👤 Huiqun Wang

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Also published as: Junli Wang, Xindi Wang, Junpeng Wang, Tingyu Wang, Guoqiang Wang, Yuxuan Wang, Hanzhi Wang, Zhi-Long Wang, Shanshan Wang, Wenfei Wang, Dengbin Wang, Yen-Sheng Wang, Chuanxin Wang, Zeyu Wang, Beibei Wang, Taicheng Wang, Xingguo Wang, Z P Wang, Yue-Min Wang, Chenghua Wang, Xianqiang Wang, Congrong Wang, Yanhai Wang, Du Wang, Xianzhe Wang, Zuoheng Wang, Yongyi Wang, Zhihui Wang, Yanhua Wang, Limeng Wang, H J Wang, Pei-Jian Wang, Yana Wang, Congrui Wang, Larry Wang, Yu-Zhuo Wang, Sihua Wang, Wanchun Wang, Jialin Wang, Xinying Wang, Shuguang Wang, Yinhuai Wang, Xiaobin Wang, Yuying Wang, Hebo Wang, Leli Wang, Jiayu Wang, Zhaojun Wang, Hai Wang, Si Wang, Re-Hua Wang, Xuping Wang, Bo Wang, Shubao Wang, Songjiao Wang, Hongjia Wang, Victoria Wang, Ling Wang, Jianjie Wang, Haining Wang, Dali Wang, Ji-Yang Wang, Cheng Wang, Weifan Wang, Yuanqiang Wang, Zhixiao Wang, Yaxian Wang, Zhigang Wang, Haochen Wang, Jia-Ying Wang, Shichao Wang, Ruosu Wang, N Wang, Haixing Wang, Guiqun Wang, Zhiting Wang, Dan Wang, Wangxia Wang, Jing-Long Wang, Yaqian Wang, Yafang Wang, Xing-Jun Wang, Dapeng Wang, Zhongyuan Wang, Junsheng Wang, Zhaohai Wang, He-Ping Wang, Minmin Wang, Wenzhou Wang, Zhaohui Wang, Yanfang Wang, Pengtao Wang, Leran Wang, Qianwen Wang, Hongkun Wang, Sa Wang, Y Alan Wang, Liyan Wang, Jou-Kou Wang, Mingda Wang, Chenfei Wang, Yuehan Wang, Simeng Wang, Yuhua Wang, Ruibin Wang, Haibo Wang, Ni Wang, Guoxiu Wang, Zhuangzhuang Wang, Yajie Wang, Zhixiang Wang, Sangui Wang, Xiantao Wang, Yan-Yang Wang, Mengjun Wang, Ruling Wang, Peihe Wang, Miao Wang, Zaihua Wang, Jun-Jie Wang, Mengyao Wang, Zhiyu Wang, Changzhen Wang, Xijun Wang, Chengjian Wang, Yiyi Wang, Mo Wang, Xiaolun Wang, Danan Wang, Fanchang Wang, Zilin Wang, Fanhua Wang, Supeng Perry Wang, Gavin Wang, Yi-Ying Wang, Yani Wang, Zhuowei Wang, Weiwei Wang, Haifeng Wang, Yi-Shiuan Wang, Yan-Chao Wang, Xiaotong Wang, Jia-Qi Wang, Yongliang Wang, Yongming Wang, Fengchong Wang, Jianyong Wang, Zeping Wang, Huaquan Wang, Xiaojia Wang, Tao Wang, Tianjun Wang, Siying Wang, Zhenze Wang, Zhijian Wang, Li Wang, Heming Wang, Jingtong Wang, Xuefei Wang, Yingqiao Wang, Xiao Qun Wang, Chun-Chieh Wang, Shuang-Xi Wang, Laiyuan Wang, Zhaoming Wang, Yinggui Wang, Qi-Jia Wang, Wen-Yan Wang, Mingming Wang, Peipei Wang, Chien-Hsun Wang, Qiuhong Wang, Monica Wang, Lexin Wang, Xiufen Wang, Yuehua Wang, Pingfeng Wang, Caiyan Wang, Weijie Wang, Yigang Wang, Jieyan Wang, Huiquan Wang, Chunsheng Wang, Yunhe Wang, Changtu Wang, Qingliang Wang, Guanghua Wang, Yongbin Wang, Zhaobo Wang, Minghui Wang, Junshi Wang, Jingyu Wang, Longsheng Wang, Fen Wang, Xianshu Wang, Jianwu Wang, Jun-Zhuo Wang, Zhixing Wang, Lei Wang, Yiyan Wang, Jinglin Wang, Jinhe Wang, Enhua Wang, Yuecong Wang, Xueying Wang, Jennifer T Wang, Xin-Hua Wang, Shijie Wang, Chun-Xia Wang, Yuanjiang Wang, Xiaojun Wang, Shunjun Wang, Chun-Juan Wang, M Wang, Jinfei Wang, Jinghuan Wang, Xuru Wang, Xiao-Lan Wang, Yu-Chen Wang, Zhi-Guo Wang, Luya Wang, Shuwei Wang, Pingchuan Wang, Qifan Wang, Xing-Quan Wang, Weiding Wang, Xuebin Wang, Yaling Wang, Chenyin Wang, Allen Wang, Liyuan Wang, Rong-Rong Wang, Wusan Wang, Wayseen Wang, Qianru Wang, Yi-Xin Wang, Hailin Wang, Yu-Hang Wang, Xuesong Wang, Haojie Wang, Wanxia Wang, Mengwen Wang, Hanping Wang, Yuhang Wang, Lueli Wang, Xinchang Wang, Oliver Wang, Shuge Wang, Jianhao Wang, Chong Wang, Kui Wang, Litao Wang, Zining Wang, Ming-Yang Wang, Hongxia Wang, Mingyi Wang, Hai Bo Wang, Bingnan Wang, Hongqian Wang, Jisheng Wang, Jiakun Wang, Maoju Wang, Xiaoqiu Wang, Dongyi Wang, Hai Yang Wang, Pengju Wang, Xiaofeng Wang, Huming Wang, Jian'an Wang, Qianrong Wang, Xiaowei Wang, Xiangkun Wang, Da Wang, Hongying Wang, Changying Wang, Changyu Wang, Xiaoqin Wang, Zhenxi Wang, Qiaoqiao Wang, Yu Tian Wang, Yupeng Wang, Xinli Wang, YueJiao Wang, Jian-chun Wang, Pengchao Wang, Xiao-Juan Wang, Siqing Wang, C Z Wang, Pengbo Wang, Baoli Wang, Yu-Zhe Wang, Gui-Qi Wang, Dazhi Wang, Yanwen Wang, Xingqin Wang, Shijin Wang, Wenming Wang, Fanxiong Wang, Tiansong Wang, Shuzhe Wang, Jie Wang, Jinling Wang, Yunfang Wang, Luyao Wang, Cun-Yu Wang, Zikang Wang, Quan-Ming Wang, Yingying Wang, Chia-Chuan Wang, Xintong Wang, Jufeng Wang, Xuejun Wang, Xiao-Qian Wang, Yijin Wang, Meng Yu Wang, Tianyi Wang, Chia-Lin Wang, Zhuo-Jue Wang, Yaohe Wang, Rong Wang, Hao-Hua Wang, Yong-Jun Wang, Xubo Wang, Dalong Wang, Yan-Ge Wang, Erika Y Wang, Ruixian Wang, Jin-Liang Wang, Shicung Wang, Saifei Wang, Jintao Wang, Zhenzhen Wang, Jiawei Wang, Beilei Wang, Huabo Wang, Huiyu Wang, Hongtao Wang, Chengjun Wang, Guo-Du Wang, Taoxia Wang, Zitao Wang, Jingwen Wang, Yibin Wang, Long Wang, Xinjing Wang, Qunzhi Wang, Liangliang Wang, Bangchen Wang, Yu-Fen Wang, Shibin Wang, Congcong Wang, Xiong Wang, Zhiren Wang, Xiaozhu Wang, Hong-Xia Wang, Qingyong Wang, Tianying Wang, Tammy C Wang, Huijie Wang, Tiansheng Wang, Mengzhao Wang, Jianshu Wang, Xinlong Wang, Benzhong Wang, Zhipeng Wang, Kaijie Wang, Xiaomin Wang, Peijun Wang, Zhiqiang Wang, Jundong Wang, Zheng Wang, Yueze Wang, Sujuan Wang, Qing-Yun Wang, Xiaoqing Wang, Zongqi Wang, Zhicun Wang, Fudi Wang, Seok Mui Wang, Wanbing Wang, Kejun Wang, Nanping Wang, Mingyang Wang, Wenxia Wang, Yaru Wang, Zikun Wang, Shidong Wang, Bei Bei Wang, Yu-Hui Wang, Rui Wang, Yige Wang, Tongxin Wang, Xiaohua Wang, Changjing Wang, Xingjin Wang, Bingjie Wang, Shaoyu Wang, Hui-Hui Wang, Zhenyu Wang, Baoying Wang, Yang-Yang Wang, Shi-Yao Wang, Lifei Wang, Fangfang Wang, Zhimei Wang, Kunpeng Wang, Binglong Wang, Daijun Wang, Qinghang Wang, Zi Wang, Shushu Wang, QingDong Wang, Qing K Wang, Fuhua Wang, Yanni Wang, Jianle Wang, Wenyan Wang, Jinning Wang, Ziqi Wang, Wei-Qi Wang, Yaolou Wang, Haoming Wang, Jian-Wei Wang, Tian Wang, Peixi Wang, Iris X Wang, Tongxia Wang, Mei-Xia Wang, Haiying Wang, Tielin Wang, Hongze Wang, Chung-Hsi Wang, Peiyao Wang, Linli Wang, Guanru Wang, Yuzhong Wang, Yunhan Wang, Jianan Wang, Menglong Wang, Yingxue Wang, Jiayi Wang, Dingxiang Wang, Ting Wang, Fenglin Wang, Jianqun Wang, Ran Wang, Kuan Hong Wang, Liusong Wang, Wen-Der Wang, Yixuan Wang, Feng Wang, Kaicen Wang, Eryao Wang, Yulei Wang, Huaibing Wang, Zhongzhi Wang, Jinrong Wang, Sujie Wang, Xiaozhong Wang, Xiao-Pei Wang, Li-Na Wang, H X Wang, Linjie Wang, Zhaosong Wang, Yafen Wang, Chuan-Wen Wang, Xiaoning Wang, Li-Xin Wang, Silas L Wang, Baocheng Wang, Hongyi Wang, Zhi-Xiao Wang, Shengjie Wang, Zhi-Hao Wang, Yaokun Wang, Shao-Kang Wang, Qunxian Wang, Jianghui Wang, Zhao Wang, Di Wang, Jianzhi Wang, Ruijing Wang, Ling Jie Wang, Qingshi Wang, Jianye Wang, Yuqiang Wang, Kangling Wang, Anxin Wang, Shengli Wang, Zhulin Wang, Hua-Wei Wang, Yiwen Wang, Yang Wang, Hanqi Wang, Changwei Wang, Honglei Wang, Yi Lei Wang, Wenkang Wang, Junjie Wang, Yazhou Wang, Peng-Cheng Wang, Chenzi Wang, Anqi Wang, Yuemiao Wang, Xuelin Wang, Rujie Wang, Dongyan Wang, Yuxue Wang, Wengong Wang, Qigui Wang, Junqing Wang, Ruhan Wang, Xinye Wang, Huihui Wang, Gengsheng Wang, Mark Wang, Zhidong Wang, Mengmeng Wang, Yuwen Wang, Liang Wang, Huaxiang Wang, Fangjun Wang, Huixia Wang, Haijiao Wang, Hong-Hui Wang, Yi-Shan Wang, Yunchao Wang, Junjun Wang, Binghai Wang, Xinguo Wang, Jun-Sing Wang, Lingzhi Wang, Yuexiang Wang, Hong-Gang Wang, Yen-Feng Wang, Xidi Wang, Jiawen Wang, Liangfu Wang, Lifeng Wang, Shihan Wang, Wentian Wang, Sa A Wang, Lee-Kai Wang, Yu-Wei Wang, Zumin Wang, Shau-Chun Wang, Jianjiao Wang, Tian-Tian Wang, Jiantao Wang, Edward Wang, Jianbo Wang, Qingfeng Wang, Wenran Wang, Xiaolin Wang, Fenghua Wang, Rongjia Wang, Shiqiang Wang, Caixia Wang, Guihu Wang, Xindong Wang, Wenxiu Wang, Xueguo Wang, YiLi Wang, Aizhong Wang, Qiqi Wang, Chengcheng Wang, D Wang, L Wang, Jianhua Wang, Qiuling Wang, Shaolian Wang, Wen-Qing Wang, Wenqing Wang, Yuchuan Wang, Guangdi Wang, Yiquan Wang, Huimei Wang, Genghao Wang, Zun Wang, Miranda C Wang, Annette Wang, Chi-Ping Wang, Hanmin Wang, Zhaoxi Wang, Shifeng Wang, Runze Wang, Mangju Wang, Junjiang Wang, Dong D Wang, Xiu-Ping Wang, Haijiu Wang, Linghuan Wang, Yiying Wang, Renqian Wang, Nana Wang, Xiangdong Wang, Shiyin Wang, Chaoyi Wang, Menghan Wang, Shuyue Wang, Yongmei Wang, Nanbu Wang, Lihua Wang, Hongyue Wang, Jianli Wang, Chunli Wang, Minghua Wang, Junkai Wang, Chenguang Wang, Siyue Wang, Jun Wang, Shu-Song Wang, Bingyan Wang, Qingping Wang, Zhong-Yu Wang, Fei-Fei Wang, Jennifer E Wang, Z-Y Wang, Dongxia Wang, Dang Wang, Zi-Hao Wang, Rihua Wang, Jutao Wang, Yanzhe Wang, Guohao Wang, Liming Wang, Yishu Wang, Xuemin Wang, Xianfeng Wang, Zixu Wang, Jingfan Wang, Guang-Jie Wang, Guixue Wang, Jiaojiao Wang, Yaxin Wang, Haibing Wang, Weizhong Wang, Hairong Wang, Hai-Jun Wang, Mingji Wang, Yongrui Wang, Huizhi Wang, Longfei Wang, Chongmin Wang, Jingyang Wang, Zhong-Ping Wang, Huanhuan Wang, Baisong Wang, Xiaohui Wang, Fengyang Wang, Wanliang Wang, Ziqiang Wang, Chuan Wang, Jeffrey Wang, Ying-Zi Wang, Ziwei Wang, Xian Wang, Hanyu Wang, Qiming Wang, Dedong Wang, Fengying Wang, Xiaoya Wang, Zhenhua Wang, Yanchun Wang, Keming Wang, Zi-Yi Wang, Dezhong Wang, Jingying Wang, Shouli Wang, Lan-lan Wang, Weiyu Wang, Yuhuai Wang, Jun Yi Wang, Wenying Wang, Xue-Feng Wang, Xing-Lei Wang, Yuehong Wang, Pengyu Wang, Yihe Wang, Guodong Wang, Weijian Wang, Wu-Wei Wang, Y Wang, Ruonan Wang, Jianbing Wang, Mian Wang, Dennis Qing Wang, Nannan Wang, Zuo Wang, Christine Wang, Ruixin Wang, Yaxiong Wang, Siwei Wang, Yuanzhen Wang, Wen-Chang Wang, Haijing Wang, X Wang, Melissa T Wang, Haixia Wang, Qianghu Wang, Hongsheng Wang, Xiurong Wang, Shaowei Wang, Shuo Wang, Zengtao Wang, Yun-Xing Wang, Songtao Wang, Mei Wang, Mengyun Wang, Qingming Wang, Ke-Feng Wang, Zhihao Wang, Haoqi Wang, X E Wang, Xin-Shang Wang, Dongmei Wang, Lingli Wang, Huai-Zhou Wang, Hua Wang, Kunzheng Wang, Mao-Xin Wang, Jingzhou Wang, Jiaqi Wang, Xingbang Wang, Wence Wang, Yongdi Wang, Xin-Qun Wang, Guoyi Wang, Jian-Guo Wang, Jiafu Wang, Pin Wang, Libo Wang, Junling Wang, J Z Wang, Haozhou Wang, Jing Wang, Hezhi Wang, T Q Wang, Xi-Hong Wang, Yuanfan Wang, Endi