Showing 261 - 280 results of 299 for search '"biobank"', query time: 0.07s Refine Results
  1. 261

    Neurotrophin-3 as a mediator in the link between PM2.5 exposure and psychiatric disorders: A Mendelian randomization study by Yuan Zhang, Wei Wang, Xuening Zhang, Ran Jing, Xin Wen, Peng Xiao, Xinjie Liu, Zengle Zhao, Tongmin Chang, Yufei Li, Wen Liu, Chenxi Sun, Xiaorong Yang, Lejin Yang, Ming Lu

    Published 2025-01-01
    “…Methods: We used genome-wide association study datasets from the UK Biobank and Psychiatric Genomics Consortium to systematically investigate the causal relationship between PM2.5 and nine common psychiatric disorders using two-sample Mendelian randomization (TSMR) methods. …”
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  2. 262

    Can social adversity and mental, physical and oral multimorbidity form a syndemic? A concept and protocol paper by Easter Joury, Easter Joury, Eliana Nakhleh, Ed Beveridge, Derek Tracy, Derek Tracy, Derek Tracy, Derek Tracy, Ellie Heidari, Ellie Heidari, David Shiers, David Shiers, David Shiers, Silke Vereeken, Emily Peckham, Simon Gilbody, Simon Gilbody, Jayati Das-Munshi, Farida Fortune, Farida Fortune, Vishal R. Aggarwal, Masuma Mishu, Masuma Mishu, Joseph Firth, Joseph Firth, Kamaldeep Bhui, Kamaldeep Bhui, Kamaldeep Bhui, Kamaldeep Bhui

    Published 2025-01-01
    “…Thus, the present project aimed to (i) test for syndemic interactions between social adversity (socioeconomic adversity and traumatic events) and mental, physical and oral multimorbidity using the syndemic theoretical framework; and (ii) determine whether the syndemic relationships vary by age, sex and ethnicity.MethodsData from three large-scale population-based databases: UK BioBank, US National Health and Nutrition Examination Survey (NHANES) and the Research with East London Adolescents Community Health Survey (RELACHS) will be analysed. …”
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  3. 263

    Association of accelerated phenotypic aging, genetic risk, and lifestyle with progression of type 2 diabetes: a prospective study using multi-state model by Lulu Pan, Yahang Liu, Chen Huang, Yifang Huang, Ruilang Lin, Kecheng Wei, Ye Yao, Guoyou Qin, Yongfu Yu

    Published 2025-02-01
    “…Methods We conducted a prospective cohort study that included 376,083 adults free of T2D and diabetes-related events at baseline in UK Biobank. PhenoAgeAccel > 0 and ≤ 0 were defined as biologically older and younger than chronological age. …”
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  4. 264

    Early-life tobacco smoke elevating later-life osteoporosis risk: Mediated by telomere length and interplayed with genetic predisposition by Dongsheng Di, Haolong Zhou, Zhangbo Cui, Jianli Zhang, Qian Liu, Tingting Yuan, Tingting Zhou, Xiao Luo, Danyang Ling, Qi Wang

    Published 2025-02-01
    “…Methods: Data on in utero tobacco smoke exposure (IUTSE) status and age of tobacco use initiation from the UK Biobank were used to estimate early-life tobacco smoke exposure. …”
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  5. 265
  6. 266

    Accelerometer-measured physical activity, sedentary behavior, and incidence of macrovascular and microvascular events in individuals with type 2 diabetes mellitus and prediabetes by Yannis Yan Liang, Yu He, Piao Huang, Hongliang Feng, Haiteng Li, Sizhi Ai, Jing Du, Huachen Xue, Yaping Liu, Jun Zhang, Lu Qi, Jihui Zhang

    Published 2025-12-01
    “…Methods: This study included 11,474 individuals with T2DM and prediabetes from the UK Biobank. PA, including total PA, moderate-to-vigorous intensity PA (MVPA), light-intensity PA (LPA), and SB, were measured by accelerometers over 7 days. …”
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  7. 267

    Relationship between physical activity level, leisure sedentary time, and frailty among residents aged 30 – 79 years in Wuzhong district, Suzhou city: a baseline surveillance data... by Xikang FAN, Jiang HUA, Jian SU, Hao YU, Yan LU, Yujie HUA, Jianrong JIN, Pei PEI, Dianjianyi SUN, Canqing YU, Jun LYU, Jinyi ZHOU, Ran TAO

