Metabolomics: An emerging potential approach to decipher critical illnesses

Metabolomics for the identification of biomarkers in endometriosis

BACKGROUND

Endometriosis affects the quality of life in women during their reproductive years, causing immense pain and can result in infertility. It is characterized by inflammation and the growth of the endometrium outside the uterine cavity. Metabolomics has the potential to resolve the major bottleneck of endometriosis which is delay in diagnosis due to the invasive diagnostic approach.In this review, the author has summarized the identified biomarkers of endometriosis from different bodily fluids. Metabolomics promises a non-invasive diagnostic approach for endometriosis that could aid in earlier diagnosis and prognosis.

METHODS

Patients with endometriosis keywords were searched in correspondence with the assigned keywords, including metabolomics from PubMed, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study.

RESULTS

This article provides information regarding metabolomics studies in endometrisis.

CONCLUSIONS

We demonstrated that metabolomics is about to change the world of endometriosis by analyzing and detecting the diagnosis, prognosis, mortality and treatment response biomarkers.

Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol

BACKGROUND

This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction.

METHODS

Data from ICU patients (December 2016 to December 2021), including serum levels of trace elements, vitamins, cholesterol, and prealbumin, were retrospectively analyzed using AI models. Models employed included category boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and multilayer perceptron (MLP). Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1-score. The performance was evaluated using 10-fold crossvalidation. The SHapley Additive exPlanations (SHAP) method provided interpretability.

RESULTS

CatBoost emerged as the top-performing individual AI model with an AUROC of 0.756, closely followed by LGBM, MLP, and XGBoost. Furthermore, the ensemble model combining these four models achieved the highest AUROC of 0.776 and more balanced metrics, outperforming all models. SHAP analysis indicated significant influences of prealbumin, Acute Physiology and Chronic Health Evaluation II score, and age on predictions. Notably, the ratios of selenium to age and low-density lipoprotein to total cholesterol also had a notable impact on the models' output.

CONCLUSION

The study underscores the critical role of nutrition-related parameters in ICU patient outcomes. Advanced AI models, particularly in an ensemble approach, demonstrated improved predictive accuracy. SHAP analysis offered insights into specific factors influencing patient survival, highlighting the need for broader consideration of these biomarkers in critical care management.

Unveiling Pathophysiological Insights: Serum Metabolic Dysregulation in Acute Respiratory Distress Syndrome Patients with Acute Kidney Injury

Acute respiratory distress syndrome (ARDS) is associated with high mortality rates, which are further exacerbated when accompanied by acute kidney injury (AKI). Presently, there is a lack of comprehensive studies thoroughly elucidating the metabolic dysregulation in ARDS patients with AKI leading to poor outcomes. We hypothesized that metabolomics can be a potent tool to highlight the differences in the metabolic profile unraveling unidentified pathophysiological mechanisms of ARDS patients with and without AKI. 1H nuclear magnetic resonance spectroscopy was used to identify key metabolites in the serum samples of 75 patients. Distinct clusters of both groups were obtained as the study's primary outcome using multivariate analysis. Notable alternations in the levels of nine metabolites were identified. Pathway analysis revealed the dysregulation of five significant cycles, which resulted in various complications, such as hyperammonemia, higher energy requirements, and mitochondrial dysfunction causing oxidative stress. Identified metabolites also showed a significant correlation with clinical scores, indicating severity. This study shows the alterations in the metabolite concentration highlighting the difference in the pathophysiology of both patient groups and its association with outcome, pointing in the direction of a personalized medicine approach and holding significant promise for application in critical care settings to improve clinical outcomes.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

High-resolution plasma metabolomics and thiamine status in critically Ill adult patients

INTRODUCTION

Thiamine (Vitamin B1) is an essential micronutrient and is classically considered a co-factor in energy metabolism. The association between thiamine status and whole-body metabolism in critical illness has not been studied.

OBJECTIVES

To determine association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites and connected metabolic pathways using high resolution metabolomics (HRM) in critically ill patients.

METHODS

Cross-sectional study performed at Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were critically ill adults with an expected length of intensive care unit stay longer than 48 h and receiving chronic furosemide therapy. A total of 76 participants were included. Mean age was 69 years (range 33-92 years); 65% were female. Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP was measured by HPLC and plasma HRM was performed using liquid chromatography/mass spectrometry. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies (MWAS). MWAS using the highest and lowest TPP concentration tertiles was performed as a secondary analysis.

RESULTS

Specific metabolic pathways associated with whole blood TPP levels in regression and tertile analysis included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways.

CONCLUSIONS

Plasma HRM revealed that thiamine status, determined by whole blood TPP concentrations, was significantly associated with metabolites and metabolic pathways related to metabolism of energy, carbohydrates, amino acids, lipids, and the gut microbiome in adult critically ill patients.