Wang, Hua-Qin Wang, Jeremy Wang, Songping Wang, Suyun Wang, Jiqing Wang, Shu-Ling Wang, Jennifer X Wang, Lily Wang, Yin-Hu Wang, Jen-Chywan Wang, Qingqing Wang, Shuangyuan Wang, Haihong Wang, Luyun Wang, Yake Wang, Ya-Nan Wang, Weicheng Wang, Jianxiang Wang, Zihua Wang, Lin Wang, Fu-Sheng Wang, Zongbao Wang, Tong-Hong Wang, Xianze Wang, Ting-Ting Wang, Haibin Wang, Xin-Yue Wang, Zhi-Gang Wang, Ziying Wang, Shukang Wang, Wen-Jun Wang, Delin Wang, Yating Wang, Xuehao Wang, Yefu Wang, Yi-Ning Wang, Cheng-zhang Wang, Jing J Wang, Xinglong Wang, Yanqing Wang, Tongyao Wang, Dongyang Wang, Deqi Wang, Qiao Wang, Alice Wang, Yunzhi Wang, Dayong Wang, Renxi Wang, Yeh-Han Wang, Mingya Wang, Longxiang Wang, Hualin Wang, Hailei Wang, Ao Wang, Wanyu Wang, Jiale Wang, Qiangcheng Wang, Huishan Wang, Yunqiong Wang, Xudong Wang, Xifu Wang, Wen-Xuan Wang, Dao Wen Wang, Zhi-Wei Wang, Xingchen Wang, Yanyang Wang, Yutao Wang, Huizhen Wang, Hu WANG, Y P Wang, Wen Wang, Qingsong Wang, Baofeng Wang, Ruo-Ran Wang, Yaobin Wang, Changliang Wang, Pintian Wang, Dai Wang, Su-Guo Wang, Ruting Wang, Fengzhen Wang, Qinrong Wang, HuiYue Wang, Baosen Wang, Shuhe Wang, Yifei Wang, Jiun-Ling Wang, Junhui Wang, Guangzhi Wang, Qijia Wang, Yushe Wang, Jinlong Wang, Zhouguang Wang, Huiyao Wang, Shuxin Wang, Yingyi Wang, Jing-Yi Wang, Yongxiang Wang, Zhi Wang, Dehao Wang, Yi-sheng Wang, Jiazhi Wang, Yunfei Wang, Mingjin Wang, Yaozhi Wang, Jinyu Wang, Jinmeng Wang, LiLi Wang, Shuai Wang, Yan Wang, Jun Kit Wang, Cui Wang, Zhan Wang, Dong-Jie Wang, Yangyang Wang, Xiangguo Wang, Runuo Wang, Ruimin Wang, Pengpu Wang, Nuan Wang, Guangyan Wang, Xin-Liang Wang, Minxiu Wang, Ruifang Wang, Hui Wang, Hongda Wang, Xiyan Wang, Jinxia Wang, Xinchen Wang, Haihua Wang, Delong Wang, Yayu Wang, Xue-Hua Wang, Xin-Peng Wang, Changqian Wang, Bei Wang, Ya-Han Wang, Chih-Liang Wang, P N Wang, Xiaoqian Wang, Xianshi Wang, Zhiruo Wang, Xueding Wang, Renxiao Wang, Yi-Ming Wang, Tianqi Wang, Ledan Wang, Rongyun Wang, Gan Wang, Qinqin Wang, Yuxiang Wang, Feimiao Wang, Mengyuan Wang, Chaofan Wang, Linshuang Wang, Yanhui Wang, Zhenglong Wang, Zongkui Wang, Zhenwei Wang, Xiyue Wang, Yi Fan Wang, Xiao-Ai Wang, Po-Jen Wang, Xinyang Wang, Linying Wang, Fa-Kai Wang, Yimeng Wang, Dong-Mei Wang, Anli Wang, Hui-Li Wang, Jianqing Wang, Honglun Wang, Wei-Feng Wang, Kaihao Wang, Jialing Wang, Shuren Wang, Cui-Fang Wang, Wenqi Wang, Peilin Wang, Wen-Fei Wang, Guang-Rui Wang, T Wang, Weiqing Wang, Ciyang Wang, Biao Wang, Kaihe Wang, Jieh-Neng Wang, Tony Wang, Yuehu Wang, Zhicheng Wang, Tongtong Wang, Zi Xuan Wang, Yingtai Wang, Xin-Xin Wang, Chu Wang, Tianhao Wang, Shukui Wang, Ching C Wang, Yulin Wang, Chunyang Wang, Yeqi Wang, Yinbo Wang, Kongyan Wang, Weiling Wang, Linxuan Wang, Shengya Wang, Yaqi Wang, Huating Wang, Aiting Wang, Ya Xing Wang, Daoping Wang, Shasha Wang, Wei-Lien Wang, Quanli Wang, Yanru Wang, L M Wang, Bijue Wang, H Wang, Jipeng Wang, Xiaoxia Wang, Shuu-Jiun Wang, Baitao Wang, Haimeng Wang, Chung-Hsing Wang, Weining Wang, M Y Wang, Wenwen Wang, Zhongsu Wang, Xiaochen Wang, Ligang Wang, Shaohsu Wang, Bing Qing Wang, Jiangbin Wang, Yajun Wang, Chunting Wang, Hemei Wang, En-hua Wang, H-Y Wang, Zixi Wang, Wenjing Wang, Haikun Wang, Ruxin Wang, Jianru Wang, Yongqiang Wang, Ouchen Wang, Jianyu Wang, Shen Wang, Yixi Wang, Zhi-Hong Wang, Li Dong Wang, Zhou-Ping Wang, Wen-Yong Wang, Meng-Lan Wang, Xiaojie Wang, Leying Wang, Yi-Zhen Wang, Y Y Wang, Jianlin Wang, Guoqing Wang, Jiani Wang, Guan-song Wang, You Wang, Xiangding Wang, Ke Wang, Wendong Wang, Yue Wang, Zhe Wang, K Wang, Zhuo Wang, Su'e Wang, Cangyu Wang, Erfei Wang, Xiaoming Wang, Aijun Wang, Xiaoye Wang, Jun-Sheng Wang, Wenxiang Wang, Yanjun Wang, Qiangqiang Wang, Yachun Wang, Haitao Wang, Tiancheng Wang, Gangyang Wang, Jianmin Wang, Jiabo Wang, Yijing Wang, Mengzhi Wang, Yinuo Wang, Zhou Wang, Guiying Wang, Xuezheng Wang, Shan Wang, Aoli Wang, Fuqiang Wang, Yawei Wang, Xianxing Wang, Ya-Long Wang, Yuyang Wang, Dong Hao Wang, Y-S Wang, Zelin Wang, Liqun Wang, Cunyi Wang, Qian-Zhu Wang, Yinan Wang, Panfeng Wang, Guangwen Wang, J Q Wang, Guang Wang, Yu-Ping Wang, John Wang, Jiaping Wang, Zhisheng Wang, Xuan-Ren Wang, Xiaowu Wang, Zhengyu Wang, Baowei Wang, Zhijun Wang, Zhong-Hao Wang, Fengzhong Wang, Jin-Da Wang, Zhaoqing Wang, Yuanbo Wang, Haixin Wang, Yaping Wang, Lixiu Wang, Mingxia Wang, Neng Wang, Guozheng Wang, Yan-Feng Wang, Huafei Wang, Yuhan Wang, Xingxing Wang, Wenhe Wang, Xing-Huan Wang, Xiansong Wang, Yishan Wang, Ruming Wang, Ya Qi Wang, Yueying Wang, Chunle Wang, Shihua Wang, W Wang, Hengjun Wang, Meihui Wang, Huanyu Wang, Ruinan Wang, Qiwei Wang, Zhong Wang, Shiyao Wang, Jian-Zhi Wang, Ruimeng Wang, Jinxiang Wang, Jinsong Wang, Bin-Xue Wang, Fuwen Wang, Yiou Wang, Shifa Wang, Yin Wang, Yanzhu Wang, Jia Bin Wang, Siyang Wang, Zhanggui Wang, Yueting Wang, Qingyu Wang, Qianqian Wang, Xiu-Lian Wang, Fengling Wang, Chenxi Wang, Cheng An Wang, Yipeng Wang, Weipeng Wang, Zechen Wang, Shuaiqin Wang, Xueqian Wang, Chan Wang, Guohang Wang, Cai-Yun Wang, Jiang Wang, Huei Wang, Yufeng Wang, Heng Wang, Qing-Liang Wang, Chuang Wang, Xiaofang Wang, Hao-Ching Wang, Junying Wang, Jianwei Wang, Jinhai Wang, Hanchao Wang, Penglai Wang, I-Ching Wang, S L Wang, Tianhu Wang, Sheng-Min Wang, Pan-Pan Wang, Duan Wang, Xuqiao Wang, Minghuan Wang, Wei-Wei Wang, Xiaojian Wang, Shuping Wang, Jinfu Wang, Biqi Wang, Zhenguo Wang, Fangyan Wang, Sainan Wang, Peijuan Wang, Pei-Yu Wang, Yuyan Wang, Fuxin Wang, Ji M Wang, Yange Wang, Yali Wang, Wenhui Wang, Leishen Wang, Lichan Wang, Xianna Wang, Wenbin Wang, Kenan Wang, Chih-Yuan Wang, Yanlei Wang, Ju Wang, Yanliang Wang, Keqing Wang, Bangshing Wang, Dayan Wang, Yongsheng Wang, Dinghui Wang, Zheyue Wang, Xinke Wang, Daqing Wang, Yan Ming Wang, He-Ling Wang, Shengyao Wang, Jiwen Wang, Xizhi Wang, Luxiang Wang, Dandan Wang, RongRong Wang, Heng-Cai Wang, Jindan Wang, Xiaoding Wang, Yumeng Wang, Heling Wang, Xiao-Yun Wang, Meiding Wang, Zhilun Wang, Guo-hong Wang, Na Wang, Yanli Wang, Fubing Wang, Feixiang Wang, Zhiyuan Wang, Yi-Cheng Wang, Zhengwei Wang, Wenyuan Wang, Yu-Ying Wang, Jianqin Wang, Sijia Wang, Chuansen Wang, Huawei Wang, Kaiyan Wang, Qingyuan Wang, Yujia Wang, Lian Wang, Junrui Wang, Chao-Yung Wang, Zehao Wang, Ruixue Wang, Minjun Wang, Jin Wang, Xiaoxiao Wang, Jun-Feng Wang, Binquan Wang, Shuxia Wang, Donggen Wang, Deming Wang, Chenggang Wang, Chuduan Wang, Haichuan Wang, Catherine Ruiyi Wang, Hai-Feng Wang, Anthony Z Wang, Guanghui Wang, Jiahao Wang, Xiaosong Wang, Zijue Wang, Wenbo Wang, M-J Wang, Yu Wang, Yingping Wang, Zhengbing Wang, G Q Wang, Mengjing Wang, Zhendong Wang, Kailu Wang, Jinfeng Wang, Zhiguo Wang, Yusha Wang, Jianmei Wang, Kun Wang, Lihong Wang, Haoxin Wang, Haowei Wang, Ziqing Wang, Aihua Wang, Yuanyong Wang, Sanwang Wang, Doudou Wang, Hao-Yu Wang, Peirong Wang, Wenting Wang, Yibing Wang, He Wang, Jia-Peng Wang, Shixin Wang, En-bo Wang, Dong-Dong Wang, Hualing Wang, Hongyan Wang, Shaoying Wang, Yingjie Wang, Tianqing Wang, Guo-Hua Wang, Yongfei Wang, Lijing Wang, Hongli Wang, Zixian Wang, Niansong Wang, Liangxu Wang, Xinrong Wang, X-T Wang, Zhenning Wang, Dake Wang, Yu-Ting Wang, Zonggui Wang, Daping Wang, Joy Wang, Chenji Wang, Jingmin Wang, Yuyin Wang, Jin-Cheng Wang, Jiangbo Wang, Huiyang Wang, Chi Chiu Wang, He-Cheng Wang, Zhongjing Wang, Weina Wang, Qiaohong Wang, Qintao Wang, Jenny Y Wang, Zheyi Wang, Robert Yl Wang, Zhaotong Wang, Ya Wang, Fangyu Wang, Haobin Wang, Tianyuan Wang, Xinrui Wang, Zhehao Wang, Yihan Wang, Chuan-Jiang Wang, Jianjun Wang, Yongfeng Wang, Gaofu Wang, Ying-Piao Wang, Jingwei Wang, Mengjiao Wang, Chuyao Wang, Yanping Wang, Xinchun Wang, Shu Wang, Guibin Wang, Hong-Ying Wang, Linping Wang, Yugang Wang, Xinru Wang, Fengyun Wang, Heyong Wang, Ziping Wang, Yuegang Wang, Xiangyu Wang, Haoran Wang, Xiaomei Wang, Fang Wang, Lina Wang, Guowen Wang, Liyun Wang, Qingshui Wang, Baoyun Wang, Li-Juan Wang, Tongsong Wang, Jingyun Wang, Huiguo Wang, Zhibo Wang, Lou-Pin Wang, Renjun Wang, Huiting Wang, Junfeng Wang, Zihan Wang, Linhua Wang, Zhiji Wang, Fubao Wang, Eunice S Wang, Xiaojuan Wang, Yuewei Wang, Shuang Wang, Ruey-Yun Wang, Xiaoling Wang, Weihua Wang, Yanggan Wang, Jia Wang, Chaoqun Wang, Xiao-liang Wang, Manli Wang, Yongkang Wang, Huiwen Wang, Ting Chen Wang, Yixian Wang, Xinlin Wang, Shuya Wang, Bochu Wang, Kehao Wang, Sasa Wang, Mengshi Wang, Qiu-Ling Wang, Chengshuo Wang, Mengru Wang, Yiwei Wang, Xueyun Wang, Yijun Wang, Haomin Wang, Meng C Wang, Mengxiao Wang, Huan-You Wang, Jingheng Wang, Carol A Wang, Benjamin H Wang, Penglong Wang, Pei-Wen Wang, Jian-Long Wang, Wang Wang, Jinhui Wang, Yuanqing Wang, Jacob E Wang, Jian-Xiong Wang, Wenyu Wang, Chengze Wang, Hongmei Wang, Fengqiang Wang, Zijun Wang, Shaochun Wang, Qinwen Wang, Ruicheng Wang, Aixian Wang, Yanling Wang, Lu-Lu Wang, Linyuan Wang, Yeming Wang, Ye Wang, Tian-Yi Wang, Zhichao Wang, Dangfeng Wang, Jiucun Wang, Guo-Liang Wang, Guandi Wang, Zhuo-Xin Wang, Aili Wang, Fengliang Wang, Yingzi Wang, Lirong Wang, Xuekai Wang, Wei-En Wang, Jing-Xian Wang, Hesuiyuan Wang, Yuexin Wang, Suzhen Wang, Luping Wang, Xiuyu Wang, Zicheng Wang, Jiliang Wang, Rikang Wang, Xue Wang, Shudan Wang, Chun Wang, Hongxin Wang, Chenglong Wang, Junxiao Wang, Zhiqing Wang, Shawn Wang, Shunran Wang, Tiantian Wang, Youhua Wang, Xiao-Hui Wang, Qing-Yan Wang, Hanying Wang, Qiuping Wang, Yongzhong Wang, Jin-Xia Wang, Xiao-Tong Wang, Shun Wang, Xiaoqun Wang, Ching-Jen Wang, Xin Wang, Hanbin Wang, Yingwen Wang, Jia Bei Wang, Xiaodan Wang, Wenhan Wang, Jia-Yu Wang, Xiaozhi Wang, Xinkun Wang, Jinhao Wang, KeShan Wang, Shengdong Wang, Jinzhu Wang, Lihui Wang, Bicheng Wang, Chao-Jun Wang, Shaoyi Wang, Yajing Wang, Qing-Bin Wang, Feiyan Wang, Geng Wang, Chen Wang, Zhimin Wang, Cenxuan Wang, Wenjun Wang, Chuan-Chao Wang, Zexin Wang, Shu-Huei Wang, Yonggang Wang, Zhaoyu Wang, Xiaochuan Wang, Chuan-Hui Wang, Junshuang Wang, X F Wang, Li-Ting Wang, Chenxin Wang, Qiao-Ping Wang, Jingqi Wang, Xiongjun Wang, Shuang-Shuang Wang, Xu Wang, Houchun Wang, Yaodong Wang, Lujuan