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Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics

机译:队列概况:LifeLines DEEP,一项在荷兰北部进行的前瞻性,总体人群队列研究:研究设计和基线特征

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Purpose There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects, and thereby contributing to systems epidemiology. Participants This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. In this unique subcohort, LifeLines DEEP, we included 1539 participants aged 18?years and older. Findings to date We collected additional blood (n=1387), exhaled air (n=1425) and faecal samples (n=1248), and elicited responses to gastrointestinal health questionnaires (n=1176) for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation for a wide range of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP. Future plans We have established a cohort of which multiple data levels allow for the integrative analysis of populations for translation of this information into biomarkers for disease, and which will offer new insights into disease mechanisms and prevention.
机译:目的迫切需要进行基于人群的前瞻性队列研究,因为它们会在疾病发作之前关注个体,从而开展能够识别生物标志物和改善疾病的作用的研究,从而有助于系统流行病学。参加者本文介绍了经过深入研究的荷兰LifeLines人群的设计和基线特征。在这个独特的子群体LifeLines DEEP中,我们包括1539名18岁以上的参与者。迄今为止的发现我们收集了更多的血液(n = 1387),呼出的空气(n = 1425)和粪便样本(n = 1248),并引起了对胃肠健康问卷的回答(n = 1176),以分析基因组,表观基因组,转录组,微生物组,代谢组和其他生物学水平。在这里,我们概述了LifeLines DEEP中的不同数据层,并介绍了研究人群的基线特征,包括食物摄入量和生活质量。我们还将描述LifeLines DEEP队列如何对广泛的表型结果进行遗传,基因组和代谢变异的详细研究。最后,我们检查了胃肠道健康的决定因素,LifeLines DEEP可以解决我们特别感兴趣的领域。未来计划我们已经建立了一个队列,该队列的多个数据级别允许对人群进行综合分析,以将这些信息转换为疾病的生物标记,这将为疾病机制和预防提供新的见识。

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