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Blood-Based DNA Methylation Biomarkers for Type 2 Diabetes: Potential for Clinical Applications.

ABSTRACT: Type 2 diabetes (T2D) is a leading cause of death and disability worldwide. It is a chronic metabolic disorder that develops due to an interplay of genetic, lifestyle, and environmental factors. The biological onset of the disease occurs long before clinical symptoms develop, thus the search for early diagnostic and prognostic biomarkers, which could facilitate intervention strategies to prevent or delay disease progression, has increased considerably in recent years. Epigenetic modifications represent important links between genetic, environmental and lifestyle cues and increasing evidence implicate altered epigenetic marks such as DNA methylation, the most characterized and widely studied epigenetic mechanism, in the pathogenesis of T2D. This review provides an update of the current status of DNA methylation as a biomarker for T2D. Four databases, Scopus, Pubmed, Cochrane Central, and Google Scholar were searched for studies investigating DNA methylation in blood. Thirty-seven studies were identified, and are summarized with respect to population characteristics, biological source, and method of DNA methylation quantification (global, candidate gene or genome-wide). We highlight that differential methylation of the TCF7L2, KCNQ1, ABCG1, TXNIP, PHOSPHO1, SREBF1, SLC30A8, and FTO genes in blood are reproducibly associated with T2D in different population groups. These genes should be prioritized and replicated in longitudinal studies across more populations in future studies. Finally, we discuss the limitations faced by DNA methylation studies, which include including interpatient variability, cellular heterogeneity, and lack of accounting for study confounders. These limitations and challenges must be overcome before the implementation of blood-based DNA methylation biomarkers into a clinical setting. We emphasize the need for longitudinal prospective studies to support the robustness of the current findings of this review.

SUBMITTER: Willmer T

PROVIDER: S-EPMC6288427 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Blood-Based DNA Methylation Biomarkers for Type 2 Diabetes: Potential for Clinical Applications.

Willmer Tarryn T Johnson Rabia R Louw Johan J Pheiffer Carmen C

Frontiers in endocrinology 20181204

Type 2 diabetes (T2D) is a leading cause of death and disability worldwide. It is a chronic metabolic disorder that develops due to an interplay of genetic, lifestyle, and environmental factors. The biological onset of the disease occurs long before clinical symptoms develop, thus the search for early diagnostic and prognostic biomarkers, which could facilitate intervention strategies to prevent or delay disease progression, has increased considerably in recent years. Epigenetic modifications re ...[more]

PMID: 30564199

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Project description:BackgroundDNA methylation is associated with cardiovascular (CV) disease. However, in type 2 diabetes (T2D) patients, the role of gene methylation in the development of CV disease is under-studied. We aimed to identify the CV disease-related DNA methylation loci in patients with T2D and to explore the potential pathways underlying the development of CV disease using a two-stage design.MethodsThe participants were from the Jinan Diabetes Cohort Study (JNDCS), an ongoing longitudinal study designed to evaluate the development of CV risk in patients with T2D. In the discovery cohort, 10 diabetic patients with CV events at baseline were randomly selected as the case group, and another 10 diabetic patients without CV events were matched for sex, age, smoking status, and body mass index as the control group. In 1438 T2D patients without CV disease at baseline, 210 patients with CV events were identified after a mean 6.5-year follow-up. Of whom, 100 patients who experienced CV events during the follow-up were randomly selected as cases, and 100 patients who did not have CV events were randomly selected as the control group in the validation cohort. Reduced representation bisulfite sequencing and Targeted Bisulfite Sequencing were used to measure the methylation profiles in the discovery and validation cohort, respectively.ResultsIn the discover cohort, 127 DMRs related to CV disease were identified in T2D patients. Further, we validated 23 DMRs mapped to 25 genes, of them, 4 genes (ARSG, PNPLA6, NEFL, and CRYGEP) for the first time were reported. There was evidence that the addition of DNA methylation data improved the prediction performance of CV disease in T2D patients. Pathway analysis identified some significant signaling pathways involved in CV comorbidities, T2D, and inflammation.ConclusionsIn this study, we identified 23 DMRs mapped to 25 genes associated with CV disease in T2D patients, of them, 4 DMRs for the first time were reported. DNA methylation testing may help identify a high CV-risk population in T2D patients.

Project description:BackgroundVariation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification.ResultsWe developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers.ConclusionsOur results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

Dataset Information

Blood-Based DNA Methylation Biomarkers for Type 2 Diabetes: Potential for Clinical Applications.

Publications

Blood-Based DNA Methylation Biomarkers for Type 2 Diabetes: Potential for Clinical Applications.

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