👤 Yinghui Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, 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Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueyi Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, 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Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Chunxiao Dang, Xiaofeng Wang, Pengfei Liu +2 more · 2024 · International journal of women's health · added 2026-04-24
Observational studies have investigated the association between lipid-lowering drugs and breast cancer (BC) and endometrial cancer (EC), but some controversy remains. This paper aims to explore the ca Show more
Observational studies have investigated the association between lipid-lowering drugs and breast cancer (BC) and endometrial cancer (EC), but some controversy remains. This paper aims to explore the causal relationship between genetic proxies for lipid-lowering drugs and breast and endometrial cancers using drug-target Mendelian randomization (MR). Analyses were mainly performed using inverse variance weighted (IVW), heterogeneity and horizontal pleiotropy tests, and sensitivity analysis to assess the robustness of the results and causal relationship. HMGCR, APOB, and NPC1L1 increased the risk of breast cancer, LPL increased the risk of endometrial cancer, and APOC3 decreased the risk of breast and endometrial cancer. No heterogeneity or horizontal pleiotropy was detected, and nor was there any evidence of an association between other lipid-lowering drugs and breast and endometrial cancer. Our study demonstrated genetically that HMGCR inhibition, APOB inhibition, and NPC1L1 inhibition decrease the risk of breast cancer, LPL agonist increases the risk of endometrial cancer, and APOC3 inhibition decreases the risk of breast cancer and endometrial cancer, and these findings provide genetic insights into the potential risks of lipid-lowering drug therapy. Show less
📄 PDF DOI: 10.2147/IJWH.S468733
APOB
Chang Su, Juan Tian, Xueqing He +3 more · 2024 · ImmunoTargets and therapy · added 2026-04-24
Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering dr Show more
Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering drugs influence the development of autoimmune thyroid disease (AITD) remains unclear. This study aims to evaluate the effects of lipid-lowering drugs on AITD and explore their potential mechanisms. Two-sample and two-step Mendelian randomization (MR) studies were performed to assess the causal relationships between circulating lipids (LDL-C, TC, TG, and ApoB) and seven lipid-lowering drug targets ( There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD ( There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors. Show less
📄 PDF DOI: 10.2147/ITT.S487319
APOB
Dong-Juan Xu, Yi-Lei Shen, Meng-Meng Hu +6 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Parkinson's disease (PD) and Alzheimer's disease (AD) are common neurodegenerative diseases with multifaceted etiology. Nutritional and metabolic abnormalities are frequently implicated in PD and AD. Show more
Parkinson's disease (PD) and Alzheimer's disease (AD) are common neurodegenerative diseases with multifaceted etiology. Nutritional and metabolic abnormalities are frequently implicated in PD and AD. In this observational study, we analyzed a series of nutritional markers, and aimed to understand their association with AD and PD risk. A total of 424 PD patients, 314 AD patients, and 388 healthy controls were included. Nutritional markers including Hemoglobin A1c, vitamin B12, folate, apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), low-density lipoprotein (LDL), high-density lipoprotein, triglyceride, total cholesterol (TC), uric acid and homocysteine (HCY) were measured. Significance for odds ratios examining was Multifactor risk analysis showed that ApoB, LDL, and TC reduce PD risk, while HCY increase PD risk. ApoA1 and HCY are protective and risk factors for AD, respectively. The cross-sectional study demonstrates that HCY and lipid metabolism markers are associated with PD and AD risks. Our findings support the involvement of one-carbon metabolism and lipid metabolism disturbance in PD and AD. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e40191
APOB
Qinghan Meng, Haina Ma, Nannan Tian +12 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Type 2 diabetes (T2DM) is a significant risk factor for coronary heart disease (CHD). This study aimed to assess the variations in biomarkers associated with CHD in T2DM patients across different age Show more
Type 2 diabetes (T2DM) is a significant risk factor for coronary heart disease (CHD). This study aimed to assess the variations in biomarkers associated with CHD in T2DM patients across different age groups in the Han Chinese population. A strict selection process was employed, involving three groups: a control group (n = 300) with no medical history, a new-onset T2DM group (n = 300), and a new-onset T2DM + CHD group (n = 300). Participants in each group were further categorized based on age: Group 1 (<60 years), Group 2 (60-75 years), and Group 3 (>75 years). Fasting glucose, hemoglobin A1c (HbA1c), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), ApoB/ApoA1 ratio, lipoprotein(a) [Lp(a)], high-sensitivity C-reactive protein (hsCRP), and homocysteine (HCY) levels were analyzed in all groups. Both T2DM and T2DM + CHD groups exhibited elevated levels of TG, TC, LDL-C, ApoB, ApoB/ApoA1, Lp(a), hsCRP, and HCY, alongside decreased levels of HDL-C and ApoA1 in comparison to the control group. Notably, when comparing the T2DM to the T2DM + CHD groups, significant increases were noted in ApoB, Lp(a), and hsCRP levels in the T2DM + CHD group, whereas other biomarkers did not show significant differences. Across all age groups, the patterns remained consistent, with the T2DM and T2DM + CHD groups showing elevated levels of TG, TC, LDL-C, ApoB, ApoB/ApoA1, Lp(a), hsCRP, and HCY, and decreased levels of HDL-C and ApoA1 compared to their respective age-matched control groups. Furthermore, within each age category, significant increases in ApoB, Lp(a), and hsCRP were specifically observed with advancing age in the T2DM + CHD group, with Lp(a) and hsCRP levels showing particularly notable elevations, underscoring their potential as significant indicators of CHD risk in the T2DM population. Lp(a) and hsCRP may serve as valuable risk biomarkers for the development of CHD in T2DM patients. Understanding the variations in these biomarkers across different age groups can assist in risk assessment and the development of personalized management strategies for CHD in T2DM patients. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e40074
