Effects of race and socioeconomic status on the epigenetic aging clock

我们设计了一个来自多样性社区健康老龄化(HANDLS)研究的个人子队列，这项研究将使我们能够解决种族和社会经济状况对dNaM年龄的影响。根据能量和样本量的计算，以及与我们协作团队的广泛讨论，我们从HANDLS的第一波中挑选了508名参与者。我们选择了这些外周血DNA样本同时来自波1和波3的个体，这将使我们能够进行加速dNaM年龄的纵向研究。这些人包括贫困以上和贫困以下、再生障碍性贫血和白人，在HANDLS研究的年龄范围内有男性和女性(30-)。 我们使用发光甲基化EPIC微珠芯片检测了487名中年AA(N=244)、白人(N=243)、男性(N=248)和女性(N=239)血液DNA的全基因组DNA甲基化(866,836 cpgs)。AA和白人的平均年龄(SD)分别为48.4岁(8.8岁)和49.0岁(8.7岁)(p=0.48)。我们在AA和白人中发现了4930个显著相关的aDMP和469个。其中，75.6%和53.1%是新的，主要是由于EPIC阵列中测得的CPG数量的增加，分别在AA和白人中。在与年龄相关的疾病和与生长发育有关的细胞通路中，AAS比白人有更多与年龄相关的dNaM变化。我们评估了三种表观遗传年龄加速措施(普遍的、内在的和外在的)。与白人相比，AAS的外在衰老明显较慢。此外，与AA女性相比，AA和白人男性在普遍年龄加速指标中的衰老速度都更快(分别为+2.04岁和+1.24岁，p&lt；0.05)。AAS比白人有更广泛的甲基化变化。种族和性别的相互作用是生物年龄加速的基础，这表明DNA甲基化模式的改变在与年龄相关的健康差异中可能是重要的。 未来，我们还将使用第三波DNA样本进行全基因组DNA甲基化的纵向分析。一旦第三波甲基化测量完成，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理解影响表观遗传年龄加速以及全基因组CpG甲基化的纵向变化的因素。 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)是导致全因死亡、心血管疾病和癌症的一系列心脏代谢危险因素。在非裔美国人(AAs)和白人中识别与Met相关的表观遗传学改变可能有助于深入了解影响其不同健康结局的基因。我们检测了DNA甲基化(DNaM)，并对有无甲硫氨酸的AA和白色人群中的甲硫氨酸进行了表观基因组范围的关联研究(EWAS)。我们使用NCEP-ATPIII指南评估了AAS(n=225)和白人(n=233)成年人中与dNaM相关的年龄、种族和贫困状况。全基因组dNaM测量使用Illumina Infinium甲基化EPIC珠芯片进行评估。用dmpFinder和Bumphunter鉴定差异甲基化位置(DMP)和差异甲基化区域(DMRS)。使用CpGassoc进行电切术。我们在每个种族中发现了与年龄、贫困状况和Met相关的显著DMP。GSTT1(Glutathione S-Transferase Theta 1，谷胱甘肽-S转移酶Theta 1)是AAs中最高甲基化的基因之一，而MIPEP(线粒体中间肽酶)是AAs中最低甲基化的基因之一。PPP1R13L(蛋白磷酸酶1调节亚基13样)是白种人中最高甲基化的基因，而SCD(硬脂酰辅酶A脱饱和酶-1)是有或没有蛋氨酸的白人中最低甲基化的基因之一。Ewas结果表明，dNaM的差异可能在白色和白色之间导致蛋氨酸风险，因为在白色和白色中发现了不同的基因。我们复制了之前发现的蛋氨酸相关基因，发现硫氧还蛋白相互作用蛋白(TXN1P)只有在白人中有统计学差异表达。我们的结果可能有助于进一步研究AAA和白人之间存在的蛋氨酸差异的基因。 我们还开始研究表观遗传年龄加速与与健康差距和衰老相关的重要行为状况之间的关系；这些行为状况包括抑郁症状、感知到的歧视和认知。

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.