Wang, Jilin Wang, Peichang Wang, Keyun Wang, Ruixuan Wang, Zhangying Wang, Lianyong Wang, Dongyu Wang, Xinghui Wang, Binghan Wang, Guanduo Wang, Xian-e Wang, Guimin Wang, Xiaomeng Wang, Yuh-Hwa Wang, Jinru Wang, Mingyu Wang, Binbin Wang, Chaokui Wang, Linhui Wang, Youzhi Wang, Zhenqian Wang, Jialiang Wang, Sufang Wang, Haiyan Wang, Yankun Wang, Yingbo Wang, Zilong Wang, Xiao-Qun Wang, Lin-Fa Wang, Wenhao Wang, P Wang, Rui-Hong Wang, Xiao-jian WANG, Pei Chang Wang, Zhengkun Wang, Vivian Wang, Ying Wang, Zihuan Wang, Peiwen Wang, Chao Wang, Da-Zhi Wang, He-Tong Wang, Mofei Wang, Zezhou Wang, Liyong Wang, Bruce Wang, Hao-Tian Wang, Jin-Juan Wang, Yucheng Wang, Yong-Gang Wang, Saili Wang, Xiuwen Wang, Ruiquan Wang, Xinmei Wang, Zhezhi Wang, Xiao-Jie Wang, H Y Wang, Li-Dong Wang, Duanyang Wang, Kaiting Wang, Yikang Wang, Yichen Wang, Ting-Chen Wang, Meixia Wang, ZhenXue Wang, Juan Wang, Shouling Wang, Lan Wang, Li Chun Wang, Xingxin Wang, Ruibing Wang, Xue-Ying Wang, Bi-Dar Wang, Jiayang Wang, Suxia Wang, Yumin Wang, Qing Jun Wang, Xinbo Wang, Youli Wang, Yi-Ni Wang, Xinran Wang, Lixian Wang, Kan Wang, Ruiming Wang, Qing-Yuan Wang, Kai-Kun Wang, Yaoxian Wang, Qing-Jin Wang, Junmei Wang, Xin Wei Wang, J P Wang, Xufei Wang, Yuqin Wang, Handong Wang, Li-San Wang, Guoling Wang, Wenrui Wang, Zhongwei Wang, Shi-Han Wang, Ruoxi Wang, Huiping Wang, Mu Wang, Weihong Wang, Minzhou Wang, Yakun Wang, Da-Cheng Wang, Pengjie Wang, Qihua Wang, Ji-Nuo Wang, Deshou Wang, Xiaowen Wang, Yaochun Wang, Qihao Wang, Ruiying Wang, Tiange Wang, Xi Wang, Yindan Wang, Lixin Wang, Zhaofeng Wang, Guixin Wang, Erming Wang, Haoyu Wang, Kexin Wang, Yiqiao Wang, Qi-Qi Wang, Shuiyun Wang, Xi-Rui Wang, Cai-Hong Wang, Zhizheng Wang, Mingxun Wang, Liangli Wang, Theodore Wang, Alexander Wang, Huayang Wang, Yinyin Wang, Shuzhong Wang, Tingting Wang, Jiao Wang, Wenxian Wang, Jianghua Wang, Furong Wang, Shijun Wang, Le Wang, Guihua Wang, Xiaokun Wang, Xia Wang, Jiabei Wang, Guoying Wang, Zeyuan Wang, Jue Wang, Jin-E Wang, Jingru Wang, Chun-Li Wang, Xiaole Wang, Ermao Wang, Lanlan Wang, Ye-Ran Wang, Hao Wang, Xv Wang, Shikang Wang, Yufei Wang, Siyi Wang, Xiujuan Wang, Qinyun Wang, Xiangwei Wang, Jian-Hong Wang, David Q-H Wang, Chunjuan Wang, Weiyan Wang, Jia-Liang Wang, Yanxing Wang, Sheri Wang, Chenwei Wang, Haoping Wang, Sheng-Quan Wang, Xiangrong Wang, Xiao-Yi Wang, Huan Wang, Zhitao Wang, Xinyan Wang, J Wang, Kaixi Wang, Huihua Wang, Renwei Wang, Xiaoliang Wang, Xiao-Lin Wang, Tian-Lu Wang, Jiou Wang, Weiqin Wang, Jiamin Wang, Dennis Wang, Ji-Yao Wang, Pingping Wang, Jinyang Wang, Chen-Cen Wang, Chien-Wei Wang, Daolong Wang, Rong-Tsorng Wang, Yuwei Wang, Guo-Ping Wang, Zhentang Wang, F Wang, Xueju Wang, Saisai Wang, Zhehai Wang, Y B Wang, Xiao Wang, Guobing Wang, Kangmei Wang, Chunguo Wang, Longcai Wang, Haina Wang, Chih-Hsien Wang, Yuli Wang, Ling-Ling Wang, Zhangshun Wang, Xue-Lian Wang, Jianxin Wang, Da-Yan Wang, Xianghua Wang, Peng Wang, Yu Qin Wang, Zhao-Jun Wang, Rui-Rui Wang, Xingyue Wang, Man Wang, Daozhong Wang, Tian-Li Wang, Luhui Wang, Gaopin Wang, Mengze Wang, Jizheng Wang, Hong-Yan Wang, Dongying Wang, Wenkai Wang, Stephani Wang, Dan-Dan Wang, Yicheng Wang, Yusheng Wang, Junwen Wang, Gao Wang, Ruo-Nan Wang, Yifan Wang, Jueqiong Wang, Xuewei Wang, Jianning Wang, Yonglun Wang, Shiwen Wang, Lifang Wang, Fuyan Wang, Jian-Bin Wang, Chonglong Wang, Haiwei Wang, Yike Wang, Chunxia Wang, Kaijuan Wang, Minglei Wang, Jingxiao Wang, Luting Wang, David Wang, Ben Wang, Ji-zheng Wang, Yuncong Wang, Lei P Wang, Tingye Wang, Wenke Wang, Ping Wang, Min Wang, Qiang-Sheng Wang, Xuejing Wang, Zhanju Wang, Xixi Wang, Xiaodong Wang, Chaomeng Wang, Yanong Wang, Xinghao Wang, Jiaming Wang, Siyuan Wang, Jiu Wang, Ruizhi Wang, Qing Mei Wang, Wenyi Wang, Yiqing Wang, Cai Ren Wang, Lianchun Wang, Xing-Ping Wang, Xiaoman Wang, Yanjin Wang, Xueqin Wang, Chenliang Wang, Zhenshan Wang, Junhong Wang, Guiping Wang, Xianrong Wang, Xumeng Wang, Dajia Wang, Huang Wang, Huie Wang, Weiwen Wang, Ruiwen Wang, Qing Wang, Haohao Wang, Bao-Long Wang, P Jeremy Wang, Chengqiang Wang, Suli Wang, Lingyan Wang, Chi Wang, Meng Wang, Luwen Wang, Quan Wang, Yan-Jun Wang, Sen Wang, Ruining Wang, Xiaozhen Wang, Zhiping Wang, Xue-Yao Wang, Yuming Wang, Jingjing Wang, Jiazheng Wang, Yunong Wang, Chongze Wang, Rufang Wang, Qiuning Wang, Tiannan Wang, Liqing Wang, Wencheng Wang, Xuefeng Wang, Yongli Wang, Xinwen Wang, Runzhi Wang, Chaojie Wang, Wentao Wang, Zhifeng Wang, Yanan Wang, Mengqi Wang, Limin Wang, Donglin Wang, Shujin Wang, Chengbin Wang, Qiu-Xia Wang, Zhengxuan Wang, Yancun Wang, Yuhuan Wang, Wei Wang, G-W Wang, Bangmao Wang, Kejia Wang, Jinjin Wang, Qifei Wang, Guobin Wang, Chun-Lin Wang, Jing-Shi Wang, Jiheng Wang, Huajing Wang, Yanlin Wang, Chuansheng Wang, Cailian Wang, Beilan Wang, Luofu Wang, Yangpeng Wang, Jieqi Wang, Weilin Wang, Xiaoxuan Wang, Yangyufan Wang, Xiao-Fei Wang, Chen-Ma Wang, Yun Yong Wang, Shizhi Wang, B Wang, Yuling Wang, Yi-Yi Wang, Fanwen Wang, Aiyun Wang, Jian Wang, Chengyu Wang, Jing-Huan Wang, Ning Wang, Yichuan Wang, L F Wang, Chau-Jong Wang, Xin-Yang Wang, Yunzhe Wang, Xuewen Wang, Sheng-Ping Wang, Bi Wang, Qiuting Wang, Yan-Jiang Wang, Dongshi Wang, Yingna Wang, Jingyue Wang, Hongshan Wang, Chunjiong Wang, Hong-Yang Wang, Yingmei Wang, Danfeng Wang, Zhongyi Wang, Teng Wang, Chih-Hao Wang, Mingchao Wang, Yi-Chuan Wang, Chuning Wang, Shihao Wang, Ming-Wei Wang, Menglu Wang, Zhulun Wang, Wuji Wang, Dao-Xin Wang, Han Wang, Jincheng Wang, Thomas T Y Wang, Qingyun Wang, Guoliang Wang, Jihong Wang, Hong-Qin Wang, G Wang, Hsei-Wei Wang, Linfang Wang, Xiao Ling Wang, Ganyu Wang, Zhengdong Wang, Cuizhe Wang, Hongyu Wang, Tieqiao Wang, Lijuan Wang, Jingchun Wang, Youzhao Wang, Zijian Wang, Ziheng Wang, Xingyu Wang, Shuning Wang, Shaokun Wang, Zhifu Wang, Xinqi Wang, Jinqiu Wang, ZhongXia Wang, Yanyun Wang, Dadong Wang, Xingjie Wang, Yiting Wang, Zhongli Wang, Junyu Wang, Jianding Wang, Meng-Wei Wang, Yingge Wang, Zhenchang Wang, Qun Wang, Jin-Xing Wang, Lijun Wang, Shuqing Wang, Fu-Yan Wang, Sheng-Nan Wang, Feijie Wang, Qiuyan Wang, Ying-Wei Wang, Shitao Wang, Meng-hong Wang, Zhengyang Wang, Jinghong Wang, Zhiying Wang, Pei Wang, Weixue Wang, Shiyue Wang, Xiaohong Wang, Daiwei Wang, Jinghua Wang, S X Wang, Jian-Yong Wang, Zeying Wang, Can Wang, Kehan Wang, Yunzhang Wang, Jinping Wang, Chenchen Wang, Chun-Ting Wang, Yujiao Wang, Xinxin Wang, Ji Wang, Sui Wang, Wenqiang Wang, Yingwei Wang, Shuzhen Wang, Daixi Wang, Yanming Wang, Lin-Yu Wang, Hongyin Wang, Zhongqun Wang, Er-Jin Wang, Yi Wang, Ziyi Wang, Lianghai Wang, Zhendan Wang, Xiao-Ming Wang, Chengyan Wang, Hui Miao Wang, Jingyi Wang, Ranran Wang, Banghui Wang, Huilun Wang, Ai-Ting Wang, Wenxuan Wang, Yuan-Hung Wang, Zixuan Wang, Hailing Wang, Xuan-Ying Wang, Jiqiu Wang, Yalong Wang, Xiaogang Wang, Shu-qiang Wang, Yun-Jin Wang, Zijie Wang, Tianlin Wang, Mingqiang Wang, Lufang Wang, Jin'e Wang, Xiru Wang, Cuili Wang, GuoYou Wang, Zhizhong Wang, Haifei Wang, Guorong Wang, Xinyue Wang, Pei-Juan Wang, Jiangong Wang, Yingte Wang, Huajin Wang, Ruibo Wang, Kejian Wang, Cheng-Cheng Wang, Xusheng Wang, Shu-Na Wang, Panliang Wang, Mingxi Wang, Shenqi Wang, Zifeng Wang, Chaozhan Wang, Xiuyuan Hugh Wang, Yuping Wang, Xujing Wang, Kai Wang, Hongbing Wang, Sheng-Yang Wang, Jianfei Wang, Hang Wang, Jing-Jing Wang, Weizhi Wang, Jixuan Wang, De-He Wang, P L Wang, Ningjian Wang, Chunyi Wang, Isabel Z Wang, Yong Wang, Yiming Wang, Mingzhi Wang, Jiying Wang, Qian-Wen Wang, Shusen Wang, Xiaoting Wang, Baogui Wang, Mingsong Wang, Zixia Wang, Demin Wang, Shiyuan Wang, Qiuli Wang, C Wang, Dongliang Wang, Weixiao Wang, Yinsheng Wang, Chunmei Wang, Huaili Wang, Xuelian Wang, Yongjun Wang, Zhi-Qin Wang, Jiaying Wang, Yulong Wang, Ren Wang, Jingnan Wang, Qishan Wang, Zeneng Wang, Guangsuo Wang, Chijia Wang, Hongcai Wang, Donghao Wang, Xing-Jin Wang, Zongji Wang, Shenao Wang, Jiaqian Wang, Xiaoying Wang, Yilin Wang, Hangzhou Wang, Wenchao Wang, Jieyu Wang, Li-E Wang, Xuezhen Wang, Liuyang Wang, Zhiqian Wang, Fang-Tao Wang, Qiong Wang, Meng-Meng Wang, Youji Wang, Jiafeng Wang, Xiaojing Wang, William Wang, Junmin Wang, Laijian Wang, Xuexiang Wang, Huiyan Wang, T Y Wang, Zhaofu Wang, Wen-mei Wang, Yalin Wang, Xinshuai Wang, Daqi Wang, Zhen Wang, Shi-Cheng Wang, Anni Wang, Chunhong Wang, Hai-Long Wang, Pan Wang, Charles C N Wang, Pengxiang Wang, Xianzong Wang, Xike Wang, Qianliang Wang, Chunyan Wang, Xuan Wang, Xiaofen Wang, Zhi-Jian Wang, Feng-Sheng Wang, Xiangru Wang, R Wang, Yi-Shu Wang, Jia-Lin Wang, Yonghong Wang, Lintao Wang, Pai Wang, Yanfei Wang, Xuanwen Wang, Lei-Lei Wang, Chenxuan Wang, James Wang, Xinhui Wang, Shengqi Wang, Yueshen Wang, Shan-Shan Wang, Dingting Wang, Zhige Wang, Jingfeng Wang, Yongqing Wang, Chenyang Wang, Ziliang Wang, Bao Wang, Xueyan Wang, Liping Wang, Xingde Wang, Weijun Wang, Sibo Wang, Yaoling Wang, Donghong Wang, Chenyu Wang, Justin Wang, Baolong Wang, Yiqi Wang, Fengyong Wang, Lichao Wang, Yachen Wang, Quanren Wang, Shiyu Wang, Boyu Wang, Aimin Wang, Zhenghui Wang, Hengjiao Wang, Xiaoxin X Wang, Weimin Wang, Mutian Wang, Zhuo-Hui Wang, Xingye Wang, Zou Wang, Yu-Wen Wang, Shaoli Wang, Xin-Ming Wang, Weirong Wang, Kangli Wang, Yaoxing Wang, Xuejie Wang, Qifeng Wang, Xiaoxin Wang, Yinghui Wang, Jianzhang Wang, Tom J Wang, Yaqiong Wang, Zongwei Wang, Yun-Hui Wang, Haiyun Wang, Zhiyou Wang, Lijin Wang, Jifei Wang, Haiyong Wang, Xiao-Xia Wang, Shyi-Gang P Wang, Chih-Yang Wang, Zhixin Wang, Jun-Jun Wang, Tianjing Wang, Zhixia Wang, Chuanhai Wang, Zhijie Wang, Silu Wang, Jianguo Wang, Ming-Hsi Wang, Liling Wang, Yanting Wang, Haolong Wang, Xue-Lei Wang, Ru Wang, Qinglin Wang, Christina Wang, Mimi Wang, Menghui Wang, Wenju Wang, Junhua Wang, S S Wang, Fangyong Wang, Lifen Wang, Zhenbin Wang, Yapeng Wang, Shaoshen Wang, B R Wang, Sugai Wang, Hequn Wang, Songlin Wang, Wenjie Wang, Xiang-Dong Wang, Ting-Hua Wang, Mingliang Wang, Chengniu Wang, Guoxiang Wang, E Wang, Xiaochun Wang, Xueting Wang, Ming-Jie Wang, Zhaojing Wang, Dongxu Wang, Yirui Wang, Jiatao Wang, Jing-Min