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C反应蛋白组学测量分析将甲基化风险评分与睡眠健康及相关健康结果相关联。

Analysis of C-reactive protein omics-measures associates methylation risk score with sleep health and related health outcomes.

作者信息

Wang Ziqing, Wallace Danielle A, Spitzer Brian W, Huang Tianyi, Taylor Kent, Rotter Jerome I, Rich Stephen S, Liu Peter Y, Daviglus Martha L, Hou Lifang, Ramos Alberto R, Kaur Sonya, Durda J Peter, González Hector M, Fornage Myriam, Redline Susan, Isasi Carmen R, Sofer Tamar

机构信息

Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Department of Medicine, Harvard Medical School, Boston, MA, USA.

出版信息

medRxiv. 2024 Sep 4:2024.09.04.24313008. doi: 10.1101/2024.09.04.24313008.

DOI:10.1101/2024.09.04.24313008
PMID:
39281736
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11398435/
Abstract

INTRODUCTION

DNA methylation (DNAm) predictors of high sensitivity C-reactive protein (CRP) offer a stable and accurate means of assessing chronic inflammation, bypassing the CRP protein fluctuations secondary to acute illness. Poor sleep health is associated with elevated inflammation (including elevated blood CRP levels) which may explain associations of sleep insufficiency with metabolic, cardiovascular and neurological diseases. Our study aims to characterize the relationships among sleep health phenotypes and CRP markers -blood, genetic, and epigenetic indicators-within the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

METHODS

In HCHS/SOL, methylation risk scores (MRS)-CRP and polygenetic risk score (PRS)-CRP were constructed separately as weighted sums of methylation beta values or allele counts, respectively, for each individual. Sleep health phenotypes were measured using self-reported questionnaires and objective measurements. Survey-weighted linear regression established the association between the multiple sleep phenotypes (obstructive sleep apnea (OSA), sleep duration, insomnia and excessive sleepiness symptom), cognitive assessments, diabetes and hypertension with CRP markers while adjusting for age, sex, BMI, study center, and the first five principal components of genetic ancestry in HCHS/SOL.

RESULTS

We included 2221 HCHS/SOL participants (age range 37-76 yrs, 65.7% female) in the analysis. Both the MRS-CRP (95% confidence interval (CI): 0.32-0.42, p = 3.3 × 10) and the PRS-CRP (95% CI: 0.15-0.25, p = 1 × 10) were associated with blood CRP level. Moreover, MRS-CRP was associated with sleep health phenotypes (OSA, long sleep duration) and related conditions (diabetes and hypertension), while PRS-CRP markers were not associated with these traits. Circulating CRP level was associated with sleep duration and diabetes. Associations between OSA traits and metabolic comorbidities weakened after adjusting for MRS-CRP, most strongly for diabetes, and least for hypertension.

CONCLUSIONS

MRS-CRP is a promising estimate for systemic and chronic inflammation as reflected by circulating CRP levels, which either mediates or serves as a common cause of the association between sleep phenotypes and related comorbidities, especially in the presence of diabetes.

摘要

引言

高敏C反应蛋白(CRP)的DNA甲基化(DNAm)预测指标提供了一种稳定且准确的评估慢性炎症的方法,可绕过急性疾病导致的CRP蛋白波动。睡眠健康状况不佳与炎症升高(包括血液CRP水平升高)有关,这可能解释了睡眠不足与代谢、心血管和神经疾病之间的关联。我们的研究旨在描述西班牙裔社区健康研究/拉丁裔研究(HCHS/SOL)中睡眠健康表型与CRP标志物(血液、基因和表观遗传指标)之间的关系。

方法

在HCHS/SOL中,甲基化风险评分(MRS)-CRP和多基因风险评分(PRS)-CRP分别作为每个个体甲基化β值或等位基因计数的加权和构建。睡眠健康表型通过自我报告问卷和客观测量进行评估。调查加权线性回归确定了多种睡眠表型(阻塞性睡眠呼吸暂停(OSA)、睡眠时间、失眠和过度嗜睡症状)、认知评估、糖尿病和高血压与CRP标志物之间的关联,同时在HCHS/SOL中对年龄、性别、BMI、研究中心和遗传血统的前五个主成分进行了调整。

结果

我们纳入了2221名HCHS/SOL参与者(年龄范围37 - 76岁,65.7%为女性)进行分析。MRS-CRP(95%置信区间(CI):0.32 - 0.42,p = 3.3×10)和PRS-CRP(95%CI:0.15 - 0.25,p = 1×10)均与血液CRP水平相关。此外,MRS-CRP与睡眠健康表型(OSA、睡眠时间长)及相关疾病(糖尿病和高血压)相关,而PRS-CRP标志物与这些特征无关。循环CRP水平与睡眠时间和糖尿病相关。在调整MRS-CRP后,OSA特征与代谢合并症之间的关联减弱,对糖尿病影响最强,对高血压影响最小。

结论

MRS-CRP有望作为循环CRP水平所反映的全身和慢性炎症的估计指标,其要么介导睡眠表型与相关合并症之间的关联,要么作为它们的共同原因,尤其是在存在糖尿病的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/ea245f961c2c/nihpp-2024.09.04.24313008v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/714acdc61829/nihpp-2024.09.04.24313008v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/bc046571c430/nihpp-2024.09.04.24313008v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/ea245f961c2c/nihpp-2024.09.04.24313008v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/714acdc61829/nihpp-2024.09.04.24313008v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/bc046571c430/nihpp-2024.09.04.24313008v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0045/11398435/ea245f961c2c/nihpp-2024.09.04.24313008v1-f0003.jpg

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