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Song Yang, Kun He, Weikang Zhang +10 more · 2024 · International journal of biological macromolecules · Elsevier · added 2026-04-24
Adult tethered cord syndrome (ATCS) has a hidden onset and delayed clinical symptoms. The purpose of this study is to identify hub proteins in the cerebrospinal fluid of ATCS patients through bioinfor Show more
Adult tethered cord syndrome (ATCS) has a hidden onset and delayed clinical symptoms. The purpose of this study is to identify hub proteins in the cerebrospinal fluid of ATCS patients through bioinformatics analysis, and to find significant heterogeneity in these proteins between ATCS patients and non ATCS patients (control group). Firstly, differential genes were screened based on proteomic results. Compared with the control group, 18 differentially expressed proteins were upregulated and 18 differentially expressed proteins were downregulated in the cerebrospinal fluid of ATCS patients. Then, GO, KEGG, and GESA functional enrichment analysis showed that ATCS patients were active in biological processes such as coagulation, inflammatory response, and regulation of humoral immune response, suggesting the possibility of spinal cord injury. In addition, protein network interaction analysis indicates that APOB, APOC3, FGA, and FGG are defined as hub proteins. The correlation between ATCS patients and immune characteristics was analyzed using the CIBERSORT algorithm, which may have generated a unique immune microenvironment. Finally, Western blotting was used to experimentally validate APOB, APOC3, FGA, and FGG. The results showed that APOB, APOC3, FGA, and FGG were upregulated in the cerebrospinal fluid of ATCS patients and had an important impact on the repair and functional maintenance of spinal cord injury. They can be used as key proteins for early and accurate diagnosis and treatment of spinal cord thrombosis syndrome, and suggest that the spinal cord of ATCS patients may be damaged, which can serve as potential therapeutic targets. Show less
no PDF DOI: 10.1016/j.ijbiomac.2024.137534
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Yuhui Huang, Xuehui Sun, Qingxia Huang +13 more · 2024 · Translational psychiatry · Nature · added 2026-04-24
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites a Show more
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P < 0.001; AUPRC: 0.705 vs. 0.405, P < 0.001). This study identified specific plasma metabolites related to cognitive impairment. Incorporation of specific metabolites substantially improved the prediction performance for cognitive impairment. Show less
📄 PDF DOI: 10.1038/s41398-024-03147-9
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Haomin Huang, Lamei Li, Anni Yang +5 more · 2024 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in Show more
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in CAD risk between FH and non-FH patients with high low-density lipoprotein cholesterol (LDL-C) levels. Individuals (≥18 years) who underwent coronary angiography (CAG) from June 2016 to September 2020 were consecutively enrolled. Participants with LDL-C levels ≥4.0 mmol/L were ultimately included in this study. For all participants, next-generation sequencing was performed with expanded gene panels including 11 genes (LDLR, APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, LIPA, LPA, APOBR, LRPAP1, and STAP1). A total of 223 individuals were included in this study. According to the CAG findings, 199 CAD patients and 24 non-CAD patients were included. The proportions of FH genes, regardless of whether 3 major genes or all 11 genes were sequenced, were not significantly different between the CAD and non-CAD groups ( FH mutation did not increase the rate of CAD in individuals with an MLDL-C level ≥4.0 mmol/L. However, among CAD patients (MLDL-C level ≥4.0 mmol/L) with almost normal renal function (≥87.4 ml/min/1.73 m Show less
📄 PDF DOI: 10.3389/fcvm.2024.1434392
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Hui Zhang, Yiming Liu, Li Feng +9 more · 2024 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during d Show more
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during disease progression. Pathological features and serum lipid profiles of 179 patients with stage III-IV gastric adenocarcinoma were analyzed. Lipid parameters examined included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), apolipoprotein AI (Apo AI), apolipoprotein B (Apo B), lipoprotein(a) (Lp(a)), among others. The total cholesterol-lymphocyte score (TL score) and BMI were also calculated. The association between lipid parameters and clinicopathological characteristics such as age, gender, family history, and metastasis sites was assessed. In GC patients, females had higher TG levels than males. Patients with peritoneal metastasis had significantly lower levels of TC, LDL-C, Apo B, and B/A ratio. Those with lung metastasis exhibited higher LDL-C levels and lower levels of VLDL-C. No significant associations were found between lipid levels and metastasis to distant lymph nodes, liver, or bone. Female patients with ovarian metastasis had significantly lower VLDL-C levels. Multivariate analysis revealed low TC as an independent risk factor for peritoneal metastasis, high LDL-C and low VLDL-C levels for lung metastasis, and younger age and low VLDL-C for ovarian metastasis. Specific blood lipid levels are significantly associated with metastatic sites in advanced gastric cancer. Lipid profiles could serve as potential biomarkers for predicting metastatic sites in GC patients. Show less
📄 PDF DOI: 10.1186/s12876-024-03479-2
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Chia-Hsuan Cheng, Hiromi Yatsuda, Han-Hsiang Chen +3 more · 2024 · Sensors (Basel, Switzerland) · MDPI · added 2026-04-24
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. Show more
