Serum metabolomics as a novel diagnostic approach for disease: a systematic review

Zhang, Aihua; Sun, Hui; Wang, Xijun

doi:10.1007/s00216-012-6117-1

Cited by 226 publications

(175 citation statements)

References 59 publications

Supporting

Mentioning

173

Contrasting

Unclassified

Order By: Relevance

“…Increasing evidence shows that metabolomics using easily accessible human biosamples has become an effective tool to explore diagnostic biomarkers and investigate disease progression [21][22][23][24]. Metabolomics analysis in endometriosis has been performed in peripheral blood, peritoneal fluid, follicular fluid and urine [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Endometrium metabolomic profiling reveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages

Liang

2018

Fertility and Sterility

View full text Add to dashboard Cite

Background: The sensitivity and specificity of non-invasive diagnostic methods for endometriosis, especially at early stages, are not optimal. The clinical diagnostic indicator cancer antigen 125 (CA125) performs poorly in the diagnosis of minimal endometriosis, with a sensitivity of 24%. Therefore, it is urgent to explore novel diagnostic biomarkers. We evaluated the metabolomic profile variation of the eutopic endometrium between minimal-mild endometriosis patients and healthy women by ultra-high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (UHPLC-ESI-HRMS). Methods: Our study comprised 29 patients with laparoscopically confirmed endometriosis at stages I-II and 37 infertile women who underwent diagnostic laparoscopy combined with hysteroscopy from January 2014 to January 2015. Eutopic endometrium samples were collected by pipelle endometrial biopsy. The metabolites were quantified by UHPLC-ESI-HRMS. The best combination of biomarkers was then selected by performing step-wise logistic regression analysis with backward elimination. Results: Twelve metabolites were identified as endometriosis-associated biomarkers. The eutopic endometrium metabolomic profile of the endometriosis patients was characterized by a significant increase in the concentration of hypoxanthine, L-arginine, L-tyrosine, leucine, lysine, inosine, omega-3 arachidonic acid, guanosine, xanthosine, lysophosphatidylethanolamine and asparagine. In contrast, the concentration of uric acid was decreased. Metabolites were filtered by step-wise logistic regression with backward elimination, and a model containing uric acid, hypoxanthine, and lysophosphatidylethanolamine was constructed. Receiver-operating characteristic (ROC) analysis confirmed the prognostic value of these parameters for the diagnosis of minimal/mild endometriosis with a sensitivity of 66.7% and a specificity of 90.0%. Conclusions: Metabolomics analysis of the eutopic endometrium in endometriosis was effectively characterized by UHPLC-ESI-HRMS-based metabolomics. Our study supports the importance of purine and amino acid metabolites in the pathophysiology of endometriosis and provides potential biomarkers for semi-invasive diagnosis of early-stage endometriosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Endometrium metabolomic profiling reveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages

Liang

2018

Fertility and Sterility

View full text Add to dashboard Cite

show abstract

“…Identification of small molecular metabolites in bodily fluids will enable determination of drug effectiveness and prediction of treatment according to the phenotype of patients. Furthermore, this technology can identify mortality and morbidity associated with the disease and prognostic potential biomarkers of diseases and drug response [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Mechanism by which Metformin Influence Metabolic Changes in Human: A Systematic-Review

Park¹,

Kim²,

Shin³

2016

J Diabetes Metab

View full text Add to dashboard Cite

Metformin is the most widely used oral antihyperglycemic agent for the improvement of type 2 diabetes mellitus (T2DM). In patients with T2DM, metformin increases insulin sensitivity and reduces glucose production. In this systematic review, we identified and compared metabolic changes induced by metformin administration.We searched articles published within the past 5 years on Pubmed.gov by using the keywords "metformin and metabolomics." No restrictions were placed on the languages, species, or gender during the selection of articles. From the 29 identified articles, we excluded non-human in vivo or in vitro studies, studies in cancer patients, and studies in which the focus was not on variation in the levels of metabolites after administration of medication such as pharmacogenetic studies. Finally, we selected 8 articles and compared their results.In the selected study results, a treatment with metformin and a combination of metformin and other anti-diabetic agents decreased the levels of phenylalanine, tyrosine, lysine, and glutamate to 5.58 ± 2.23%, 11.50 ± 2.12%, 25.28 ± 27.90%, and 35.49 ± 26.18% (mean ± SD), respectively, but increased the levels of alanine to 16.00% in 3 months-5 years of follow-up after metformin treatment compared to the control. The levels of total cholesterol and low-density lipoprotein (LDL) cholesterol (5.28%) decreased, whereas those of high-density lipoprotein (HDL) cholesterol increased by 34.65% in 18-30 months of follow-up after administration of metformin.Metformin administration in human alters the blood concentration of phenylalanine, tyrosine, glutamate and lysine. Further confirmatory studies are required to understand how these metabolites related to the drug mechanism and also drug response, and to develop the biomarkers for predicting metformin response in humans.

show abstract

“…At the same time, metabolomics provides a means of detecting early biochemical changes in organisms before the appearance 5 of a disease and thus, a means of finding predictive biomarkers [2]. Among the analytical techniques used in metabolomics, gas chromatography-mass spectrometry (GC-MS) is a well stablished platform due to its robustness and its applicability to a wide range of matrices and metabolites through silylation of the 10 polar groups.…”

Section: Introductionmentioning

confidence: 99%

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation

Domingo-Almenara

Perera

Ramírez

et al. 2015

Journal of Chromatography A

View full text Add to dashboard Cite

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most reported technique for that purpose, and more recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to inference a model to describe the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the inference of an optimal model to describe the observed data is a critical step and these algorithms should prevent undue influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which instead uses principal component analysis to determine the compound spectra.The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR using pure standards and human serum samples. Results shows how ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study proves that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices.

show abstract

Serum metabolomics as a novel diagnostic approach for disease: a systematic review

Cited by 226 publications

References 59 publications

Endometrium metabolomic profiling reveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages

Endometrium metabolomic profiling reveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages

Mechanism by which Metformin Influence Metabolic Changes in Human: A Systematic-Review

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation

Contact Info

Product

Resources

About