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102. A Sequential Pattern Mining Approach to Tourist Movement: The Case of a Mega Event.

Author

Cheng, Mingming, Jin, Xin, Wang, Ying, Wang, Xiaowei, and Chen, Jinyan

Subjects
SEQUENTIAL pattern mining, SPECIAL events, INTERNATIONAL visitors, SOCIAL impact
Abstract

The movement of tourists has important economic and social implications for destination management. However, tracking and analyzing such movement can be a challenge both conceptually and methodologically. Using four different sequential pattern mining algorithms, this study investigates the movement of international visitors during the Gold Coast Commonwealth Games (GC2018) at the level of a specific destination through Twitter data. Results indicate that sequential pattern mining is a powerful technique to reveal complex travel patterns and provides insights into the potential associated destinations of visitors beyond the current point-to-point analysis. This approach can assist destination management and event organizers in identifying the event's contribution to tourists' local visitation. [ABSTRACT FROM AUTHOR]

Published
2023
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110. Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study

Author

Chan, Ka Hung, primary, Wright, Neil, additional, Xiao, Dan, additional, Guo, Yu, additional, Chen, Yiping, additional, Du, Huaidong, additional, Yang, Ling, additional, Millwood, Iona Y, additional, Pei, Pei, additional, Wang, Junzheng, additional, Turnbull, Iain, additional, Gilbert, Simon, additional, Avery, Daniel, additional, Kartsonaki, Christiana, additional, Yu, Canqing, additional, Chen, Junshi, additional, Lv, Jun, additional, Clarke, Robert, additional, Collins, Rory, additional, Peto, Richard, additional, Li, Liming, additional, Wang, Chen, additional, Chen, Zhengming, additional, Bennett, Derrick, additional, Boxall, Ruth, additional, Burgess, Sushila, additional, Chan, Peter Ka Hung, additional, Clarke, Johnathan, additional, Mohamed, Ahmed Edris, additional, Fry, Hannah, additional, Hill, Mike, additional, Im, Becky Pek Kei, additional, Iona, Andri, additional, Kakkoura, Maria, additional, Lam, Hubert, additional, Lin, Kuang, additional, Mazidi, Mohsen, additional, Morris, Sam, additional, Nie, Qunhua, additional, Pozarickij, Alfred, additional, Ryder, Paul, additional, Said, Saredo, additional, Schmidt, Dan, additional, Sherliker, Paul, additional, Stevens, Rebecca, additional, Walters, Robin, additional, Wang, Lin, additional, Yang, Xiaoming, additional, Yao, Pang, additional, Han, Xiao, additional, Hou, Can, additional, Xia, Qingmei, additional, Liu, Chao, additional, Chen, Naying, additional, Liu, Duo, additional, Tang, Zhenzhu, additional, Chen, Ningyu, additional, Jiang, Qilian, additional, Lan, Jian, additional, Li, Mingqiang, additional, Liu, Yun, additional, Meng, Fanwen, additional, Meng, Jinhuai, additional, Pan, Rong, additional, Qin, Yulu, additional, Wang, Ping, additional, Wang, Sisi, additional, Wei, Liuping, additional, Zhou, Liyuan, additional, Dong, Caixia, additional, Ge, Pengfei, additional, Ren, Xiaolan, additional, Li, Zhongxiao, additional, Mao, Enke, additional, Wang, Tao, additional, Zhang, Hui, additional, Zhang, Xi, additional, Chen, Jinyan, additional, Hu, Ximin, additional, Wang, Xiaohuan, additional, Guo, Zhendong, additional, Li, Huimei, additional, Li, Yilei, additional, Weng, Min, additional, Wu, Shukuan, additional, Yan, Shichun, additional, Zou, Mingyuan, additional, Zhou, Xue, additional, Guo, Ziyan, additional, Kang, Quan, additional, Li, Yanjie, additional, Yu, Bo, additional, Xu, Qinai, additional, Chang, Liang, additional, Fan, Lei, additional, Feng, Shixian, additional, Zhang, Ding, additional, Zhou, Gang, additional, Gao, Yulian, additional, He, Tianyou, additional, He, Pan, additional, Hu, Chen, additional, Sun, Huarong, additional, Zhang, Xukui, additional, Chen, Biyun, additional, Fu, Zhongxi, additional, Huang, Yuelong, additional, Liu, Huilin, additional, Xu, Qiaohua, additional, Yin, Li, additional, Long, Huajun, additional, Xu, Xin, additional, Zhang, Hao, additional, Zhang, Libo, additional, Su, Jian, additional, Tao, Ran, additional, Wu, Ming, additional, Yang, Jie, additional, Zhou, Jinyi, additional, Zhou, Yonglin, additional, Hu, Yihe, additional, Hua, Yujie, additional, Jin, Jianrong, additional, Liu, Fang, additional, Liu, Jingchao, additional, Lu, Yan, additional, Ma, Liangcai, additional, Tang, Aiyu, additional, Zhang, Jun, additional, Cheng, Liang, additional, Du, Ranran, additional, Gao, Ruqin, additional, Li, Feifei, additional, Li, Shanpeng, additional, Liu, Yongmei, additional, Ning, Feng, additional, Pang, Zengchang, additional, Sun, Xiaohui, additional, Tian, Xiaocao, additional, Wang, Shaojie, additional, Zhai, Yaoming, additional, Zhang, Hua, additional, Hou, Wei, additional, Lv, Silu, additional, Chen, Xiaofang, additional, Wu, Xianping, additional, Zhang, Ningmei, additional, Zhou, Weiwei, additional, Li, Jianguo, additional, Liu, Jiaqiu, additional, Luo, Guojin, additional, Sun, Qiang, additional, Zhong, Xunfu, additional, Gong, Weiwei, additional, Hu, Ruying, additional, Wang, Hao, additional, Wang, Meng, additional, Yu, Min, additional, Chen, Lingli, additional, Gu, Qijun, additional, Pan, Dongxia, additional, Wang, Chunmei, additional, Xie, Kaixu, additional, and Zhang, Xiaoyi, additional

Published
2022
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114. Risks of Stroke and Heart Disease Following Hysterectomy and Oophorectomy in Chinese Premenopausal Women