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. The majority of lipid measurements conducted in hospital settings employ optical detection, which necessitates the use of relatively large-sized detection machines. It is, therefore, necessary to develop point-of-care testing (POCT) for lipoprotein in order to monitor CVD. To enhance the management and surveillance of CVD, this study sought to develop a POCT approach for apolipoprotein B (ApoB) utilizing a shear horizontal surface acoustic wave (SH-SAW) platform to assess the risk of heart disease. The platform employs a reflective SH-SAW sensor to reduce the sensor size and enhance the phase-shifted signals. In this study, the platform was utilized to monitor the impact of a weekly almond and oat milk or statins intervention on alterations in CVD risk. The SH-SAW ApoB test exhibited a linear range of 0 to 212 mg/dL, and a coefficient correlation (R) of 0.9912. Following a four-week intervention period, both the almond and oat milk intervention (-23.3%, Show less
📄 PDF DOI: 10.3390/s24206517
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Weiyong Xu, Zhenchang Wang, Huaqing Yao +2 more · 2024 · International journal of general medicine · added 2026-04-24
To investigate the distribution of arteriosclerotic vessels of arteriosclerosis, differential serum lipid profiles, and differences in the proportion of dyslipidaemia between patients with single-site Show more
To investigate the distribution of arteriosclerotic vessels of arteriosclerosis, differential serum lipid profiles, and differences in the proportion of dyslipidaemia between patients with single-site arteriosclerosis and multi-site arteriosclerosis (significant hardening of ≥2 arteries). The data of 6581 single-site arteriosclerosis patients and 5940 multi-site arteriosclerosis patients were extracted from the hospital medical record system. Serum total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein (Apo) A1, ApoB concentrations and C-reactive protein (CRP) between patients with single-site arteriosclerosis and multi-site arteriosclerosis were collected and analyzed. The most diseased arteries were coronary arteries (n=7099, 33.7%), limb arteries (n=6546, 31.1%), and carotid arteries (n=5279, 25.1%). TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C levels were higher and CRP level was lower in multi-site arteriosclerosis patients than those in single-site arteriosclerosis patients. The TC, LDL-C levels in non-elderly (<65 years old) female patients were higher and TG/HDL-C, TC/HDL-C, LDL-C/HDL-C levels were lower than those in non-elderly male patients, while the TG, TC, LDL-C, and TG/HDL-C levels in elderly (≥65 years old) female patients were higher and LDL-C/HDL-C level was lower than those in elderly male patients. The proportion of dyslipidemia in descending order was as follows: low HDL-C (31.9%), elevated TG (16.9%), elevated TC (9.0%), and elevated LDL-C (4.2%). The levels of TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C in patients with peripheral arteriosclerosis were higher than those in patients with cardio-cerebrovascular arteriosclerosis. There were differences in serum lipid levels in patients with arteriosclerosis with different age, gender and distribution of arteriosclerotic vessels. Show less
📄 PDF DOI: 10.2147/IJGM.S483324
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Yufeng Wang, Linbo Guan, Xinghui Liu +6 more · 2024 · The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians · Taylor & Francis · added 2026-04-24
Gestational diabetes mellitus (GDM) is associated with metabolic abnormalities such as an altered serum lipid profile. This study investigated the influence of polymorphisms in the lipid metabolism-re Show more
Gestational diabetes mellitus (GDM) is associated with metabolic abnormalities such as an altered serum lipid profile. This study investigated the influence of polymorphisms in the lipid metabolism-related cholesteryl ester transfer protein gene ( This prospective case-control study included 665 women with GDM and 1,044 women with uncomplicated pregnancies. The PCR-restriction fragment length polymorphism method was used to genotype rs708272 and rs1800775 single nucleotide polymorphisms (SNPs). Lipid and glucose metabolic parameters were assessed. Genetic associations with related traits were analyzed. Genotype distributions of rs708272 and rs1800775 in patients with GDM were similar to those in normal controls. However, the two In patients with GDM, the rs708272 polymorphism was associated with atherogenic lipid levels (TG, TC, LDL-C, and ApoB), whereas the rs708272 and rs1800775 polymorphisms were associated with glucose metabolism and insulin resistance parameters, which were influenced by the body mass index. These results suggest that genetic associations with atherogenic metabolic factors may increase the risk of adverse outcomes in mothers with GDM and their offspring. Show less
no PDF DOI: 10.1080/14767058.2024.2415375
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X N Zhang, Q T Meng, H W Zhang +5 more · 2024 · Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] · added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112150-20240708-00546
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Haizhen Wang, Cyrus Nikain, Konstantinos I Fortounas +15 more · 2024 · Molecular metabolism · Elsevier · added 2026-04-24
Triglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely Show more
Triglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely understood. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytosolic lipid droplet (LD) biogenesis in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen. Experiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of the Fitm2 gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretion in vitro and in vivo were measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, stimulated Raman scattering (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy. 1) FITM2-deficient hepatic cells in vitro and in vivo secrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to low density lipoprotein (LDL) density; 3) Both in vitro and in vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress. The results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be an important factor in the partitioning of TG between cytosolic LDs and VLDL particles. Show less
📄 PDF DOI: 10.1016/j.molmet.2024.102048
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Wang-Dong Xu, You-Yue Chen, Xiang Wang +2 more · 2024 · Seminars in arthritis and rheumatism · Elsevier · added 2026-04-24
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstiti Show more
