Pancreatic cancer (PC) has one of the highest fatality rates and the currently available therapeutic options are not sufficient to improve its overall poor prognosis. In addition to insufficient effectiveness of anticancer treatments, the lack of clear early symptoms and early metastatic spread maintain the PC survival rates at a low level. Metabolic reprogramming is among the hallmarks of cancer and could be exploited for the diagnosis and treatment of PC. PC is characterized by its heterogeneity and, apart from molecular subtypes, the identification of metabolic subtypes in PC could aid in the development of more individualized therapeutic approaches and may lead to improved clinical outcomes. In addition to the deregulated utilization of glucose in aerobic glycolysis, PC cells can use a wide range of substrates, including branched‑chain amino acids, glutamine and lipids to fulfil their energy requirements, as well as biosynthetic needs. The tumor microenvironment in PC supports tumor growth, metastatic spread, treatment resistance and the suppression of the host immune response. Moreover, reciprocal interactions between cancer and stromal cells enhance their metabolic reprogramming. PC stem cells (PCSCs) with an increased resistance and distinct metabolic properties are associated with disease relapses and cancer spread, and represent another significant candidate for therapeutic targeting. The present review discusses the metabolic signatures observed in PC, a disease with a multifaceted and often transient metabolic landscape. In addition, the metabolic pathways utilized by PC cells, as well as stromal cells are discussed, providing examples of how they could present novel targets for therapeutic interventions and elaborating on how interactions between the various cell types affect their metabolism. Furthermore, the importance of PCSCs is discussed, focusing specifically on their metabolic adaptations.

The skin\'s ability to function optimally is affected by many diverse factors. Metabolomics has a great potential to improve our understanding of the underlying metabolic changes and the affected pathways. Therefore, the objective of this study was to review the current state of the literature and to perform further metabolic pathway analysis on the obtained data. The aim was to gain an overview of the metabolic changes under altered conditions and to identify common and different patterns as a function of the investigated factors. A cross-study comparison of the extracted studies from different databases identified 364 metabolites, whose concentrations were considerably altered by the following factor groups: irradiation, xenobiotics, ageing and skin diseases (mainly psoriasis). Using metabolic databases and pathway analysis tools, the individual metabolites were assigned to the corresponding metabolic pathways and the most strongly affected signalling pathways were identified. All factors induced oxidative stress. Thus, antioxidant defence systems, especially coenzyme Q10  (ageing) and the glutathione system (irradiation, ageing, xenobiotics), were impacted. Lipid metabolism was also impacted by all factors studied. The carnitine shuttle as part of β-oxidation was activated by all factor groups except ageing. Glycolysis, Krebs (TCA) cycle and purine metabolism were mainly affected by irradiation and xenobiotics. The pentose phosphate pathway was activated, and Krebs cycle was downregulated in response to oxidative stress. In summary, it can be ascertained that mainly energy metabolism, lipid metabolism, antioxidative defence and DNA repair systems were impacted by the factors studied.

Proper preoperative ovarian cancer (OC) diagnosis remains challenging. Serum free amino acid (SFAA) profiles were investigated to identify potential novel biomarkers of OC and assess their performance in ovarian tumor differential diagnosis. Serum samples were divided based on the histopathological result: epithelial OC (n = 38), borderline ovarian tumors (n = 6), and benign ovarian tumors (BOTs) (n = 62). SFAA profiles were evaluated using aTRAQ methodology based on high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). Levels of eleven amino acids significantly differed between OC+borderline and BOTs. The highest area under the receiver operating characteristic curve (AUC of ROC) (0.787) was obtained for histidine. Cystine and histidine were identified as best single markers for early stage OC/BOT and type I OC. For advanced stage OC, seven amino acids differed significantly between the groups and citrulline obtained the best AUC of 0.807. Between type II OC and BOTs, eight amino acids differed significantly and the highest AUC of 0.798 was achieved by histidine and citrulline (AUC of 0.778). Histidine was identified as a potential new biomarker in differential diagnosis of ovarian tumors. Adding histidine to a multimarker panel together with CA125 and HE4 improved the differential diagnosis between OC and BOTs.

UNASSIGNED: Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.
UNASSIGNED: A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines using the MEDLINE, Embase, PubMed, and Cochrane databases. Studies included were case series, case control series, and cohort studies looking specifically at HSF.
UNASSIGNED: The primary analysis, which pooled the results from 17 published studies and four meeting abstracts, identified over 200 metabolites. Seven of these studies (six published studies, one meeting abstract) had asymptomatic control groups and collectively suggested 26 putative biomarkers in osteoarthritis, inflammatory arthropathies, and trauma. These can broadly be categorized into amino acids plus related metabolites, fatty acids, ketones, and sugars.
UNASSIGNED: The role of metabolic profiling in orthopaedics is fast evolving with many metabolites already identified in a variety of pathologies. However, these results need to be interpreted with caution due to the presence of multiple confounding factors in many of the studies. Future research should include largescale epidemiological metabolic profiling studies incorporating various confounding factors with appropriate statistical analysis to account for multiple testing of the data.Cite this article: Bone Joint Res. 2020;9(3):108-119.

