Effects of race and socioeconomic status on the epigenetic aging clock

我们设计了一个子队列的人从健康老龄化社区的多样性在整个生命周期（HANDLS）的研究，这将使我们能够解决种族和社会经济地位对DNA年龄的影响。基于功效和样本量计算，以及与我们的合作团队的广泛讨论，我们从HANDLS的第一波中选择了508名参与者。我们选择了这些具有来自第1波和第3波的外周血DNA样本的个体，这将使我们能够进行加速DNA年龄的纵向研究。这些人包括HANDLS研究年龄段（30-64岁）的贫困以上和贫困以下、AA和白色、男性和女性。 我们使用Illumina MethylationEPIC BeadChip测量了从487名中年AA（N=244）和白色（N=243），男性（N = 248）和女性（N =239）提取的血液DNA中的全基因组DNA甲基化（866，836个CpG）。AA和白人的平均（sd）年龄分别为48.4（8.8）和49.0（8.7）（p=0.48）。我们确定了4，930个显著相关的aDMPs在AA和469白人。其中，75.6%和53.1%是新的，主要是由于在AA和白人中EPIC阵列中测量的CpG数量增加。在与年龄相关的疾病和参与生长和发育的细胞通路相关的基因中，AA比白人有更多与年龄相关的DNAm变化。我们评估了三个表观遗传年龄加速措施（普遍，内在和外在）。与白人相比，AA的外在衰老明显较慢。此外，与AA女性相比，AA和白色男性在通用年龄加速测量中的衰老速度更快（分别为+2.04和+1.24岁，p<0.05）。AA比白人有更广泛的甲基化变化。种族和性别相互作用，以生物学年龄加速的基础上，改变DNA甲基化模式可能是重要的年龄相关的健康差异。 将来，我们还将使用来自第3波的DNA样本纵向分析全基因组DNA甲基化。一旦完成第3波甲基化测量，我们将遵循类似的质量控制标准，并使用线性混合效应回归模型进行纵向数据分析：（i）评估性别，种族，SES及其相互作用对表观遗传年龄加速纵向变化的影响;（ii）识别与年龄相关的差异甲基化CpG位点和纵向变化的差异甲基化区域。我们还将对纵向数据进行全基因组甲基化分析，以确定与年龄相关的差异甲基化CpG位置和区域，以确定可能在衰老过程和途径中发挥作用的基因。产生的数据将有助于了解影响表观遗传年龄加速以及全基因组CpG甲基化纵向变化的因素。 我们还研究了健康差异的一个重要领域，代谢综合征。 代谢综合征（MetS）是一组导致全因死亡、心血管疾病和癌症的心血管代谢危险因素。识别与非裔美国人（AAs）和白人中MetS相关的表观遗传学改变可能会深入了解影响其不同健康结果的基因。我们检查了DNA甲基化（DNAm），并在有和没有MetS的AA和白人中进行了MetS的表观全基因组关联研究（EWAS）。我们使用NCEP-ATP III指南评估了AA（n = 225）和白色（n = 233）成人中年龄、种族和贫困状况相关的DNA m。使用Illumina Infinium甲基化EPIC BeadChip评估全基因组DNAm测量。差异甲基化的位置（DMPs）和差异甲基化的区域（DMR）进行了鉴定，使用dmpdenium和bumphunter。EWAS使用CpGasstrom进行。我们发现显着的DMP与年龄，贫困状况和MetS在每个种族。当比较有和没有MetS的AA时，GSTT 1（Glucosides S-Transferase Theta 1）是最高甲基化的基因之一，MIPEP（Mitochondrial Intermediate Peptidase）是最低甲基化的基因之一。PPP 1 R13 L（蛋白磷酸酶1调节亚基13样）是最高的高甲基化和SCD（硬脂酰辅酶A去饱和酶-1）是最低甲基化的基因之一，白人与和没有MetS。EWAS结果表明，DNA m差异可能有助于白人和AA之间的MetS风险，因为不同的基因被确定在AA和白人。我们复制了以前鉴定的MetS相关基因，发现硫氧还蛋白相互作用蛋白（TXN 1 P）仅在白人中有统计学显著差异表达。我们的研究结果可能是有用的，在AA和白人之间的MetS差异的基因的进一步研究。 我们还开始研究表观遗传年龄加速与健康差异和衰老相关的重要行为条件之间的关系;这些包括抑郁症状，感知歧视和认知。

We designed a sub-cohort of individuals from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study that would enable us to address the effects of race and SES on DNAm age. Based on power and sample size calculations, and extensive discussions with our collaborative team, we have selected 508 participants from Wave 1 of HANDLS. We chose these individuals with peripheral blood DNA samples from both Wave 1 and Wave 3, which will allow us to perform longitudinal study of accelerated DNAm age. These individuals were comprised of above and below poverty, AA and white, male and female across the age-span of the HANDLS study (30-64 yrs.). We measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N=244) and white (N=243), men (N=248) and women (N=239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p=0.48). We identified 4,930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+2.04 and +1.24 years, respectively, p<0.05). AAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities. In future, we will also analyze genome-wide DNA methylation longitudinally using DNA samples from wave 3. Once the wave 3 methylation measurement is complete, we will follow similar quality control standards, and perform longitudinal data analysis using linear mixed-effects regression models (i) to assess the effect of sex, race, SES, and their interaction on the longitudinal changes of epigenetic age acceleration; and (ii) to identify age-associated differentially methylated CpG sites and differentially methylated regions that change longitudinally. We will also perform genome-wide methylation analysis on the longitudinal data to identify differentially methylated CpG positions and regions associated with age to identify genes that could play role in the aging processes and pathways. Data generated will help to understand factors affecting the longitudinal changes in epigenetic age acceleration as well as in genome-wide CpG methylation. We also have examined an important area of health disparities, metabolic syndrome. Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors for all-cause mortality, cardiovascular disease, and cancer. Identifying epigenetic alterations associated with MetS in African Americans (AAs) and Whites may provide insight into genes that influence its differential health outcomes. We examined DNA methylation (DNAm) and performed an epigenome-wide association study (EWAS) of MetS among AAs and Whites with and without MetS. We assessed age, race and poverty status associated DNAm among AAs (n = 225) and White (n = 233) adults using NCEP-ATP III guidelines. Genome-wide DNAm measurement was assessed using Illumina Infinium Methylation EPIC BeadChip. Differentially methylated positions (DMPs) and differentially methylated regions (DMRs) were identified using dmpFinder and bumphunter. EWAS was performed using CpGassoc. We found significant DMPs associated with age, poverty status and MetS in each race. GSTT1(Glutathione S-Transferase Theta 1) was one of the top-hypermethylated genes and MIPEP (Mitochondrial Intermediate Peptidase) was one of the most hypomethylated genes when comparing AAs with and without MetS. PPP1R13L (Protein Phosphatase 1 Regulatory Subunit 13 Like) was the top hypermethylated and SCD (stearoyl-CoA desaturase-1) was one of the most hypomethylated genes for Whites with and without MetS. EWAS results showed that DNAm differences might contribute to MetS risk among Whites and AAs since different genes were identified in AAs and Whites. We replicated previously identified MetS associated genes and found that Thioredoxin-interacting protein (TXN1P) was statistically significantly differentially expressed only in Whites. Our results may be useful in further studies of genes underlying differences in MetS among AAs and Whites. We have also begun to examine the relationships between epigenetic age acceleration and important behavioral conditions relevant to health disparities and aging; these include depressive symptoms, perceived discrimination and cognition.