Author

Poorthuis, Michiel H.F., primary, Yao, Pang, additional, Chen, Yiping, additional, Guo, Yu, additional, Shi, Liya, additional, Li, Liming, additional, Chen, Zhengming, additional, Clarke, Robert, additional, Yang, Ling, additional, Chen, Junshi, additional, Collins, Rory, additional, Lv, Jun, additional, Peto, Richard, additional, Walters, Robin, additional, Avery, Daniel, additional, Bennett, Derrick, additional, Boxall, Ruth, additional, Bragg, Fiona, additional, Chang, Yumei, additional, Du, Huaidong, additional, Gilbert, Simon, additional, Hacker, Alex, additional, Holmes, Michael, additional, Kartsonaki, Christiana, additional, Kerosi, Rene, additional, Lin, Kuang, additional, Millwood, Iona, additional, Nie, Qunhua, additional, Ryder, Paul, additional, Sansome, Sam, additional, Schmidt, Dan, additional, Sohoni, Rajani, additional, Turnbull, Iain, additional, Wang, Lin, additional, Wright, Neil, additional, Yang, Xiaoming, additional, Han, Xiao, additional, Hou, Can, additional, Jing, Biao, additional, Liu, Chao, additional, Pei, Pei, additional, Tan, Yunlong, additional, Yu, Canqing, additional, Pang, Zengchang, additional, Gao, Ruqin, additional, Li, Shanpeng, additional, Wang, Shaojie, additional, Liu, Yongmei, additional, Du, Ranran, additional, Zang, Yajing, additional, Cheng, Liang, additional, Tian, Xiaocao, additional, Zhang, Hua, additional, Zhai, Yaoming, additional, Ning, Feng, additional, Sun, Xiaohui, additional, Li, Feifei, additional, Lv, Silu, additional, Wang, Junzheng, additional, Hou, Wei, additional, Zeng, Mingyuan, additional, Jiang, Ge, additional, Zhou, Xue, additional, Yang, Liqiu, additional, He, Hui, additional, Yu, Bo, additional, Li, Yanjie, additional, Xu, Qinai, additional, Kang, Quan, additional, Guo, Ziyan, additional, Wang, Dan, additional, Hu, Ximin, additional, Wang, Hongmei, additional, Chen, Jinyan, additional, Fu, Yan, additional, Fu, Zhenwang, additional, Wang, Xiaohuan, additional, Weng, Min, additional, Guo, Zhendong, additional, Wu, Shukuan, additional, Li, Yilei, additional, Li, Huimei, additional, Fu, Zhifang, additional, Wu, Ming, additional, Zhou, Yonglin, additional, Zhou, Jinyi, additional, Tao, Ran, additional, Yang, Jie, additional, Su, Jian, additional, Liu, Fang, additional, Zhang, Jun, additional, Hu, Yihe, additional, Lu, Yan, additional, Ma, Liangcai, additional, Tang, Aiyu, additional, Zhang, Shuo, additional, Jin, Jianrong, additional, Liu, Jingchao, additional, Tang, Zhenzhu, additional, Chen, Naying, additional, Huang, Ying, additional, Li, Mingqiang, additional, Meng, Jinhuai, additional, Pan, Rong, additional, Jiang, Qilian, additional, Lan, Jian, additional, Liu, Yun, additional, Wei, Liuping, additional, Zhou, Liyuan, additional, Chen, Ningyu, additional, Wang, Ping, additional, Meng, Fanwen, additional, Qin, Yulu, additional, Wang, Sisi, additional, Wu, Xianping, additional, Zhang, Ningmei, additional, Chen, Xiaofang, additional, Zhou, Weiwei, additional, Luo, Guojin, additional, Li, Jianguo, additional, Zhong, Xunfu, additional, Liu, Jiaqiu, additional, Sun, Qiang, additional, Ge, Pengfei, additional, Ren, Xiaolan, additional, Dong, Caixia, additional, Zhang, Hui, additional, Mao, Enke, additional, Wang, Xiaoping, additional, Wang, Tao, additional, Zhang, Xi, additional, Zhang, Ding, additional, Zhou, Gang, additional, Feng, Shixian, additional, Chang, Liang, additional, Fan, Lei, additional, Gao, Yulian, additional, He, Tianyou, additional, Sun, Huarong, additional, He, Pan, additional, Hu, Chen, additional, Zhang, Xukui, additional, Wu, Huifang, additional, Yu, Min, additional, Hu, Ruying, additional, Wang, Hao, additional, Qian, Yijian, additional, Wang, Chunmei, additional, Xie, Kaixu, additional, Chen, Lingli, additional, Zhang, Yidan, additional, Pan, Dongxia, additional, Gu, Qijun, additional, Huang, Yuelong, additional, Chen, Biyun, additional, Yin, Li, additional, Liu, Huilin, additional, Fu, Zhongxi, additional, Xu, Qiaohua, additional, Xu, Xin, additional, Zhang, Hao, additional, Long, Huajun, additional, Li, Xianzhi, additional, Zhang, Libo, additional, and Qiu, Zhe, additional

Published
2022
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116. Weaning period and growth patterns of captive Sunda pangolin (Manis javanica) cubs

Author

Yan, Dingyu, primary, Zeng, Xiangyan, additional, Jia, Miaomiao, additional, Guo, Xiaobing, additional, Que, Tengcheng, additional, Tao, Li, additional, Li, Mingzhe, additional, Li, Baocai, additional, Chen, Jinyan, additional, Xu, Shanghua, additional, Hua, Yan, additional, Wu, Shibao, additional, Zeng, Peng, additional, Li, Shousheng, additional, and Wei, Yongjie, additional

Published
2022
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121. Tweets Topic Classification and Sentiment Analysis Based on Transformer-Based Language Models.

