medRxivGenetic & Genomic MedicinePreprint
Nuclear regulatory disturbances precede and predict the development of Type-2 diabetes in Asian populations
Pritesh JainiD, Hong Kiat Ng, Darwin TayiD, Theresia Handayani MinaiD, Dorrain Yanwen LowiD, Nilanjana SadhuiD, Ishminder K. Kooner, Ananya GuptaiD, T. Li, Nicolas BertiniD, Calvin Woon Loong Chin, C. Fang, Liuh Ling GohiD, Shi Qi Mok, Su Qin Peh, Charumathi SabanayagamiD, Vinitaa JhaiD, Anuradhani KasturiratneiD, Prasad KatulandaiD, Khadija Irfan Khawaja, Weng Khong LimiD, Khai Pang LeongiD, Ching‐Yu ChengiD, Jian‐Min YuaniD, Paul ElliottiD, , John Campbell ChambersiD
Abstract
To identify biomarkers and pathways to Type-2 diabetes (T2D), a major global disease, we completed array-based epigenome-wide association in whole blood in 5,709 Asian people. We found 323 Sentinel CpGs (from 314 genetic loci) that predict future T2D. The CpGs reveal coherent, nuclear regulatory disturbances in canonical immune activation pathways, as well as metabolic networks involved in insulin signalling, fatty acid metabolism and lipid transport, which are causally linked to development of T2D. The CpGs have potential clinical utility as biomarkers. An array-based composite Methylation Risk Score (MRS) is predictive for future T2D (RR: 5.2 in Q4 vs Q1; P=7x10-25), and is additive to genetic risk. Targeted methylation sequencing revealed multiple additional CpGs predicting T2D, and synthesis of a sequencing-based MRS that is strongly predictive for T2D (RR: 8.3 in Q4 vs Q1; P=1.0x10-11). Importantly, MRS varies between Asian ethnic groups, in a way that explains a large fraction of the difference in T2D risk between populations. We thus provide new insights into the nuclear regulatory disturbances that precede development of T2D, and reveal the potential for sequence-based DNA methylation markers to inform risk stratification in diabetes prevention.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 2
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