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免疫与肌肉减少症之间因果关系网络的综合图谱。

Comprehensive landscapes of the causal network between immunity and sarcopenia.

机构信息

Department of Traumatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

出版信息

Front Immunol. 2024 Aug 20;15:1443885. doi: 10.3389/fimmu.2024.1443885. eCollection 2024.

Abstract

BACKGROUND

Inflammaging, an immune status characterized by a sustained increase in pro-inflammatory markers and a decline in anti-inflammatory mechanisms, is a critical risk factor in the development of sarcopenia. Landscapes of the causal relationships between immunity and sarcopenia are needed to understand the mechanism of sarcopenia and provide novel treatments comprehensively.

METHODS

We used Mendelian Randomization (MR) as the basic method in this study. By setting immune proteins, immune cells, and sarcopenia as exposures and outcomes alternatively, and then combining them in different directions, we potentially estimated their causal relationships and directions and subsequently mapped the comprehensive causal landscape based on this information efficiently. To further understand the network, we developed a method based on rank-sums to integrate multiple algorithms and identify the key immune cells and proteins.

RESULTS

More than 1,000 causal relationships were identified between immune cell phenotypes, proteins, and sarcopenia traits (p < 0.05), and the causal maps of these linkages were established. In the threshold of FDR < 0.05, hundreds of causal linkages were still significant. The final comprehensive map included 13 immune cell phenotypes and 8 immune proteins. The star factors in the final map included EM CD8br %CD8br, EM DN (CD4- CD8-) %DN, SIRT2, and so on.

CONCLUSION

By reading the landscapes in this study, we may not only find the factors and the pathways that have been reported and proven but also identify multiple novel immunity cell phenotypes and proteins with enriched upstream and downstream pathways.

摘要

背景

炎症衰老,一种以促炎标志物持续增加和抗炎机制下降为特征的免疫状态,是肌肉减少症发展的关键风险因素。需要了解免疫与肌肉减少症之间的因果关系景观,以全面了解肌肉减少症的发病机制并提供新的治疗方法。

方法

我们在这项研究中使用了孟德尔随机化(MR)作为基本方法。通过交替将免疫蛋白、免疫细胞和肌肉减少症作为暴露和结果,并以不同的方向将它们组合起来,我们可以有效地估计它们的因果关系和方向,然后基于这些信息构建全面的因果关系图。为了进一步了解网络,我们开发了一种基于秩和的方法,整合了多种算法,并识别关键的免疫细胞和蛋白。

结果

在免疫细胞表型、蛋白和肌肉减少症特征之间确定了 1000 多个因果关系(p<0.05),并建立了这些联系的因果图。在 FDR<0.05 的阈值下,仍有数百个因果联系具有统计学意义。最终的综合图谱包括 13 种免疫细胞表型和 8 种免疫蛋白。最终图谱中的关键因素包括 EM CD8br%CD8br、EM DN(CD4-CD8-)%DN、SIRT2 等。

结论

通过阅读本研究中的图谱,我们不仅可以找到已经报道和证实的因素和途径,还可以识别多种具有丰富上下游途径的新型免疫细胞表型和蛋白。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d4a/11368746/87bf7c4392f3/fimmu-15-1443885-g001.jpg

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