Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis.

IF 0.4 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Statistics in Biosciences Pub Date : 2021-12-01 Epub Date: 2021-03-21 DOI:10.1007/s12561-021-09305-7
Yu-Bo Wang, Cuilin Zhang, Zhen Chen
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引用次数: 0

Abstract

Growing evidence supports a positive association between childhood obesity and chronic diseases in later life. It is also suggested that childhood obesity is more prevalent for children born from pregnancies complicated by metabolic disorders such as gestational diabetes, and can be related to maternal dietary factors during gestation. Extending conventional analyses that report only the marginal associations within non-causal mediation frameworks, we present mediation analysis in the case of multiple exposures and multiple mediators using a regularized two-stage approach. By placing shrinkage priors on each parameter relating to direct and indirect effects, a parsimonious model can be obtained, and consequently, the most relevant pathways will be selected to inform the development of efficient prevention programs. We apply this method to data from the Danish site of the Diabetes & Women's Health Study, Danish National Birth Cohort (DNBC), and find 6 significant maternal risk factors either directly or indirectly affecting childhood body mass index z score at age 7. Simulations with data-generating mechanisms similar to the DNBC data demonstrate good performance of the proposed model.

母亲饮食与儿童肥胖的代际关联:贝叶斯正则中介分析
越来越多的证据支持儿童肥胖与晚年慢性病之间的正相关。研究还表明,患有代谢紊乱(如妊娠糖尿病)的孕妇所生的儿童肥胖更为普遍,这可能与孕妇妊娠期间的饮食因素有关。扩展仅报告非因果中介框架内边缘关联的传统分析,我们使用正则化的两阶段方法在多重暴露和多重中介的情况下进行中介分析。通过对与直接和间接影响相关的每个参数设置收缩先验,可以获得一个简约的模型,因此,将选择最相关的途径,以告知有效预防方案的发展。我们将此方法应用于丹麦糖尿病与妇女健康研究网站丹麦国家出生队列(DNBC)的数据,发现6个显著的母亲风险因素直接或间接影响儿童7岁时的体重指数z得分。用与DNBC数据相似的数据生成机制进行了仿真,结果表明该模型具有良好的性能。
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来源期刊
Statistics in Biosciences
Statistics in Biosciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.00
自引率
0.00%
发文量
28
期刊介绍: Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science. SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
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