Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
复制标题
产妇日志研究：纵向生命日志监测和多组学分析，用于早期预测复杂妊娠
DOI：
10.1136/bmjopen-2018-025939
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发表时间：
2019
期刊：
影响因子：
2.9
通讯作者：
Nagasaki M
中科院分区：
文献类型：
--
作者：
Sugawara J;Ochi D;Yamashita R;Yamauchi T;Saigusa D;Wagata M;Obara T;Ishikuro M;Tsunemoto Y;18名略;Hiyama S;Nagasaki M
一项针对孕妇的前瞻性队列研究--Maternity Log研究，旨在构建一个高分辨率的孕期生物信息学数据的时间进程参考目录，并探讨基因组和环境因素与妊娠并发症（如妊娠期高血压疾病、妊娠期糖尿病和早产）发生之间的关系，使用连续的生活方式监测结合基因组、转录组、蛋白质组、代谢组和微生物组的多组学数据。孕妇在日本仙台东北大学医院首次常规产前访视时招募，在2015年9月至2016年11月之间。在被邀请的合格妇女中，65.4%同意参加，共有302名妇女参加。入选标准为年龄≥20岁，能够使用日语智能手机上网。迄今为止的研究结果研究参与者上传了日常一般健康信息，包括睡眠质量，排便情况以及恶心，疼痛和子宫收缩的存在。参与者还使用多种家庭医疗设备收集生理数据，如体重、血压、心率和体温。每个生活日志项目的平均上传率从67.4%（胎动）到85.3%（体力活动）不等，数据点总数超过600万。收集孕妇血浆、血清、尿液、唾液、牙菌斑和脐带血等生物样本进行多组学分析，未来将利用Lifelog和多组学数据构建一个高分辨率的妊娠时程参考目录。该参考目录将使我们能够发现多维表型和妊娠期新风险标志物之间的关系，以便将来个性化地早期预测妊娠并发症。
PurposeA prospective cohort study for pregnant women, the Maternity Log study, was designed to construct a time-course high-resolution reference catalogue of bioinformatic data in pregnancy and explore the associations between genomic and environmental factors and the onset of pregnancy complications, such as hypertensive disorders of pregnancy, gestational diabetes mellitus and preterm labour, using continuous lifestyle monitoring combined with multiomics data on the genome, transcriptome, proteome, metabolome and microbiome.ParticipantsPregnant women were recruited at the timing of first routine antenatal visits at Tohoku University Hospital, Sendai, Japan, between September 2015 and November 2016. Of the eligible women who were invited, 65.4% agreed to participate, and a total of 302 women were enrolled. The inclusion criteria were age ≥20 years and the ability to access the internet using a smartphone in the Japanese language.Findings to dateStudy participants uploaded daily general health information including quality of sleep, condition of bowel movements and the presence of nausea, pain and uterine contractions. Participants also collected physiological data, such as body weight, blood pressure, heart rate and body temperature, using multiple home healthcare devices. The mean upload rate for each lifelog item was ranging from 67.4% (fetal movement) to 85.3% (physical activity), and the total number of data points was over 6 million. Biospecimens, including maternal plasma, serum, urine, saliva, dental plaque and cord blood, were collected for multiomics analysis.Future plansLifelog and multiomics data will be used to construct a time-course high-resolution reference catalogue of pregnancy. The reference catalogue will allow us to discover relationships among multidimensional phenotypes and novel risk markers in pregnancy for the future personalised early prediction of pregnancy complications.