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Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy

PURPOSE: A 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 complicati...

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Autores principales: Sugawara, Junichi, Ochi, Daisuke, Yamashita, Riu, Yamauchi, Takafumi, Saigusa, Daisuke, Wagata, Maiko, Obara, Taku, Ishikuro, Mami, Tsunemoto, Yoshiki, Harada, Yuki, Shibata, Tomoko, Mimori, Takahiro, Kawashima, Junko, Katsuoka, Fumiki, Igarashi-Takai, Takako, Ogishima, Soichi, Metoki, Hirohito, Hashizume, Hiroaki, Fuse, Nobuo, Minegishi, Naoko, Koshiba, Seizo, Tanabe, Osamu, Kuriyama, Shinichi, Kinoshita, Kengo, Kure, Shigeo, Yaegashi, Nobuo, Yamamoto, Masayuki, Hiyama, Satoshi, Nagasaki, Masao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398744/
https://www.ncbi.nlm.nih.gov/pubmed/30782942
http://dx.doi.org/10.1136/bmjopen-2018-025939
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author Sugawara, Junichi
Ochi, Daisuke
Yamashita, Riu
Yamauchi, Takafumi
Saigusa, Daisuke
Wagata, Maiko
Obara, Taku
Ishikuro, Mami
Tsunemoto, Yoshiki
Harada, Yuki
Shibata, Tomoko
Mimori, Takahiro
Kawashima, Junko
Katsuoka, Fumiki
Igarashi-Takai, Takako
Ogishima, Soichi
Metoki, Hirohito
Hashizume, Hiroaki
Fuse, Nobuo
Minegishi, Naoko
Koshiba, Seizo
Tanabe, Osamu
Kuriyama, Shinichi
Kinoshita, Kengo
Kure, Shigeo
Yaegashi, Nobuo
Yamamoto, Masayuki
Hiyama, Satoshi
Nagasaki, Masao
author_facet Sugawara, Junichi
Ochi, Daisuke
Yamashita, Riu
Yamauchi, Takafumi
Saigusa, Daisuke
Wagata, Maiko
Obara, Taku
Ishikuro, Mami
Tsunemoto, Yoshiki
Harada, Yuki
Shibata, Tomoko
Mimori, Takahiro
Kawashima, Junko
Katsuoka, Fumiki
Igarashi-Takai, Takako
Ogishima, Soichi
Metoki, Hirohito
Hashizume, Hiroaki
Fuse, Nobuo
Minegishi, Naoko
Koshiba, Seizo
Tanabe, Osamu
Kuriyama, Shinichi
Kinoshita, Kengo
Kure, Shigeo
Yaegashi, Nobuo
Yamamoto, Masayuki
Hiyama, Satoshi
Nagasaki, Masao
author_sort Sugawara, Junichi
collection PubMed
description PURPOSE: A 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. PARTICIPANTS: Pregnant 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 DATE: Study 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 PLANS: Lifelog 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.
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spelling pubmed-63987442019-03-20 Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy Sugawara, Junichi Ochi, Daisuke Yamashita, Riu Yamauchi, Takafumi Saigusa, Daisuke Wagata, Maiko Obara, Taku Ishikuro, Mami Tsunemoto, Yoshiki Harada, Yuki Shibata, Tomoko Mimori, Takahiro Kawashima, Junko Katsuoka, Fumiki Igarashi-Takai, Takako Ogishima, Soichi Metoki, Hirohito Hashizume, Hiroaki Fuse, Nobuo Minegishi, Naoko Koshiba, Seizo Tanabe, Osamu Kuriyama, Shinichi Kinoshita, Kengo Kure, Shigeo Yaegashi, Nobuo Yamamoto, Masayuki Hiyama, Satoshi Nagasaki, Masao BMJ Open Obstetrics and Gynaecology PURPOSE: A 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. PARTICIPANTS: Pregnant 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 DATE: Study 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 PLANS: Lifelog 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. BMJ Publishing Group 2019-02-09 /pmc/articles/PMC6398744/ /pubmed/30782942 http://dx.doi.org/10.1136/bmjopen-2018-025939 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Obstetrics and Gynaecology
Sugawara, Junichi
Ochi, Daisuke
Yamashita, Riu
Yamauchi, Takafumi
Saigusa, Daisuke
Wagata, Maiko
Obara, Taku
Ishikuro, Mami
Tsunemoto, Yoshiki
Harada, Yuki
Shibata, Tomoko
Mimori, Takahiro
Kawashima, Junko
Katsuoka, Fumiki
Igarashi-Takai, Takako
Ogishima, Soichi
Metoki, Hirohito
Hashizume, Hiroaki
Fuse, Nobuo
Minegishi, Naoko
Koshiba, Seizo
Tanabe, Osamu
Kuriyama, Shinichi
Kinoshita, Kengo
Kure, Shigeo
Yaegashi, Nobuo
Yamamoto, Masayuki
Hiyama, Satoshi
Nagasaki, Masao
Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title_full Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title_fullStr Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title_full_unstemmed Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title_short Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
title_sort maternity log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
topic Obstetrics and Gynaecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398744/
https://www.ncbi.nlm.nih.gov/pubmed/30782942
http://dx.doi.org/10.1136/bmjopen-2018-025939
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