Author

Mandal, Ranju, Chen, Jinyan, Becken, Susanne, and Stantic, Bela

Subjects
LANGUAGE models, SOCIAL media, NATURAL language processing, DEEP learning, MACHINE learning
Abstract

People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such 'big data' allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts' topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes. [ABSTRACT FROM AUTHOR]

Published
2023
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126. Synthesizing Five-Body Interaction in a Superconducting Quantum Circuit

Author

Zhang, Ke, primary, Li, Hekang, additional, Zhang, Pengfei, additional, Yuan, Jiale, additional, Chen, Jinyan, additional, Ren, Wenhui, additional, Wang, Zhen, additional, Song, Chao, additional, Wang, Da-Wei, additional, Wang, H., additional, Zhu, Shiyao, additional, Agarwal, Girish S., additional, and Scully, Marlan O., additional

Published
2022
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128. Acoustofluidic black holes for multifunctional in-droplet particle manipulation

Author

Liu, Pengzhan, primary, Tian, Zhenhua, additional, Yang, Kaichun, additional, Naquin, Ty Downing, additional, Hao, Nanjing, additional, Huang, Huiyu, additional, Chen, Jinyan, additional, Ma, Qiuxia, additional, Bachman, Hunter, additional, Zhang, Peiran, additional, Xu, Xiahong, additional, Hu, Junhui, additional, and Huang, Tony Jun, additional

Published
2022
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129. Additional file 1 of A novel inflammation-related prognostic biomarker for predicting the disease-free survival of patients with colorectal cancer

Author

Cai, Xiaoling, Chen, Fa, Liang, Lisheng, Jiang, Weizhong, Liu, Xing, Wang, Dong, Wu, Yunli, Chen, Jinyan, Guan, Guoxian, and Peng, Xian-e

Subjects
neoplasms
Abstract

Additional file 1: Table S1. Baseline tumor markers and inflammation index of patients with colorectal cancer. Fig. S1. Coefficients deviance of the LASSO analysis. Fig. S2. A, B, C X-tile analysis on the optimal cutoff points of NSAP. Fig. S3. A Kaplan-Meier curves for disease-free survival of CRC patients. according to the stage; B according to alpha-fetoprotein (AFP); C according to carcinoembryonic antigen (CEA). Fig. S4. A Calibration curve of nomogram of 1-Year DFS. B Calibration curve of nomogram of 2-Year DFS.

Published
2022
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131. A novel analytical approach for outcome prediction in newly diagnosed NSCLC based on [18F]FDG PET/CT metabolic parameters, inflammatory markers, and clinical variables.

Author

Zhang, Lixia, Xu, Caiyun, Zhang, Xiaohui, Wang, Jing, Jiang, Han, Chen, Jinyan, and Zhang, Hong

Subjects
NON-small-cell lung carcinoma, POSITRON emission tomography, BIOMARKERS, INFLAMMATION, BLOOD serum analysis
Abstract

Objectives: To develop a novel analytical approach based on 18F-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC. Methods: A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [18F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis. Results: The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model. Conclusions: We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC. Key Points: • The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification. [ABSTRACT FROM AUTHOR]

Published
2023
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132. Comparison of Remimazolam-Flumazenil versus Propofol for Rigid Bronchoscopy: A Prospective Randomized Controlled Trial.

Author

Pan, Yafei, Chen, Mo, Gu, Fulei, Chen, Jinyan, Zhang, Wen, Huang, Zhangxiang, Zhu, Dapeng, Song, Jia, Fang, Jun, Yu, Weifeng, and Xie, Kangjie

Subjects
BRONCHOSCOPY, RANDOMIZED controlled trials, PROPOFOL, LOSS of consciousness, GENERAL anesthesia, TUMOR surgery
Abstract

Background: Remimazolam is a novel ultrashort-acting intravenous benzodiazepine sedative–hypnotic that significantly reduces the times to sedation onset and recovery. This trial was conducted to confirm the recovery time from anesthesia of remimazolam-flumazenil versus propofol in patients undergoing endotracheal surgery under rigid bronchoscopy. Methods: Patients undergoing endotracheal tumor resection or stent implantation were randomly allocated into a remimazolam group (Group R) or a propofol group (Group P). The primary outcome was the recovery time from general anesthesia. The secondary outcomes were the time to loss of consciousness (LoC), hemodynamic fluctuations, and adverse events. Results: A total of 34 patients were screened, and 30 patients were enrolled in the study. The recovery time was significantly shorter for Group R (140 ± 52 s) than for Group P (374 ± 195 s) (p < 0.001). The times to LoC were 76 ± 40 s in Group R and 75 ± 25 s in Group P and were not significantly different. There were also no significant differences in hemodynamic fluctuations or adverse events between the two groups. Conclusions: The recovery time from general anesthesia in rigid bronchoscopy patients was shorter using remimazolam-flumazenil than with propofol, with no dramatic hemodynamic fluctuations and adverse events or differences between the agents. Remimazolam-flumazenil allows for faster recovery from anesthesia than propofol. [ABSTRACT FROM AUTHOR]

Published
2023
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141. Tracking Social Media to Understand Visitors' Travel Patterns: a Big Data Approach

Author

Chen, Jinyan

Subjects
FOS: Economics and business, big data analytics, social media data, visitors travel patterns, Tourism
Abstract

