral blood stem cells mobilized by granulocyte colony-stimulating factor (G-CSF) from healthy donors are commonly used for allogeneic stem cell transplantation. The effect of G-CSF administration on global serum metabolite profiles has not been investigated before.

Objectives

This study aims to examine the systemic metabolomic profiles prior to and following administration of G-CSF in healthy adults.

Methods

Blood samples were collected from 15 healthy stem cell donors prior to and after administration of G-CSF 10 µg/kg/day for 4 days. Using a non-targeted metabolomics approach, metabolite levels in serum were determined using ultrahigh performance liquid chromatography-tandem mass spectrometry and gas chromatography/mass spectrometry.

Results

Comparison of the metabolite profiles of donors before and after G-CSF treatment revealed 239 metabolites that were significantly altered. The major changes of the metabolite profiles following G-CSF administration included alteration of several fatty acids, including increased levels of several medium and long-chain fatty acids, as well as polyunsaturated fatty acids; while there were lower levels of other lipid metabolites such as phospholipids, lysolipids, sphingolipids. Furthermore, there were significantly lower levels of several amino acids and/or their metabolites, including several amino acids with known immunoregulatory functions (methionine, tryptophan, valine). Lastly, the levels of several nucleotides and nucleotide metabolites (guanosine, adenosine, inosine) were also decreased after G-CSF administration, while methylated products were increased. Some of these altered products/metabolites may potentially have angioregulatory effects whereas others may suggest altered intracellular epigenetic regulation.

Conclusion

Our results show that G-CSF treatment alters biochemical serum profiles, in particular amino acid, lipid and nucleotide metabolism. Additional studies are needed to further evaluate the relevance of these changes in healthy donors.

     

Metabolomic signatures of increases in temperature and ocean acidification from the reef-building coral, <Emphasis Type="Italic">Pocillopora damicornis</Emphasis>

Introduction

As a changing climate threatens the persistence of terrestrial and marine ecosystems by altering community composition and function, differential performance of taxa highlights the need for predictive metrics and mechanistic understanding of the factors underlying positive performance in the face of environmental disturbances. Biochemical reactions within cells provide a snapshot of molecular regulation and flexibility during exposure to environmental stressors. However, because the organism is the unit of selection there is a need for the integration of metabolite data with organism physiology to understand mechanisms responsible for individual success under a changing climate.

Objectives

Our study aims to characterize the molecular response of reef corals to simulated global climate change stressors. Furthermore, we seek to relate changes in the molecular physiology to observations in overall colony response.

Methods

To this end, we applied a non-targeted metabolomic approach to describe lipid and primary metabolite composition after exposure of the reef-building coral Pocillopora damicornis to ambient and elevated experimental climate change conditions. We compared these metabolite data to organism physiology, specifically the key processes of photosynthesis, respiration, and calcification.

Results

Corals significantly altered their lipid and primary metabolite profiles in response to experimental treatments. Primary metabolite profiles predicted organisms’ net photosynthesis, but not calcification or respiration measures. Despite challenges in metabolome annotation, our data indicated corals alter carbohydrate composition, cell structural lipids, and signaling compounds in response to elevated treatment conditions.

Conclusions

The integration of metabolite and physiological data highlights the predictive power of metabolomics in defining organism performance and provides biomarkers for future studies. Here, we present a multivariate biomarker approach to assess climate change impacts and advance our mechanistic understanding of stress response in this keystone species.

     

Metabolite profiling of yam (<Emphasis Type="Italic">Dioscorea spp</Emphasis>.) accessions for use in crop improvement programmes

Introduction

Ninety-seven percent of yam (Dioscorea spp.) production takes place in low income food deficit countries (LIFDCs) and the crop provides 200 calories a day to approximately 300 million people. Therefore, yams are vital for food security. Yams have high-yield potential and high market value potential yet current breeding of yam is hindered by a lack of genomic information and genetic resources. New tools are needed to modernise breeding strategies and unlock the potential of yam to improve livelihood in LIFDCs.

Objectives

Metabolomic screening has been undertaken on a diverse panel of Dioscorea accessions to assess the utility of the approach for advancing breeding strategies in this understudied crop.

Methods

Polar and lipophilic extracts from tubers of accessions from the global yam breeding program have been comprehensively profiled via gas chromatography-mass spectrometry.

