oa The triple interaction DietMicrobiomeEpigenome: new approach to the noncommunicable diseases

Background/Objective: Qatar suffers the highest percentage of non-communicable diseases (NCD), such as diabetes and obesity among few countries in the world. There are two major components that could explain the NCD, which are diet and the genetic background. It's well demonstrated that diet affects the genome function in terms of epigenetic and microbiome modification. Recent scientific advancement or studies have shown that all these components could be the factors in defining the development of the NCD, but, there is no study that explains the direct connection of Diet-Microbiome-Epigenome. We want to explore the interaction of these three factors in a small pilot study on type 1 diabetes (T1D) creating a methodology that will be applicable for all NCDs and nutrition-related diseases. This study aims to identify specific nutrients that modulate gut microbiome; to define different microbiome composition and metabolites; and to define differential methylated regions (DMR) that is possibly affected by nutrients, gut microbiome and its metabolite. This abstract presents the study design and discusses methodologies that will be used in the study. Study design and methodology: We will compare 4 groups of subjects: T1D, T1D obese, pure obese, and lean controls. Pediatric patients will be recruited from Sidra OPC based on major inclusion criteria, (such as age should be between 6-12 yrs, no antibiotic treatment in the past 3 months, no chronic diseases except T1D and no history of cancer) and divided into four groups: (1) healthy lean children (5-84th percentile of BMI), (2) Obese ( ≥  95th percentile of BMI;), (3) T1D and (4) Obese T1D. A comprehensive set of physical measurements (body weight, height and Waist circumference), clinical biomarkers for diabetes and obesity (mainly, blood glucose level, lipid and liver profile, HbA1c will be done with blood sample) and family history of diabetes, treatment history will be collected and most importantly dietary habits will be collected by 24 hrs food recall. Two sets of stool samples (one for microbiome analysis by 16S rDNA-sequencing; and one for fatty acids analysis by gas chromatography) and blood samples (one for DNA extraction for methylation analysis by Illumina DNA-methylation Array and one for RNA extraction for gene expression analysis using Fluidigm platform) will be collected from each subject. Data analysis will investigate any association of diet to the clinical phenotypes comparing the four groups of subjects, using logistic regression; two-sided P-value of < 0.05 will be considered statistically significant. Possible Research Outcomes/Conclusion: At the end of pilot study, we can able to define the (1) methodology workflow, (2) nutrients list or diet patterns that increase the risk of T1D in obese children, (3) specific microbiome pattern, in terms of composition and metabolite, in obese T1D children, and (4) specific nutrients and microbiome metabolites that alter DNA-methylation and gene expression in obese T1D children. This methodology workflow will be applied to other studies on NCD and nutrition-related diseases.