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将体力活动与乳腺癌联系起来:文本挖掘结果及系统综述三个潜在机制途径的方案。

Linking Physical Activity to Breast Cancer: Text Mining Results and a Protocol for Systematically Reviewing Three Potential Mechanistic Pathways.

机构信息

Cancer Epidemiology Division, Cancer Council Victoria, Victoria, Australia.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.

出版信息

Cancer Epidemiol Biomarkers Prev. 2022 Jan;31(1):11-15. doi: 10.1158/1055-9965.EPI-21-0435. Epub 2021 Oct 20.

Abstract

Epidemiologic research suggests that physical activity is associated with a reduced risk of breast cancer, but the causal nature of this link is not clear. Investigating mechanistic pathways can provide evidence of biological plausibility and improve causal inference. This project will examine three putative pathways (sex steroid hormones, insulin signaling, and inflammation) in a series of two-stage systematic reviews. Stage 1 used Text Mining for Mechanism Prioritisation (TeMMPo) to identify and prioritize relevant biological intermediates. Stage 2 will systematically review the findings from studies of (i) physical activity and intermediates and (ii) intermediates and breast cancer. Ovid MEDLINE, EMBASE, and SPORTDiscus will be searched using a combination of subject headings and free-text terms. Human intervention and prospective, observational studies will be eligible for inclusion. Meta-analysis will be performed where possible. Risk of bias will be assessed using the Cochrane Collaboration tool, or the ROBINS-I or ROBINS-E tool, depending on study type. Strength of evidence will be assessed using the GRADE system. In addition to synthesizing the mechanistic evidence that links physical activity with breast cancer risk, this project may also identify priority areas for future research and help inform the design and implementation of physical activity interventions..

摘要

流行病学研究表明,身体活动与降低乳腺癌风险有关,但这种关联的因果性质尚不清楚。研究机制途径可以提供生物学合理性的证据,并提高因果推断的能力。本项目将通过两阶段系统评价系列研究三个假定的途径(性激素、胰岛素信号和炎症)。第 1 阶段使用 Text Mining for Mechanism Prioritisation (TeMMPo) 来识别和优先考虑相关的生物学中间产物。第 2 阶段将系统地回顾关于(i)身体活动与中间产物和(ii)中间产物与乳腺癌的研究发现。将使用主题词和自由文本词的组合在 Ovid MEDLINE、EMBASE 和 SPORTDiscus 上进行搜索。人类干预和前瞻性观察研究将有资格入选。在可能的情况下,将进行荟萃分析。将使用 Cochrane 协作工具、ROBINS-I 或 ROBINS-E 工具评估偏倚风险,具体取决于研究类型。使用 GRADE 系统评估证据的强度。除了综合身体活动与乳腺癌风险之间的机制证据外,本项目还可能确定未来研究的优先领域,并有助于为身体活动干预措施的设计和实施提供信息。

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