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Abstract：
OBJECTIVE: This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method.
METHODS: The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran\'s Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study.
RESULTS: By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC.
CONCLUSIONS: The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC\'s intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.
METHODS: The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran\'s Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study.
RESULTS: By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC.
CONCLUSIONS: The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC\'s intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.
摘要：
目的：本研究旨在使用双样本孟德尔随机（TSMR）方法和贝叶斯模型平均孟德尔随机（BMA-MR）方法来研究特定脂质与乳腺癌（BC）风险之间的关系。
方法：该研究分析了来自179个脂质粒的大规模GWAS数据集的数据，以评估不同分子亚型中脂质粒与BC风险之间的关系。TSMR被用来探索因果关系，同时进行BMA-MR方法以验证结果。这项研究通过Cochran的Q评估了异质性和水平多效性，MR-Egger截距测试,MR-PRESSO此外，我们进行了留一法敏感性分析,以评估个体单核苷酸多态性对MR研究的影响.
结果：通过检查179个脂质组特征作为暴露，BC作为结果，这项研究揭示了甘油磷脂的显著因果效应，鞘脂，和甘油脂对BC的风险。具体来说,对于雌激素受体阳性BC（ER+BC），磷脂酰胆碱(P<0.05)和磷脂酰肌醇(OR:0.916-0.966,P<0.05)在甘油磷脂中发挥显著作用,随着甘油酯的重要性（二酰基甘油（OR=0.923，P<0.001）和三酰基甘油，OR：0.894-0.960，P<0.05））。然而,本研究未观察到鞘脂对ER+BC的显著影响.在雌激素受体阴性BC（ER-BC）的情况下，不仅仅是甘油磷脂，鞘脂（OR=1.085，P=0.008），和甘油脂(OR=0.909，P=0.002)产生了影响，但也发现了甾醇的保护作用（OR：1.034-1.056，P<0.05）。在ER-BC中，甘油脂的突出度最小。磷脂酰乙醇胺（OR：1.091-1.119，P<0.05）是ER-BC的重要因果效应。
结论：研究结果表明，磷脂酰肌醇和甘油三酯水平降低了BC的风险，表明这些脂质分子具有潜在的保护作用。此外，这项研究阐明了BC复杂的脂质代谢途径，突出显示不同的脂质组结构变异，这些变异可能在不同的分子亚型中产生不同的影响。
方法：该研究分析了来自179个脂质粒的大规模GWAS数据集的数据，以评估不同分子亚型中脂质粒与BC风险之间的关系。TSMR被用来探索因果关系，同时进行BMA-MR方法以验证结果。这项研究通过Cochran的Q评估了异质性和水平多效性，MR-Egger截距测试,MR-PRESSO此外，我们进行了留一法敏感性分析,以评估个体单核苷酸多态性对MR研究的影响.
结果：通过检查179个脂质组特征作为暴露，BC作为结果，这项研究揭示了甘油磷脂的显著因果效应，鞘脂，和甘油脂对BC的风险。具体来说,对于雌激素受体阳性BC（ER+BC），磷脂酰胆碱(P<0.05)和磷脂酰肌醇(OR:0.916-0.966,P<0.05)在甘油磷脂中发挥显著作用,随着甘油酯的重要性（二酰基甘油（OR=0.923，P<0.001）和三酰基甘油，OR：0.894-0.960，P<0.05））。然而,本研究未观察到鞘脂对ER+BC的显著影响.在雌激素受体阴性BC（ER-BC）的情况下，不仅仅是甘油磷脂，鞘脂（OR=1.085，P=0.008），和甘油脂(OR=0.909，P=0.002)产生了影响，但也发现了甾醇的保护作用（OR：1.034-1.056，P<0.05）。在ER-BC中，甘油脂的突出度最小。磷脂酰乙醇胺（OR：1.091-1.119，P<0.05）是ER-BC的重要因果效应。
结论：研究结果表明，磷脂酰肌醇和甘油三酯水平降低了BC的风险，表明这些脂质分子具有潜在的保护作用。此外，这项研究阐明了BC复杂的脂质代谢途径，突出显示不同的脂质组结构变异，这些变异可能在不同的分子亚型中产生不同的影响。
