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骨密度与乳腺癌风险之间的因果关系:基于东亚人群的孟德尔随机化研究。

A causal relationship between bone mineral density and breast cancer risk: a mendelian randomization study based on east Asian population.

机构信息

First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.

Department of Endocrinology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.

出版信息

BMC Cancer. 2024 Sep 14;24(1):1148. doi: 10.1186/s12885-024-12908-0.

DOI:10.1186/s12885-024-12908-0
PMID:39277718
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11401392/
Abstract

BACKGROUND

Breast cancer (BC) poses significant burdens on women globally. While past research suggests a potential link between bone mineral density (BMD) and BC risk, findings remain inconsistent. Our study aims to elucidate the causal relationship between BMD and BC in East Asians using bidirectional Mendelian randomization (MR).

METHODS

Genetic association data for bone mineral density T-scores (BMD-T) and Z-scores (BMD-Z) (Sample size = 92,615) and BC from two different sources (Sample size1 = 98,283; Sample size2 = 79,550) were collected from publicly available genome-wide association studies (GWAS). Single-nucleotide polymorphisms (SNPs) associated with BMD-T and BMD-Z as phenotype-related instrumental variables (IVs) were used, with BC as the outcome. As the primary means of causal inference, the inverse variance weighted (IVW) approach was employed. Heterogeneity analysis was conducted using Cochran's Q test, while MR-Egger regression analysis was implemented to assess the pleiotropic effects of the IVs. Sensitivity analyses were performed using methods such as MR-Egger, weighted median, and weighted mode to analyze the robustness and reliability of the results. The MR-PRESSO method and the RadialMR were used to detect and remove outliers. The PhenoScanner V2 website was utilized to exclude confounding factors shared between BMD and BC. Besides, the Bonferroni correction was also used to adjust the significance threshold. Then, the meta-analysis method was applied to combine the MR analysis results from the two BC sources. Finally, a reverse MR analysis was conducted.

RESULTS

The results of the IVW method were consolidated through meta-analysis, revealing a positive correlation between genetically predicted BMD-T ([Formula: see text], [Formula: see text], [Formula: see text]) and BMD-Z ([Formula: see text],[Formula: see text], [Formula: see text]) with increased BC risk. The Cochran's [Formula: see text] test and MR-Egger regression suggested that neither of these causal relationships was affected by heterogeneity or horizontal pleiotropy. The sensitivity analyses supported the IVW results, indicating the robustness of the findings. Reverse MR analysis showed no causal relationship between BC and BMD.

CONCLUSION

Our MR study results provide evidence for the causal relationship between BMD and BC risk in East Asian populations, suggesting that BMD screening is of great significance in detecting and preventing BC.

摘要

背景

乳腺癌(BC)在全球范围内给女性带来了巨大负担。虽然过去的研究表明骨密度(BMD)与 BC 风险之间存在潜在联系,但研究结果仍不一致。我们的研究旨在使用双向孟德尔随机化(MR)来阐明东亚人群中 BMD 与 BC 之间的因果关系。

方法

从两个不同来源(样本量 1=98283;样本量 2=79550)收集了与骨矿物质密度 T 评分(BMD-T)和 Z 评分(BMD-Z)相关的遗传关联数据(样本量=92615)和 BC。使用与 BMD-T 和 BMD-Z 相关的单核苷酸多态性(SNP)作为表型相关的工具变量(IVs),并将 BC 作为结果。作为因果推断的主要手段,采用了逆方差加权(IVW)方法。使用 Cochran's Q 检验进行异质性分析,同时使用 MR-Egger 回归分析评估 IVs 的偏倚效应。使用 MR-Egger、加权中位数和加权模式等方法进行敏感性分析,以分析结果的稳健性和可靠性。使用 MR-PRESSO 方法和 RadialMR 检测和去除异常值。使用 PhenoScanner V2 网站排除 BMD 和 BC 之间共享的混杂因素。此外,还使用 Bonferroni 校正调整显著性阈值。然后,应用荟萃分析方法将来自两个 BC 来源的 MR 分析结果合并。最后,进行了反向 MR 分析。

结果

通过荟萃分析整合 IVW 方法的结果表明,遗传预测的 BMD-T([Formula: see text],[Formula: see text],[Formula: see text])和 BMD-Z([Formula: see text],[Formula: see text],[Formula: see text])与 BC 风险增加呈正相关。Cochran's [Formula: see text]检验和 MR-Egger 回归表明,这些因果关系都不受异质性或水平偏倚的影响。敏感性分析支持 IVW 结果,表明结果的稳健性。反向 MR 分析显示 BC 与 BMD 之间没有因果关系。

结论

我们的 MR 研究结果为东亚人群中 BMD 与 BC 风险之间的因果关系提供了证据,表明 BMD 筛查在检测和预防 BC 方面具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/0b5f181253b4/12885_2024_12908_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/18726d8480c2/12885_2024_12908_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/422fdfc1eadf/12885_2024_12908_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/0ceb11c5cea1/12885_2024_12908_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/b564a82c4569/12885_2024_12908_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/e8a7216e1e90/12885_2024_12908_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/b318790a6dbf/12885_2024_12908_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/0b5f181253b4/12885_2024_12908_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/18726d8480c2/12885_2024_12908_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/422fdfc1eadf/12885_2024_12908_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/0ceb11c5cea1/12885_2024_12908_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/b564a82c4569/12885_2024_12908_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/e8a7216e1e90/12885_2024_12908_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/b318790a6dbf/12885_2024_12908_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca86/11401392/0b5f181253b4/12885_2024_12908_Fig7_HTML.jpg

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