Wang, Shih-Wei Wang, Zhengchun Wang, Chaoxian Wang, Zehua Wang, Qiyu Wang, Shuye Wang, Baojun Wang, Qing Kenneth Wang, Xichun Wang, Jianliu Wang, Junping Wang, Yudong Wang, Mingzhu Wang, Kangning Wang, Wei-Ting Wang, Hongfang Wang, Chengwen Wang, Changduo Wang, Jinkang Wang, Junya Wang, Fengge Wang, Jianping Wang, Chang Wang, Zhifang Wang, Deli Wang, Linghua Wang, Shitian Wang, Lingling Wang, Zhihua Wang, Jun-Ling Wang, Keyi Wang, Lingbing Wang, Peijia Wang, Ruizhe Wang, X O Wang, Wanyi Wang, Ganggang Wang, Pei-Hua Wang, Kaiyue Wang, Xiaojiao Wang, Xun Wang, Shiyang Wang, Ya-Ping Wang, Yirong Wang, Lixing Wang, Danyang Wang, Xiaotang Wang, Taian Wang, Ming Wang, Xiangcheng Wang, Xuemei Wang, Zhixiong Wang, Mengying Wang, Li-Yong Wang, Xinchao Wang, Jianlong Wang, Jinjie Wang, Nan Wang, Weidong Wang, Mei-Gui Wang, L-S Wang, Wuqing Wang, Z Wang, Ya-Zhou Wang, Xincheng Wang, Jing-Wen Wang, Jinyue Wang, Hongyun Wang, Huaizhi Wang, Yan-Zi Wang, Danling Wang, Dongqin Wang, Hongzhuang Wang, Chung-Teng Wang, Yan-Chun Wang, Shi-Xin Wang, Muxuan Wang, Yujie Wang, Yunbing Wang, Yahui Wang, Zhihong Wang, Xiaoshan Wang, Tienju Wang, Chiou-Miin Wang, Yuqian Wang, Shengyuan Wang, Yumei Wang, Ningyuan Wang, Minjie Wang, Zhenda Wang, Qing-Dong Wang, Horng-Dar Wang, Siqi Wang, Kaihong Wang, Hong-Kai Wang, Meiling Wang, Jiaxing Wang, Xueyi Wang, Zhuozhong Wang, Anlai Wang, Julie Wang, Jin-Bao Wang, Keke Wang, Zhang Wang, Yintao Wang, Yong-Bo Wang, Bing Wang, Dalu Wang, Minxian Wang, Zulong Wang, Gao T Wang, Gang Wang, Sophie H Wang, Xinquan Wang, Yi-Ting Wang, Honglian Wang, Ruyue Wang, Jia-Qiang Wang, Seungwon Wang, Shusheng Wang, Yanbin Wang, Chang-Yun Wang, Le-Xin Wang, Juling Wang, Haohui Wang, Chuanyue Wang, Tianqin Wang, Danqing Wang, Keyan Wang, Yeou-Lih Wang, Qinglu Wang, Sun Wang, Rui-Min Wang, Yong-Tang Wang, Xianwei Wang, Lixia Wang, Tong Wang, Xiaonan Wang, Feida Wang, Jiaxuan Wang, Mingrui Wang, Zixiang Wang, Y Z Wang, Yuliang Wang, Ming-Chih Wang, J J Wang, Huina Wang, Jingang Wang, Jinyun Wang, Min-sheng Wang, Wanyao Wang, Ziqiu Wang, Guo-Quan Wang, Xueping Wang, Qixue Wang, Hechuan Wang, Shang Wang, Chaohan Wang, M H Wang, L Z Wang, Jianhui Wang, Xifeng Wang, Xiaorong Wang, Yinong Wang, Zhixiu Wang, Jiaxi Wang, Jiahui Wang, Xiaofei Wang, Feifei Wang, Kesheng Wang, Rong-Chun Wang, Zhi-Xin Wang, Chaoyu Wang, Yongkuan Wang, Zuoyan Wang, Hsueh-Chun Wang, Xixiang Wang, Guanrou Wang, Songsong Wang, Hongyuan Wang, Yubing Wang, Xuliang Wang, Wen-Ying Wang, Xinglei Wang, Dao-Wen Wang, Yun Wang, Ze Wang, Jiyan Wang, Zai Wang, Guan Wang, Chih-Chun Wang, Yiqin Wang, X S Wang, Hongzhan Wang, Exing Wang, Shu-Jin Wang, Shangyu Wang, Shouzhi Wang, Yunduan Wang, Jiyong Wang, Dongdong Wang, Qingzhong Wang, Zi-Qi Wang, Renyuan Wang, Siyu Wang, Donghui Wang, Ming-Yuan Wang, Juxiang Wang, Muxiao Wang, Fu Wang, Fei Wang, Qiuyu Wang, Ertao Wang, Zhi Xiao Wang, Zunxian Wang, Hui-Nan Wang, Rongping Wang, Won-Jing Wang, Leiming Wang, Pu Wang, Shen-Nien Wang, Xiaona Wang, Meng-Ying Wang, Wen-Jie Wang, Jiaxin Wang, RuNan Wang, Jiemei Wang, Ningli Wang, Zhong-Hui Wang, Hong Wang, Hui-Yu Wang, Ziqian Wang, Xinzhou Wang, Zhoufeng Wang, Weiguang Wang, Zusen Wang, Jiajia Wang, Bin Wang, Shu-Xia Wang, Yu'e Wang, Laidi Wang, Xiao-Li Wang, Lu Wang, Zhugang Wang, Maojie Wang, Ganglin Wang, Xinyu Wang, Junlin Wang, Dong Wang, Yao Wang, Ya-Jie Wang, Zhiwu Wang, DongWei Wang, Hongdan Wang, Yanxia Wang, Maiqiu Wang, Guansong Wang, Qingtong Wang, Yingcheng Wang, Wenjuan Wang, Liying Wang, Xiaolong Wang, Weihao Wang, Qiushi Wang, Yingfei Wang, Haoyang Wang, Li-Li Wang, Yanbing Wang, Yingchun Wang, Guangming Wang, Kaiyuan Wang, Shiqi Wang, Qi-En Wang, Song Wang, Jing-Hao Wang, Lynn Yuning Wang, Zekun Wang, Rui-Ping Wang, Yining E Wang, Yuzhou Wang, Liu Wang, Maochun Wang, Cindy Wang, Qian-Liang Wang, Duo-Ping Wang, Linlin Wang, Taishu Wang, Xiang Wang, Qirui Wang, Baoming Wang, Liting Wang, Jiapan Wang, Lingda Wang, Xietong Wang, Jia-Mei Wang, Liwei Wang, Shaozheng Wang, Q Wang, Timothy C Wang, Mengyue Wang, Xing Wang, Yahong Wang, Yuyong Wang, Yujiong Wang, Guangliang Wang, Ya-Qin Wang, Yezhou Wang, Hongjian Wang, Su-Hua Wang, Qian-fei Wang, Meng-Dan Wang, Yuchen Wang, Hongpin Wang, Pengfei Wang, Ge Wang, Meijun Wang, Yan-Ming Wang, Haichao Wang, Tzung-Dau Wang, Runci Wang, Yan-Yi Wang, Cheng-Jie Wang, Chen-Yu Wang, Cong Wang, Yaxuan Wang, Y H Wang, Yongjie Wang, Yuntai Wang, Ranjing Wang, Yiru Wang, Anxiang Wang, Q Z Wang, Shimiao Wang, Guoping Wang, Junke Wang, Xingyun Wang, Zhengyi Wang, Shi-Qi Wang, Yanfeng Wang, Danxin Wang, Chaodong Wang, Zhiqi Wang, Chunyu Wang, Lijia Wang, Chunlong Wang, Haiping Wang, Qingfa Wang, Yu-Fan Wang, Baihan Wang, Chunxue Wang, Liewei Wang, Xinyi Wang, Fu-Zhen Wang, Qing-Mei Wang, Sheng Wang, Yi-Tao Wang, Dawei Wang, Xiaoyu Wang, Ziling Wang, Zhonglin Wang, Rurong Wang, Qingchun Wang, Qiang Wang, Suiyan Wang, Xu-Hong Wang, Jie Jin Wang, Chenyao Wang, Fei-Yan Wang, Shi Wang, Zhiyong Wang, Jieda Wang, Xiaoqi Wang, Linshu Wang, Ruxuan Wang, Qian Wang, Qianxu Wang, Fangjie Wang, Zhaoxia Wang, Jeremy R Wang, Mingmei Wang, Jingkang Wang, Jen-Chun Wang, Changyuan Wang, Chenglin Wang, Meng-Ru Wang, Tianpeng Wang, Zhongfang Wang, Xuedong Wang, Zhuoying Wang, Bingyu Wang, Xuelai Wang, Weilong Wang, Mengge Wang, Qin Wang, Da-Li Wang, Xuanyi Wang, Hongjuan Wang, Zhi-Hua Wang, Hong-Wei Wang, Yulai Wang, Gongming Wang, Yongni Wang, Mengya Wang, Yadong Wang, Chenghao Wang, Hongbo Wang, Kaiming Wang, Haonan Wang, Guanyun Wang, Yilu Wang, Quanxi Wang, Weiyuan Wang, Xiujun Wang, Liang-Yan Wang, Jianshe Wang, Yingxiong Wang, Cunchuan Wang, Jing-Zhai Wang, Yuelong Wang, Yuqi Wang, Xiaorui Wang, Qianjin Wang, Huijun Wang, Xiaobo Wang, Guoqian Wang, Luhong Wang, Kaining Wang, Chaohui Wang, Yanhong Wang, J-Y Wang, Qi-Bing Wang, Xiaohu Wang, Jiayan Wang, Cui-Shan Wang, Lulu Wang, Yong-Jie Wang, Shixuan Wang, Yuanyuan Wang, Jianying Wang, Haizhen Wang, Shuiliang Wang, Qianbao Wang, Jung-Pan Wang, Rixiang Wang, A Wang, Hanbing Wang, Caiqin Wang, Peigeng Wang, Yuan Wang, Yuzhuo Wang, Yubo Wang, Xianding Wang, Qiaoqi Wang, Cuiling Wang, Ai-Ling Wang, Hailong Wang, Yihao Wang, Lan-Wan Wang, Haihe Wang, S Wang, Sha Wang, Xiaoli Wang, David Q H Wang, Jianfang Wang, Yuting Wang, Jinhuan Wang, Kaixu Wang, Hongwei Wang, Yi-Wen Wang, Yizhe Wang, Shengyu Wang, Yanmei Wang, Huimin Wang, Youjie Wang, Kunhua Wang, Chongjian Wang, Ziyun Wang, Tianhui Wang, Huiying Wang, Yue-Nan Wang, Peiyin Wang, Hongbin Wang, Hong Yi Wang, Xinjun Wang, Yian Wang, Liyi Wang, Yunce Wang, Yi-Xuan Wang, Yitao Wang, Jiali Wang, Junqin Wang, Yuebing Wang, Yiping Wang, Yunpeng Wang, Yuxing Wang, Shuqi Wang, Ziyu Wang, Hongjie Wang, Xiaoyan Wang, Lianshui Wang, Xiaolu Wang, Wenya Wang, Fan Wang, Jinhua Wang, Sidan Wang, Lixiang Wang, Y L Wang, Xue-Rui Wang, Kai-Wen Wang, Zhongyu Wang, Xiaoyang Wang, Hongyang Wang, Rencheng Wang, Yinxiong Wang, Yuanli Wang, Zhuqing Wang, Y-H Wang, Yuhui Wang, Xitian Wang, Weizhen Wang, Qi Wang, Qiyuan Wang, Changlong Wang, Yatao Wang, Tengfei Wang, Yehan Wang
articles
Hua Liu, Jinrong Wang, Wenming Wang +2 more · 2025 · Journal of inflammation research · added 2026-04-24
Traumatic brain injury (TBI) is a leading cause of disability and death worldwide, involving complex pathophysiological responses such as metabolic disturbance and systemic inflammation. This study ai Show more
Traumatic brain injury (TBI) is a leading cause of disability and death worldwide, involving complex pathophysiological responses such as metabolic disturbance and systemic inflammation. This study aimed to evaluate the prognostic value of selected metabolic and inflammatory biomarkers in predicting short- and medium-term mortality in patients with moderate-to-severe TBI. We conducted a retrospective cohort study of patients with TBI admitted between March 29, 2018, and July 31, 2023. Clinical data, including a panel of metabolic (eg, triglyceride-glucose index [TYG], APOB/A1 ratio) and inflammatory biomarkers (eg, neutrophil-to-platelet ratio [NPR]), were collected within 24 hours of admission. Mortality was assessed at 14 days, 30 days, and hospital discharge. Multivariate Cox regression models and ROC curve analysis were used to assess prognostic associations and model performance. A total of 2555 patients were enrolled, of whom 579 (22.67%) underwent surgical treatment. Multivariate Cox proportional hazards regression analysis revealed that the triglyceride-glucose index (TYG) was an independent predictor of short-term mortality in TBI patients, while the neutrophil-to-platelet ratio (NPR) and apolipoprotein B/A1 (APOB/A1) ratio were independent predictors of both short- and mid-term mortality. In addition, surgical treatment was associated with an increased risk of mid-term mortality, while tracheostomy significantly reduced mortality risk across all time points. Receiver operating characteristic (ROC) curve analysis showed that the regression model incorporating inflammatory markers had the highest areas under the curve (AUCs) of 0.904, 0.897, and 0.897, demonstrating superior performance in predicting short- and mid-term mortality. Additionally, in the subgroup analysis of non-operation patients, TYG and NPR had a more significant impact on mortality risk. Metabolic and inflammatory biomarkers, including TYG, NPR, and APOB/A1 ratio, provide valuable prognostic information in patients with TBI. These markers may assist clinicians in early risk stratification and personalized treatment planning. Show less
📄 PDF DOI: 10.2147/JIR.S519606
APOB
Hui Wang, Sensen Wu, Dikang Pan +6 more · 2025 · Nutrition & diabetes · Nature · added 2026-04-24
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic re Show more