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstitial lung disease (ILD). Clinical, laboratory data of SLE patients were first collected. Meteorological data were then gathered according to the geographical locations of the SLE patients. Diagnostic results, univariate logistic regression, elastic net regression, and multivariate logistic regression were used to screen for risk factors for female SLE patients of reproductive age complicated with ILD. A nomogram was constructed using these risk factors and was internally and externally validated through methods such as calculating the concordance index, plotting calibration curves, drawing receiver operating characteristic curves, and clinical decision curves. A total of 4798 SLE patients were included in this study, with 2488 patients in the development set and 2310 patients in the external validation set. The patients in the development set were randomly divided into a training set (N = 1742) and an internal testing set (N = 746) at a ratio of 7:3. Eight independent risk factors for ILD were identified, including APOB, APOA1, ALP, PLT, HCT, EOS-R, LYM-R, and age. The nomogram model was developed, and the areas under the receiver operating characteristic curve was 0.811 (0.748, 0.875), 0.820 (0.727,0.913), and 0.889 (0.869, 0.909) for the three sets, respectively. We established a nomogram model using easily accessible clinical and laboratory data to predict the probability of female SLE patients of reproductive age developing ILD. Show less
no PDF DOI: 10.1016/j.semarthrit.2024.152556
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Erin O Jacob, Adam D McIntyre, Jian Wang +1 more · 2024 · The Journal of international medical research · SAGE Publications · added 2026-04-24
To investigate the relationship between plasma lipoprotein (a) (Lp[a]) and lipid profiles in patients with severe hypertriglyceridaemia (HTG). This case-control study undertook a retrospective chart r Show more
To investigate the relationship between plasma lipoprotein (a) (Lp[a]) and lipid profiles in patients with severe hypertriglyceridaemia (HTG). This case-control study undertook a retrospective chart review of patients from the Lipid Genetics Clinic at London Health Sciences Centre in Ontario, Canada. Plasma Lp(a) was compared between patients with severe HTG and healthy normolipidaemic control subjects. Severe HTG was defined by plasma triglycerides (TG) ≥ 10 mmol/l. Pairwise correlations between Lp(a), TG, apolipoprotein B (apo B) and non-high-density lipoprotein cholesterol (non-HDL-C) were evaluated. This study reviewed 4400 patients and identified 154 patients with severe HTG, which were compared with 272 control subjects. The median Lp(a) was significantly lower in patients with severe HTG compared with control subjects (5.0 versus 10.2 mg/dl, respectively). No correlation was observed between Lp(a) and TG or non-HDL-C. Lp(a) and apo B were modestly correlated in patients with severe HTG ( Patients with severe HTG have lower plasma Lp(a) than normolipidaemic control subjects. The basis for this relationship is not immediately apparent but is hypothesis-generating and warrants further investigation. Show less
📄 PDF DOI: 10.1177/03000605241289294
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Dong Liu, Jin Zhang, Xiaoyu Zhang +9 more · 2024 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
In recent years, the position of PCSK9 inhibitors as adjuvant therapy to statins in guidelines has further improved. However, there remained a dearth of direct comparative studies among different PCSK Show more
In recent years, the position of PCSK9 inhibitors as adjuvant therapy to statins in guidelines has further improved. However, there remained a dearth of direct comparative studies among different PCSK9 inhibitors. Therefore, this study aimed to conduct a network meta-analysis to evaluate the efficacy and safety of different PCSK9 inhibitors combined with statins. A comprehensive literature search was conducted from the study's inception to 12 November 2023, encompassing multiple online databases including PubMed, Embase, Cochrane Central, Web of Science, and ClinicalTrials.gov to obtain relevant randomized controlled trials. Frequentist network meta-analysis was employed to compare the efficacy and safety of different PCSK9 inhibitors. The efficacy endpoints were low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (ApoB), and lipoprotein (a) (Lp(a)). The safety endpoints were any adverse events (AE), severe adverse events (SAE), AE leading to treatment discontinuation, and injection-site reaction. Compared with placebo and ezetimibe, all PCSK9 inhibitors demonstrated significant reductions in LDL-C levels. Notably, evolocumab exhibited the most pronounced effect with a treatment difference of -63.67% (-68.47% to -58.87%) compared with placebo. Regarding dosage selection for evolocumab, the regimen of 140 mg Q2W (-69.13%, -74.55% to -63.72%) was superior to 420 mg QM (-61.51%, -65.97% to -57.05%). Based on rankings and Compared with placebo and ezetimibe, PCSK9 inhibitors can significantly reduce LDL-C, ApoB, and Lp(a) when combined with statins to treat hypercholesterolemia. Furthermore, PCSK9 inhibitors and ezetimibe exhibit similar safety profiles. [PROSPERO], identifier [CRD42023490506]. Show less
📄 PDF DOI: 10.3389/fcvm.2024.1454918
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Di Feng, Xiao Wang, Jiahui Song +8 more · 2024 · Human reproduction (Oxford, England) · Oxford University Press · added 2026-04-24
Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose lev Show more
Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose levels, and elevated serum fructose levels are an independent risk factor for hyperuricemia in women with PCOS. Our previous study suggested a link between elevated serum fructose levels and PCOS. Fructose is unique as it generates uric acid during metabolism, and high uric acid levels are associated with metabolic disorders and an increased risk of anovulation. However, the relationship between serum uric acid and fructose levels in women with PCOS remains unclear. In a case-control study of 774 women (482 controls and 292 patients with PCOS) between May and October 2020 at the Shengjing Hospital of China Medical University, the relationship between uric acid and fructose levels in women with PCOS was examined. Participants were divided into subgroups based on various factors, including BMI, insulin resistance, dyslipidemia, metabolic syndrome, and hyperuricemia. Serum uric acid concentrations were measured using enzymatic assays, and serum fructose levels were determined using a fluorescent enzyme immunoassay. Dietary fructose data were collected through a validated food-frequency questionnaire of 81 food items. We applied restricted cubic splines to a flexibly model and visualized the linear/nonlinear relationships between serum uric acid