Upon dietary exposure, the endogenous metabolism responds to the diet-derived nutrients and bioactive compounds, such as phytochemicals. However, the responses vary remarkably due to the interplay with other dietary components, lifestyle exposures, and intrinsic factors, which lead to differences in endogenous regulatory metabolism. These physiological processes are evidenced as a signature profile composed of various metabolites constituting metabolic phenotypes, or metabotypes. The metabolic profiling of biological samples following dietary intake hence would provide information about diet-that is, as the intake biomarkers and the ongoing physiological reactions triggered by this intake-thereby enable evaluation of the metabolic basis required to distinguish the different metabotypes. The capacity of nontargeted metabolomics to also encompass the unprecedented metabolite species has enabled the profiling of multiple metabolites and the corresponding metabotypes with a single analysis, decoding the complex interplay between diet, other relevant factors, and health. In this systematic review, we screened 345 articles published in English in January 2007-July 2018, which applied the metabolomics approach to profile the changes of endogenous metabolites in the blood related to dietary interventions, either derived by metabolism of gut microbiota or the human host. We excluded all the compounds that were directly derived from diet, and also the dietary interventions focusing on supplementation with individual compounds. After the removal of less relevant studies and assessment of eligibility, 49 articles were included in this review. First, we mention the contribution of individual factors, either modifiable or nonmodifiable factors, in shaping metabolic profile. Then, how different aspects of the diet would affect the metabolic profiles are disentangled. Next, the classes of endogenous metabolites altered following included dietary interventions are listed. We also discuss the current challenges in the field, along with future research opportunities.

Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).
We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.
We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.
Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.
The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and \'in house\' metabolite databases available.
This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.
The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Metabolomics and lipidomics generally targets a huge number of intermediate and end products of cellular metabolism in body fluids, tissues, and cells etc. At present, mass spectrometry (MS) based metabolic or lipid profiling of routine biological specimens including the whole cells, tissues, plasma, serum and urine etc., can cover hundreds of metabolites or lipid species in one analysis, which has qualified deep elucidation of global metabolic and lipid networks. Mitochondria are important intracellular organelles and many critical biochemical reactions occur here, they provide building block for new cells, control redox balance, participate in apoptosis and behave as a signalling platform. Evidence suggests high prevalence of mitochondrial dysfunction occurs in a variety of cancers and other diseases, thus there is an urgent demand for investigating and clarifying mitochondrial metabolic and lipid alterations induced by diseases. Nevertheless, mitochondria contribute a small fraction to cellular contents, profiling of whole cell is probably unsuitable for monitoring alterations in mitochondria. Therefore, metabolomics and lipidomics analyses specially for mitochondria are necessary to understand disturbed metabolic and lipid pathways induced by environment and diseases. However, methods for comprehensively profiling metabolites and lipids in mitochondria have been limited at present. This review summarizes the current states and progress of MS-based mitochondrial metabolomics and lipidomics study. Details of mitochondrial isolation procedure, analytical methods and their applications are described. The challenges and opportunities are also given.

Bile acids (BAs) are important regulatory factors of life activities, which are involved in the regulation of glucose, lipid and energy metabolisms, and closely associated with intestinal hormones, microbiotas and energy balance. BAs abnormalities easily lead to inflammation and metabolic diseases, in turn, the progress of diseases could influence characteristics of BAs. Therefore, accurate detection of BAs contents is of great significance to disease prevention, diagnosis and treatment. At present, the most widely used enzymatic method in clinical practice is applicable to the detection of total bile acid (TBA). In laboratory research, different types of BAs can be accurately separated and quantified by liquid chromatography-mass spectrometry (LC-MS). The metabolic profiling of BAs based on detection technologies can completely and accurately monitor their types and contents, playing a crucial role in disease prevention, diagnosis and treatment. We herein reviewed the main detection technologies of BAs and the application of metabolic profiling in related diseases in recent years.

OBJECTIVE: This review aims to identify metabolomic biomarkers of oesophago-gastric (OG) cancer in human biological samples, and to discuss the dominant metabolic pathways associated with the observed changes.
METHODS: A systematic review of the literature, up to and including 9th November 2012, was conducted for experimental studies investigating the metabolomic profile of human biological samples from patients with OG cancer compared to a control group. Inclusion criteria for analytical platforms were mass spectrometry or nuclear magnetic resonance spectroscopy. The QUADAS-2 tool was used to assess the quality of the included studies.
RESULTS: Twenty studies met the inclusion criteria and samples utilised for metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). Several metabolites of glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and control samples. Lactate and fumurate were the most commonly recognised biomarkers of OG cancer related to cellular respiration. Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers. Products of lipid metabolism including saturated and un-saturated free fatty acids, ketones and aldehydes and triacylglycerides were also identified as biomarkers of OG cancer. Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment.
CONCLUSIONS: The application of metabolomics for biomarker detection in OG cancer presents new opportunities for the purposes of screening and therapeutic monitoring. Future studies should provide clear details of patient selection and develop metabolite assays suitable for progress beyond phase 1 pre-clinical exploratory studies.