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic regression to assess the relationship between ApoB and DN risk, utilizing data from the National Health and Nutrition Examination Survey spanning 2007-2016. Then, we used restricted cubic splines (RCS) to flexibly model and visualize the relation of predicted ApoB levels with DN risk. Subsequently, a bidirectional two-sample Mendelian randomization study using genome-wide association study summary statistics was performed. The primary Inverse Variance Weighted method, along with supplementary MR approaches, was employed to verify the causal link between ApoB and DN. Sensitivity analyses were conducted to confirm the robustness of the results. Our observational study enrolled 2242 participants with diabetes mellitus from NHANES. The multivariable logistic regression model indicated that elevated ApoB levels (>1.2 g/L), compared to low levels (<0.8 g/L), were significantly associated with DN risk (P < 0.05). The RCS model revealed a positive linear association with the risk of DN when ApoB levels exceeded 1.12 g/L (OR = 1.29, 95% CI: 1.07-1.57, P = 0.008). However, the MR IVW method did not reveal a direct causal effect of DN on ApoB (OR: 0.976; 95% CI: 0.950-1.004; P = 0.095), nor a direct causal effect of ApoB on DN (OR: 0.837; 95% CI: 0.950-1.078; P = 0.428). The evidence from observational studies indicates a positive correlation between ApoB levels exceeding 1.12 g/L and the onset of DN. However, the causal effects of ApoB on DN and vice versa were not supported by the MR analysis. Show less
📄 PDF DOI: 10.1038/s41387-025-00370-1
APOB
Chunyu Yang, Xin Chai, Yachen Wang +8 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
Existing evidence suggests that elevated 1-hour post-load plasma glucose (1-h PG ≥ 8.6 mmol/L) during an oral glucose tolerance test (OGTT) is associated with atherogenic lipid parameters which are li Show more
Existing evidence suggests that elevated 1-hour post-load plasma glucose (1-h PG ≥ 8.6 mmol/L) during an oral glucose tolerance test (OGTT) is associated with atherogenic lipid parameters which are linked to an increased risk of cardiovascular disease (CVD). However, it remains unclear whether normal glucose tolerance (NGT) individuals with elevated 1-h PG (NGT-1hPG-high) should still be considered low-risk. Therefore, this study aims to demonstrate comprehensive lipid characteristics in individuals with different glycemic status stratified by 1-h PG, with a particular focus on those with NGT-1hPG-high. This cross-sectional study included individuals aged 25-55 years with high-risk of diabetes from the Daqing Diabetes Prevention Study II (Daqing DPS-II). Individuals were categorized into different glycemic status based on the World Health Organization's 1999 criteria and the International Diabetes Federation's 2024 position statement on 1-h PG. Traditional (TC, TG, HDL-C, LDL-C) and non-traditional lipid parameters [ApoA-1, ApoB, sdLDL-C, Lp(a), non-HDL-C, remnant cholesterol (RC), ApoB/ApoA-1, LDL-C/ApoB] were measured. Dyslipidemia was defined according to the 2023 Chinese Guidelines for Lipid Management. The China-PAR equation was used to estimate 10-year CVD risk. Spearman's correlation coefficients were calculated to evaluate the correlation between lipid parameters and 10-year CVD risk. Logistic and multiple linear regression models were performed to assess the association between 1-h PG and dyslipidemia as well as lipid parameters adjusting for covariates. Among 2 469 individuals, 22.7% had NGT with normal 1-h PG (NGT-1hPG-normal), 19.9% had NGT-1hPG-high, 2.6% had prediabetes with normal 1-h PG (PDM-1hPG-normal), 34.2% had prediabetes with elevated 1-h PG (PDM-1hPG-high), and 20.6% had newly diagnosed diabetes. The prevalence of dyslipidemia did not significantly differ between NGT-1hPG-high and PDM-1hPG-high (OR = 1.13, 95%CI: 0.88-1.44, P > 0.05). Higher 1-h PG levels were consistently associated with an atherogenic lipid profile, characterized by increased TC, TG, LDL-C, ApoB, sdLDL-C, non-HDL-C, RC and ApoB/ApoA-1, along with decreased ApoA-1, HDL-C and LDL-C/ApoB (all P < 0.05). Among lipid parameters, TG, sdLDL-C, RC, ApoB/ApoA-1, LDL-C/ApoB and HDL-C showed the strongest correlation with 10-year CVD risk, with Spearman's correlation coefficients of 0.41, 0.38, 0.35, 0.31, - 0.37 and - 0.36, respectively. In the NGT-1hPG-high, TG, sdLDL-C, and ApoB/ApoA-1 levels were significantly higher, while HDL-C and LDL-C/ApoB levels were significantly lower compared to counterparts with NGT-1hPG-normal (all P < 0.05). Moreover, except for TG and RC (both P < 0.01), the majority of lipid parameter levels in NGT-1hPG-high did not significantly differ from those in PDM (all P > 0.05). NGT-1hPG-high exhibited a similar atherogenic lipid profile to that observed in PDM. 1-h PG could serve as a potential indicator for the early identification of at-risk individuals who may otherwise go undetected among NGT population. Show less
📄 PDF DOI: 10.1186/s12933-025-02722-8
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Xiang Lian, Xiaoyan Li, Kexin Wang +3 more · 2025 · Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · added 2026-04-24
To investigate the gene detection results of 2 patients with familial hypercholesterolemia (FH) caused by complex heterozygous variation, and to clarify the relationship between clinical manifestation Show more
To investigate the gene detection results of 2 patients with familial hypercholesterolemia (FH) caused by complex heterozygous variation, and to clarify the relationship between clinical manifestations and gene variation. Two patients (patient 1 and 2) with FH who visited Beijing Anzhen Hospital Affiliated to Capital Medical University in 2018 were selected as research subjects. A retrospective study method was used to collect clinical and family history data of the two patients. And 2 mL of peripheral venous blood from each of the two patients was collected, and genomic DNA extraction was performed on the blood samples. Sanger sequencing was used to validate the variant sites of the two patients detected by whole-exome sequencing (WES). Pathogenicity of variants was classified based on the American College of Medical Genetics and Genomics (ACMG) Standards and Guidelines for the Classification of Genetic Variants (hereinafter referred to as the "ACMG Guidelines"), and the impact of variant was analyzed using multiple bioinformatics tools including SIFT, PolyPhen-2, and SWISS-MODEL. This study has been approved by Beijing Anzhen Hospital Affiliated to Capital Medical University (Ethics No. 2024215X). Patient 1 initially presented with early-onset coronary heart disease, with initial lipid levels of serum total cholesterol (TC) 9.86 mmol/L (normal reference value: 3.10~5.20 mmol/L) and serum low-density lipoprotein cholesterol (LDL-C) 8.37 mmol/L (normal reference value: 1.27~3.12 mmol/L) on admission. Patient 1 initially underwent treatment with rosuvastatin combined with ezetimibe for one month, but the lipid-lowering effect was not significant. The lipid-lowering therapy was then adjusted to atorvastatin combined with ezetimibe and probucol. After one year of treatment, the patient developed paroxysmal chest pain symptoms. A follow-up lipid profile showed a serum TC level of 4.50 mmol/L and a LDL-C level of 3.55 mmol/L. The lipid-lowering regimen was continued, and the serum LDL-C levels were maintained between 2.65 and 3.66 mmol/L. Patient 2 was found to have an abnormally high blood lipid level and carotid artery hardening during physical examination, with an initial blood lipid level of serum TC 11.82 mmol/L and serum LDL-C 9.63 mmol/L. After receiving rosuvastatain therapy, the lipid-lowering effect was significant. WES revealed that patient 1 carried the heterozygous variants c.1871₁₈₇₃del(p.Ile624del) and c.1747C>T (p.His583Tyr) in the LDLR gene (NM₀₀₀₅₂₇.4), while patient 2 carried the heterozygous variants c.1747C>T (p.His583Tyr) in the LDLR gene and c.6936₆₉₃₇inv (p.Ile2313Val) in the APOB gene (NM₀₀₀₃₈₄₎. According to the ACMG Guidelines, the LDLR gene c.1747C>T (p.His583Tyr) was classified as a pathogenic variant (PS3+PM1+PM2_supporting+PM5+PP2+PP3), and c.1871₁₈₇₃del (p.Ile624del) was classified as a pathogenic variant (PS3+PS4+PM2_supporting+PM1+PM4); the APOB gene c.6936₆₉₃₇inv (p.Ile2313Val) was classified as a variant of uncertain clinical significance (PM2_supporting BP4). Patients 1 and 2 in this study were patients with complex heterozygous variant FH, and their genotypic differences may be related to the differences in clinical serum LDL-C levels and the efficacy of hypolipidemic agents. Show less
no PDF DOI: 10.3760/cma.j.cn511374-20241026-00562
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Xiaobing Luo, Hongying Cai, Xiaofeng Wang +4 more · 2025 · Scientific reports · Nature · added 2026-04-24
Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective s Show more
Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective study analysed 323 consecutive patients with GBS who underwent rigid choledochoscopic gallbladder-preserving cholecystolithotomy to determine the detection rate, clinical features, and potential risk factors of gallbladder intramural stones (IS). IS were found in 24.1% (78/323) of patients, characterised by distinct cholangioscopic findings, including stone shadows, yellow floating bands, or a combination of both within the gallbladder wall. Compared to patients without IS, those with IS had a higher prevalence of Clonorchis sinensis (C. sinensis) eggs (60.3% vs. 40.8%, P < 0.05) and elevated serum cholesterol, LDL cholesterol, and Apo-B levels (P < 0.05). However, stone composition and C. sinensis egg detection rates did not differ between intraluminal stones and IS within the same patient (P > 0.05). Logistic regression analysis revealed that IS were associated with C. sinensis infection and elevated Apo-B levels. In conclusion, IS share homology with intraluminal stones in the same patient with GBS and exhibit unique appearances in rigid choledochoscopy. For patients with GBS and IS, elevated serum Apo-B levels and C. sinensis infection were independent risk factors. Show less
📄 PDF DOI: 10.1038/s41598-025-00721-z