Tourism involves the movement of people. Visitors move between destinations differently, which creates patterns and networks that link destinations within travel itineraries. These patterns contain valuable information that can be analysed for tourism product development, to improve marketing services and assist in infrastructure and transportation development decisions. Traditionally, research on visitors' travel patterns relied on data collected from interviews, surveys, or direct observation. Normally the size of this type of data is relatively small, and processing of data collection is costly as well as time consuming. The introduction of big data analytics within tourism research has seen a diversification within data sources, and social media has become a particularly valuable source. Social media generates vast volumes of data, which in addition to text, images, and video content contains additional information in the form of metadata, such as geo-location (where posts were made) and user origins. When combined with advances in big data analytics, this type of data opens an opportunity to model temporal and spatial travel patterns and especially understand the travel pattern drivers behind the travel patterns. Due to the flexibility of social media data collection, understanding visitors' travel patterns is not limited to one destination but can be expanded to the national or global scale. This thesis took advantage of the value of social media data and big data analytics, in five independent but interconnected chapters to report, and advance knowledge on visitors travel patterns. The thesis is partly comprised of papers with a compilation of five manuscripts in addition to an introduction, an overview of the methodology, and a conclusion chapter. Manuscript one consisted of a literature review that synthesises the relevant literature relating to social media, tourism, and travel patterns. In the review, key parameters were defined to describe and synthesise the emerging field of tracking visitor flows with social media data. The review discussed commonly used methods and technologies and makes recommendations for future research approaches. Manuscript two focused on data analytics and theory validation. It applied social network analysis and core-periphery theory using social media data to build the travel networks. The case study followed the travel of Chinese visitors between selected Australian destinations to examine travel structures. The Chinese social media platform Weibo was the data source. The results demonstrated that Chinese visitors in Australia mostly visit iconic Australian destinations first, before visiting destinations classified as semi-core or periphery. The findings indicated the suitability of applying core-periphery theory to social media data. It was also revealed that consideration should be given to the number of destinations within visitors' travel itineraries when analysing the core-periphery travel structures. The rationale for this focus was that the same destination may play a different role if the number of destinations is different. Manuscript three explored global tourist mobility from social media data and expanded the focus study to a global perspective. The case study investigated travel patterns of Chinese visitors before and after travel to Australia. This work addressed global tourist mobility and how Australia is positioned as an international travel destination. Results showed that Chinese visitors visited thirteen regions before or after their travel to Australia, and they appeared to travel directly from Australia to China, which indicated that Australia was the most prominent gateway country. The proposed method for identifying global travel patterns may be helpful to understand the impacts of the coronavirus pandemic and highlights that tracking mobility is important to understand the spread of diseases as well as opportunities for tourism recovery. Manuscript four proposed a method that identified Chinese travel sentiment in Chinese language. The method was tested using data from Weibo, and a case that focused on Weibo users’ posts related to visiting the Great Barrier Reef (GBR) was conducted. The manuscript detailed the process of capturing the Weibo posts describing the creation of lexicon and presented an algorithm for sentiment calculation. By investigating the sentiment towards different GBR destinations, it was demonstrated that the proposed method was effective in obtaining insights and may be suitable to monitor visitor opinions. Manuscript five introduced a new methodology that conducted importance-performance analysis (IPA) to understand visitor satisfaction with travel patterns. The new method indirectly measured performance and importance values from publicly available social media data (Weibo). A lexicon-based method for sentiment analysis was applied to identify performance value. Importance was calculated as the adjusted association rule mining algorithm Support, which assessed the frequency of posts at each destination. The results indicated that the proposed methodology offers an opportunity to conduct IPA indirectly from social media data with larger sample sizes, but at a lower cost with flexible data collection. The research used Chinese visitors to Australia as a case study, but the proposed approach could benefit a range of decision-makers directly or indirectly related to tourism.

Published
2021
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144. Association of heart rate and diabetes among 0.5 million adults in the China Kadoorie biobank: Results from observational and Mendelian randomization analyses

Author

Wang, Wenxiu, primary, Wang, Jingjia, additional, Lv, Jun, additional, Yu, Canqing, additional, Shao, Chunli, additional, Tang, Yida, additional, Guo, Yu, additional, Bian, Zheng, additional, Du, Huaidong, additional, Yang, Ling, additional, Millwood, Iona Y., additional, Walters, Robin G., additional, Chen, Yiping, additional, Chang, Liang, additional, Fan, Lei, additional, Chen, Junshi, additional, Chen, Zhengming, additional, Huang, Tao, additional, Li, Liming, additional, Clarke, Robert, additional, Collins, Rory, additional, Peto, Richard, additional, Walters, Robin, additional, Avery, Daniel, additional, Boxall, Ruth, additional, Bennett, Derrick, additional, Chang, Yumei, additional, Gilbert, Simon, additional, Hacker, Alex, additional, Hill, Mike, additional, Holmes, Michael, additional, Iona, Andri, additional, Kartsonaki, Christiana, additional, Kerosi, Rene, additional, Kong, Ling, additional, Kurmi, Om, additional, Lancaster, Garry, additional, Lewington, Sarah, additional, Lin, Kuang, additional, McDonnell, John, additional, Millwood, Iona, additional, Nie, Qunhua, additional, Radhakrishnan, Jayakrishnan, additional, Ryder, Paul, additional, Sansome, Sam, additional, Schmidt, Dan, additional, Sherliker, Paul, additional, Sohoni, Rajani, additional, Stevens, Becky, additional, Turnbull, Iain, additional, Wang, Jenny, additional, Wang, Lin, additional, Wright, Neil, additional, Yang, Xiaoming, additional, Han, Xiao, additional, Hou, Can, additional, Pei, Pei, additional, Liu, Chao, additional, Tan, Yunlong, additional, Pang, Zengchang, additional, Gao, Ruqin, additional, Li, Shanpeng, additional, Wang, Shaojie, additional, Liu, Yongmei, additional, Du, Ranran, additional, Zang, Yajing, additional, Cheng, Liang, additional, Tian, Xiaocao, additional, Zhang, Hua, additional, Zhai, Yaoming, additional, Ning, Feng, additional, Sun, Xiaohui, additional, Li, Feifei, additional, Lv, Silu, additional, Wang, Junzheng, additional, Hou, Wei, additional, Zeng, Mingyuan, additional, Jiang, Ge, additional, Zhou, Xue, additional, Yang, Liqiu, additional, He, Hui, additional, Yu, Bo, additional, Li, Yanjie, additional, Xu, Qinai, additional, Kang, Quan, additional, Guo, Ziyan, additional, Wang, Dan, additional, Hu, Ximin, additional, Chen, Jinyan, additional, Fu, Yan, additional, Fu, Zhenwang, additional, Wang, Xiaohuan, additional, Weng, Min, additional, Guo, Zhendong, additional, Wu, Shukuan, additional, Li, Yilei, additional, Li, Huimei, additional, Fu, Zhifang, additional, Wu, Ming, additional, Zhou, Yonglin, additional, Zhou, Jinyi, additional, Tao, Ran, additional, Yang, Jie, additional, Su, Jian, additional, liu, Fang, additional, Zhang, Jun, additional, Hu, Yihe, additional, Lu, Yan, additional, Ma, Liangcai, additional, Tang, Aiyu, additional, Zhang, Shuo, additional, Jin, Jianrong, additional, Liu, Jingchao, additional, Tang, Zhenzhu, additional, Chen, Naying, additional, Huang, Ying, additional, Li, Mingqiang, additional, Meng, Jinhuai, additional, Pan, Rong, additional, Jiang, Qilian, additional, Lan, Jian, additional, Liu, Yun, additional, Wei, Liuping, additional, Zhou, Liyuan, additional, Ping Wang, Ningyu Chen, additional, Meng, Fanwen, additional, Qin, Yulu, additional, Wang, Sisi, additional, Wu, Xianping, additional, Zhang, Ningmei, additional, Chen, Xiaofang, additional, Zhou, Weiwei, additional, Luo, Guojin, additional, Li, Jianguo, additional, Zhong, Xunfu, additional, Liu, Jiaqiu, additional, Sun, Qiang, additional, Ge, Pengfei, additional, Ren, Xiaolan, additional, Dong, Caixia, additional, Zhang, Hui, additional, Mao, Enke, additional, Wang, Xiaoping, additional, Wang, Tao, additional, zhang, Xi, additional, Zhang, Ding, additional, Zhou, Gang, additional, Feng, Shixian, additional, Gao, Yulian, additional, He, Tianyou, additional, Sun, Huarong, additional, He, Pan, additional, Hu, Chen, additional, Zhang, Xukui, additional, Wu, Huifang, additional, Yu, Min, additional, Hu, Ruying, additional, Wang, Hao, additional, Qian, Yijian, additional, Wang, Chunmei, additional, Xie, Kaixu, additional, Chen, Lingli, additional, Zhang, Yidan, additional, Pan, Dongxia, additional, Gu, Qijun, additional, Huang, Yuelong, additional, Chen, Biyun, additional, Yin, Li, additional, Liu, Huilin, additional, Fu, Zhongxi, additional, Xu, Qiaohua, additional, Xu, Xin, additional, Zhang, Hao, additional, Long, Huajun, additional, Li, Xianzhi, additional, Zhang, Libo, additional, and Qiu, Zhe, additional

