揭示代谢综合征与表观遗传衰老的关系:来自NHANES 1999-2002和孟德尔随机化研究的证据。

Yujun Zhang, Jiawei Gui, JingJing Song, Benjie Li, Qixian Wang, Xinmeng Lv, Chong Li, Guoyang Zhang, Zaihua Cheng, Xiao Huang
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引用次数: 0

摘要

背景:表观遗传年龄加速(EAA)是反映生物年龄与实足年龄差异的一种新的生物衰老标志物。有证据表明代谢综合征(MetS)会影响衰老相关的生理机能,但MetS与EAA之间的关系尚不清楚,值得进一步研究。方法:我们分析1999-2002年全国健康与营养检查调查(NHANES)中1972名个体的数据。从13个表观遗传时钟的残差中测定了eaa。采用加权逻辑回归、线性回归和限制三次样条(RCS)模型来研究eaa与MetS之间的相关性。通过遗传相关性和双样本孟德尔随机化(MR)分析来评估因果关系,并辅以基于汇总数据的MR (SMR)和生物信息学分析来探索与这些关联相关的基因调控。结果:MetS参与者表现出显著更高的EAAs水平,DNA甲基化表型加速(PhenoAgeAccel)增加0.84年(95% CI: 0.04-1.64), DNA甲基化GrimAge加速(GrimAgeAccel)增加0.83年(95% CI: 0.32-1.34), DNA甲基化Grim2Age加速(GrimAge2Accel)增加1.33年(95% CI: 0.77-1.89)。eaa升高与met风险增加显著相关,RCS模型进一步证实了这一相关性。遗传相关性和MR分析显示MetS和GrimAgeAccel之间存在显著关联。SMR鉴定出met和GrimAgeAccel之间共有的风险基因。随后的生物信息学分析表明,这些基因与表型相关,如葡萄糖依赖性促胰岛素原肽水平。结论:我们建立了MetS与EAAs之间的因果关系,表明MetS可能为个性化的衰老预防和干预提供新的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unraveling the Relationship Between Metabolic Syndrome and Epigenetic Aging: Evidence from NHANES 1999-2002 and Mendelian Randomization Study.

Background: Epigenetic age acceleration (EAA), reflecting the difference between biological and chronological age, serves as a novel biomarker for biological aging. Evidence shows metabolic syndrome (MetS) affects aging-related physiology, but the relationship between MetS and EAA remains unclear and warrants further investigation.

Methods: We analyzed data from 1,972 individuals in the National Health and Nutrition Examination Survey (NHANES) 1999-2002. EAAs were determined from the residuals of 13 epigenetic clocks regressed on chronological age. Weighted logistic regression, linear regression, and restricted cubic spline (RCS) models were utilized to investigate correlations between EAAs and MetS. Genetic correlation and two-sample Mendelian randomization (MR) analyses were performed to assess causal associations, complemented by summary-data-based MR (SMR) and bioinformatics analyses to explore gene regulation related to these associations.

Results: Participants with MetS exhibited significantly higher levels of EAAs, with DNA methylation PhenoAge acceleration (PhenoAgeAccel) increasing by 0.84 years (95% CI: 0.04-1.64), DNA methylation GrimAge acceleration (GrimAgeAccel) increasing by 0.83 years (95% CI: 0.32-1.34), and DNA methylation Grim2Age acceleration (GrimAge2Accel) increasing by 1.33 years (95% CI: 0.77-1.89). Elevated EAAs were significantly associated with increased risks of MetS, a correlation further substantiated by RCS models. Genetic correlation and MR analyses revealed significant associations between MetS and GrimAgeAccel. SMR identified shared risk genes between MetS and GrimAgeAccel. Subsequent bioinformatics analyses showed that these genes were associated with phenotypes such as glucose-dependent proinsulinotropic peptide levels.

Conclusion: We established a causal relationship between MetS and EAAs, indicating that MetS may provide new strategies for personalized aging prevention and intervention.

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