Results

A visual pathway representation of the measured yam tuber metabolome has been delivered as a resource for biochemical evaluation of yam germplasm. Over 200 compounds were routinely measured in tubers, providing a major advance for the chemo-typing of this crop. Core biochemical redundancy concealed trends that were only elucidated following detailed mining of global metabolomics data. Combined analysis on leaf and tuber material identified a subset of metabolites which allow accurate species classification and highlighted the potential of predicting tuber composition from leaf profiles. Metabolic variation was accession-specific and often localised to compound classes, which will aid trait-targeting for metabolite markers.

Conclusions

Metabolomics provides a standalone platform with potential to deliver near-future crop gains for yam. The approach compliments the genetic advancements currently underway and integration with other ‘–omics’ studies will deliver a significant advancement to yam breeding strategies.

     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

     

Metabolic risks at birth of neonates exposed in utero to HIV-antiretroviral therapy relative to unexposed neonates: an NMR metabolomics study of cord blood

Introduction

Antiretroviral therapy (ART) for HIV-infected pregnant women is highly effective in preventing mother-to-child transmission (PMTCT) of the virus, but deleterious metabolic and mitochondrial observations in infants born to HIV-infected women treated with ART during pregnancy are periodically reported.

Objectives

This study addresses the concern of HIV-ART-induced metabolic perturbations through a metabolomics study of cord blood collected during transitional neonatal hypoglycaemia following birth from newborns either exposed or unexposed to fetal HIV-ART.

Methods

Proton magnetic resonance spectra from cord blood of 11 in utero HIV-ART-exposed and 14 unexposed newborns, as well as serum from 8 control infants, generated 114 spectral bins which were used to identify significant metabolites by means of univariate and multivariate statistical analyses.

Results

The metabolite profiles differed significantly between that from the unexposed newborns and that from infants—interpreted to characterize the state of transitional neonatal hypoglycaemia (low glucose and high lactic acid and ketone bodies). Quantitative analysis of potential ATP generation showed no meaningful difference in the global metabolite profiles of HIV-ART-exposed and unexposed neonates, but Volcano plot analysis, affirmed by odds ratios, indicated that exposure to HIV-ART affected the plasma 3-hydroxybutyric acid and hypoxanthine concentrations.

Conclusions

The metabolite profile for transitional neonatal hypoglycaemia indicated that HIV-ART did not compromise the exposed neonates to the energy stress of allostasis experienced at birth. Increased hypoxanthine and 3-hydroxybutyric acid indicates metabolic stress at birth in some of the newborns exposed to HIV-ART and raises a concern about unrecognized prolonged allostasis with potential neurological consequences for these infants.

     

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies

Background

Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method.

Objectives

To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system.

Methods

This is a case–control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment.

Results

Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation.

Conclusion

The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.

     

Metabolomics as a tool for understanding the evolution of <Emphasis Type="Italic">Tabebuia sensu lato</Emphasis>

Background

Plant systematic studies have changed substantially in the last years, stimulated by new strategies for phylogenetic studies. In this regard, chemistry data has been a useful tool for understanding plant phylogenetic relationships.

Objective

Our aim was to apply metabolomic approaches, followed by multivariate statistical analysis and dereplication of Tabebuia sensu lato species, and compare our results with classifications based on traditional taxonomy and molecular phylogeny. We also evaluated the application of metabolomics as a chemotaxonomic identification tool, as well as to enlighten plant chemical evolution.

Methods

Metabolomic data was generated through a high-resolution mass spectrometry with electrospray ionization of 27 Tabebuia sensu lato specimens from different populations, consisting of 15 Handroanthus (from four species) and 12 Tabebuia sensu stricto (from three species). Chemometric tools, such as principal component analysis and metabolite heatmaps, were used to scrutinize the metabolic changes among species.

Results

Tabebuia and Handroanthus species presented different secondary metabolite storage capacity. The genus Tabebuia revealed higher levels of glycosylated iridoids esterified with a phenylpropanoid moiety, such as specioside, verminoside, and minecoside, while Handroanthus accumulated iridoids linked to a simple phenol, lignans, and verbascoside derivatives.

Conclusion

These results corroborate splitting the Tabebuia s.l., which was supported by profound changes in secondary metabolism, suggesting metabolomics as an excellent tool for understanding species evolution.

     

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

Introduction

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

Objectives

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

Method

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

Result

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.

Conclusion

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.