Abstract
Background: There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, in relation to body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study we dissect the subcutaneous adipose tissue (SAT) methylome signature of visceral fat accumulation and integrate results with genetic blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data.
Methods: Epigenome wide association analyses were carried out to determine visceral fat mass associated differentially methylated positions (VF-DMPs) in adipose tissue SAT samples from 538 TwinsUK participants. Validation and replication was pursued in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including AGR, lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health.
Results: We identified 1,181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC, CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone.
Conclusions: Our findings help characterise the adiposity-associated molecular landscape of SATadipose tissue, with insights for metabolic disease risk and show the power of integrating multiple omics.
Methods: Epigenome wide association analyses were carried out to determine visceral fat mass associated differentially methylated positions (VF-DMPs) in adipose tissue SAT samples from 538 TwinsUK participants. Validation and replication was pursued in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including AGR, lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health.
Results: We identified 1,181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC, CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone.
Conclusions: Our findings help characterise the adiposity-associated molecular landscape of SATadipose tissue, with insights for metabolic disease risk and show the power of integrating multiple omics.
| Original language | English |
|---|---|
| Journal | Genome medicine |
| DOIs | |
| Publication status | Published - 22 Jul 2022 |
Keywords
- epigenetics
- metabolomics
- adiposity
- diet