Identification of Key Factors in Cartilage Tissue During the Progression of Osteoarthritis Using a Non-targeted Metabolomics Strategy

This research was to reveal the key factors in the progression of osteoarthritis (OA) using non-targeted metabolomics and to find targeted therapies for patients with OA. Twenty-two patients with knee OA scheduled for total knee arthroplasty were divided into two groups: Kellgren-Lawrence (KL) grade 3 (n = 16) and grade 4 (n = 6), according to plain X-rays of the knee. After the operation, the cartilages of femur samples were analyzed using non-targeted metabolomics. When compared with grade 3 patients, the levels of choline, 2-propylpiperidine, rhamnose, and monomethyl glutaric acid were higher; while 1-methylhistamine, sphingomyelin (SM) (d18:1/14:0), zeranol, 3- (4-hydroxyphenyl)-1-propanol, 5-aminopentanamide, dihydrouracil, 2-hydroxypyridine, and 3-amino-2-piperidone were lower in grade 4 patients. Furthermore, some metabolic pathways were found to be significantly different in two groups such as the pantothenate and coenzyme A (CoA) biosynthesis pathway, the glycerophospholipid metabolism pathway, histidine metabolism pathway, lysine degradation pathway, glycine, serine and threonine metabolism pathway, fructose and mannose metabolism pathway, the pyrimidine metabolism pathway, and beta-alanine metabolism pathway. This work used non-targeted metabolomics and screened out differential metabolites and metabolic pathways, providing a reliable theoretical basis for further study of specific markers and their specific pathways in the progression of OA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00123-z.

Sepsis, Management & Advances in Metabolomics

Though there have been developments in clinical care and management, early and accurate diagnosis and risk stratification are still bottlenecks in septic shock patients. Since septic shock is multifactorial with patient-specific underlying co-morbid conditions, early assessment of sepsis becomes challenging due to variable symptoms and clinical manifestations. Moreover, the treatment strategies are traditionally based on their progression and corresponding clinical symptoms, not personalized. The complex pathophysiology assures that a single biomarker cannot identify, stratify, and describe patients affected by septic shock. Traditional biomarkers like CRP, PCT, and cytokines are not sensitive and specific enough to be used entirely for a patient's diagnosis and prognosis. Thus, the need of the hour is a sensitive and specific biomarker after comprehensive analysis that may facilitate an early diagnosis, prognosis, and drug development. Integration of clinical data with metabolomics would provide means to understand the patient's condition, stratify patients better, and predict the clinical outcome.

Multi-omics analysis reveals overactive inflammation and dysregulated metabolism in severe community-acquired pneumonia patients

BACKGROUND

Severe community-acquired pneumonia (S-CAP) is a public health threat, making it essential to identify novel biomarkers and investigate the underlying mechanisms of disease severity.

METHODS

Here, we profiled host responses to S-CAP through proteomics analysis of plasma samples from a cohort of S-CAP patients, non-severe (NS)-CAP patients, diseases controls (DCs), and healthy controls (HCs). Then, typical differentially expressed proteins were then validated by ELISA in an independent cohort. Metabolomics analysis was further performed on both the cohort 1 and cohort 2. Then, the proteomic and metabolomic signatures were compared between the adult and child cohorts to explore the characteristics of severe pneumonia patients.

RESULTS

There were clear differences between CAP patients and controls, as well as substantial differences between the S-CAP and NS-CAP. Pathway analysis of changes revealed excessive inflammation, suppressed immunity, and lipid metabolic disorders in S-CAP cases. Interestingly, comparing these signatures between the adult and child cohorts confirmed that overactive inflammation and dysregulated lipid metabolism were common features of S-CAP patients, independent of age. The change proportion of glycerophospholipids, glycerolipids, and sphingolipids were obviously different in the adult and child S-CAP cases.

CONCLUSION

The plasma multi-omics profiling revealed that excessive inflammation, suppressed humoral immunity, and disordered metabolism are involved in S-CAP pathogenesis.

High-Resolution Plasma Metabolomics and Thiamine Status in Critically Ill Adult Patients

BACKGROUND AND AIM

Thiamine (Vitamin B1) is an essential micronutrient and a co-factor for metabolic functions related to energy metabolism. We determined the association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites using high resolution metabolomics in critically ill patients. Methods Cross-sectional study performed in Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were ≥ 18 years of age, with an expected length of ICU stay longer than 48 hours, receiving furosemide therapy for at least 6 months before ICU admission. Results Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP concentrations were measured using high-performance liquid chromatography (HPLC). Liquid chromatography/mass spectrometry was used for plasma high-resolution metabolomics. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies. We also compared metabolomic features from patients in the highest TPP concentration tertile to patients in the lowest TPP tertile as a secondary analysis. We enrolled 76 participants with a median age of 69 (range, 62.5-79.5) years. Specific metabolic pathways associated with whole blood TPP levels, using both regression and tertile analysis, included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways. Conclusions Plasma high-resolution metabolomics analysis showed that whole blood TPP concentrations are significantly associated with metabolites and metabolic pathways linked to the metabolism of energy, amino acids, lipids, and the gut microbiome in adult critically ill patients.

