Metabolomics is the comprehensive assessment of endogenous metabolites and attempts to systematically identify and quantify metabolites from a biological sample. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols and carbohydrates, which makes global analysis a difficult challenge. The recent rapid development of a range of analytical platforms, including GC, HPLC, UPLC, CE coupled to MS and NMR spectroscopy, could enable separation, detection, characterization and quantification of such metabolites and related metabolic pathways. Owing to the complexity of the metabolome and the diverse properties of metabolites, no single analytical platform can be applied to detect all metabolites in a biological sample. The combined use of modern instrumental analytical approaches has unravelled the ideal outcomes in metabolomics, and is beneficial to increase the coverage of detected metabolites that can not be achieved by single-analysis techniques. Integrated platforms have been frequently used to provide sensitive and reliable detection of thousands of metabolites in a biofluid sample. Continued development of these analytical platforms will accelerate widespread use and integration of metabolomics into systems biology. Here, the application of each hyphenated technique is discussed and its strengths and limitations are discussed with selected illustrative examples; furthermore, this review comprehensively highlights the role of integrated tools in metabolomic research.

To provide comprehensive estimates of the global, regional, and national burden of infertility from 1990 to 2017, using findings from a 2017 study on the global burden of disease (GBD), we assessed the burden of infertility in 195 countries and territories from 1990 to 2017. DisMod-MR 2.1 is a Bayesian meta-regression method that estimates non-fatal outcomes using sparse and heterogeneous epidemiological data. Globally, the age-standardized prevalence rate of infertility increased by 0.370% per year for females and 0.291% per year for males from 1990 to 2017. Additionally, age-standardized disability-adjusted life-years (DALYs) of infertility increased by 0.396% per year for females and 0.293% per year for males during the observational period. An increasing trend to these burden estimates was observed throughout the all socio-demographic index (SDI) countries. Interestingly, we found that high SDI countries had the lowest level of prevalence and DALYs in both genders. However, the largest increasing trend was observed in high-SDI countries for females. By contrast, low-SDI countries had the largest increasing trend in males. Negative associations were observed between these burden estimates and the SDI level. The global disease burden of infertility has been increasing throughout the period from 1990 to 2017.

Metabolomics is a promising "omics" field in systems biology; its objective is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could result in earlier intervention and provide valuable insights into the mechanisms of diseases. Because of the possible discovery of clinically relevant biomarkers, metabolomics has potential advantages that routine approaches to clinical diagnosis do not. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of a disease. Serum is a readily accessible and informative biofluid, making it ideal for early detection of a wide range of diseases, and analysis of serum has several advantages over analysis of other biofluids. Metabolite profiles of serum can be regarded as important indicators of physiological and pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level, and provide information enabling identification of early and differential metabolic markers of disease. Analysis of these crucial metabolites in serum has become important in monitoring the state of biological organisms and is widely used for diagnosis of disease. Emerging metabolomics will drive serum analysis, facilitate and improve the development of disease treatments, and provide great benefits for public health in the long-term.

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases.

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value>0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. Metabolomics, an omic science in systems biology, is the comprehensive profiling of metabolic changes occurring in living systems (1). It attempts to capture global changes and overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations, and has shown great promise as a means to identify biomarkers of diseases (2, 3). One area of considerable interest in the field of metabolomics is the detection of potential biomarkers associated with diseases, and the metabolic profiling could provide global changes of endogenous metabolites of patients. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. It could facilitate biomarker discovery by distinguishing between diseased and nondiseased patients. Biomarker metabolites can also be therapeutic targets (4). Detecting changes in metabolite concentrations reveals the range of biochemical effects induced by a disease condition.