Y M Geurts1, P-A Dugué1,2, J E Joo3, E Makalic2, C-H Jung4, W Guan5, S Nguyen6, M L Grove7, E M Wong3, A M Hodge1,2, J K Bassett1, L M FitzGerald1,8, H Tsimiklis3, L Baglietto9, G Severi10,11, D F Schmidt2, D D Buchanan2,12,13,14, R J MacInnis1,2, J L Hopper2, J S Pankow6, E W Demerath6, M C Southey3, G G Giles1,2, D R English1,2, R L Milne1,2. 1. Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia. 2. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia. 3. Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, VIC, Australia. 4. Melbourne Bioinformatics, University of Melbourne, Parkville, VIC, Australia. 5. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 6. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 7. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA. 8. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia. 9. Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy. 10. Human Genetics Foundation (HuGeF), Torino, Italy. 11. CESP (U1018 INSERM, Équipe Générations et Santé), Facultés de médecine Université Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif, France. 12. Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia. 13. University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia. 14. Genetic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC, Australia.
Abstract
BACKGROUND/ OBJECTIVES: There is increasing evidence of a relationship between blood DNA methylation and body mass index (BMI). We aimed to assess associations of BMI with individual methylation measures (CpGs) through a cross-sectional genome-wide DNA methylation association study and a longitudinal analysis of repeated measurements over time. SUBJECTS/ METHODS: Using the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined in baseline peripheral blood samples from 5361 adults recruited to the Melbourne Collaborative Cohort Study (MCCS) and selected for nested case-control studies, 2586 because they were subsequently diagnosed with cancer (cases) and 2775 as controls. For a subset of 1088 controls, these measures were repeated using blood samples collected at wave 2 follow-up, a median of 11 years later; weight was measured at both time points. Associations between BMI and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. These were applied to cases and controls separately, with results combined through fixed-effects meta-analysis. RESULTS: Cross-sectional analysis identified 310 CpGs associated with BMI with P<1.0 × 10-7, 225 of which had not been reported previously. Of these 225 novel associations, 172 were replicated (P<0.05) using the Atherosclerosis Risk in Communities (ARIC) study. We also replicated using MCCS data (P<0.05) 335 of 392 associations previously reported with P<1.0 × 10-7, including 60 that had not been replicated before. Associations between change in BMI and change in methylation were observed for 34 of the 310 strongest signals in our cross-sectional analysis, including 7 that had not been replicated using the ARIC study. CONCLUSIONS: Together, these findings suggest that BMI is associated with blood DNA methylation at a large number of CpGs across the genome, several of which are located in or near genes involved in ATP-binding cassette transportation, tumour necrosis factor signalling, insulin resistance and lipid metabolism.
BACKGROUND/ OBJECTIVES: There is increasing evidence of a relationship between blood DNA methylation and body mass index (BMI). We aimed to assess associations of BMI with individual methylation measures (CpGs) through a cross-sectional genome-wide DNA methylation association study and a longitudinal analysis of repeated measurements over time. SUBJECTS/ METHODS: Using the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined in baseline peripheral blood samples from 5361 adults recruited to the Melbourne Collaborative Cohort Study (MCCS) and selected for nested case-control studies, 2586 because they were subsequently diagnosed with cancer (cases) and 2775 as controls. For a subset of 1088 controls, these measures were repeated using blood samples collected at wave 2 follow-up, a median of 11 years later; weight was measured at both time points. Associations between BMI and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. These were applied to cases and controls separately, with results combined through fixed-effects meta-analysis. RESULTS: Cross-sectional analysis identified 310 CpGs associated with BMI with P<1.0 × 10-7, 225 of which had not been reported previously. Of these 225 novel associations, 172 were replicated (P<0.05) using the Atherosclerosis Risk in Communities (ARIC) study. We also replicated using MCCS data (P<0.05) 335 of 392 associations previously reported with P<1.0 × 10-7, including 60 that had not been replicated before. Associations between change in BMI and change in methylation were observed for 34 of the 310 strongest signals in our cross-sectional analysis, including 7 that had not been replicated using the ARIC study. CONCLUSIONS: Together, these findings suggest that BMI is associated with blood DNA methylation at a large number of CpGs across the genome, several of which are located in or near genes involved in ATP-binding cassette transportation, tumour necrosis factor signalling, insulin resistance and lipid metabolism.
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