PDF(Pubmed)
Abstract：
BACKGROUND: This study leverages a two-sample Mendelian Randomization (MR) approach to explore the causal relationships between 1,400 metabolites and pulmonary fibrosis, using genetic variation as instrumental variables. By adhering to stringent criteria for instrumental variable selection, the research aims to uncover metabolic pathways that may influence the risk and progression of pulmonary fibrosis, providing insights into potential therapeutic targets.
METHODS: Utilizing data from the OpenGWAS project, which includes a significant European cohort, and metabolite GWAS data from the Canadian Longitudinal Aging Study (CLSA), the study employs advanced statistical methods. These include inverse variance weighting (IVW), weighted median estimations, and comprehensive sensitivity analyses conducted using the R software environment to ensure the robustness of the causal inferences.
RESULTS: The study identified 62 metabolites with significant causal relationships with pulmonary fibrosis, highlighting both risk-enhancing and protective metabolic factors. This extensive list of metabolites presents a broad spectrum of potential therapeutic targets and biomarkers for early detection, underscoring the metabolic complexity underlying pulmonary fibrosis.
CONCLUSIONS: The findings from this MR study significantly advance our understanding of the metabolic underpinnings of pulmonary fibrosis, suggesting that alterations in specific metabolites could influence the risk and progression of the disease. These insights pave the way for the development of novel diagnostic and therapeutic strategies, emphasizing the potential of metabolic modulation in managing pulmonary fibrosis.
METHODS: Utilizing data from the OpenGWAS project, which includes a significant European cohort, and metabolite GWAS data from the Canadian Longitudinal Aging Study (CLSA), the study employs advanced statistical methods. These include inverse variance weighting (IVW), weighted median estimations, and comprehensive sensitivity analyses conducted using the R software environment to ensure the robustness of the causal inferences.
RESULTS: The study identified 62 metabolites with significant causal relationships with pulmonary fibrosis, highlighting both risk-enhancing and protective metabolic factors. This extensive list of metabolites presents a broad spectrum of potential therapeutic targets and biomarkers for early detection, underscoring the metabolic complexity underlying pulmonary fibrosis.
CONCLUSIONS: The findings from this MR study significantly advance our understanding of the metabolic underpinnings of pulmonary fibrosis, suggesting that alterations in specific metabolites could influence the risk and progression of the disease. These insights pave the way for the development of novel diagnostic and therapeutic strategies, emphasizing the potential of metabolic modulation in managing pulmonary fibrosis.
摘要：
背景：这项研究利用了两个样本的孟德尔随机化（MR）方法来探索1,400种代谢物与肺纤维化之间的因果关系，利用遗传变异作为工具变量。通过坚持工具变量选择的严格标准，这项研究旨在揭示可能影响肺纤维化风险和进展的代谢途径，提供对潜在治疗目标的见解。
方法：利用OpenGWAS项目中的数据，其中包括一个重要的欧洲群体，和来自加拿大纵向衰老研究(CLSA)的代谢物GWAS数据，这项研究采用了先进的统计方法。这些包括方差逆加权(IVW)，加权中位数估计，以及使用R软件环境进行的全面敏感性分析，以确保因果推断的稳健性。
结果：该研究确定了62种与肺纤维化有显著因果关系的代谢物，强调风险增强和保护性代谢因素。这些广泛的代谢物清单为早期检测提供了广泛的潜在治疗靶标和生物标志物，强调肺纤维化背后的代谢复杂性。
结论：这项MR研究的发现极大地促进了我们对肺纤维化代谢基础的理解，这表明特定代谢物的改变可能影响疾病的风险和进展。这些见解为开发新的诊断和治疗策略铺平了道路，强调代谢调节在控制肺纤维化中的潜力。
方法：利用OpenGWAS项目中的数据，其中包括一个重要的欧洲群体，和来自加拿大纵向衰老研究(CLSA)的代谢物GWAS数据，这项研究采用了先进的统计方法。这些包括方差逆加权(IVW)，加权中位数估计，以及使用R软件环境进行的全面敏感性分析，以确保因果推断的稳健性。
结果：该研究确定了62种与肺纤维化有显著因果关系的代谢物，强调风险增强和保护性代谢因素。这些广泛的代谢物清单为早期检测提供了广泛的潜在治疗靶标和生物标志物，强调肺纤维化背后的代谢复杂性。
结论：这项MR研究的发现极大地促进了我们对肺纤维化代谢基础的理解，这表明特定代谢物的改变可能影响疾病的风险和进展。这些见解为开发新的诊断和治疗策略铺平了道路，强调代谢调节在控制肺纤维化中的潜力。