and fructose levels in PCOS. Multivariate logistic analysis was executed to assess the association between serum fructose levels and hyperuricemia in PCOS. Human granulosa cell and oocyte mRNA profile sequencing data were downloaded for mapping uric acid and fructose metabolism genes in PCOS. Further downstream analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interactions were then carried out on the differentially expressed genes (DEGs). The correlation between uric acid and fructose metabolism genes was calculated using the Pearson correlation coefficient. The GeneCards database was used to identify DEGs related to uric acid and fructose metabolism in PCOS, and then several DEGs were confirmed by quantitative real-time PCR. Both serum fructose and uric acid levels were significantly increased in women with PCOS compared with the control women (P  <  0.001), and there was no statistically significant difference in dietary fructose intake between PCOS and controls, regardless of metabolic status. There was a positive linear correlation between serum uric acid and fructose levels in women with PCOS (Poverall < 0.001, Pnon-linear = 0.30). In contrast, no correlation was found in control women (Poverall = 0.712, Pnon-linear = 0.43). Additionally, a non-linear association was observed in the obese subgroup of patients with PCOS (Poverall < 0.001, Pnon-linear = 0.02). Serum uric acid levels were linearly and positively associated with serum fructose levels in patients with PCOS with insulin resistance, dyslipidemia, and metabolic syndrome. Furthermore, even after adjusting for confounding factors, elevated serum fructose levels were an independent risk factor for hyperuricemia in patients with PCOS (P  =  0.001; OR, 1.380; 95% CI, 1.207-1.577). There were 28 uric acid and 25 fructose metabolism genes which showed a significant correlation in PCOS. Seven upregulated genes (CAT, CRP, CCL2, TNF, MMP9, GCG, and APOB) related to uric acid and fructose metabolism in PCOS ovarian granulosa cells were ultimately successfully validated using quantitative real-time PCR. Due to limited conditions, more possible covariates (such as smoking and ethnicity) were not included, and the underlying molecular mechanism between fructose and uric acid levels in women with PCOS remains to be further investigated. The results of this study and our previous research indicate that the high uric acid status of PCOS may be mediated by fructose metabolism disorders, highlighting the importance of analyzing fructose metabolism, and especially its metabolic byproduct uric acid, during the clinical diagnosis of PCOS. These results suggest the adverse effects of high uric acid in PCOS, and the importance of taking early interventions regarding uric acid levels to reduce the occurrence and development of further clinical signs, such as metabolic disorders in women with PCOS. This work was supported by: the National Natural Science Foundation of China (No. 82371647, No. 82071607, and No. 32100691); LiaoNing Revitalization Talents Program (No. XLYC1907071); Fok Ying Tung Education Foundation (No. 151039); and Outstanding Scientific Fund of Shengjing Hospital (No. 202003). No competing interests were declared. N/A. Show less
no PDF DOI: 10.1093/humrep/deae219
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Yongjiang Cheng, Jingyan Ye, Junyuan Huang +1 more · 2024 · PeerJ · added 2026-04-24
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Int Show more
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Intrahepatic cholestasis, being the most significant type of cholestasis, further complicates the assessment of lipid abnormalities. However, the accuracy of low-density lipoprotein cholesterol (LDL-C) measurement in intrahepatic cholestasis patients remains uncertain. This study aimed to evaluate the consistency of the homogeneous assay and the Friedewald formula in detecting LDL-C levels and identify factors influencing LDL-C test results in intrahepatic patients with cholestasis. Retrospective analysis of laboratory data was conducted on intrahepatic cholestatic patients. Correlations between LDL-C values obtained using the homogeneous method (LDL-C(D)) and the Friedewald formula (LDL-C(F)), as well as associations between high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (ApoA1), LDL-C(D) and LDL-C(F), and apolipoprotein B (ApoB), were analyzed. Logistic regression analyses were employed to identify diagnostic indicators for inaccurate LDL-C measurements in intrahepatic cholestatic patients. Compared to patients with intrahepatic cholestasis without jaundice, the correlation between LDL-C(F) and LDL-C(D) was weaker in those with jaundice. Additionally, HDL-C exhibited a strong correlation with ApoA1 in both jaundice and non-jaundice cholestasis cases. Elevated non-HDL-C to APOB ratio (NH-C/B Ratio) levels (>4.5) were identified as a reliable predictor of inaccurate LDL-C measurements in patients with chronic intrahepatic cholestasis accompanied by jaundice. LDL-C measurement reliability is moderately weaker in patients with intrahepatic cholestasis accompanied by jaundice. Elevated levels of the NH-C/B ratio serve as a significant predictor of inaccurate LDL-C measurements in this chronic patient population, highlighting its clinical relevance for diagnostic assessments. Show less
📄 PDF DOI: 10.7717/peerj.18224
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DanDan Zhang, Jing Wu, Guoqiang Ren +5 more · 2024 · Brain and behavior · Wiley · added 2026-04-24
To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. T Show more
To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. The Mini-Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent-sample t test or Mann-Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. Compared with the healthy control group, the levels of serum total cholesterol (TC), low-density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high-density lipoprotein (HDL) level was lower in the AD group (both p < 0.05). The hippocampal volume in the AD group was lower than in the control group (p < 0.01). The serum Apo B100 level was negatively correlated with MMSE score (r = -0.646), whereas hippocampal volume was positively correlated with MMSE score (r = 0.630). ROC curve analysis showed that the AUC of the combined serum Apo B100 level and hippocampal volume for AD was higher than that of either alone (AUC = 0.821, p < 0.01). Serum Apo B100 level is elevated, and the hippocampal volume is reduced in AD patients. The combined detection of the two has a higher diagnostic efficiency for AD than other alone and has the potential to become an important indicator for the diagnosis of AD in the future. Show less