Published
2021
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146. Vitamin D and cause-specific vascular disease and mortality: a Mendelian randomisation study involving 99,012 Chinese and 106,911 European adults

Author

Huang, Tao, Afzal, Shoaib, Yu, Canqing, Guo, Yu, Bian, Zheng, Yang, Ling, Millwood, Iona Y., Walters, Robin G., Chen, Yiping, Chen, Ningyu, Gao, Ruqin, Chen, Junshi, Clarke, Robert, Chen, Zhengming, Ellervik, Christina, Nordestgaard, Børge G., Lv, Jun, Li, Liming, Collins, Rory, Peto, Richard, Avery, Daniel, Boxall, Ruth, Bennett, Derrick, Chang, Yumei, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Hill, Mike, Holmes, Michael, Iona, Andri, Kartsonaki, Christiana, Kerosi, Rene, Kong, Ling, Kurmi, Om, Lancaster, Garry, Lewington, Sarah, Lin, Kuang, McDonnell, John, Nie, Qunhua, Radhakrishnan, Jayakrishnan, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Jenny, Wang, Lin, Wright, Neil, Yang, Xiaoming, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Tan, Yunlong, Pang, Zengchang, Li, Shanpeng, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Zeng, Mingyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Xu, Qinai, Kang, Quan, Guo, Ziyan, Wang, Dan, Hu, Ximin, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Fu, Zhifang, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Lan, Jian, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Wang, Ping, Meng, Fanwen, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Zhou, Weiwei, Luo, Guojin, Li, Jianguo, Chen, Xiaofang, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, Hu, Chen, Zhang, Xukui, Wu, Huifang, He, Pan, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixu, Chen, Lingli, Zhang, Yidan, Pan, Dongxia, Gu, Qijun, Huang, Yuelong, Chen, Biyun, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Long, Huajun, Li, Xianzhi, Zhang, Libo, and Qiu, Zhe

Subjects
Adult, medicine.medical_specialty, lcsh:Medicine, Disease, Lower risk, Gastroenterology, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Internal medicine, Causal effect, Vitamin D and neurology, Humans, Medicine, Vascular Diseases, 030212 general & internal medicine, Myocardial infarction, Vitamin D, Mendelian randomisation, Stroke, business.industry, Vascular disease, Confounding, lcsh:R, General Medicine, Vitamin D Deficiency, medicine.disease, Lipids, 3. Good health, Cardiovascular diseases, Population study, Gene-Environment Interaction, business, 030217 neurology & neurosurgery, Research Article
Abstract

Background Randomised control trials and genetic analyses have demonstrated that vitamin D or 25-hydroxyvitamin D (25[OH]D) levels may not play a causal role in the development of cardiovascular disease. However, it is unclear if 25(OH)D is causally associated with cause-specific vascular disease and lipids. Therefore, we examined the causal association of 25(OH)D with myocardial infarction, stroke, ischaemic heart disease, ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, and lipid levels among both Chinese and Europeans. Methods We used a Mendelian randomisation (MR) design in the China Kadoorie Biobank, the Copenhagen City Heart Study, and the Copenhagen General Population Study. The 25(OH)D-related genetic variants in the CYP2R1 and DCHR7 genes were genotyped in 99,012 Chinese adults and 106,911 Danish adults. Results In Chinese adults, plasma 25(OH)D levels were not significantly associated with cause-specific vascular disease or mortality, with the exception of intracerebral haemorrhage (HR, 1.09 [95% CI, 1.01,1.18] per 25 nmol/L higher plasma 25(OH)D). In Europeans, plasma 25(OH)D levels were inversely associated with all types of vascular diseases and mortality. However, MR analysis did not demonstrate causal associations of genetically increased 25(OH)D levels with cause-specific vascular diseases, or mortality in both Chinese and European adults. In addition, each 25 nmol/L higher 25(OH)D was observationally associated with lower total cholesterol and low-density lipoprotein cholesterol levels, but higher high-density lipoprotein cholesterol levels. Likewise, MR analysis showed that 25(OH)D levels were not causally associated with lipids in both Chinese and European adults after Bonferroni correction. Conclusions We found no evidence to support that genetically increased 25(OH)D was associated with a lower risk of ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and lipid levels in both Chinese and European adults. These results suggest that the inverse associations of vitamin D with vascular disease could be the result of confounding. Electronic supplementary material The online version of this article (10.1186/s12916-019-1401-y) contains supplementary material, which is available to authorized users.

