Cross-Ancestry DNA Methylation Marks of Insulin Resistance in Pregnancy: An Integrative Epigenome-Wide Association Study.
Nicolas Fragoso-BargasHannah R ElliottSindre Lee-ØdegårdJulia O OpsahlLine SletnerAnne Karen JenumChristian A DrevonElisabeth QvigstadGunn-Helen MoenKåre Inge BirkelandRashmi B PrasadChristine SommerPublished in: Diabetes (2023)
Although there are some epigenome-wide association studies (EWAS) of insulin resistance, for most of them authors did not replicate their findings, and most are focused on populations of European ancestry, limiting the generalizability. In the Epigenetics in Pregnancy (EPIPREG; n = 294 Europeans and 162 South Asians) study, we conducted an EWAS of insulin resistance in maternal peripheral blood leukocytes, with replication in the Born in Bradford (n = 879; n = 430 Europeans and 449 South Asians), Methyl Epigenome Network Association (MENA) (n = 320), and Botnia (n = 56) cohorts. In EPIPREG, we identified six CpG sites inversely associated with insulin resistance across ancestry, of which five were replicated in independent cohorts (cg02988288, cg19693031, and cg26974062 in TXNIP; cg06690548 in SLC7A11; and cg04861640 in ZSCAN26). From methylation quantitative trait loci analysis in EPIPREG, we identified gene variants related to all five replicated cross-ancestry CpG sites, which were associated with several cardiometabolic phenotypes. Mediation analyses suggested that the gene variants regulate insulin resistance through DNA methylation. To conclude, our cross-ancestry EWAS identified five CpG sites related to lower insulin resistance.
Keyphrases
- dna methylation
- genome wide
- insulin resistance
- copy number
- gene expression
- peripheral blood
- adipose tissue
- genome wide association study
- metabolic syndrome
- high fat diet
- type diabetes
- skeletal muscle
- polycystic ovary syndrome
- high fat diet induced
- pregnancy outcomes
- preterm birth
- social support
- high resolution
- glycemic control
- transcription factor
- gestational age
- low birth weight
- network analysis