衰老的血浆microRNA特征及其与健康结果和死亡率的联系:一项基于人群的队列研究的发现

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY
Lieke M Kuiper, Michelle M J Mens, Julia W Wu, Jaap Goudsmit, Yuan Ma, Liming Liang, Albert Hofman, Trudy Voortman, M Arfan Ikram, Jeroen G J van Rooij, Joyce B J van Meurs, Mohsen Ghanbari
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引用次数: 0

摘要

背景:MicroRNAs是一种小的非编码rna,它在转录后调节基因表达,并在衰老表型的各种组织中表现出差异表达。在循环中可检测到的细胞外microrna反映(病理)生理过程,并有望作为健康衰老和年龄相关疾病的生物标志物。本研究旨在利用人群水平数据探索血浆细胞外microrna作为生物衰老指标及其与健康结局的关系。方法:我们在基于人群的鹿特丹研究队列的2684名参与者中,使用靶向rna测序量化了2083种细胞外microrna的血浆表达水平。训练集和测试集包括1930名来自老年第一和第二亚队列(RS-I/RS-II;中位年龄:70.6),而验证集包括754名来自中年第四亚队列(RS-IV;中位年龄:53.5岁)。基于591个在血浆中表达良好的microrna,我们检测了microrna在实足年龄、表型(年龄和9个多系统血液生物标志物的综合评分)、虚弱指数和死亡率方面的差异表达。接下来,采用弹性网络模型构建基于microrna的复合衰老生物标志物,预测实足年龄(mirAge)、表型年龄(mirPA)、脆弱指数(mirFI)和死亡率(mirMort)。在测试和验证集中,使用Cox比例风险、线性回归和逻辑回归模型评估这些衰老生物标志物与不同年龄相关健康结局的关联。结果:我们在RS-I/RS-II老年人群(ntraining = 1158, ntest = 772)中鉴定出188个随实足年龄差异表达的microrna,其中177个(94.1%)在中年RS-IV亚队列中重复(nvalidation = 754)。此外,227个mirna与表型年龄密切相关,61个与FI相关,16个与10年死亡率无关。随后,我们构建了四种基于血浆microrna的衰老生物标志物:mirAge有108个,mirPA有153个,mirFI有81个,mirMort有50个。这些基于微rna的衰老生物标志物得分升高与不利的健康结果相关,包括主观身体功能和自我报告的健康状况降低、死亡率和虚弱风险增加,但与首次或多次发病无关。总的来说,与mirAge相比,mirPA、mirFI和mirMort的效应估计更大。结论:本研究描述了不同的血浆microrna衰老特征,并介绍了四种基于microrna的衰老生物标志物,这些标志物有可能识别加速衰老和年龄相关的衰退,为人类衰老的复杂过程提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma microRNA signatures of aging and their links to health outcomes and mortality: findings from a population-based cohort study.

Background: MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally and show differential expression in various tissues with aging phenotypes. Detectable in circulation, extracellular microRNAs reflect (patho)physiological processes and hold promise as biomarkers for healthy aging and age-related diseases. This study aimed to explore plasma extracellular microRNAs as a biological aging indicator and their associations with health outcomes using population-level data.

Methods: We quantified plasma expression levels of 2083 extracellular microRNAs using targeted RNA-sequencing in 2684 participants from the population-based Rotterdam Study cohort. The training and test sets included 1930 participants from the advanced-aged initial and second subcohort (RS-I/RS-II; median age: 70.6), while the validation set comprised 754 participants from the middle-aged fourth subcohort (RS-IV; median age: 53.5). Based on 591 microRNAs well-expressed in plasma, we examined differential expression of microRNAs with chronological age, PhenoAge-a composite score of age and nine multi-system blood biomarkers-the frailty index, and mortality. Next, elastic net models were employed to construct composite microRNA-based aging biomarkers predicting chronological age (mirAge), PhenoAge (mirPA), frailty index (mirFI), and mortality (mirMort). The association of these aging biomarkers with different age-related health outcomes was assessed using Cox Proportional Hazard, linear regression, and logistic regression models in the test and validation sets.

Results: We identified 188 microRNAs differentially expressed with chronological age within the RS-I/RS-II advanced-aged population (ntraining = 1158, ntest = 772), of which 177 microRNAs (94.1%) were replicated in the middle-aged RS-IV subcohort (nvalidation = 754). Moreover, 227 miRNAs showed robust associations with PhenoAge, 61 with FI, and 16 with 10-year mortality independent of chronological age. Subsequently, we constructed four plasma microRNA-based aging biomarkers: mirAge with 108, mirPA with 153, mirFI with 81, and mirMort with 50 miRNAs. Elevated scores on these microRNA-based aging biomarkers were associated with unfavorable health outcomes, including lower subjective physical functioning and self-reported health and increased mortality and frailty risk, but not with first- or multi-morbidity. Overall, larger effect estimates were observed for mirPA, mirFI, and mirMort compared to mirAge.

Conclusions: This study describes distinct plasma microRNA-aging signatures and introduces four microRNA-based aging biomarkers with the potential to identify accelerated aging and age-related decline, providing insights into the intricate process of human aging.

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来源期刊
Genome Medicine
Genome Medicine GENETICS & HEREDITY-
CiteScore
20.80
自引率
0.80%
发文量
128
审稿时长
6-12 weeks
期刊介绍: Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.
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