Published
2019

147. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Author

Bakker, Mark K., van der Spek, Rick A.A., van Rheenen, Wouter, Morel, Sandrine, Bourcier, Romain, Hostettler, Isabel C., Alg, Varinder S., van Eijk, Kristel R., Koido, Masaru, Akiyama, Masato, Terao, Chikashi, Matsuda, Koichi, Walters, Robin G., Lin, Kuang, Li, Liming, Millwood, Iona Y., Chen, Zhengming, Rouleau, Guy A., Zhou, Sirui, Rannikmäe, Kristiina, Sudlow, Cathie L.M., Houlden, Henry, van den Berg, Leonard H., Dina, Christian, Naggara, Olivier, Gentric, Jean-Christophe, Shotar, Eimad, Eugène, François, Desal, Hubert, Winsvold, Bendik S., Børte, Sigrid, Johnsen, Marianne Bakke, Brumpton, Ben M., Sandvei, Marie Søfteland, Willer, Cristen J., Hveem, Kristian, Zwart, John-Anker, Verschuren, W. M. Monique, Friedrich, Christoph M., Hirsch, Sven, Schilling, Sabine, Dauvillier, Jérôme, Martin, Olivier, Martinsen, Amy E, Aamodt, Anne Hege, Skogholt, Anne Heidi, Sandset, Else Charlotte, Kristoffersen, Espen S, Ellekjaer, Hanne, Heuch, Ingrid, Nielsen, Jonas Bille, Hagen, Knut, Fritsche, Lars, Thomas, Laurent F., Pedersen, Linda, Gabrielsen, Maiken E, Vigeland, Maria Dehli, Holmen, Oddgeir, Zhou, Wei, Chen, Junshi, Chen (PI), Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li (PI), Liming, Liu, Depei, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Boxall, Ruth, Bennett, Derrick, Chang, Yumei, Chen, Yiping, Du, Huaidong, Gan, Wei, Gilbert, Simon, Hacker, Alex, Hill, Michael, Holmes, Michael, Iona, Andri, Kartsonaki, Christiana, Kerosi, Rene, Kong, Ling, Lancaster, Garry, Lewington, Sarah, McDonnell, John, Millwood, Iona, Nie, Qunhua, Ryder, Paul, Sansome, Sam, Schmidt-Valle, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Bian, Zheng, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Yu, Canqing, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Zou, Mingyuan, Yan, Shichun, Zhou, Xue, Yu, Bo, Li, Yanjie, Xu, Qinai, Kang, Quan, Guo, Ziyan, Wang, Dan, Hu, Ximin, Chen, Jinyan, Fu, Yan, Wang, Xiaohuan, Weng, Min, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Hua, Yujie, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Lan, Jian, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Wang, Ping, Meng, Fanwen, Qin Sisi Wang, Yulu, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Zhou, Weiwei, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhou, Ding Zhang, Zhou, Gang, Feng, Shixian, Chang, Ling, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Zhang, Xukui, Wu, Huifang, Yu, Min, Hu, Ruying, Wang, Hao, Gong, Weiwei, Wang, Meng, Xie, Kaixu, Chen, Lingli, Pan, Dongxia, Gu, Qijun, Huang, Yuelong, Chen, Biyun, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Long, Huajun, Zhang, Libo, Nagai, Akiko, Muto, Kaori, Hirata, Makoto, Morisaki, Takayuki, Yamashita, Yasushi, Kamatani, Yoichiro, Kambara, Yoko, Murakami, Yoshinori, Masumoto, Akihide, Nagayama, Satoshi, Miki, Yoshio, Yoshimori, Kozo, Fujioka, Tomoaki, Takata, Ryo, Yamaji, Ken, Takahashi, Kazuhisa, Asai, Satoshi, Takahashi, Yasuo, Minami, Shiro, Yamaguchi, Hiroki, Koretsune, Yukihiro, Nishizawa, Yasuko, Kodama, Ken, Kutsumi, Hiromu, Suzuki, Takao, Sinozaki, Nobuaki, Murayama, Shigeo, Furukawa, Yoichi, Yamanashi, Yuji, Papagiannaki, Chrisanthi, Piotin, Michel, Trystram, Denis, Edjlali-Goujon, Myriam, Boulouis, Grégoire, Rodriguez, Christine, Hassen, Waghi Ben, Saleme, Suzanna, Mounayer, Charbel, Rouchaud, Aymeric, Levrier, Olivier, Aguettaz, Pierre, Combaz, Xavier, Pasco, Anne, l’Allinec, Vincent, Bintner, Marc, Molho, Marc, Pascale, Gauthier, Chivot, Cyril, Costalat, Vincent, Darganzil, Cyril, Bonafé, Alain, Januel, Anne Christine, Michelozzi, Caterina, Cognard, Christophe, Bonneville, Fabrice, Tall, Philippe, Darcourt, Jean, Biondi, Alessandra, Iosif, Cristina, Ferre, Jean Christophe, Gauvrit, Jean Yves, Eugene, François, Raoult, Hélène, Gentric, Jean Christophe, Ognard, Julien, Anxionnat, René, Gory, Benjamin, Bracard, Serge, Derelle, Anne Laure, Tonnelet, Romain, Spelle, Laurent, Ikka, Léon, Ozanne, Augustin, Gallas, Sophie, Caroff, Jildaz, Achour, Nidal Ben, Moret, Jacques, Chabert, Emmanuel, Berge, Jérôme, Marnat, Gaultier, Barreau, Xavier, Gariel, Florent, Clarencon, Frédéric, Aggour, Mohammed, Ricolfi, Frédéric, Chavent, Adrien, Thouant, Pierre, Lebidinsky, Pablo, Lemogne, Brivael, Herbreteau, Denis, Bibi, Richard, Janot, Kevin, Pierot, Laurent, Soize, Sébastien, Labeyrie, Marc Antoine, Vandendries, Christophe, Kazemi, Appoline, Leclerc, Xavier, Pruvo, Jean Pierre, Bricout, Nicolas, Velasco, Stéphane, Boucebci, Samy, Lemmens, Robin, Pandolfo, Massimo, Bodenant, Marie, Louillet, Fabien, Mas, Jean-Louis, Deltour, Sandrine, Leder, Sara, Léger, Anne, Canaple, Sandrine, Godefroy, Olivier, Giroud, Maurice, Jacquin, Agnès, Moulin, Thierry, Vuillier, Fabrice, Tzourio, Christophe, Santos, Michael Dos, Malik, Rainer, Hausser, Ingrid, Thomas-Feles, Constanze, Weber, Ralf, Grond-Ginsbach, Caspar, Hacke, Werner, Giossi, Alessia, Volonghi, Irene, Costa, Paolo, del Zotto, Elisabetta, Morotti, Andrea, Poli, Loris, Muiesan, Maria Lorenza, Salvetti, Massimo, Rosei, Enrico Agabiti, Lanfranconi, Silvia, Baron, Pierluigi, Ferrarese, Carlo, Susani, Emanuela, Giacalone, Giacomo, Paolucci, Stefano, Palmirotta, Raffaele, Guadagni, Fiorella, Paciaroni, Maurizio, Ballabio, Elena, Parati, Eugenio A., Fluri, Felix, Hatz, Florian, Gisler, Dominique, Amort, Margareth, Bevan, Steve, James, Tom, Olsson, Sandra, Holmegaard, Lukas, Altintas, Ayse, Martin, Juan José, Kittner, Steven, Mitchell, Braxton, Stine, Colin, O’Connell, Jeff, Dueker, Nicole, Koudstaal, Peter J., de Lau, Lonneke M.L., Hofman, Albert, Verhaaren, Benjamin F, Uitterlinden, Andre G, Montaner, Joan, Mendioroz, Maite, Yadav, Sunaina, Khan, Muhammad Saleem, Wilder, Michael, van Dijk, Ewoud, Maaijwee, Noortje, Rutten-Jacobs, Loes, Kramer, Jamie, Malik, Shaneela, Brott, Thomas G, Brown, Robert D, Singleton, Andrew, Hardy, John, Rich, Stephen