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Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus.

ABSTRACT: The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM-specific salivary biomarkers by literature-based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM-specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM-associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM-associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening.

SUBMITTER: Srinivasan M

PROVIDER: S-EPMC4433061 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus.

Srinivasan Mythily M Blackburn Corinne C Mohamed Mohamed M Sivagami A V AV Blum Janice J

Biomarker insights 20150514

The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant sim ...[more]

PMID: 26005324

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Project description:BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Dataset Information

Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus.

Publications

Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus.

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