    Published 2024-12-01
    “…MethodsData were collected from 53 269 residents aged 30 – 79 years in Wuzhong district, Suzhou city, who participated in the baseline survey of the China Kadoorie Biobank (CKB) from November 2004 to January 2008. …”
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  8. 268
  9. 269

    Severe obesity, high inflammation, insulin resistance with risks of all-cause mortality and all-site cancers, and potential modification by healthy lifestyles by Qianyun Jin, Siwen Liu, Yunmeng Zhang, Yuting Ji, Jie Wu, Hongyuan Duan, Xiaomin Liu, Jingjing Li, Yacong Zhang, Zhangyan Lyu, Fangfang Song, Fengju Song, Hua Li, Yubei Huang

    Published 2025-01-01
    “…The independent and joint associations of severe obesity (body mass index ≥ 35 m/kg2), inflammation (C-reactive protein > 10 mg/L and systemic inflammation markers > 9th decile), and IR surrogates with the risks of all-cause mortality and all-site cancers, were evaluated in 163,008 participants from the UK Biobank cohort. Further analyses were conducted to investigate how these associations were modified by lifestyle. …”
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  10. 270

    Healthcare and the Effect of Technology : developments, challenges and advancements / by Kabene, Stefane M

    Published 2010
    Table of Contents: “…Shah, Kaveepan Lertwachara, Anteneh Ayanso -- Biobanking : justice, social consensus, and the marginalized / Robert J. …”
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  11. 271

    Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model de... by Hayeon Lee, Seung Ha Hwang, Seoyoung Park, Yunjeong Choi, Sooji Lee, Jaeyu Park, Yejun Son, Hyeon Jin Kim, Soeun Kim, Jiyeon Oh, Lee Smith, Damiano Pizzol, Sang Youl Rhee, Hyunji Sang, Jinseok Lee, Dong Keon Yon

    Published 2025-02-01
    “…The Korean cohort (NHIS-NSC cohort; discovery cohort; n = 973,303), conducted between 1 January, 2002 and 31 December, 2013, was used for training and internal validation, whereas the Japanese cohort (JMDC cohort; validation cohort A; n = 12,143,715) and UK cohort (UK Biobank; validation cohort B; n = 416,656) were used for external validation. …”
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  12. 272

    A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context by Olivia Murrin, Ninon Mounier, Bethany Voller, Linus Tata, Carlos Gallego-Moll, Albert Roso-Llorach, Lucía A. Carrasco-Ribelles, Chris Fox, Louise M. Allan, Ruby M. Woodward, Xiaoran Liang, Jose M. Valderas, Sara M. Khalid, Frank Dudbridge, Sally E. Lamb, Mary Mancini, Leon Farmer, Kate Boddy, Jack Bowden, David Melzer, Timothy M. Frayling, Jane A.H. Masoli, Luke C. Pilling, Concepción Violán, João Delgado

    Published 2025-03-01
    “…Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co-occurrence of pairs of conditions in the primary care data. …”
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  13. 273
  14. 274