📄 PDF DOI: 10.1002/brb3.70066
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Longfei Wang, Qiong Cheng · 2024 · Cancer control : journal of the Moffitt Cancer Center · SAGE Publications · added 2026-04-24
APOBEC-1 complementation factor (A1CF) and Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-1 (APOBEC-1) constitute the minimal proteins necessary for the editing of apolipoprotein B (apoB) Show more
APOBEC-1 complementation factor (A1CF) and Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-1 (APOBEC-1) constitute the minimal proteins necessary for the editing of apolipoprotein B (apoB) mRNA in vitro. Unlike APOBEC-1 and apoB mRNA, the ubiquitous expression of A1CF in human tissues suggests its unique biological significance, with various factors such as protein kinase C, thyroid hormones, and insulin regulating the activity and expression of A1CF. Nevertheless, few studies have provided an overview of this topic. We conducted a literature review to describe the molecular mechanisms of A1CF and its relevance to human diseases. In the PubMed database, we used the keywords 'A1CF' and 'APOBEC-1 complementation factor' to collect peer-reviewed articles published in English from 2000 to 2023. Two authors independently reviewed the articles and reached the consensus. After reviewing 127 articles, a total of 61 articles that met the inclusion criteria were included in the present review. Studies revealed that A1CF is involved in epigenetic regulation of reproductive cells affecting embryonic development, and that it is closely associated with the occurrence of gout due to its editing properties on apoB. A1CF can also affect the process of epithelial-mesenchymal transition in renal tubular epithelial cells and promote liver regeneration by controlling the stability of IL-6 mRNA, but no influence on cardiac function was found. Furthermore, increasing evidence suggests that A1CF may promote the occurrence and development of breast cancer, lung cancer, renal cell carcinoma, hepatocellular carcinoma, endometrial cancer, and glioma. This review clarifies the association between A1CF and other complementary factors and their impact on the development of human diseases, aiming to provide guidance for further research on A1CF, which can help treat human diseases and promote health. Show less
📄 PDF DOI: 10.1177/10732748241284952
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Xuan Wang, Lu Lyu, Wei Li +6 more · 2024 · Diabetes & metabolic syndrome · Elsevier · added 2026-04-24
This investigation aimed to evaluate the efficacy and safety of rosuvastatin in treating moderate to severe metabolic associated fatty liver disease (MAFLD). This prospective, open-label, randomized s Show more
This investigation aimed to evaluate the efficacy and safety of rosuvastatin in treating moderate to severe metabolic associated fatty liver disease (MAFLD). This prospective, open-label, randomized study included non-diabetic participants with metabolic syndrome and intrahepatocellular lipid (IHCL) levels >10 %, as determined by proton magnetic resonance spectroscopy ( Thirty-two participants completed the study. Rosuvastatin resulted in a significant absolute (△IHCL: 7.61 ± 4.51 vs. 1.54 ± 5.33, p = 0.002) and relative reduction in IHCL (△IHCL%: -42.28 ± 24.90 % vs. -8.91 ± 31.93 %, p = 0.003) compared to the control. Reduction in IHCL correlated significantly with decreases in low-density lipoprotein cholesterol (LDL-C) (r = 0.574, p < 0.01), apolipoprotein B (ApoB) (r = 0.660, p < 0.001), and free fatty acids (FFA) (r = 0.563, p = 0.005). No significant safety differences were observed between groups. Rosuvastatin significantly reduced hepatic steatosis in individuals with moderate to severe MAFLD and metabolic syndrome over 52 weeks, while maintaining a favorable safety profile. Show less
no PDF DOI: 10.1016/j.dsx.2024.103126
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Jiayu Wang, Lisi Xu, Xuemei Chen +10 more · 2024 · Journal of Alzheimer's disease : JAD · added 2026-04-24
Apolipoproteins and cortical morphology are closely associated with memory complaints, and both may contribute to the development of Alzheimer's disease. To examine whether apolipoprotein B (ApoB), ap Show more
Apolipoproteins and cortical morphology are closely associated with memory complaints, and both may contribute to the development of Alzheimer's disease. To examine whether apolipoprotein B (ApoB), apolipoprotein A-1 (ApoA1), and their ratio (ApoB/ApoA1) are associated with cortical morphology in patients with memory complaints. Ninety-seven patients underwent neuropsychological testing, measurements of ApoB, ApoA1, ApoB/ApoA1, plasma Alzheimer's biomarker, apolipoprotein E (ApoE) genotyping, and 3T structural magnetic resonance imaging (sMRI) scans. Based on sMRI scanning locations, patients were categorized into the University of Electronic Science and Technology (UESTC) and the Fourth People's Hospital of Chengdu (FPHC). The Computational Anatomy Toolbox within Statistical Parametric Mapping was used to calculate each patient's cortical morphology index based on sMRI data. The cortical morphology index and apolipoproteins were also analyzed. Significant positive correlations were found between ApoB and sulcal depth in the lateral occipital cortex among the UESTC, the FPHC, and the total sample groups, and negative correlations were observed between sulcal depth in the lateral occipital cortex and the scores of the Shape Trails Test Part A and B. In the FPHC group, the scores of the Montreal Cognitive Assessment Basic, delayed recall of the Auditory Verbal Learning Test, Animal Fluency Test and Boston Naming Test were positively correlated with the sulcal depth. ApoB is associated with the sulcal depth in the lateral occipital cortex, potentially relating to speed/executive function in individuals with memory complaints. Show less
no PDF DOI: 10.3233/JAD-230863
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Jiajie Mei, Xiaodan Fu, Zhenzhu Liu +9 more · 2024 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the Show more
