肥胖是高血压的既定危险因素,但机制只是部分理解。我们研究了体重指数(BMI)相关的DNA甲基化(DNAm)变异是否会介导BMI与血压(BP)的关联。我们首先在单卵双生子中进行了全基因组DNA甲基化分析,以检测BMI相关的DNAm变异,然后使用因果推断检验(CIT)方法和中介分析评估了DNAm对BMI与BP水平之间关系的中介作用。使用GREAT工具对CpG进行本体富集分析。包括60对BMI和收缩压(SBP)的双胞胎和58对BMI和舒张压(DBP)的双胞胎。BMI与SBP呈正相关(β=1.86,p=.0004)。85个CpG的BMI与DNAm之间的关联达到p<1×10-4水平。LNCPRESS1、OGDHL、RNU1-44P,发现NPHS1、ECEL1P2、LLGL2、RNY4P15、MOGAT3、PHACTR3和BAI2。在85个CpG中,9映射到C10orf71-AS1,NDUFB5P1,KRT80,BAI2,ABCA2,PEX11G和FGF4与SBP水平显着相关。在9个CpG中,2在ABCA2内负介导BMI与SBP之间的关联,中介效应为-0.24(95%CI[-0.65,-0.01])。BMI也与DBP呈正相关(β=0.60,p=.0495)。193个CpG的BMI与DNAm之间的关联达到p<1×10-4水平。OGDHL内25个与BMI相关的DMRs,发现POU4F2、ECEL1P2、TTC6、SMPD4、EP400、TUBA1C和AGAP2。在193个CpG中,33映射到ABCA2,ADORA2B,CTNNBIP1,KDM4B,NAA60,RSPH6A,SLC25A19和STIL与DBP水平显著相关。在33个CpG中,12在ABCA2、SLC25A19、KDM4B、PTPRN2,DNASE1,TFCP2L1,LMNB2和C10orf71-AS1负介导BMI与DBP之间的关联,总中介效应为-0.66(95%CI[-1.07,-0.30])。有趣的是,BMI也可能负介导上述大多数CpG介质的DNAm与BP之间的关联。当按性别分层时,也发现了DNAm的中介作用。总之,DNAm变异可能部分负向介导BMI与BP的关联。我们的发现可能为进一步阐明肥胖对高血压的发病机制以及确定新的高血压诊断生物标志物和治疗靶点提供新的线索。
Obesity is an established risk factor for hypertension, but the mechanisms are only partially understood. We examined whether body mass index (BMI)-related DNA methylation (DNAm) variation would mediate the association of BMI with blood pressure (BP). We first conducted a genomewide DNA methylation analysis in monozygotic twin pairs to detect BMI-related DNAm variation and then evaluated the mediating effect of DNAm on the relationship between BMI and BP levels using the causal inference test (CIT) method and mediation analysis. Ontology enrichment analysis was performed for CpGs using the GREAT tool. A total of 60 twin pairs for BMI and systolic blood pressure (SBP) and 58 twin pairs for BMI and diastolic blood pressure (DBP) were included. BMI was positively associated with SBP (β = 1.86, p = .0004). The association between BMI and DNAm of 85 CpGs reached p < 1×10-4 level. Eleven BMI-related differentially methylated regions (DMRs) within LNCPRESS1, OGDHL, RNU1-44P, NPHS1, ECEL1P2, LLGL2, RNY4P15, MOGAT3, PHACTR3, and BAI2 were found. Of the 85 CpGs, 9 mapped to C10orf71-AS1, NDUFB5P1, KRT80, BAI2, ABCA2, PEX11G and FGF4 were significantly associated with SBP levels. Of the 9 CpGs, 2 within ABCA2 negatively mediated the association between BMI and SBP, with a mediating effect of -0.24 (95% CI [-0.65, -0.01]). BMI was also positively associated with DBP (β = 0.60, p = .0495). The association between BMI and DNAm of 193 CpGs reached p < 1×10-4 level. Twenty-five BMI-related DMRs within OGDHL, POU4F2, ECEL1P2, TTC6, SMPD4, EP400, TUBA1C and AGAP2 were found. Of the 193 CpGs, 33 mapped to ABCA2, ADORA2B, CTNNBIP1, KDM4B, NAA60, RSPH6A, SLC25A19 and STIL were significantly associated with DBP levels. Of the 33 CpGs, 12 within ABCA2, SLC25A19, KDM4B, PTPRN2, DNASE1, TFCP2L1, LMNB2 and C10orf71-AS1 negatively mediated the association between BMI and DBP, with a total mediation effect of -0.66 (95% CI [-1.07, -0.30]). Interestingly, BMI might also negatively mediate the association between the DNAm of most CpG mediators mentioned above and BP. The mediating effect of DNAm was also found when stratified by sex. In conclusion, DNAm variation may partially negatively mediate the association of BMI with BP. Our findings may provide new clues to further elucidate the pathogenesis of obesity to hypertension and identify new diagnostic biomarkers and therapeutic targets for hypertension.