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Xiao-Yan Shi, Ya-Kun Liu, Yan Chen +3 more · 2025 · Pediatric obesity · Blackwell Publishing · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent liver condition in children and teenagers with obesity. Unfortunately, there is no standardized treatment. To ex Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent liver condition in children and teenagers with obesity. Unfortunately, there is no standardized treatment. To examine the connection between apolipoprotein B (apoB), apolipoprotein A1 (apoA1), and the apoB/apoA1 ratio with the occurrence of MASLD in this population. A retrospective study was made on children and adolescents with obesity in a children's hospital between the period 2020 and 2022. Anthropometric data, ultrasound results, and blood biochemistry were analysed to assess the connection between apoB, apoA1, and the presence of MASLD. Of the 916 participants included, 313 were diagnosed with MASLD. The level of serum apoB reflected a substantial dose-response correlation with the odds of having MASLD. When apoB levels exceeded the 50th percentile, the risk increased significantly, and at the 95th percentile, the odds were 4.83 times higher than at the 50th percentile (95% CI: 2.02-11.56). The ratio of apoB/apoA1 at the 95th percentile was connected to a 2.41-fold higher prevalence compared to the 50th percentile (95% CI: 1.33-4.37). No significant correlation was found between the levels of apoA1 and MASLD prevalence. Elevated levels of apoB and the apoB/apoA1 ratio have been strongly connected to increased MASLD prevalence in children and adolescents with obesity; hence, signifying their potential usefulness as biomarkers for early detection and intervention. Show less
no PDF DOI: 10.1111/ijpo.70017
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Q Zang, F Li, Y Ju +6 more · 2025 · Scandinavian journal of rheumatology · Taylor & Francis · added 2026-04-24
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA Show more
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA disease activity remains unclear, which is the focus of this study. We evaluated dyslipidaemia and atherosclerosis in a cohort of untreated female patients. Fifty untreated female patients with TA (median age 30 years) and 98 healthy controls matched for age and body mass index (median age 30 years) were assessed for lipid profiles [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), ApoB, ApoE, lipoprotein(a)], inflammatory markers [C-reactive protein (CRP), erythrocyte sedimentation rate (ESR)], and atherosclerotic plaque frequency. TA patients exhibited significantly higher levels of TG and the non-HDL-C/HDL-C ratio than the control group, whereas TC, HDL-C, LDL-C, and ApoA1 levels were significantly lower. Pearson's correlation analysis indicated a positive correlation between CRP and ApoB, as well as the non-HDL-C/HDL-C ratio, and negative correlations with TG, HDL-C, and ApoA1. Atherosclerotic plaques were detected in 14.3% of the TA patients. Multivariate regression analysis revealed that the presence of atherosclerotic plaques was associated only with age, independent of inflammatory markers and lipoprotein levels. The results of this study indicate that untreated female TA patients exhibit a markedly dysregulated serum lipid profile. Atherosclerosis in early TA was not related to lipids or markers of inflammation. Show less
no PDF DOI: 10.1080/03009742.2025.2488096
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Jun Wang, Kefen Zhang, Xiuming Tang +2 more · 2025 · Journal of cancer research and therapeutics · added 2026-04-24
Currently, understanding of the nonlinear relationship between age and hepatocellular carcinoma (HCC) prognosis is insufficient. Thus, this study aimed to analyze the relationship between age at HCC d Show more
Currently, understanding of the nonlinear relationship between age and hepatocellular carcinoma (HCC) prognosis is insufficient. Thus, this study aimed to analyze the relationship between age at HCC diagnosis and overall survival (OS) and identify possible influencing mechanisms. Clinical data from the TCGA public database were analyzed. Restricted cubic spline and segmented logistic regression were employed to explore the nonlinear relationship between age at diagnosis and mortality risk following hepatectomy. Furthermore, bioinformatics methods were employed to understand the possible mechanisms of this nonlinear relationship at the genetic level. The results indicated a nonlinear relationship between age at diagnosis and OS, with the age of 60 years identified as a critical point. Segmented regression showed that age ≥60 years is an unfavorable prognostic factor. The "DNA mismatch repair" pathway was considerably enriched in patients aged <60 years. However, the gene mutation rate of "APOB," "MUC16," "ALB," and "PCLO" and the median tumor mutation burden were relatively more evident in patients aged ≥60 years. MGEA12 was more highly expressed in tumor tissues than in normal ones, particularly in patients aged ≥60 years. The survival rate of the high-expression group was lower than that of the low-expression group. At the mRNA level, the MGEA12 expression in Huh-7 and SUN449 was higher than that in the HSC-LX2 cell line. A nonlinear relationship was found between age at HCC diagnosis and OS, with the age of 60 years being the critical point. MGEA12 may affect the prognosis of elderly people. Show less
no PDF DOI: 10.4103/jcrt.jcrt_1690_24
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Ting Tang, Junjie Hao, Qingyan Yang +2 more · 2025 · Endocrine · Springer · added 2026-04-24
This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and tr Show more
This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and treatment strategies for clinical prevention and management of diabetes, particularly in individuals with concurrent sarcopenia. In this study, we selected inpatients aged over 60 years diagnosed with T2DM who were admitted to the Department of Geriatrics at Qinghai University Affiliated Hospital from July 2023 to June 2024 as research subjects. We collected general patient data, including gender, age, ethnicity, height, weight, and calculated body mass index (BMI). Key indices measured included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), phospholipids, lipoprotein(a) [Lp(a)], very low-density lipoprotein (VLDL), and free fatty acids (FFA). Additionally, we assessed limb skeletal muscle mass, grip strength, walking speed, and calculated the appendicular skeletal muscle mass index (ASMI). Based on Asian diagnostic criteria for sarcopenia, patients were categorized into a non-sarcopenic group or a group with T2DM combined with sarcopenia. Baseline laboratory data along with ASMI measurements, grip strength assessments, and walking speeds were statistically analyzed for both groups. Compared with T2DM patients without sarcopenia, the levels of HbA1c, Lp(a), FFA, serum albumin, TC, TG, HDL-C, ApoA and VLDL in type 2 diabetic patients with sarcopenia were statistically significant (all P < 0.05). When multivariate adjustments were made for these clinical features, age (OR = 1.18, 95%CI: 1.11-1.25, P < 0.001), BMI (OR = 0.81, 95%CI: 0.72-0.92, P < 0.001), ApoA (OR = 0.03, 95%CI: 0.00-0.90, P = 0.043), Lp(a) > = 15.5 mg/dL (OR = 3.14, 95%CI: 1.51-6.54, P = 0.002) and FFA > = 0.48 g/L (OR = 4.11, 95%CI: 1.97-8.57, P < 0.001) were independent predictors of diabetes mellitus with sarcopenia. ROC curve analysis showed that free fatty acids (AUC = 0.721, 95%CI: 0.660-0.782, P < 0.001) in T2DM with sarcopenia has good predictive value judgment. Age, BMI, ApoA, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Serum free fatty acids have a good predictive value in the judgment of T2DM complicated with sarcopenia. Show less
📄 PDF DOI: 10.1007/s12020-025-04226-7
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Da Luo, Elias Björnson, Xiaoying Wang +7 more · 2025 · International journal of cardiology · Elsevier · added 2026-04-24
The per-particle pathogenicity of very-low-density lipoprotein (VLDL) and lipoprotein(a) [Lp(a)] with risk of valvular heart diseases (VHD) other than aortic stenosis compared with low-density lipopro Show more
The per-particle pathogenicity of very-low-density lipoprotein (VLDL) and lipoprotein(a) [Lp(a)] with risk of valvular heart diseases (VHD) other than aortic stenosis compared with low-density lipoprotein (LDL) remains unclear. Single-nucleotide polymorphism specific clusters associated with LDL cholesterol (LDL-C), VLDL cholesterol (VLDL-C) and Lp(a) were identified. The relationships of genetically predicted variation in apolipoprotein B (apoB) in these lipoproteins with risk of VHD and its major types (aortic stenosis, aortic regurgitation, and mitral regurgitation) were evaluated to determine the comparative pathogenicity by Mendelian randomization (MR) analyses. The VHD odds ratio (OR) per 1 g/L higher apoB was 1.09 [95 % confidence interval (CI) 1.04-1.15] in LDL vs. 1.45 (95 % CI 1.25-1.69) in VLDL vs. 2.71 (95 % CI 1.92-3.82) in Lp(a) based on the cluster-based MR analyses. The polygenic scores for each lipoprotein weighted by apoB similarly showed a greater OR of VHD per 1 g/L apoB in VLDL [1.20 (95 % CI 1.06-1.37)] and in Lp(a) [2.54, (95 % CI 1.95-3.32)] compared with that in LDL [1.05 (95 % CI 1.01-1.08)]. Multivariable MR analyses further revealed the strong effects of VLDL-C and Lp(a) on VHD risk independent of LDL-C. In addition, significant associations between Lp(a) and all three major types of VHD were observed, while LDL and VLDL had no impact on aortic and mitral regurgitation. VLDL and Lp(a) appear to have significantly greater per-particle pathogenicity in VHD compared to LDL. The distinct impacts of lipoproteins on different VHD subtypes suggest the inadequacy of just focusing on LDL-lowering treatment for valve disorders. Show less
no PDF DOI: 10.1016/j.ijcard.2025.133218
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Xuliang Luo, Yan Guo, Xuelian Li +6 more · 2025 · BMC genomics · BioMed Central · added 2026-04-24
