Mao Jing, Gan Yanqiong, Tan Xinlin, He Yuhan, Jing Qiao, Shi Qi
Affiliated Hospital of North Sichuan Medical College, Nanchong, People's Republic of China.
Int J Womens Health. 2025 Feb 26;17:517-527. doi: 10.2147/IJWH.S500632. eCollection 2025.
High basophil count levels are associated with an increased risk of gestational diabetes mellitus (GDM). We used two-sample Mendelian randomisation (MR) to explore a potential causal relationship. It also aims to offer genetic evidence supporting the link between basophil count and the development of gestational diabetes mellitus while addressing the potential issues of confounding and reverse causality commonly encountered in observational studies.
We utilized publically accessible summary information obtained from genome-wide association studies (GWAS) for conducting a two-sample Mendelian randomization (TSMR) study. The major analysis method employed was inverse variance weighted (IVW), whereas the other four methods, namely weighted median method, MR-Egger regression, simple model and weighted model, were used as supplemental analyses. We also investigated the relationship between GDM and basophil count in the opposite direction using directional validation of MR analysis. Furthermore, the R package "ClusterProfiler" to conduct an analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms was used. Additionally, with the help of the STRING database, we have constructed a network of protein-protein interactions (PPIs).
The Inverse Variance Weighted (IVW) method revealed a significant causal association between basophil count and gestational diabetes mellitus (OR, 0.84; 95% CI; 0.74-0.96; P, 0.01). A sensitivity analysis was performed to assess the reliability of the results, indicating no indication of pleiotropy or heterogeneity, hence strengthening the validity of the findings. The reverse causation of GDM predisposition on basophil counts was not supported by the results of the directional validation of the MR analysis.
The results of this study showed a causal relationship between high basophil counts and increased risk of GDM but did not support a causal relationship between genetic susceptibility to GDM and basophil counts.
高嗜碱性粒细胞计数水平与妊娠期糖尿病（GDM）风险增加相关。我们采用两样本孟德尔随机化（MR）方法来探究潜在的因果关系。其目的还在于提供遗传证据，支持嗜碱性粒细胞计数与妊娠期糖尿病发生之间的联系，同时解决观察性研究中常见的混杂和反向因果关系等潜在问题。
我们利用从全基因组关联研究（GWAS）获得的公开汇总信息进行两样本孟德尔随机化（TSMR）研究。主要采用的分析方法是逆方差加权（IVW），而其他四种方法，即加权中位数法、MR-Egger回归、简单模型和加权模型，用作补充分析。我们还通过MR分析的方向性验证来研究GDM与嗜碱性粒细胞计数相反方向的关系。此外，使用R包“ClusterProfiler”对京都基因与基因组百科全书（KEGG）通路和基因本体（GO）术语进行分析。另外，借助STRING数据库，我们构建了蛋白质-蛋白质相互作用（PPI）网络。
逆方差加权（IVW）方法显示嗜碱性粒细胞计数与妊娠期糖尿病之间存在显著的因果关联（OR，0.84；95%CI：0.74 - 0.96；P，0.01）。进行了敏感性分析以评估结果的可靠性，表明没有多效性或异质性迹象，从而加强了研究结果的有效性。MR分析的方向性验证结果不支持GDM易感性对嗜碱性粒细胞计数的反向因果关系。
本研究结果表明高嗜碱性粒细胞计数与GDM风险增加之间存在因果关系，但不支持GDM遗传易感性与嗜碱性粒细胞计数之间的因果关系。
