Extending human healthspan and longevity: a symposium report.

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Recent Metabolic Advances for Preventing and Treating Acute and Chronic Graft Versus Host Disease.

The therapeutic efficacy of allogeneic hematopoietic stem cell transplantation (allo-HSCT) is limited by the development of graft-versus-host disease (GVHD). In GVHD, rigorous pre-conditioning regimen resets the immune landscape and inflammatory milieu causing immune dysregulation, characterized by an expansion of alloreactive cells and a reduction in immune regulatory cells. In acute GVHD (aGVHD), the release of damage- and pathogen- associated molecular patterns from damaged tissue caused by the conditioning regimen sets the stage for T cell priming, activation and expansion further exacerbating tissue injury and organ damage, particularly in the gastrointestinal tract. Studies have shown that donor T cells utilize multiple energetic and biosynthetic pathways to mediate GVHD that can be distinct from the pathways used by regulatory T cells for their suppressive function. In chronic GVHD (cGVHD), donor T cells may differentiate into IL-21 producing T follicular helper cells or tissue resident T helper cells that cooperate with germinal center B cells or memory B cells, respectively, to produce allo- and auto-reactive antibodies with subsequent tissue fibrosis. Alternatively, donor T cells can become IFN- γ/IL-17 cytokine expressing T cells that mediate sclerodermatous skin injury. Patients refractory to the first line standard regimens for GVHD treatment have a poor prognosis indicating an urgent need for new therapies to restore the balance between effector and regulatory immune cells while preserving the beneficial graft-versus-tumor effect. Emerging data points toward a role for metabolism in regulating these allo- and auto-immune responses. Here, we will discuss the preclinical and clinical data available on the distinct metabolic demands of acute and chronic GVHD and recent efforts in identifying therapeutic targets using metabolomics. Another dimension of this review will examine the changing microbiome after allo-HSCT and the role of microbial metabolites such as short chain fatty acids and long chain fatty acids on regulating immune responses. Lastly, we will examine the metabolic implications of coinhibitory pathway blockade and cellular therapies in allo-HSCT. In conclusion, greater understanding of metabolic pathways involved in immune cell dysregulation during allo-HSCT may pave the way to provide novel therapies to prevent and treat GVHD.

The evolving landscape of untargeted metabolomics.

AIMS: Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics. DATA SYNTHESIS: Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available. CONCLUSIONS: Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

The future is now? Clinical and translational aspects of "Omics" technologies.

Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID-19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalized" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health-care sector over the coming decade to integrate these resources in an effective, practical and ethical way.

Analysis of steroid profiles by mass spectrometry: A new tool for exploring adrenal tumors?

The assay of multiple steroids by mass spectrometry coupled with chromatography, combined with data analysis using an artificial intelligence approach, has become more widely accessible in recent years. Multiple applications for this technology exist for the study of adrenocortical tumors. Taking advantage of the capacity of malignant cortical tumor secretion of non-bioactive precursors, it provides an additional diagnostic approach that can point to the nature of a tumor. These encouraging perspectives have been based to date only on pilot retrospective studies. However, this has changed in 2020 with the publication of data from the EURINE-ACT study. This very large prospective European study provided more nuanced evidence for the benefit of combining the measurement of a panel of steroids with essential imaging tools. This study also facilitated our understanding and provided more precise characterisation of autonomous steroid secretion, particularly in the case of sublinical cortisol-secreting adrenocortical adenomas. This article will focus on our current knowledge on the potential utility of mass spectrometry for diagnosis of both the nature of an adrenal tumors and their secretion.

Metabolomic and lipidomic approaches to identify biomarkers for bladder cancer and interstitial cystitis (Review).

The discovery, introduction and clinical use of prognostic and diagnostic biomarkers has significantly improved outcomes for patients with various illnesses, including bladder cancer (BC) and other bladder­related diseases, such as benign bladder dysfunction and interstitial cystitis (IC). Several sensitive and noninvasive clinically relevant biomarkers for BC and IC have been identified. Metabolomic­ and lipidomic­based biomarkers have notable clinical potential in improving treatment outcomes for patients with cancer; however, there are also some noted limitations. This review article provides a short and concise summary of the literature on metabolomic and lipidomic biomarkers for BC and IC, focusing on the possible clinical utility of profiling metabolic alterations in BC and IC.

