PDF(Pubmed)
Abstract：
BACKGROUND: Androgenic alopecia (AGA) is the most common non-scarring alopecia disorder. Given its increasing incidence and onset during adolescence, AGA significantly impacts both the physical and psychological well-being of affected individuals. Emerging evidence suggests a pivotal role of metabolites in AGA. This study aims to elucidate the causal relationship between metabolites and AGA using Mendelian randomization (MR) analysis.
METHODS: We conducted a two-sample Mendelian randomization (TSMR) analysis based on a genome-wide association study (GWAS) to assess the causality of 452 metabolites on AGA. The main approach employed for inferring causal effects was inverse variance weighted (IVW), which was complemented by MR-Egger regression, weighted median, as well as MR pleiotropy residual sum and outlier (MR-PRESSO) approaches. Additionally, sensitivity analyses were performed to ensure result robustness. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) in GWAS dataset comprising 452 metabolites.
RESULTS: Notably, we identified Scyllo-inositol and Alpha-ketoglutarate as the most potent protective factors against AGA, while Heme* and 2-palmitoylglycerophosphocholine* emerged as significant risk factors for AGA. Furthermore, sensitivity analysis revealed no heterogeneity in these findings.
CONCLUSIONS: Overall, our research suggests a potential causal link between metabolites and AGA, offering a more comprehensive insight into the pathogenesis of AGA and present additional strategies for prevention and treatment.
METHODS: We conducted a two-sample Mendelian randomization (TSMR) analysis based on a genome-wide association study (GWAS) to assess the causality of 452 metabolites on AGA. The main approach employed for inferring causal effects was inverse variance weighted (IVW), which was complemented by MR-Egger regression, weighted median, as well as MR pleiotropy residual sum and outlier (MR-PRESSO) approaches. Additionally, sensitivity analyses were performed to ensure result robustness. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) in GWAS dataset comprising 452 metabolites.
RESULTS: Notably, we identified Scyllo-inositol and Alpha-ketoglutarate as the most potent protective factors against AGA, while Heme* and 2-palmitoylglycerophosphocholine* emerged as significant risk factors for AGA. Furthermore, sensitivity analysis revealed no heterogeneity in these findings.
CONCLUSIONS: Overall, our research suggests a potential causal link between metabolites and AGA, offering a more comprehensive insight into the pathogenesis of AGA and present additional strategies for prevention and treatment.
摘要：
背景：雄激素性脱发（AGA）是最常见的非瘢痕性脱发疾病。鉴于其在青春期的发病率和发病增加，AGA显著影响受影响个体的身体和心理健康。新出现的证据表明代谢物在AGA中的关键作用。本研究旨在使用孟德尔随机化（MR）分析阐明代谢物与AGA之间的因果关系。
方法：我们基于全基因组关联研究（GWAS）进行了双样本孟德尔随机化（TSMR）分析，以评估代谢物452对AGA的因果关系。用于推断因果效应的主要方法是逆方差加权(IVW)，由MR-Egger回归补充，加权中位数，以及MR多效性残差和离群值(MR-PRESSO)方法。此外,进行敏感性分析以确保结果的稳健性。在包含452个代谢物的GWAS数据集中选择单核苷酸多态性(SNP)作为工具变量(IVs)。
结果：值得注意的是，我们确定了Scyllo-肌醇和α-酮戊二酸是对抗AGA的最有效的保护因子，而血红素*和2-棕榈酰甘油磷酸胆碱*是AGA的重要危险因素。此外，敏感性分析显示，这些结果没有异质性。
结论：总体而言，我们的研究表明代谢物和AGA之间存在潜在的因果关系，提供更全面的了解AGA的发病机制，并提出其他预防和治疗策略。
方法：我们基于全基因组关联研究（GWAS）进行了双样本孟德尔随机化（TSMR）分析，以评估代谢物452对AGA的因果关系。用于推断因果效应的主要方法是逆方差加权(IVW)，由MR-Egger回归补充，加权中位数，以及MR多效性残差和离群值(MR-PRESSO)方法。此外,进行敏感性分析以确保结果的稳健性。在包含452个代谢物的GWAS数据集中选择单核苷酸多态性(SNP)作为工具变量(IVs)。
结果：值得注意的是，我们确定了Scyllo-肌醇和α-酮戊二酸是对抗AGA的最有效的保护因子，而血红素*和2-棕榈酰甘油磷酸胆碱*是AGA的重要危险因素。此外，敏感性分析显示，这些结果没有异质性。
结论：总体而言，我们的研究表明代谢物和AGA之间存在潜在的因果关系，提供更全面的了解AGA的发病机制，并提出其他预防和治疗策略。
