Department of Orthopedics, the Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, Jiangsu Province, People's Republic of China.
School of Medicine of Jiangsu University, Zhenjiang, 212000, Jiangsu, China.
Sci Rep. 2024 Apr 22;14(1):9179. doi: 10.1038/s41598-024-60059-w.
Although serum iron status and sarcopenia are closely linked, the presence of comprehensive evidence to establish a causal relationship between them remains insufficient. The objective of this study is to employ Mendelian randomization techniques to clarify the association between serum iron status and sarcopenia. We conducted a bi-directional Mendelian randomization (MR) analysis to investigate the potential causal relationship between iron status and sarcopenia. MR analyses were performed using inverse variance weighted (IVW), MR-Egger, and weighted median methods. Additionally, sensitivity analyses were conducted to verify the reliability of the causal association results. Then, we harvested a combination of SNPs as an integrated proxy for iron status to perform a MVMR analysis based on IVW MVMR model. UVMR analyses based on IVW method identified causal effect of ferritin on appendicular lean mass (ALM, β = - 0.051, 95% CI - 0.072, - 0.031, p = 7.325 × 10). Sensitivity analyses did not detect pleiotropic effects or result fluctuation by outlying SNPs in the effect estimates of four iron status on sarcopenia-related traits. After adjusting for PA, the analysis still revealed that each standard deviation higher genetically predicted ferritin was associated with lower ALM (β = - 0.054, 95% CI - 0.092, - 0.015, p = 0.006). Further, MVMR analyses determined a predominant role of ferritin (β = - 0.068, 95% CI - 0.12, - 0.017, p = 9.658 × 10) in the associations of iron status with ALM. Our study revealed a causal association between serum iron status and sarcopenia, with ferritin playing a key role in this relationship. These findings contribute to our understanding of the complex interplay between iron metabolism and muscle health.
虽然血清铁状态和肌肉减少症密切相关,但目前尚缺乏充分的综合证据来确定它们之间的因果关系。本研究旨在应用孟德尔随机化技术阐明血清铁状态与肌肉减少症之间的关联。我们进行了双向孟德尔随机化(MR)分析,以研究铁状态与肌肉减少症之间的潜在因果关系。使用逆方差加权(IVW)、MR-Egger 和加权中位数方法进行 MR 分析。此外,还进行了敏感性分析以验证因果关联结果的可靠性。然后,我们选择了一组 SNP 作为铁状态的综合代表,基于 IVW MVMR 模型进行 MVMR 分析。基于 IVW 方法的 UVMR 分析确定了铁蛋白对四肢瘦肉量(ALM,β=-0.051,95%CI-0.072,-0.031,p=7.325×10)的因果效应。敏感性分析未检测到四个铁状态对与肌肉减少症相关特征的影响估计中存在的潜在效应对或异常 SNP 导致的结果波动。在调整 PA 后,分析仍表明,遗传预测铁蛋白每增加一个标准差与 ALM 降低相关(β=-0.054,95%CI-0.092,-0.015,p=0.006)。此外,MVMR 分析确定了铁蛋白(β=-0.068,95%CI-0.12,-0.017,p=9.658×10)在铁状态与 ALM 之间的关联中起主要作用。我们的研究揭示了血清铁状态与肌肉减少症之间的因果关联,其中铁蛋白在这种关系中起着关键作用。这些发现有助于我们理解铁代谢和肌肉健康之间的复杂相互作用。