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

Therapeutic targeting of metabolic alterations in acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) remains a significant source of mortality in critically ill patients. Characterised by acute, widespread alveolar inflammation and pulmonary oedema, its pathophysiological heterogeneity has meant that targeted treatments have remained elusive. Metabolomic analysis has made initial steps in characterising the underlying metabolic derangements of ARDS as an indicator of phenotypical class and has identified mitochondrial dysfunction as a potential therapeutic target. Mesenchymal stem cells and their derived extracellular vesicles have shown significant promise as potential therapies in delivering mitochondria in order to redivert metabolism onto physiological pathways.

Power of proteomics and progress in precision medicine - 13th central and eastern European proteomic conference (CEEPC), Ustron, Poland.

INTRODUCTION: Central and Eastern European Proteomic Conference (CEEPC) provides a platform for researchers to discuss multi-disciplinary integrated approaches to address a range of challenges from present day viral pandemic to on-going progress in Precision Medicine. CEEPC brings together various multi-omics entwined with novel enabling technologies, thus facilitating conceptual advances from cell to society for the benefit of mankind. AREAS COVERED: Proteomic methodologies, databases and software has revolutionized our ability to assess protein interactions and cellular changes, allowing the establishment of biological connections and identification of important cellular regulatory proteins and pathways previously unknown or not fully understood. Additionally, Mass spectrometry (MS) remains a major driving force in the field of 'multi-omics' and a powerful technology for the structural characterization of biomolecules and for analysis of proteins and small molecules such as lipids, sugars and metabolites. Combination of measurements from proteomics, genomics, epigenomics, transcriptomics and metabolomics, present a powerful decision-making format allowing deeper interpretation of a disease scenario in Precision medicine. EXPERT COMMENTARY: Precision Medicine offers novel and promising ways to identify and treat a wide range of diseases. The future success of these therapies will be underpinned by novel proteo-genomic approaches linked to sophisticated databases to evaluate and predict drug-patient interactions.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives.

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

OMICS insights into cancer histology; Metabolomics and proteomics approach.

Metabolomics as a post-genomic research area comprising different analytical methods for small molecules analysis. One of the underlying applications of metabolomics technology for better disease diagnosis and prognosis is discovering the metabolic pathway differences between healthy individuals and patients. On the other hand, the other noteworthy applications of metabolomics include its effective role in biomarker screening for cancer detection, monitoring, and prediction. In other words, emerging of the metabolomics field can be hopeful to provide a suitable alternative for the common current cancer diagnostic methods especially histopathological tests. Indeed, cancer as a major global issue places a substantial burden on the health care system. Hence, proper management can be beneficial. In this respect, formalin-fixed paraffin-embedded tissue specimens (in histopathological tests) are considered as a valuable source for metabolomics investigations. Interestingly, formalin-fixed paraffin-embedded tissue specimens can provide informative data for cancer management. In general, using these specimens, determining the cancer stage, individual response to the different therapies, personalized risk prediction are possible and high-quality clinical services are the promise of OMICS technologies for cancer disease. However, considering all of these beneficial characteristics, there are still some limitations in this area that need to be addressed in order to optimize the metabolomics utilizations and advancement.

[Analysis of metabolomics and proteomics based on capillary electrophoresis-mass spectrometry].