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the risk factors and establish an accurate clinical prediction model for the rapid progression of NTLs post-PCI. PCSK9 inhibitors lower LDL-c levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI. We tried to test this hypothesis and explore the potential mechanisms. This retrospective study included 1250 patients who underwent the first PCI and underwent repeat coronary angiography for recurrence of chest pain within 24 months. General characteristics, laboratory tests and inflammatory factors(IL-10, IL-6, IL-8, IL-1β, sIL-2R, and TNF-α) were collected. Machine learning (LASSO regression) was mainly employed to select the important characteristic risk factors for the rapid progression of NTLs post-PCI and build prediction models. Finally, mediator analysis was employed to explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI. There were more diabetes, less beta-blockers and PCSK9 inhibitors application, higher HbA1c, LDL-c, ApoB, TG, TC, uric acid, hs-CRP, TNF-α, IL-6, IL-8, and sIL-2R in NTL progressed group. LDL-c, hs-CRP, IL-8, and sIL-2R were characteristic risk factors for the rapid progression of NTLs post-PCI, combining LDL-c, hs-CRP, IL-8, and sIL-2R builds the optimal model for predicting the rapid progression of NTLs post-PCI (AUC = 0.632). LDL-c had a clear and incomplete mediating effect (95% CI, mediating effect: 51.56%) in the reduction of the progression of NTLs by PCSK9 inhibitors, and there was a possible mediating effect of IL-8 (90% CI), and sIL-2R (90% CI). LDL-c, hs-CRP, IL-8, and sIL-2R may be the key characteristic risk factors for the rapid progression of NTLs post-PCI, and combining these parameters might predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors had a negative correlation with the rapid progression of NTLs. In addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs by reducing local inflammation of plaque. ChiCTR2200058529; Date of registration: 2022-04-10. Show less
📄 PDF DOI: 10.1186/s12872-024-04186-2
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Zhenqian Wang, Jiaying Zhang, Feng Jiao +3 more · 2024 · Journal of clinical lipidology · Elsevier · added 2026-04-24
Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remain unclear. This study aimed to investigate the Show more
Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remain unclear. This study aimed to investigate the relationships of lipids and their fractions with CKD in patients with T2D. Our prospective analysis involved 8,607 White participants with T2D but no CKD at baseline from the UK Biobank. Five common lipid traits were included as exposures. Weighted genetic risk scores (GRSs) for these lipid traits were developed. The causal associations between lipid traits, as well as lipid fractions, and CKD were explored using linear or nonlinear Mendelian randomization (MR). The 10-year predicted probabilities of CKD were evaluated via integrating MR and Cox models. Higher GRS of apolipoprotein B (ApoB) was associated with an increased CKD risk (hazard ratio (HR) [95% confidence interval (CI)]:1.07[1.02,1.13] per SD; P = 0.008) after adjusting for potential confounders. Linear MR indicated a positive association between genetically predicted ApoB levels and CKD (HR [95% CI]:1.53 [1.12,2.09]; P = 0.008), but no evidence of associations was found between other lipid traits and CKD in T2D. Regarding 12 ApoB- containing lipid fractions, a significant causal association was found between medium very-low-density lipoprotein particles and CKD (HR[95% CI]:1.16[1.02,1.32];P = 0.020). Nonlinear MR did not support nonlinearity in these causal associations. The 10-year probability curve showed that ApoB level was positively associated with the risk of CKD in patients with T2D. Lower ApoB levels were causally associated with a reduced risk of CKD in patients with T2D, positioning ApoB as a potential therapeutic target for CKD prevention in this population. Show less
no PDF DOI: 10.1016/j.jacl.2024.07.004
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Xiaohui Zhu, Dongmei Jiang, Hongjie Zhang +3 more · 2024 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2 Show more
The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications. Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed. Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses ( DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE. Show less
📄 PDF DOI: 10.2147/DMSO.S470297
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Xinyue Ming, Shirui Chen, Huijuan Li +3 more · 2024 · Cellular signalling · Elsevier · added 2026-04-24
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) les Show more
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) lesions and to clarify the antiatherosclerotic mechanism of 6-methylcoumarin (6-MC) via the modulation of miR-122. Bioinformatics analysis revealed that miR-122 was putatively overexpressed in a liver-specific manner and was downregulated in steatotic livers. miR-122 was shown to suppress the expression of Sortilin by complementarily pairing to the 3'-untranslated region (3'-UTR) of Sortilin mRNA via bioinformatics and dual-luciferase reporter assays, impeding Sortilin-mediated apoB-100 secretion from HepG2 cells. Administration of 6-MC significantly upregulated hepatocellular miR-122 levels, reducing Sortilin expression and apoB-100 secretion in HepG2 cells. The miR-122 mimic vigorously enhanced 6-MC-depressed Sortilin expression, while miR-122 inhibitor repealed the inhibitory effect of 6-MC on Sortilin expression to some extent in HepG2 cells. After internal intervention with the miR-122 precursor, and 6-MC supplementation alone or in combination with the miR-122 sponge led to the reduction in blood triglyceride (TG) levels, low-density lipoprotein-cholesterol (LDL-C) and apoB-100 and a reduction in aortic lipid deposition and AS lesions in apolipoprotein E-deficient (ApoE Show less
no PDF DOI: 10.1016/j.cellsig.2024.111384
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Enmin Ding, Fuchang Deng, Jianlong Fang +23 more · 2024 · Environmental health perspectives · added 2026-04-24
Environmental contaminants (ECs) are increasingly recognized as crucial drivers of dyslipidemia and cardiovascular disease (CVD), but the comprehensive impact spectrum and interlinking mechanisms rema Show more
