Differential sexual network connectivity offers a parsimonious explanation for population-level variations in the prevalence of bacterial vaginosis: a data-driven, model-supported hypothesis
Differential sexual network connectivity offers a parsimonious explanation for population-level variations in the prevalence of bacterial vaginosis: a data-driven, model-supported hypothesis
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DOI：
10.1186/s12905-018-0703-0
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发表时间：
2019-01-10
影响因子：
2.5
通讯作者：
Brotman, Rebecca M.
中科院分区：
文献类型：
--
作者：
Kenyon, Chris R.;Delva, Wim;Brotman, Rebecca M.
背景细菌性阴道病（BV）的患病率和阴道微生物群类型在世界各地的不同人群之间存在显著差异。了解这些差异的基础是什么是重要的，因为与BV相关的高多样性微生物群与不良妊娠结局有关，并增加了对性传播感染的易感性和传播。我们认为：1）夫妇水平的数据表明，BV相关细菌可以通过性传播，因此预计高性网络连接将促进BV相关细菌的传播。流行病学研究发现，网络连通性指标与BV患病率呈正相关：（2）BV患病率与STI发病率/患病率之间的关系可以用网络连通性差异来粗略解释; 3）来自其他哺乳动物的研究普遍支持网络连通性和高多样性阴道微生物群之间的关联。结论为了验证这一假设，我们提出了一个经验和模拟为基础的研究设计相结合。
BackgroundThe prevalence of bacterial vaginosis (BV) and vaginal microbiota types varies dramatically between different populations around the world. Understanding what underpins these differences is important, as high-diversity microbiotas associated with BV are implicated in adverse pregnancy outcomes and enhanced susceptibility to and transmission of sexually transmitted infections.Main textWe hypothesize that these variations in the vaginal microbiota can, in part, be explained by variations in the connectivity of sexual networks. We argue: 1) Couple-level data suggest that BV-associated bacteria can be sexually transmitted and hence high sexual network connectivity would be expected to promote the spread of BV-associated bacteria. Epidemiological studies have found positive associations between indicators of network connectivity and the prevalence of BV; 2) The relationship between BV prevalence and STI incidence/prevalence can be parsimoniously explained by differential network connectivity; 3) Studies from other mammals are generally supportive of the association between network connectivity and high-diversity vaginal microbiota.ConclusionTo test this hypothesis, we propose a combination of empirical and simulation-based study designs.