    Prevalence of chronic kidney disease among Chinese adults with diabetes: a nationwide population-based cross-sectional studyResearch in context by Weiping Jia, Rong Yu, Limin Wang, Dalong Zhu, Lixin Guo, Jianping Weng, Hong Li, Mei Zhang, Xiaoqi Ye, Zhiguang Zhou, Dajin Zou, Qiuhe Ji, Xiaohui Guo, Yinan Zhang, Dong Lang, Jiarui Wu, Jing Wu, Xuhong Hou, Xiaohui Guo, Kai Wu, Liming Chen, Demin Liu, Guangyao Song, Linyi Shu, Jing Yang, Yan Wang, Dongmei Li, Qiansha Guo, Ling Li, Na Wu, Yadong Sun, Huifang Cheng, Hongyu Kuang, Huijuan Zhang, Weiping Jia, Xuhong Hou, Dalong Zhu, Jian Zhu, Hong Li, Fenping Zheng, Qiu Zhang, Honglin Hu, Gang Chen, Xingquan Yang, Xiaoyang Lai, Jianping Liu, Li Chen, Ming Dong, Zhigang Zhao, Jian Liu, Guangda Xiang, Junxia Zhang, Zhiguang Zhou, Jian Peng, Jianping Weng, Longyi Zeng, Yuzhen Liang, Guoqiao Li, Kaining Chen, Leweihua Lin, Qifu Li, Qingfeng Cheng, Xingwu Ran, Dawei Chen, Hong Li, Xin Nian, Lihui Yang, Shuyou Meng, Jing Xu, Junhong Long, Jing Liu, Qi Zhang, Qingxiang Dai, Xiaomin Xie, Guirong Bai, Jun Li, Tao Li, Zhong Dong, Tao Zhang, Sijia Liu, Yong Yang, Kun Geng, Wenlong Zheng, Zhihong Li, Hui Wang, Zuojun Wu, Miao Wang, Jixin Sun, Jianwei Zhou, Jun An, Huaqing Shen, Yanfang Li, Zeping Ren, Xiuli Xue, Shaohui Jia, Yongping Zheng, Jiaoxia Li, Yonggang Qian, Ping Ma, Yan Su, Liang Cao, Xuyi Yang, Guowei Pan, Guangzhi Pan, Ling Jin, Quanfu Yu, Yunfei Liu, Yingli Zhu, Zhifang Cheng, Jun Yang, Chundi Jia, Qing Wang, Shichun Yan, Jingyang Huang, Yumei Chi, Li Zhang, Ruifa Pang, Yan Shi, Yan Lu, Yiling Wu, Jie Yu, Chunxiang Wu, Jinyi Zhou, Yunxia Wu, Ning Zhang, Jing Sun, Xiumei Tian, Jieming Zhong, Jingying Chen, Kaixu Xie, Lingjuan Fu, Xiaohua Wang, Yeji Chen, Ling Zeng, Zhenqian Cao, Maomin Yang, Biao Hu, Wenling Zhong, Qingyan Liu, Zhiding Huang, Qingbin Lai, Yuyu Zhang, Liping Zhu, Yujing Zhao, Wuming Tao, Yonghai Tu, Lin Zhou, Xiaolei Guo, Benzheng Chai, Liping Ding, Yuewei Zou, Dongzhi Li, Li Hua, Shixian Feng, Jianli Guo, Wanguang Liu, Qingxiang Li, Yunzhi Zheng, Minli Xu, Lan Zhang, Chunbo Li, Yingchao Wan, Chi Hu, Shengping Xu, Biyun Chen, Huilin Liu, Xiaojia Bian, Wenwei Chen, Honghua Li, Yanjun Xu, Fan Weng, Hong Wu, Weijin Zhang, Liankai Zhu, Jun Meng, Jin Yang, Yongsong Zeng, Gaoke Zhai, Lingfeng Qin, Yi Lu, Zhenwang Fu, Shukuan Wu, Caigang Li, Na Wang, Lianfen Chen, Xianbin Ding, Qi Zhou, Yiling Xie, Rui Chen, Dongsheng Yang, Ying Deng, Youping Hu, Bo Huang, Ping Wang, Peng Cai, Changyan Peng, Xiaofang Chen, Ling Li, Liang Xiao, Chenglin Wang, Chaogang Tan, Daobin Chen, Min Chen, Mingfang Qin, Zhaomin Lei, Jianzhi Qiu, Wei Yu, Yunfei Li, Guoxia Bai, Ying Wang, Lin Qiu, Ning Wang, Xudong Zhao, Ani Yan, Jianfei Liu, Tingcai Wang, Kaihua Hao, Zaihong Zhang, Zhanqi Xian, Tenghuan Ma, Zhihua Xu, Yi Yang, Hongli Wang, Rong Li, Zhonggang Zhao, Caixia Fan, Purhati, Hongjun Xu, Liping Gao

    Published 2025-02-01
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  18. 278

    Umbilical Cord as Prospective Source for Mesenchymal Stem Cell-Based Therapy by Irina Arutyunyan, Andrey Elchaninov, Andrey Makarov, Timur Fatkhudinov

    Published 2016-01-01
    “…Prospects for the use of mesenchymal stem cells, derived from the umbilical cord, in cell transplantation are associated with the need for specialized biobanking and transplant standardization criteria.…”
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