Metabolic fingerprint of patients showing responsiveness to treatment of septic shock in intensive care unit

OBJECTIVE

An early metabolic signature associated with the responsiveness to treatment can be useful in the better management of septic shock patients. This would help clinicians in designing personalized treatment protocols for patients showing non-responsiveness to treatment.

METHODS

We analyzed the serum on Day 1 (n = 60), Day 3 (n = 47), and Day 5 (n = 26) of patients with septic shock under treatment using NMR-based metabolomics. Partial least square discriminant analysis (PLS-DA) was performed to generate the list of metabolites that can be identified as potential disease biomarkers having statistical significance (that is, metabolites that had a VIP score > 1, and p value < 0.05, False discovery rate (FDR) < 0.05).

RESULTS

Common significant metabolites amongst the three time points were obtained that distinguished the patients being responsive (R) and non-responsive (NR) to treatments, namely 3 hydroxybutyrate, lactate, and phenylalanine which were lower, whereas glutamate and choline higher in patients showing responsiveness.

DISCUSSION

The study gave these metabolic signatures identifying patients' responsiveness to treatment. The results of the study will aid in the development of targeted therapy for ICU patients.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Assessment of microplastic contamination in an eastern Pacific tuna (Katsuwonus pelamis) and evaluation of its health risk implication through molecular docking and metabolomics studies

This work investigated microplastic (MP) pollution in a commercially-important tuna species Katsuwonus pelamis (K. pelamis) from the Eastern Pacific and health implications. 125 MPs were extracted from gills, esophagus, stomachs, intestinal tracts, and muscle of K. pelamis. MPs in the esophagus was the highest, ∼7.6 times higher than that in the gill. Polyester and polyethylene terephthalate (PET) were dominant. Molecular docking implied that PET stabilized the complex via forming 4 new hydrogen bonds that interacted with Arg83, Gln246, Thr267, and Gly268, given that PET can enter glycerol kinase protein active pocket. Metabonomic results suggested that Glycerol 3-phosphate up expressed 1.66 more times that of control groups with no MPs in the muscle. This confirmed that MPs would lie in the glycerol kinase protein active pocket, which triggered menace to K. pelamis. The results provided insights into suggested the potential influence of MPs on the sustainability of fisheries and seafood safety.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolomics profile in acute respiratory distress syndrome by nuclear magnetic resonance spectroscopy in patients with community-acquired pneumonia

Background

Acute respiratory distress syndrome (ARDS) is a challenging clinical problem. Discovering the potential metabolic alterations underlying the ARDS is important to identify novel therapeutic target and improve the prognosis. Serum and urine metabolites can reflect systemic and local changes and could help understanding metabolic characterization of community-acquired pneumonia (CAP) with ARDS.

Methods

Clinical data of patients with suspected CAP at the First Affiliated Hospital of Wenzhou Medical University were collected from May 2020 to February 2021. Consecutive patients with CAP were enrolled and divided into two groups: CAP with and without ARDS groups. ¹H nuclear magnetic resonance-based metabolomics analyses of serum and urine samples were performed before and after treatment in CAP with ARDS (n = 43) and CAP without ARDS (n = 45) groups. Differences metabolites were identifed in CAP with ARDS. Furthermore, the receiver operating characteristic (ROC) curve was utilized to identify panels of significant metabolites for evaluating therapeutic effects on CAP with ARDS. The correlation heatmap was analyzed to further display the relationship between metabolites and clinical characteristics.

Results

A total of 20 and 42 metabolites were identified in the serum and urine samples, respectively. Serum metabolic changes were mainly involved in energy, lipid, and amino acid metabolisms, while urine metabolic changes were mainly involved in energy metabolism. Elevated levels of serum 3-hydroxybutyrate, lactate, acetone, acetoacetate, and decreased levels of serum leucine, choline, and urine creatine and creatinine were detected in CAP with ARDS relative to CAP without ARDS. Serum metabolites 3-hydroxybutyrate, acetone, acetoacetate, citrate, choline and urine metabolite 1-methylnicotinamide were identified as a potential biomarkers for assessing therapeutic effects on CAP with ARDS, and with AUCs of 0.866 and 0.795, respectively. Moreover, the ROC curve analysis revealed that combined characteristic serum and urine metabolites exhibited a better classification system for assessing therapeutic effects on CAP with ARDS, with a AUC value of 0.952. In addition, differential metabolites strongly correlated with clinical parameters in patients with CAP with ARDS.