S, Tanislav, Christian, Jungehülsing, Jan, Werring, David, Alg, Varinder, Hostettler, Isabel, Bonner, Stephen, Walsh, Daniel, Bulters, Diederik, Kitchen, Neil, Brown, Martin, Grieve, Joan, Roberts, Gareth, Jones, Timothy, Critchley, Giles, Sharma, Pankaj, Nelson, Richard, Whitfield, Peter, Ross, Stuart, Patel, Hiren, Eldridge, Paul, Saastamoinen, Kari, Patel, Umang, Lawrance, Enas, Vandabona, Subha, Mendelow, David, Teal, Rachel, Warner, Orlando, Kirkpatrick, Peter, Seshadri, Sudha, Kilarski, Laura, Hyacinth, Hyacinth I, Oliveira, Jamary, Marini, Sandro, Nyquist, Paul, Lewis, Cathryn, Norrving, Bo, Smith, Gustav, Rosand, Jonathan, Biffi, Alessandro, Kourkoulis, Christina, Anderson, Chris, Giese, Anne-Katrin, Bang, Oh Young, Chung, Jong-Won, Kim, Gyeong-Moon, Zhuang, Qishuai, Sheu, Wayne, Smalley, June, Howson, Joanna, Granata, Alessandra, Markus, Hugh, Wardlaw, Joanna, Cole, John, Thalamuthu, Anbupalam, Hopewell, Jemma, Worrall, Bradford, Bis, Josh, Tirschwell, David, Reiner, Alex, Dhar, Raj, Lee, Jin-Moo, Mortenson, Janne, Wassertheil-Smoller, Sylvia, Prasad, Kameshwar, Fisher, Mark, Traenka, Christopher, Wang, Xingwu, Wang, Yongjun, Rouanet, Francois, Sibon, Igor, Sarnowski, Chloé, Maillard, Pauline, Aparicio, Hugo Javier, Dupuis, Josee, Yang, Qiong, Luvizutto, Gustavo, Chasman, Daniel, Rexrode, Kathryn, Harriot, Andrea, Phuah, Chia-Ling, Santo, Gustavo, Gerard, Jen, Liu, Guiyou, Aaron, Sanjith, Christudass, Christhunesa S., Salomi, BSB, Sanghera, Dharambir, Boehme, Amelia, Elkind, Mitchell, Gretarsdottir, Solveig, Lange, Leslie, Rost, Natalia, James, Michael, Stewart, Jill, Goldstein, Larry, Waddy, Salina, Vojinovic, Dina, Ikram, Arfan, Thijs, Vincent, Parati, Eugenio, Boncoraglio, Giorgio, Kooperberg, Charles, Abboud, Sherrine, Zand, Ramin, Bijlenga, Philippe, Selim, Magdy, Happola, Olli, Strbian, Daniel, Tomppo, Liisa, Pathak, Abhishek, Pfeiffer, Dorothea, Aires, de Buenos, de Carvalho, Joao Jose Freitas, Ribeiro, Priscila, Torres, Nuria, Barboza, Miguel, Plomaritoglou, Androniki, Bjorkegren, Johan, Chan, Yu-Feng Yvonne, Gudnason, Villi, Jimenez-Conde, Jordi, Soriano, Carolina, Roquer, Jaume, Bentley, Paul, Tournier-Lasserve, Elisabeth, Dufouil, Carole, Debette, Stephanie, Mishra, Aniket, Wee, Lawrence, Siddiqi, Saima, Wu, Jer-Yuarn, Ko, Tai-Ming, Bione, Silvia, Jood, Katarina, Tatlisumak, Turgut, Arauz, Antonio, Korostynski, Michal, Launer, Lenore, Yue, Suo, bersano, anna, Juchniewicz, Karol Józef, Mateusz, Adamski, Pera, Joanna, Wnuk, Marcin, Levi, Christopher, Gusdon, Aaron, Kostulas, Konstantinos, Maxwell, Jessye, Duering, Marco, Jagiella, Jeremiasz, Hata, Jun, Ninomiya, Toshiharu, Nguyen, Vinh, Thorarinsson, Bjorn Logi, Lee, Tsong-Hai, Rakitko, Alexandr, Dichgans, Martin, Lindgren, Arne, Wasselius, Johan, Drake, Mattias, Stenman, Martin, Ilinca, Andreea, Staals, Julie, Sadr-Nabavi, Ariane, Crawford, Katherine, Lena, Umme, Mateen, Farrah, Ay, Hakan, Wu, Ona, Schirmer, Markus, Romero, Javier, Cramer, Steve, Golland, Polina, Mueller, Bertram, Brown, Robert, Meschia, James, Ross, Owen A., Pare, Guillaume, Chong, Mike, mansour, Ossama yassin, Karaszewski, Bartosz, Enzinger, Christian, Schmidt, Reinhold, Seiler, Stephan, Pichler, Alexander, Ovbiagele, Bruce, Yamada, Yoshiji, Rundek, Tatjana, Blanton, Susan, P, John, Chern, Joseph, O'Donnell, Chris, Corriveau, Roderick, Bhattacharya, Pallab, Gwinn, Katrina, CHANDRA, BHARATENDU, Chen, Christopher, Kalaria, Raj, Koenig, Jim, Singh, Om Prakash, Olugbodi, Akintomi, Giralt, Eva, Saleheen, Danish, de Leeuw, Frank-Erik, Klijn, Karin, Olesen, Jes, Kubo, Michiaki, Spence, David, Pedersen, Annie, Olsson, Maja, Martín, Juan José, Braga, Gabriel, Xu, Huichun, Assimes, Tim, Raskurazhev, Anton, Lee, Wei Ling, Burri, Philippe, Frid, Petrea, GmbH, Heilbronn, Deng, Zhen, Habibi-koolaee, Mahdi, Vijayan, Murali, Leung, Thomas, Wong, Lawrence, Mok, Vincent, Choy, Richard, Jern, Christina, Lebedeva, Elena, Farrall, Martin, Jiayuan, Xu, Loo, Keat Wei, Rinkel, Gabriel, Magnus, Rudolf, Goncalves, Anderson, Franca, Paulo, Cendes, Iscia, Carrera, Caty, Fernandez-Cadenas, Israel, Kim, Helen, Rolfs, Arndt, Owolabi, Mayowa, Bakker, Mark, Ruigrok, Ynte, Hauer, Allard, Pulit, Sara L., Algra, Ale, van der Laan, Sander W., Macleod, Mary, Howard, George, Tiwari, Hemant, Irvin, Ryan, Albright, Karen C., Perry, Rodney, Kidwell, Chelsea, Pavlovic, Aleksandra, Sargurupremraj, Murali, Schilling, Sabrina, Pezzini, Alessandro, Abd-Allah, Foad, DeCarli, Charles, Liebeskind, David, Traylor, Matthew, Tan, Rhea, Danesh, John, Larsson, Susanna C., Rutten, Loes, Donatti, Amanda, Avelar, Wagner, Broderick, Joseph, Woo, Daniel, Kissela, Brett, Ibenez, Laura Garcia, Salman, Rustam, Sudlow, Cathie, McDonough, Caitrin Wheeler, Silliman, Scott, Magvanjav, Oyunbileg, van Agtmael, Tom, Walters, Matthew, Lorentzen, Erik, Stanne, Tara, Olsson, Martina, Nakagawa, Kazuma, Akinyemi, Rufus, Cotlatciuc, Ioana, O'Connell, Jeff, Sparks, Mary, Sorkin, John, Dave, Tushar, Naylor, Jill, Brown, Devin, Du, Rose, Kulik, Tobias B., Attia, John, Faber, James E, Rothwell, Peter, Márquez, Elsa Valdés, Mancuso, Michelangelo, Souza, Doralina Brum, de Silva, Ranil, Vibo, Riina, Korv, Janika, Maguire, Jane, Fornage, Myriam, Illoh, Kachikwu, Milewicz, Dianna, Majersik, Jennifer, DeHavenon, Adam, Kalani, Yashar, Alexander, Matthew, Cushman, Mary, Sale, Michele, Owens, Debra, Keene, Keith, Rich, Stephe, Psaty, Bruce, Longstreth, Will, Atadzhanov, Masharip, Wolfe, Stacey Quintero, Langefeld, Carl, Bushnell, Cheryl, Cruchaga, Carlos, Konrad, Jan, Liu, Junfeng, Sheth, Kevin, Falcone, Guido, Donahue J, Kathleen, Jones, Gregory T., Bown, Matthew J., Ko, Nerissa U., Coleman, Jonathan R.I., Breen, Gerome, Zaroff, Jonathan G., Klijn, Catharina J.M., Sargurupremraj, Muralidharan, Amouyel, Philippe, Debette, Stéphanie, Rinkel, Gabriel J.E., Worrall, Bradford B., Slowik, Agnieszka, Gaál-Paavola, Emilia I., Niemelä, Mika, Jääskeläinen, Juha E., von Und Zu Fraunberg, Mikael, Lindgren, Antti, Broderick, Joseph P., Werring, David J., Redon, Richard, Veldink, Jan H., Ruigrok, Ynte M., Stroke, HUNT All-In, Group, China Kadoorie Biobank Collaborative, Consortium, BioBank Japan Project, Group, ICAN Study, Group, CADISP, investigators, Genetics and Observational Subarachnoid Haemorrhage (GOSH) Study, (ISGC), International Stroke Genetics Consortium, Morel, Sandrine, and Bijlenga, Philippe Alexandre Pierre

