利用多变量孟德尔随机化检测代谢特征与骨质疏松症之间的因果关系。
Detecting causal relationship between metabolic traits and osteoporosis using multivariable Mendelian randomization.
机构信息
School of Nursing and Health, Zhengzhou University, NO.101 Kexue Road, High-Tech Development Zone of States, Zhengzhou, 450001, People's Republic of China.
Center for Bioinformatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA.
出版信息
Osteoporos Int. 2021 Apr;32(4):715-725. doi: 10.1007/s00198-020-05640-5. Epub 2020 Sep 24.
UNLABELLED
By adopting the extension approaches of Mendelian randomization, we successfully detected and prioritized the potential causal risk factors for BMD traits, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.
INTRODUCTION
Osteoporosis (OP) is a common metabolic skeletal disease characterized by reduced bone mineral density (BMD). The identified SNPs for BMD can only explain approximately 10% of the variability, and very few causal factors have been identified so far.
METHODS
The Mendelian randomization (MR) approach enables us to assess the potential causal effect of a risk factor on the outcome by using genetic IVs. By using extension methods of MR-multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA)-we intend to estimate the causal relationship between fifteen metabolic risk factors for BMD and try to prioritize the most potential causal risk factors for BMD.
RESULTS
Our analysis identified three risk factors T2D, FG, and HCadjBMI for FN BMD; four risk factors FI, T2D, HCadjBMI, and WCadjBMI for FA BMD; and three risk factors FI, T2D, and HDL cholesterol for LS BMD, and all risk factors were causally associated with heel BMD except for triglycerides and WCadjBMI. Consistent with the mvMR results, MR-BMA confirmed those risk factors as top risk factors for each BMD trait individually.
CONCLUSIONS
By combining MR approaches, we identified the potential causal risk factors for FN, FA, LS, and heel BMD individually and we also prioritized and ranked the potential causal risk factors for BMD, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.
未加标签
通过采用孟德尔随机化的扩展方法，我们成功地检测和优先考虑了与 BMD 特征相关的潜在因果风险因素，这可能为我们治疗和干预与骨骼相关的复杂特征和疾病提供新的思路。
简介
骨质疏松症（OP）是一种常见的代谢性骨骼疾病，其特征是骨矿物质密度（BMD）降低。已鉴定出的与 BMD 相关的 SNP 只能解释大约 10%的可变性，而且到目前为止很少有因果因素被鉴定出来。
方法
孟德尔随机化（MR）方法可以通过使用遗传工具变量来评估风险因素对结果的潜在因果影响。通过使用 MR 多变量 MR（mvMR）和基于贝叶斯模型平均的 MR（MR-BMA）的扩展方法，我们旨在估计 15 种代谢性 BMD 风险因素之间的因果关系，并尝试确定 BMD 的最潜在因果风险因素。
结果
我们的分析确定了三个与 FN BMD 相关的风险因素 T2D、FG 和 HCadjBMI；四个与 FA BMD 相关的风险因素 FI、T2D、HCadjBMI 和 WCadjBMI；以及三个与 LS BMD 相关的风险因素 FI、T2D 和 HDL 胆固醇，除了甘油三酯和 WCadjBMI 外，所有风险因素均与跟骨 BMD 呈因果关系。与 mvMR 结果一致，MR-BMA 确认了这些风险因素是每个 BMD 特征的首要风险因素。
结论
通过结合 MR 方法，我们单独确定了 FN、FA、LS 和跟骨 BMD 的潜在因果风险因素，我们还对 BMD 的潜在因果风险因素进行了优先排序，这可能为我们治疗和干预与骨骼相关的复杂特征和疾病提供新的思路。
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