OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.

https://care.diabetesjournals.org/content/diacare/39/5/833.full.pdf

Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have “personalized” diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Metabolomics toward personalized medicine.

https://serval.unil.ch/resource/serval:BIB_78CB07C09AAA.P001/REF.pdf

Plasma Dihydroceramides Are Diabetes Susceptibility Biomarker Candidates in Mice and Humans

The number of individuals with prediabetes is expected to grow substantially and estimated to globally affect 482 million people by 2040. Therefore, effective methods for diagnosing prediabetes will be required to reduce the risk of progressing to diabetes and its complications. The current biomarkers, glycated hemoglobin (HbA1c), fructosamine, and glycated albumin have limitations including moderate sensitivity and specificity and are inaccurate in certain clinical conditions. Therefore, identification of additional biomarkers is being explored recognizing that any single biomarker will also likely have inherent limitations. Therefore, combining several biomarkers may more precisely identify those at high risk for developing prediabetes and subsequent progression to diabetes. This review describes recently identified biomarkers and their potential utility for addressing the burgeoning epidemic of dysglycemic disorders.

/pdf/novel-biomarkers-for-prediabetes-diabetes-and-associated-1ymjgu4ygs.pdf

Novel biomarkers for prediabetes, diabetes, and associated complications

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Translational Metabolomics: Current Challenges and Future Opportunities.

The oral glucose tolerance test (OGTT) is the only method to diagnose patients having impaired glucose tolerance (IGT), but its use has diminished considerably in recent years. Metabolomic profiling studies have identified a number of metabolites whose fasting levels are associated with dysglycemia and type 2 diabetes. These metabolites may serve as the basis of an alternative test for IGT. Using the stable isotope dilution technique, quantitative assays were developed for 23 candidate biomarker metabolites. These metabolites were measured in fasting plasma samples taken just prior to an OGTT from 1623 nondiabetic subjects: 955 from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study (RISC Study; 11.7% IGT) and 668 subjects from the Diabetes Mellitus and Vascular Health Initiative (DMVhi) cohort from the DEXLIFE project (11.8% IGT). The associations between metabolites, anthropometric, and metabolic parameters and 2hPG values were assessed by Pearson correlation coefficients and Random Forest classification analysis to rank variables for their ability to distinguish IGT from normal glucose tolerance (NGT). Multivariate logistic regression models for estimating risk of IGT were developed and evaluated using AUCs calculated from the corresponding ROC curves. A model based on the fasting plasma levels of glucose, α-hydroxybutyric acid, β-hydroxybutyric acid, 4-methyl-2-oxopentanoic acid, linoleoylglycerophosphocholine, oleic acid, serine and vitamin B5 was optimized in the RISC cohort (AUC = 0.82) and validated in the DMVhi cohort (AUC = 0.83). A novel, all-metabolite-based test is shown to be a discriminate marker of IGT. It requires only a single fasted blood draw and may serve as a more convenient surrogate for the OGTT or as a means of identifying subjects likely to be IGT.

https://journals.sagepub.com/doi/pdf/10.1177/1932296814553622

A novel test for IGT utilizing metabolite markers of glucose tolerance.

Background: Insulin resistance (IR) can precede the dysglycemic states of prediabetes and type 2 diabetes mellitus (T2DM) by a number of years and is an early marker of risk for metabolic and cardiovascular disease. There is an unmet need for a simple method to measure IR that can be used for routine screening, prospective study, risk assessment, and therapeutic monitoring. We have reported several metabolites whose fasting plasma levels correlated with insulin sensitivity. These metabolites were used in the development of a novel test for IR and prediabetes. Methods: Data from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study were used in an iterative process of algorithm development to define the best combination of metabolites for predicting the M value derived from the hyperinsulinemic euglycemic clamp, the gold standard measure of IR. Subjects were divided into a training set and a test set for algorithm development and validation. The resulting calculated M score, M Q , was utilized to predict IR and the risk of progressing from normal glucose tolerance to impaired

/pdf/a-novel-fasting-blood-test-for-insulin-resistance-and-5e1qrkkxq3.pdf

A Novel Fasting Blood Test for Insulin Resistance and Prediabetes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Eric Button

Author Tools

Chat about Author

Papers

A novel test for IGT utilizing metabolite markers of glucose tolerance.

A Novel Fasting Blood Test for Insulin Resistance and Prediabetes