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) Show more
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) are nanoscale vesicles that carry and transmit signaling molecules, thereby affecting semen quality. Currently it is still unclear whether SPEV are involved in the process of that aromatase affects the quality semen in chicken. To clarify this issue, lentivirus carrying Cyp19a1 (LV-CYP19A1) for over-expression of aromatase was constructed and injected to testis of 35-week-old roosters. Semen quality and seminal plasma hormone were measured, and SPEV were also extracted and proteome sequencing was performed after treatment of LV-CYP19A1. The results indicated that semen volume, fertility, sperm motility, testosterone (T) levels were significantly decreased, and estradiol (E Our results reveal that aromatase can down-regulate the protein expression related to regulation of ATP synthesis and metabolism, and sperm motility in SPEV, thereby reducing semen quality in roosters. Show less
📄 PDF DOI: 10.1186/s12864-025-11500-5
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Chunbo Zhuang, Fangfang Cui, Jin Chen +3 more · 2025 · Biochimica et biophysica acta. Molecular basis of disease · Elsevier · added 2026-04-24
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we id Show more
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we identified RNA binding motif protein 39 (Rbm39) as a key modulator of hepatic lipid homeostasis during MASLD progression. To establish in vivo MASLD model, mice were fed either a high-fat diet (HFD) or a Gubra-Amylin NASH (GAN) diet. We employed adeno-associated virus to manipulate Rbm39 expression levels to assess its role in MASLD. Transcriptome analysis was conducted to pinpoint the genes targeted by Rbm39. Western blot, RT-PCR, dual-luciferase reporter gene assays, and alternative splicing analysis were utilized to delve into the molecular mechanisms. Our results showed that Rbm39 expression was notably decreased in the livers of MASLD mice. Knockdown of hepatic Rbm39 aggravated HFD-induced hepatic steatosis and GAN diet-induced MASH, along with a notable decrease in serum lipid levels. Conversely, overexpression of Rbm39 attenuated MASLD development and progression. RNA sequencing data analysis indicated that Rbm39 regulated the expression of apolipoprotein B (Apob) and fatty acid-binding protein 4 (Fabp4), both of which are crucial for lipid transport. Mechanistically, Rbm39 enhanced the transcription of Apob by upregulating hepatocyte nuclear factor 4α (Hnf4α), while it suppressed Fabp4 transcription by regulating alternative splicing of hypoxia inducible factor-1α (Hif-1α). These findings highlight the pivotal role of Rbm39 in maintaining hepatic lipid homeostasis and suggest its potential as a therapeutic target for MASLD. Show less
no PDF DOI: 10.1016/j.bbadis.2025.167815
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Pengfei Xie, Weinan Xie, Zhaobo Wang +8 more · 2025 · Diabetology & metabolic syndrome · BioMed Central · added 2026-04-24
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relat Show more
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relationships. This study employed bidirectional Mendelian randomization (MR) to explore these links. Using genome-wide association study (GWAS) data, the primary analysis used the inverse-variance weighted (IVW) method, which was supported by MR-Egger regression and a weighted median estimator (WME). Sensitivity analyses, including heterogeneity, pleiotropy tests, leave-one-out, and reverse causality analyses, were conducted. The IVW model revealed the following: (1) causal relationships between triglycerides (TG) (OR: 1.5807, 95% CI: 1.2578-1.9865, P = 0.0001), high-density lipoprotein cholesterol (HDL-C) (OR: 0.7342, 95% CI: 0.5729-0.9409, P = 0.0146), and apolipoprotein A1 (ApoA1) (OR: 0.6506, 95% CI: 0.5190-0.8156, P = 0.0002) and DN; (2) causal relationships between TG (OR: 1.0607, 95% CI: 1.0143-1.1093, P = 0.0098), HDL-C (OR: 0.9453, 95% CI: 0.9053-1.9871, P = 0.0109), and apolipoprotein B (ApoB) (OR: 1.0672, 95% CI: 0.0070-1.1310, P = 0.0280) and the urinary albumin-creatinine ratio (UACR); (3) no causal relationship between total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), ApoB and DN, or between TC, LDL-C, ApoA1 and UACR; (4) none of the results showed reverse causality. TG is a risk factor for DN and UACR; HDL-C is protective for both; ApoA1 protects against DN; and ApoB is a risk factor for UACR. To further explore the underlying mechanisms between TG, HDL-C, ApoA1, ApoB, and their associations with DN and UACR, and to provide reference for the selection of lipid management and treatment strategies for clinical DN patients. This study demonstrated that causal relationships between TG, HDL-C, and ApoA1 with DN and between TG, HDL-C, and ApoB with the UACR. Show less
📄 PDF DOI: 10.1186/s13098-025-01641-8
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Anqi Wang, Hui Ren, Yanyan Zhang +2 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Fatty liver hemorrhagic syndrome (FLHS) is a common nutritional and metabolic disease in laying hens, leading to a rapid decline in egg production. This study aims to evaluate the antioxidant effects Show more
Fatty liver hemorrhagic syndrome (FLHS) is a common nutritional and metabolic disease in laying hens, leading to a rapid decline in egg production. This study aims to evaluate the antioxidant effects of dietary supplementation with Pueraria Lobatae Radix polysaccharide (PLRP) on laying hens with FLHS induced by a high-energy low-protein (HELP) diet. A total of 72 thirty-seven-wk-old Hy-Line Brown laying hens were divided into 4 groups: basal diet (CON), HELP diet (HELP), HELP + 100 mg/kg PLRP (HELP-Low), and HELP + 300 mg/kg PLRP (HELP-High), with 6 replicates of 3 hens each. After 4 weeks on the HELP diet, PLRP was added to the diet of the HELP-Low and HELP-High groups for 8 weeks. The results demonstrated that PLRP supplementation significantly improved laying rate compared to the HELP group, with the HELP-Low and HELP-High groups exhibiting respective increases of 23.81% and 28.57% (P < 0.01). PLRP also promoted follicular development, increasing the number of stratified, primary, and secondary follicles and improving the ovarian index. Biochemical analysis revealed enhanced antioxidant activity, with increased levels of T-AOC, T-SOD, and GSH-Px and reduced MDA in the liver and ovaries of PLRP-treated hens (P < 0.05). At the molecular level, PLRP upregulated mRNA expression of ER-α, ER-β, MTTP, APOB, APOVLDL-II, and VTG-II in the liver, as well as VLDLR, LHR, and FSHR in the ovaries, facilitating yolk precursor biosynthesis and follicular development (P < 0.05). It indicated that PLRP supplementation mitigates oxidative stress and enhances yolk precursor synthesis, thereby improving egg production in FLHS-affected hens. PLRP shows promise as an effective feed additive for preventing and alleviating FLHS in laying hens. Future studies will investigate the regulatory effects of PLRP on gut microbiota composition and its potential interactions with FLHS in laying hens. Show less
📄 PDF DOI: 10.1016/j.psj.2025.105062
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Song Liu, Xingjin Wang, Jiaqiang Hu +2 more · 2025 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
To evaluate the efficacy and safety of siRNA drugs that lower Lp(a) in patients with dyslipidaemia. A network meta-analysis and systematic review were conducted to compare siRNA drugs targeting Lp(a), Show more
To evaluate the efficacy and safety of siRNA drugs that lower Lp(a) in patients with dyslipidaemia. A network meta-analysis and systematic review were conducted to compare siRNA drugs targeting Lp(a), based on relevant randomized controlled trials (RCTs). A comprehensive search was performed in PubMed, Embase, Web of Science and the Cochrane Library (up to October 24, 2024). RCTs with an intervention duration of at least 12 weeks were included. Eligible studies compared siRNA drugs that reduce Lp(a), including both Lp(a)-targeted and non-targeted agents, with placebo or other siRNA drugs that reduce Lp(a). The primary outcomes were the percentage reduction and absolute reduction in Lp(a), percentage reduction in low-density lipoprotein cholesterol (LDL-C), percentage reduction in apolipoprotein B (apo(B)), adverse events and serious adverse events, including injection-site reactions. The risk of bias was assessed using the Cochrane Risk of Bias Tool (ROB2), and a random-effects network meta-analysis was performed using the frequentist approach. Confidence in effect estimates was evaluated using the Confidence In Network Meta-Analysis (CINeMA) framework. A total of 14 trials involving 5646 participants were included. Lp(a)-targeted siRNA agents, particularly Olpasiran, demonstrated strong efficacy in significantly reducing Lp(a) levels, with the greatest percentage reduction in Lp(a) (mean difference [MD]: -92.06%; 95% CI: -102.43% to -81.69%; P-score: 0.98). Olpasiran also showed the greatest absolute reduction in Lp(a) (MD: -250.70 nmol/L; 95% confidence interval [CI]: -279.89 to -221.50; P-score: 0.99). Certain non-Lp(a)-targeted siRNA agents, such as inclisiran and zodasiran, also showed modest reductions in Lp(a) levels, reducing Lp(a) by approximately 15%. Lp(a)-targeted siRNA agents reduced LDL-C by more than 20% and decreased apo(B) by approximately 15%. In terms of safety, most drugs exhibited favourable safety profiles with no significant differences compared to placebo. However, zerlasiran raised concerns regarding injection-site reactions and other adverse events when compared to placebo. Lp(a)-targeted siRNA agents have shown robust effectiveness in substantially reducing Lp(a) levels, including both percentage and absolute reductions, with moderate improvements in LDL-C and apo(B) concentrations. Non-Lp(a)-targeted siRNA agents also demonstrate modest reductions in Lp(a) levels. The safety profile is generally favourable, but zerlasiran and inclisiran may increase the incidence of injection-site reactions. Show less
