A prospective multicenter birth cohort in China: pregnancy health atlas

IF 7.7 1区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH European Journal of Epidemiology Pub Date : 2024-11-15 DOI:10.1007/s10654-024-01157-x
Si Zhou, Niya Zhou, Hanbo Zhang, Wenzhi Yang, Qingsong Liu, Lianshuai Zheng, Yuting Xiang, Dan Zheng, Yan Zhou, Siyi Wang, Danling Cheng, Jun He, Hong Wang, Wenbin Zhang, Liping Guan, Qiaoling Geng, Shihao Zhou, Hongbo Zhai, Hua Jin, Fei Hou, Shuzhen Wu, Jie Gao, Jing Yi, Luming Sun, Fengxiang Wei, Jianguo Zhang, Lei Yu, Xiao Yang, Leilei Wang, Lijian Zhao, Hongbo Qi
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Abstract

The China Prospective Multi-Center Birth Cohort Study was launched in 2022. In collaboration with medical centers in 12 cities, it aims to establish a high-quality, multidimensional cohort comprising 20,000 natural pregnancy and assisted reproductive families. As of June 26, 2024, 12,911 pregnant women have participated in this study, and 161,122 biological samples have been collected. These samples cover four critical periods (early pregnancy, mid-pregnancy, late pregnancy, and postpartum) and comprise 10 different types such as serum, plasma, and urine. The study has collected comprehensive information from early pregnancy to newborns. The participants have an average age of 29.76 years, an average height of 160.46 cm, an average pre-pregnancy BMI of 23.11, and an average BMI of 27.25 before delivery. The cohort includes individuals from 26 ethnic groups, with 25 minority groups comprising 5.03% of the population. Guizhou Province exhibits the highest percentage of ethnic minorities at 24.96% and Guangdong Province owns the highest proportion (12.22%) of women with two or more children. The prevalence of thalassemia in Guangdong Province is seven times higher than in other provinces. Among pregnant women over 35 years old, the prevalence of gestational diabetes mellitus is twice that of women under 35. Additionally, the prevalence of preeclampsia in women with assisted reproductive pregnancies is more than twice that of those with natural pregnancies. The study extensively collected diverse data and biological samples, making this cohort an ideal candidate for DOHaD field and multi-omics research.

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中国前瞻性多中心出生队列:孕期健康图谱
中国前瞻性多中心出生队列研究于 2022 年启动。该研究与 12 个城市的医疗中心合作,旨在建立一个由 20 000 个自然妊娠和辅助生殖家庭组成的高质量、多维度队列。截至 2024 年 6 月 26 日,已有 12911 名孕妇参与了这项研究,收集了 161122 份生物样本。这些样本涵盖四个关键时期(孕早期、孕中期、孕晚期和产后),包括血清、血浆和尿液等 10 种不同类型。研究收集了从孕早期到新生儿的全面信息。参与者的平均年龄为 29.76 岁,平均身高为 160.46 厘米,孕前平均体重指数为 23.11,产前平均体重指数为 27.25。人群包括 26 个民族,其中 25 个少数民族占总人口的 5.03%。贵州省的少数民族比例最高,为 24.96%,广东省拥有两个或两个以上孩子的妇女比例最高(12.22%)。广东省的地中海贫血患病率是其他省份的 7 倍。在 35 岁以上的孕妇中,妊娠糖尿病的患病率是 35 岁以下孕妇的两倍。此外,辅助生殖妊娠妇女的子痫前期发病率是自然妊娠妇女的两倍多。该研究广泛收集了各种数据和生物样本,使该队列成为 DOHaD 实地和多组学研究的理想候选对象。
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来源期刊
European Journal of Epidemiology
European Journal of Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
21.40
自引率
1.50%
发文量
109
审稿时长
6-12 weeks
期刊介绍: The European Journal of Epidemiology, established in 1985, is a peer-reviewed publication that provides a platform for discussions on epidemiology in its broadest sense. It covers various aspects of epidemiologic research and statistical methods. The journal facilitates communication between researchers, educators, and practitioners in epidemiology, including those in clinical and community medicine. Contributions from diverse fields such as public health, preventive medicine, clinical medicine, health economics, and computational biology and data science, in relation to health and disease, are encouraged. While accepting submissions from all over the world, the journal particularly emphasizes European topics relevant to epidemiology. The published articles consist of empirical research findings, developments in methodology, and opinion pieces.
期刊最新文献
Differential risk of healthcare workers versus the general population during outbreak, war and pandemic crises The Jiangsu Biobank for the Prevention and Control of Diabetes (JBPCD) in China How to mitigate selection bias in COVID-19 surveys: evidence from five national cohorts A prospective multicenter birth cohort in China: pregnancy health atlas Machine learning in causal inference for epidemiology
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