Subjects
genetics [Blood Pressure], Medizin, Genome-wide association study, Blood Pressure, Disease, ddc:616.07, Bioinformatics, 616: Innere Medizin und Krankheiten, 0302 clinical medicine, Risk Factors, physiopathology [Hypertension], genetics [Genetic Predisposition to Disease], Genetic risk factor, Stroke, 0303 health sciences, Smoking, genetics [Smoking], genetics [Intracranial Aneurysm], Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], Cerebrovascular disorder, 3. Good health, genetics [European Continental Ancestry Group], Hypertension, genetics [Polymorphism, Single Nucleotide], Subarachnoid hemorrhage, pathology [Intracranial Aneurysm], genetics [White People], Biology, Genetic correlation, pathology [Endothelial Cells], Polymorphism, Single Nucleotide, Article, White People, 03 medical and health sciences, Aneurysm, Asian People, ddc:570, Genetics, medicine, Humans, Genetic Predisposition to Disease, cardiovascular diseases, 030304 developmental biology, genetics [Subarachnoid Hemorrhage], genetics [Asian Continental Ancestry Group], 572: Biochemie, genetics [Asian People], pathology [Subarachnoid Hemorrhage], adverse effects [Smoking], Endothelial Cells, Subarachnoid Hemorrhage, medicine.disease, Intracranial aneurysm, Genetic architecture, ddc:616.8, Case-Control Studies, genetics [Hypertension], 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

An author correction to this article published in December 2020 is available at https://doi.org/10.1038/s41588-020-00760-4. Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Published
2021

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