Hakyung Kim1, Jae Hyun Bae2, Kyong Soo Park3,4,5, Joohon Sung1,6, Soo Heon Kwak5. 1. Genome & Health Big Data Branch, Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea. 2. Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea. 3. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. 4. Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea. 5. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea. 6. Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.
Abstract
CONTEXT: There is a growing body of evidence that epigenetic changes including DNA methylation influence the risk of type 2 diabetes (T2D) and its microvascular complications. OBJECTIVE: We conducted a methylome-wide association study (MWAS) to identify differentially methylated sites (DMSs) of T2D and diabetic kidney disease (DKD) in a Korean population. METHODS: We performed an MWAS in 232 participants with T2D and 197 nondiabetic controls with the Illumina EPIC bead chip using peripheral blood leukocytes. The T2D group was subdivided into 87 DKD patients and 80 non-DKD controls. An additional 819 individuals from 2 population-based cohorts were used to investigate the association of identified DMSs with quantitative metabolic phenotypes. A mendelian randomization (MR) approach was applied to evaluate the causal effect of metabolic phenotypes on identified DMSs. RESULTS: We identified 8 DMSs (each at BMP8A, NBPF20, STX18, ZNF365, CPT1A, and TRIM37, and 2 at TXNIP) that were significantly associated with the risk of T2D (P < 9.0 × 10-8), including 3 that were previously known (DMSs in TXNIP and CPT1A). We also identified 3 DMSs (in COMMD1, TMOD1, and FHOD1) associated with DKD. With our limited sample size, we were not able to observe a significant overlap between DMSs of T2D and DKD. DMSs in TXNIP and CTP1A were associated with fasting glucose and glycated hemoglobin A1c. In MR analysis, fasting glucose was causally associated with DMS in CPT1A. CONCLUSION: In an East Asian population, we identified 8 DMSs, including 5 novel CpG loci, associated with T2D and 3 DMSs associated with DKD at methylome-wide statistical significance.
CONTEXT: There is a growing body of evidence that epigenetic changes including DNA methylation influence the risk of type 2 diabetes (T2D) and its microvascular complications. OBJECTIVE: We conducted a methylome-wide association study (MWAS) to identify differentially methylated sites (DMSs) of T2D and diabetic kidney disease (DKD) in a Korean population. METHODS: We performed an MWAS in 232 participants with T2D and 197 nondiabetic controls with the Illumina EPIC bead chip using peripheral blood leukocytes. The T2D group was subdivided into 87 DKD patients and 80 non-DKD controls. An additional 819 individuals from 2 population-based cohorts were used to investigate the association of identified DMSs with quantitative metabolic phenotypes. A mendelian randomization (MR) approach was applied to evaluate the causal effect of metabolic phenotypes on identified DMSs. RESULTS: We identified 8 DMSs (each at BMP8A, NBPF20, STX18, ZNF365, CPT1A, and TRIM37, and 2 at TXNIP) that were significantly associated with the risk of T2D (P < 9.0 × 10-8), including 3 that were previously known (DMSs in TXNIP and CPT1A). We also identified 3 DMSs (in COMMD1, TMOD1, and FHOD1) associated with DKD. With our limited sample size, we were not able to observe a significant overlap between DMSs of T2D and DKD. DMSs in TXNIP and CTP1A were associated with fasting glucose and glycated hemoglobin A1c. In MR analysis, fasting glucose was causally associated with DMS in CPT1A. CONCLUSION: In an East Asian population, we identified 8 DMSs, including 5 novel CpG loci, associated with T2D and 3 DMSs associated with DKD at methylome-wide statistical significance.
Authors: Elizabeth Walker-Short; Teresa Buckner; Timothy Vigers; Patrick Carry; Lauren A Vanderlinden; Fran Dong; Randi K Johnson; Ivana V Yang; Katerina Kechris; Marian Rewers; Jill M Norris Journal: Nutrients Date: 2021-11-13 Impact factor: 5.717