采用两样本孟德尔随机化（MR）方法探究BMI增加和4种女性特征性癌症之间的因果关系。

通过GWAS数据库获取BMI和4种女性特征性癌症的相关数据。按照假设筛选单核苷酸多态性（SNPs）作为工具变量（IVs），使用PhenoScanner法去除混杂因素相关的SNP，分别采用包括逆方差加权法（IVW）在内的5种孟德尔随机化分析方法进行两样本孟德尔随机化分析，采用IVW和MR-Egger法进行Cochran Q和Rochra Q异质性检验，用Egger-intercept法进行多效性检验、逐个剔除检验进行敏感性分析，并计算F值以评估是否存在弱IVs偏倚。

遗传预测的BMI的增加与乳腺癌风险降低（OR=0.648，95%CI：0.535-0.783，P=7.74e-06）和子宫内膜癌风险增加（OR=1.534，95%CI：1.195-1.970，P=7.84e-04）有因果关系。

BMI的增加可能会降低女性乳腺癌发生的风险，增加子宫内膜癌发生的风险，但没有充分证据表明BMI的增加对本文研究的其他女性特征性癌症的发生风险存在明显影响，还需要进一步的研究来阐明结果。

This study employs a two-sample Mendelian randomization (MR) approach to investigate the causal relationship between increased Body Mass Index (BMI) and four characteristic female cancers.

BMI and data pertaining to the four characteristic female cancers were obtained from the GWAS database. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) based on assumptions. The PhenoScanner method was utilized to eliminate SNPs associated with confounding factors. Five Mendelian randomization analysis methods were employed for two-sample Mendelian randomization analysis, including inverse variance-weighted (IVW). Cochran Q and Rücker Q heterogeneity tests were conducted using IVW and MR-Egger methods. The egger-intercept method was employed for pleiotropy testing, and stepwise exclusion testing was used for sensitivity analysis. F-values were calculated to assess the presence of weak IVs bias.

Genetically predicted increase in BMI was causally associated with reduced risk of breast cancer (OR=0.648, 95% CI: 0.535-0.783, P=7.74e-06) and increased risk of endometrial cancer (OR=1.534, 95% CI: 1.195-1.970, P=7.84e-04).

BMI may potentially decrease the risk of female breast cancer while increasing the risk of endometrial cancer. There is inadequate evidence to indicate a significant impact of increased BMI on the occurrence risk of other characteristic female cancers studied. Further research is warranted to elucidate these findings.