剖析血浆代谢物与骨质疏松症之间的因果关系：双向孟德尔随机化研究
Q2 Medicine
引用次数: 0
摘要
目的 通过孟德尔随机分析（MR）研究血浆代谢物与骨质疏松症之间的因果关系。方法 采用双向 MR 分析不同全基因组关联研究（GWAS）的汇总数据，研究血浆代谢物与骨质疏松症之间的因果关系。利用反方差加权法估计了血浆代谢物对骨质疏松症的因果效应，确定了从不同来源的骨质疏松症相关 GWAS 汇总数据中获得的具有统计学意义的代谢物的交叉点，然后对这些代谢物进行了敏感性分析。单核苷酸多态性之间的异质性通过 Cochran's Q 检验进行评估。横向多效性通过应用 MR-Egger 截距法和 MR-PRESSO 法进行评估。骨质疏松症对血浆代谢物的因果效应也通过逆方差加权法进行了评估。此外，还进行了通路分析，以确定参与调控骨质疏松症的潜在代谢通路。结果 经过初步分析和一系列敏感性分析，从 GCST90038656 和 GCST90044600 数据集中的 GWAS 数据中分别鉴定出 77 和 61 个血浆代谢物与骨质疏松症有因果关系。通过交集确定了五种常见代谢物。X-13684水平（GCST90038656：OR = 0.999，95% CI，0.998-1.000，P = 0.004；GCST90044600：OR = 0.834，95% CI，0.700-0.993，P = 0.042）和葡萄糖-麦芽糖比值（GCST90038656：OR = 0.998，95% CI，0.997-1.000，P = 0.025；GCST90044600：OR = 0.752，95% CI，0.998-1.000，P = 0.004）：OR = 0.752，95% CI，0.576-0.981，P = 0.036）与骨质疏松症呈负相关，而糖脱氧胆酸水平（GCST90038656：OR = 1.002，95% CI，1.000-1.003，P = 0.032；GCST90044600：OR = 1.331，95% CI，1.036-1.709，P = 0.025）和花生四烯酸肉碱（C20）水平（GCST90038656：OR = 1.001，95% CI，1.000-1.003，P = 0.039；GCST90044600：OR = 1.237; 95% CI, 1.008-1.518, P = 0.042）与骨质疏松症呈正相关。X-11299 水平与骨质疏松症的关系显示出矛盾的结果（GCST90038656：OR= 0.998，95% CI，0.997-1.000，P = 0.026；GCST90044600：OR=1.402，95% CI，1.071-1.834，P=0.014）。通路分析表明，甘氨酸、丝氨酸和苏氨酸代谢，缬氨酸、亮氨酸和异亮氨酸生物合成，半乳糖代谢，精氨酸生物合成，淀粉和蔗糖代谢通路参与了骨质疏松症的发生。结论 我们发现血浆代谢物与骨质疏松症之间存在因果关系。这些结果提供了新的视角，对在骨质疏松症的治疗中针对代谢物采取有针对性的干预措施具有重要意义。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Dissecting Causal Relationships Between Plasma Metabolites and Osteoporosis: A Bidirectional Mendelian Randomization Study
Objective
To investigate the causal relationships between plasma metabolites and osteoporosis via Mendelian randomization (MR) analysis.
Methods
Bidirectional MR was used to analyze pooled data from different genome-wide association studies (GWAS). The causal effect of plasma metabolites on osteoporosis was estimated using the inverse variance weighted method, intersections of statistically significant metabolites obtained from different sources of osteoporosis-related GWAS aggregated data was determined, and then sensitivity analysis was performed on these metabolites. Heterogeneity between single nucleotide polymorphisms was evaluated by Cochran's Q test. Horizontal pleiotropy was assessed through the application of the MR-Egger intercept method and the MRPRESSO method. The causal effect of osteoporosis on plasma metabolites was also evaluated using the inverse variance weighted method. Additionally, pathway analysis was conducted to identify potential metabolic pathways involved in the regulation of osteoporosis.
Results
Primary analysis and sensitivity analysis showed that 77 and 61 plasma metabolites had a causal relationship with osteoporosis from the GWAS data in the GCST90038656 and GCST90044600 datasets, respectively. Five common metabolites were identified via intersection. X-13684 levels and the glucose-to-maltose ratio were negatively associated with osteoporosis, whereas glycoursodeoxycholate levels and arachidoylcarnitine (C20) levels were positively associated with osteoporosis (all P < 0.05). The relationship between X-11299 levels and osteoporosis showed contradictory results (all P < 0.05). Pathway analysis indicated that glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, galactose metabolism, arginine biosynthesis, and starch and sucrose metabolism pathways were participated in the development of osteoporosis.
Conclusion
We found a causal relationship between plasma metabolites and osteoporosis. These results offer novel perspectives with important implications for targeted metabolite-focused interventions in the management of osteoporosis.
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