Conclusions

Serum- and urine-based metabolomics analyses identified characteristic metabolic alterations in CAP with ARDS and might provide promising circulatory markers for evaluating therapeutic effects on CAP with ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02075-w.

Gender-specific association of oxidative stress and immune response in septic shock mortality using NMR-based metabolomics

Background: Sepsis and septic shock are still associated with a high mortality rate. The early-stage prediction of septic shock outcomes would be helpful to clinicians for designing their treatment protocol. In addition, it would aid clinicians in patient management by understanding gender disparity in terms of clinical outcomes of septic shock by identifying whether there are sex-based differences in sepsis-associated mortality. Objective: This study aimed to test the hypothesis that gender-based metabolic heterogeneity is associated with sepsis survival and identify the biomarkers of mortality for septic shock in an Indian cohort. Method: The study was performed in an Indian population cohort diagnosed with sepsis/septic shock within 24 hours of admission. The study group was 50 patients admitted to intensive care, comprising 23 females and 27 males. Univariate and multivariate analysis were performed to identify the biomarkers for septic shock mortality and the gender-specific metabolic fingerprint in septic shock-associated mortality. Results: The energy-related metabolites, ketone bodies, choline, and NAG were found to be primarily responsible for differentiating survivors and non-survivors. The gender-based mortality stratification identified a female-specific association of the anti-inflammatory response, innate immune response, and β oxidation, and a male-specific association of the pro-inflammatory response to septic shock. Conclusion: The identified mortality biomarkers may help clinicians estimate the severity of a case, as well as predict the outcome and treatment efficacy. The study underlines that gender is one of the most significant biological factors influencing septic shock metabolomic profiles. This understanding can be utilized to identify novel gender-specific biomarkers and innovative targets relevant for gender medicine.

A line-broadening free real-time 31P pure shift NMR method for phosphometabolomic analysis

Phosphometabolomics by ³¹P NMR can be challenging, since overlapping multiplets of homonuclear coupled phosphorus nuclei complicate spectral analysis. Pure shift NMR allows to simplify such spectra by collapsing multiplets into singlets, but most pure shift methods require substantially elongated measurement times or cause disturbing spectral line broadening. Herein, we combine established pure shift NMR and artefact suppression techniques to record ³¹P pure shift NMR spectra without penalties in measurement time or line width. Examples are demonstrated in resolution of a mixture of nucleotide triphosphates and a biological sample of ¹⁸O labelled ATP isotopomers.

Mining plant metabolomes: Methods, applications, and perspectives

Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.

Using surfactants as matrix for the matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) of amino acids: Sodium dodecyl sulfate (SDS) and sodium octyl sulfate (SOS)

In the present study, the applicability of two surfactants including sodium dodecyl sulfate (SDS) and sodium octyl sulfate (SOS) as the matrix for the Matrix-Assisted Laser Desorption/Ionization (MALDI) of several amino acids (phenylalanine (Phe), valine (Val), proline (Pro), alanine (Ala), and tyrosine (Tyr)) is investigated. Also, the effect of the material of the repeller plate of the ionization part of the used time of flight (TOF) mass spectrometer on the spectral patterns of the amino acids is studied. Furthermore, the recorded MALDI spectra of amino acids are compared with their corresponding direct laser desorption/ionization (direct-LDI) TOF mass spectra. It is observed that the SDS is an appropriate matrix for the Na⁺ transfer to the Phe and Val amino acids, especially, when the Ag metal is selected as the material of the repeller plate. In this case, the peaks of the [M + Na]⁺ and [M-H + 2Na]⁺ species are considerably more intense compared to when the NaF salt is used as a Na⁺ source in the LDI of these amino acids. Unlike Phe and Val, the SDS is not a good matrix for the other selected amino acids. The decrease of the carbonic chain length of the surfactant on the MALDI spectrum of Phe is investigated and it is seen that the mentioned important peaks disappeared in the presence of SOS as the matrix. The density functional theory (DFT) calculation is employed to characterize the structure of [M + Na]⁺ and [M-H + 2Na]⁺ species and determine the interaction sites of amino acids for the Na⁺ attachment. Also, the change in standard Gibbs free energy (∆G^°) of the M + Na⁺ → [M + Na]⁺ and [M + Na]⁺ + Na⁺ → [M-H + 2Na]⁺ + H⁺ reactions are calculated. Based on the values of ∆G^°, the attachment of the first Na⁺ to the amino acid takes place in the gas phase while the attachment of the second one to [M + Na]⁺ is not a favorable process in the gas phase.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.