Environmental contaminants (ECs) are increasingly recognized as crucial drivers of dyslipidemia and cardiovascular disease (CVD), but the comprehensive impact spectrum and interlinking mechanisms remain uncertain. We aimed to systematically evaluate the association between exposure to 80 ECs across seven divergent categories and markers of dyslipidemia and investigate their underpinning biomolecular mechanisms via an unbiased integrative approach of internal chemical exposome and multi-omics. A longitudinal study involving 76 healthy older adults was conducted in Jinan, China, and participants were followed five times from 10 September 2018 to 19 January 2019 in 1-month intervals. A broad spectrum of seven chemical categories covering the prototypes and metabolites of 102 ECs in serum or urine as well as six serum dyslipidemia markers [total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein (Apo)A1, ApoB, and ApoE4] were measured. Multi-omics, including the blood transcriptome, serum/urine metabolome, and serum lipidome, were profiled concurrently. Exposome-wide association study and the deletion/substitution/addition algorithms were applied to explore the associations between 80 EC exposures detection frequency Eight main ECs [1-naphthalene, 1-pyrene, 2-fluorene, dibutyl phosphate, tri-phenyl phosphate, mono-(2-ethyl-5-hydroxyhexyl) phthalate, chromium, and vanadium] were significantly associated with most dyslipidemia markers. Multi-omics indicated that the associations were mediated by endogenous biomolecules and pathways, primarily pertinent to CVD, inflammation, and metabolism. Clinical measures of cytokines and electrocardiograms further cross-validated the association of these exogenous ECs with systemic inflammation and cardiac function, demonstrating their potential mechanisms in driving dyslipidemia pathogenesis. It is imperative to prioritize mitigating exposure to these ECs in the primary prevention and control of the dyslipidemia epidemic. https://doi.org/10.1289/EHP13864. Show less
📄 PDF DOI: 10.1289/EHP13864
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Joel T Rämö, Sean J Jurgens, Shinwan Kany +8 more · 2024 · Circulation · added 2026-04-24
Despite a proposed causal role for LDL-C (low-density lipoprotein cholesterol) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to Show more
Despite a proposed causal role for LDL-C (low-density lipoprotein cholesterol) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to assess the impact on AS and peak velocity across the aortic valve conferred by lifelong alterations in LDL-C levels mediated by protein-disrupting variants in 3 clinically significant genes for LDL (low-density lipoprotein) metabolism ( We used sequencing data and electronic health records from UK Biobank (UKB) and All of Us and magnetic resonance imaging data from UKB. We identified predicted protein-disrupting variants with the Loss Of Function Transcript Effect Estimator (LOFTEE) and AlphaMissense algorithms and evaluated their associations with LDL-C and peak velocity across the aortic valve (UK Biobank), as well as diagnosed AS and aortic valve replacement (UK Biobank and All of Us). We included 421 049 unrelated participants (5621 with AS) in UKB and 195 519 unrelated participants (1087 with AS) in All of Us. Carriers of protein-disrupting variants in Rare genetic variants that confer lifelong higher or lower LDL-C levels are associated with substantially increased and decreased risk of AS, respectively. Early and sustained lipid-lowering therapy may slow or prevent AS development. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.124.070982
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Giada Di Nunzio, Sanna Hellberg, Yuyang Zhang +18 more · 2024 · Nature cardiovascular research · Nature · added 2026-04-24
Apolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is un Show more
Apolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is unknown. Here we show that the liver responds to a dyslipidemic insult based on inducible models of familial hypercholesterolemia and APOB tracing. An acute transition to atherogenic APOB lipoprotein levels resulted in uptake by Kupffer cells and rapid accumulation of triglycerides and cholesterol in the liver. Bulk and single-cell RNA sequencing revealed a Kupffer-cell-specific transcriptional program that was not activated by a high-fat diet alone or detected in standard liver function or pathological assays, even in the presence of fulminant atherosclerosis. Depletion of Kupffer cells altered the dynamic of plasma and liver lipid concentrations, indicating that these liver macrophages help restrain and buffer atherogenic lipoproteins while simultaneously secreting atherosclerosis-modulating factors into plasma. Our results place Kupffer cells as key sentinels in organizing systemic responses to lipoproteins at the initiation of atherosclerosis. Show less
📄 PDF DOI: 10.1038/s44161-024-00448-6
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Zhehan Yang, Junpan Chen, Minghao Wen +6 more · 2024 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Aberrant lipid metabolism is intricately linked to the development of endometrial cancer, and statin lipid-lowering medications are regarded as promising adjunctive therapies for future management of Show more
Aberrant lipid metabolism is intricately linked to the development of endometrial cancer, and statin lipid-lowering medications are regarded as promising adjunctive therapies for future management of this malignancy. This study employed Mendelian randomization (MR) to explore the causal association between lipid traits and endometrial cancer while assessing the potential impact of drug targets on lower lipids on endometrial cancer. Two-sample Mendelian randomization was employed to probe the causal association between lipid traits and endometrial carcinoma. Drug-target Mendelian randomization was also utilized to identify potential drug-target genes for managing endometrial carcinoma. In instances where lipid-mediated effects through particular drug targets were notable, the impacts of these drug targets on endometrial carcinoma risk factors were investigated to bolster the findings. No causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC was found in two-sample Mendelian randomization. In drug target Mendelian randomization, genetic modeling of apolipoprotein B (APOB) (OR [95%CI]=0.31, [0.16-0.60]; The results of our MR study revealed no causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC. Among the six lipid-lowering drug targets, we observed a significant association between lower predicted APOB levels and higher CETP levels with an increased risk of endometrioid carcinoma. These findings provide novel insights into the importance of lipid regulation in individuals with endometrial carcinoma, warranting further clinical validation and mechanistic investigations. Show less
📄 PDF DOI: 10.3389/fendo.2024.1446457
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