Capillary electrophoresis-mass spectrometry (CE-MS) has the advantages of higher sensitivity, higher efficiency, and less sample consumption. Moreover, it possesses obvious advantages during the analysis of strongly charged and highly polar samples. CE-MS has been widely applied in life sciences, medicine, and pharmacology. In the past ten years, the main factors affecting its application were system stability, reproducibility, and data accuracy. In order to solve the existing problems of CE-MS, researchers have invested significant effort in technology innovation to further expand CE-MS application. In the fields of medicine and analytical chemistry, substantial research indicates that CE-MS is superior compared to other metabolomic and proteomic approaches. This study aims at reviewing the latest methods and applications developed in the fields of medicine and analytical chemistry since 2015. Furthermore, it also aims at enhancing the technology development-related application value of CE-MS and serving as a reference for future development. Further development of the CE-MS technology is discussed from the aspects of coating-sample interaction, interface types, and data processing methods. Concerning the coating types, neutral coatings had been applied extensively in CE-MS and there should be no limitation to the charge of the analyte. The coating decreased sample adsorption on the inner wall by covering the surface charge, greatly reducing the electroosmotic flow (EOF). A charged capillary coating could modify such an EOF direction. The cationic coating could reduce the hydrophobic interaction between the sample and the capillary column, resulting in higher EOF. If it is applied to the sheathless interface, the resolution could be improved by extending the capillary length. Anionic coatings are predominant among the anionic compounds, shortening the separation time by reducing the interaction between the anionic compounds and the capillary. The coating type should be chosen relative to the analyte characteristics. Concerning the interface technology, all interfaces should be simple, practical, and non-dependent on sheath liquid and background electrolytes. As far as data processing methods are concerned, it is necessary to design and develop a practical method for span space data comparison and processing. The optimized experimental conditions have effectively improved separation efficiency and data comparison analysis. Furthermore, they established a solid foundation for its application development. CE-MS analysis of complex samples in the fields of metabolomics and proteomics (e. g., of tissues, cells, body fluids, etc.) could provide a visualization method for future clinical analysis. It contributes to the development of cancer pathological analysis, drug development, disease surveillance, etc. The characteristic analysis of small molecule metabolites and protein biomarkers directly reflects on enzymatic activity in the biological systems. It could be associated with the development of various diseases/complications. Omics analysis also has an important directive to disease detection and surveillance with obvious advantages in disease diagnosis, staged treatment, drug development, and patient treatment progress. CE-MS is useful in detecting complications and promoting personalized medicine. It provides technical support for future clinical developments. In addition to a comprehensive review of the recent advances of CE-MS research, this paper also indicates the development directions of CE-MS. In order to avoid the problem of omics analysis and obtain the optimized analysis results, future analysis should be improved from the following three aspects:(i) The analysis conditions should be optimized concerning sample preparation methods and separation techniques. (ii) The analytic techniques should be supported to adjust to capillary coating and interface technology. (iii) New ideas should be developed in the fields of clinical research and statistical analysis.

Differential Metabolomic Signatures in Patients with Weight Regain and Sustained Weight Loss After Gastric Bypass Surgery: A Pilot Study.

BACKGROUND: While Roux-en-Y gastric bypass (RYGB) is one of the most effective and durable treatment options for obesity and its comorbidities, it is complicated by long-term weight regain in over 20% of patients. AIMS: We sought to determine the metabolite signatures of serum samples of patients with weight regain (RYGB-WR) after RYGB and features distinguishing these patients from patients with sustained weight loss (RYGB-SWL). METHODS: We prospectively analyzed serum samples from 21 RYGB-WR patients, 14 RYGB-SWL patients, and 11 unoperated controls. The main outcome measure was their serum metabolite profile. RESULTS: Weight regain after RYGB was associated with a unique serum metabolomic fingerprint. Most of the statistically different metabolites were involved in amino acid metabolism, one-carbon metabolism, and related nucleotide metabolism. A principal component analysis identified groups of metabolites that correlate with weight regain. Specifically, weight regain was associated with lower serum levels of metabolites related to the serine, glycine and threonine pathway, phenylalanine metabolism, tricyclic acid cycle, alanine and glutamate metabolism, and higher levels of other amino acids. CONCLUSIONS: Weight regain after RYGB is associated with unique serum metabolite signatures. Metabolite profiling may eventually help us to identify markers that could differentiate the patients who will regain weight versus those who will likely sustain weight loss.

Pharmaco-Geno-Proteo-Metabolomics and Translational Research in Cancer.

The diagnosis, prognosis and treatment of cancer has had a great improvement due to the "omics" technologies such as genomics, proteomics, epigenomics, pharmacogenomics, and metabolomics. The technological progress of these technologies has allowed precision medicine to become a clinical reality. The study of different biomolecules such as DNA, RNA and proteins has helped to detect alterations in genes, changes in gene expression profiles and loss or gain of protein function, which allows us to make associations and better understand the cancer biology. Data obtained from different "omics" technologies gives a complementary spectrum of information that helps us to understand and unveil new information for a better diagnosis, prognosis, prediction of new molecular targets of anticancer therapies, etc. This chapter presents a general landscape of the interaction between the Pharmaco-Geno-Proteo-Metabolomic and translational medicine research in cancer.

Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirrhosis leading to hepatocellular carcinoma. To date, liver biopsy is the gold standard for the diagnosis of NASH and for staging liver fibrosis. This study aimed to train models for the non-invasive diagnosis of NASH and liver fibrosis based on measurements of lipids, glycans and biochemical parameters in peripheral blood and with the use of different machine learning methods. METHODS: We performed a lipidomic, glycomic and free fatty acid analysis in serum samples of 49 healthy subjects and 31 patients with biopsy-proven NAFLD (15 with NAFL and 16 with NASH). The data from the above measurements combined with measurements of 4 hormonal parameters were analyzed with two different platforms and five different machine learning tools. RESULTS: 365 lipids, 61 glycans and 23 fatty acids were identified with mass-spectrometry and liquid chromatography. Robust differences in the concentrations of specific lipid species were observed between healthy, NAFL and NASH subjects. One-vs-Rest (OvR) support vector machine (SVM) models with recursive feature elimination (RFE) including 29 lipids or combining lipids with glycans and/or hormones (20 or 10 variables total) could differentiate with very high accuracy (up to 90%) between the three conditions. In an exploratory analysis, a model consisting of 10 lipid species could robustly discriminate between the presence of liver fibrosis or not (98% accuracy). CONCLUSION: We propose novel models utilizing lipids, hormones and glycans that can diagnose with high accuracy the presence of NASH, NAFL or healthy status. Additionally, we report a combination of lipids that can diagnose the presence of liver fibrosis. Both models should be further trained prospectively and validated in large independent cohorts.

Discovery of Gut Bacteria Specific to Alzheimer's Associated Diseases is a Clue to Understanding Disease Etiology: Meta-Analysis of Population-Based Data on Human Gut Metagenomics and Metabolomics.

Alzheimer's disease (AD)-associated sequence (ADAS) of cultured fecal bacteria was discovered in human gut targeted screening. This study provides important information to expand our current understanding of the structure/activity relationship of ADAS and putative inhibitors/activators that are potentially involved in ADAS appearance/disappearance. The NCBI database analysis revealed that ADAS presents at a large proportion in American Indian Oklahoman (C&A) with a high prevalence of obesity/diabetes and in colorectal cancer (CRC) patients from the US and China. An Oklahoman non-native group (NNI) showed no ADAS. Comparison of two large US populations reveals that ADAS is more frequent in individuals aged ≥66 and in females. Prevalence and levels of fecal metabolites are altered in the C&A and CRC groups versus controls. Biogenic amines (histamine, tryptamine, tyramine, phenylethylamine, cadaverine, putrescine, agmatine, spermidine) that present in food and are produced by gut microbiota are significantly higher in C&A (e.g., histamine/histidine 95-fold) versus NNI (histamine/histidine 16-fold). The majority of these bio-amines are cytotoxic at concentrations found in food. Inositol phosphate signaling implicated in AD is altered in C&A and CRC. Tryptamine stimulated accumulation of inositol phosphate. The seizure-eliciting tryptamine induced cytoplasmic vacuolization and vesiculation with cell fragmentation. Present additions of ADAS-carriers at different ages including infants led to an ADAS-comprising human sample size of 2,830 from 27 studies from four continents (North America, Australia, Asia, Europe). Levels of food-derived monoamine oxidase inhibitors and anti-bacterial compounds, the potential modulators of ADAS-bacteria growth and biogenic amine production, were altered in C&A versus NNI. ADAS is attributable to potentially modifiable risk factors of AD associated diseases.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions.

PURPOSE OF REVIEW: Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer. RECENT FINDINGS: Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation.

Thyroid cancers (TCs) are the most frequent endocrine malignancy with an unpredictable fast-growing incidence, especially in females all over the world. Fine-needle aspiration biopsy (FNAB) analysis is an accurate diagnostic method for detecting thyroid nodules and classification of TC. Though simplicity, safety, and accuracy of FNAB, 15-30% of cases are indeterminate, and it is not possible to determine the exact cytology of the specimen. This demands the need for innovative methods capable to find crucial biomarkers with adequate sensitivity for diagnosis and prediction in TC researches. Cancer-based metabolomics is a vast emerging field focused on the detection of a large set of metabolites extracted from biofluids or tissues. Using analytical chemistry procedures allows for the potential recognition of cancer-based metabolites for the purposes of advancing the era of personalized medicine. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with separation techniques e.g., gas chromatography (GC) and liquid chromatography (LC) are the main approaches for metabolic studies in cancers. The immense metabolite profiling has provided a chance to discover novel biomarkers for early detection of thyroid cancer and reduce unnecessary aggressive surgery. In this review, we recapitulate the recent advances and developed methods of diverse metabolomics tools and metabolic phenotypes of thyroid cancer, following a brief discussion of recent challenges in the thyroid cancer diagnosis.