no PDF DOI: 10.1111/dom.16355
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Wenxiu Wang, Rui Li, Zimin Song +4 more · 2025 · JAMA cardiology · added 2026-04-24
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lower Show more
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lowering triglycerides. Multiple clinical trials of small-interfering RNA therapeutics targeting APOC3 are currently underway. To investigate whether genetically predicted lower APOC3 is associated with a reduction in cardiovascular risk and if the combined exposure to APOC3 and LDL-C-lowering variants is associated with a reduction in the risk of coronary heart disease (CHD). This was a population-based genetic association study with 2 × 2 factorial mendelian randomization. Included were participants of European ancestry in the UK Biobank. Data were analyzed from November 2023 to July 2024. Genetic scores were constructed to mimic the effects of APOC3, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors. Plasma lipid and lipoprotein levels, CHD, and type 2 diabetes (T2D). This study included 401 548 UK Biobank participants (mean [SD] age, 56.9 [8.0] years; 216 901 female [54.0%]). Genetically predicted lower APOC3 was associated with a lower risk of CHD (odds ratio [OR], 0.96; 95% CI, 0.93-0.98) and T2D (0.97; 95% CI, 0.95-0.99). Genetically lower APOC3 and PCSK9 were associated with a similar magnitude of risk reduction in CHD per 10-mg/dL decrease in apolipoprotein B (ApoB) level (APOC3: 0.70; 95% CI, 0.59-0.83; PCSK9: 0.71; 95% CI, 0.65-0.77). Combined exposure to genetically lower APOC3 and PCSK9 was associated with an additive lower risk of CHD (APOC3: 0.96; 95% CI, 0.92-0.99; PCSK9: 0.93; 95% CI, 0.90-0.97; combined: 0.90; 95% CI, 0.86-0.93). Genetically lower HMGCR was also associated with a lower risk of CHD, and the risk was further reduced when combined with APOC3 (0.93; 95% CI, 0.90-0.97). Genetically predicted lower APOC3 was associated with a reduced risk of CHD that is comparable with that associated with lower PCSK9 per unit decrease in ApoB. Combined exposure to APOC3 and LDL-C-lowering variants was associated with an additive reduction in CHD risk. Future studies are warranted to investigate the therapeutic potential of these combined therapies, particularly among high-risk patients who cannot achieve therapeutic targets with existing lipid-lowering therapies. Show less
no PDF DOI: 10.1001/jamacardio.2025.0195
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Ruibing Li, Jinyang Wang, Jianan Wang +7 more · 2025 · Journal of inflammation research · added 2026-04-24
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in s Show more
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in seronegative cases, highlighting the need for reliable biomarkers to enhance clinical management. This study aimed to identify serum lipid biomarkers for the diagnosis and monitoring of NMOSD and to assess their potential to improve clinical decision-making. We conducted a comprehensive serum proteomic analysis in a discovery cohort of NMOSD patients and controls to identify lipid-related proteins associated with NMOSD. Subsequently, we validated the candidate biomarkers in the retrospective cohort and developed diagnostic models using a random forest algorithm. The association between these lipid biomarkers and disease activity was further evaluated in longitudinal analysis. Our analysis identified a panel of serum lipid-related biomarkers that demonstrated significant differences between NMOSD patients and controls. The diagnostic models achieved the impressive accuracy of 72% for the full NMOSD spectrum, 72% for AQP4-IgG+ NMOSD, and 68% for double seronegative NMOSD. Importantly, these biomarkers showed a correlation with disease activity, with levels changing from relapse to remission. Additionally, a combination of these lipid biomarkers was found to predict relapse with the AUC of 0.861. A user-friendly smartphone application was developed to facilitate the straightforward "input-index, output-answer" screening process, enhancing both clinical decision-making and patient care. The diagnostic model based on the serum lipid-related indexes (TC, TG, LDL, HDL, ApoA1, and ApoB) may be the useful tool for NMOSD in diagnosis and monitoring of disease stage, thereby improving the treatment outcome for patients. Future studies should focus on integrating these biomarkers into routine clinical practice to realize their full potential in enhancing NMOSD management. Show less
📄 PDF DOI: 10.2147/JIR.S496018
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Anna Tilp, Dimitrios Nasias, Andrew Carley +10 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell barrier. This process allows the heart to obtain fatty acids (FAs), its chief source of energy an Show more
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell barrier. This process allows the heart to obtain fatty acids (FAs), its chief source of energy and apolipoprotein B (apoB)-containing lipoproteins to cross the arterial endothelial barrier leading to cholesterol accumulation in the subendothelial space. Multiple studies have established elevated postprandial triglyceride-rich lipoproteins (TRLs) as an independent risk factor for cardiovascular disease (CVD). We explored how chylomicrons affect ECs and transfer their FAs across the EC barrier. We had reported that media from chylomicron-treated ECs leads to lipid droplet (LD) formation in macrophages. To determine the responsible component of this media, we assessed whether removing the extracellular vesicles (EVs) would obviate this effect. EVs from control and treated cells were then characterized by protein, lipid and microRNA (miR) content. We also studied the EV-induced transcription changes in macrophages and ECs and whether knockdown of scavenger receptor-BI (SR-BI) altered these responses. In addition, using chylomicrons labeled with [ Chylomicron treatment of ECs led to an inflammatory response that included production of EVs that drove macrophage LD accumulation. The EVs contained little free fatty acids and triglyceride, but abundant phospholipids and diacylglycerols. In concert with this, [ EC chylomicron metabolism produces EVs that increase macrophage inflammation and create LDs. Media containing these EVs also increases EC inflammation, illustrating an autocrine inflammatory process. FAs within chylomicron triglycerides are converted to phospholipids within EVs. Thus, EC uptake of chylomicrons constitutes an important pathway for vascular inflammation and tissue lipid acquisition. Show less
no PDF DOI: 10.1101/2025.02.28.640926
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Haoyu Wang, Tian Tu, Lijun Yin +2 more · 2025 · BMC cancer · BioMed Central · added 2026-04-24
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers in Show more
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care. Show less
📄 PDF DOI: 10.1186/s12885-025-13841-6
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Yu Cui, Yanzhu Chen, Mengting Hu +7 more · 2025 · Computational biology and chemistry · Elsevier · added 2026-04-24
The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential Show more
The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential for developing strategies to prevent and treat dyslipidemia and cardiovascular diseases. This study aimed to assess this relationship using two-sample Mendelian randomization (MR). Data for both exposure and outcomes were obtained from the IEU-GWAS database, with lipid profile data sourced from a publication. Genome-wide significant single nucleotide polymorphisms (SNPs), which were independent of outcome factors but correlated with exposure variables, were identified as instrumental variables. Several MR methods, including weighted analysis, maximum likelihood, inverse variance weighting (IVW), MR-Egger, and weighted median, were applied. Colocalization analysis further validated the findings. The analysis revealed microbial groups with causal relationships to ApoA1, ApoB, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, and triglycerides. Reverse MR and colocalization analysis provided additional confirmation of these results. This study offers new evidence of the causal link between gut microbiota and lipid profiles, providing insights for improving lipid profiles and reducing cardiovascular disease risk. Show less
no PDF DOI: 10.1016/j.compbiolchem.2025.108422
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Zhaoyuan Sun, Jinzhi Liu, Aihua Wang +1 more · 2025 · Scientific reports · Nature · added 2026-04-24
Small and dense LDL cholesterol (sdLDL-C) and apolipoprotein B (ApoB) have important roles in promoting the development of atherosclerosis and are highly correlated with the degree of atherosclerosis. Show more
Small and dense LDL cholesterol (sdLDL-C) and apolipoprotein B (ApoB) have important roles in promoting the development of atherosclerosis and are highly correlated with the degree of atherosclerosis. Several studies have found differences in anterior and posterior circulation strokes and in the mechanisms of their atherosclerosis, but little research has been done on the relationship of sdLDL-C and ApoB to atherosclerotic stenosis in anterior and posterior circulation strokes. We analyzed the correlation between sdLDL-C and ApoB and the degree of arterial stenosis in patients with posterior circulation stroke. We included 230 anterior circulation stroke (ACS) patients and 170 posterior circulation stroke (PCS) patients. Blood specimens were collected at admission, serum ApoB and sdLDL-C concentrations were measured, and the degree of arterial stenosis was determined on the basis of vascular imaging. We analyzed the predictive value of ApoB and sdLDL-C for the degree of cerebral artery stenosis in patients with PCS. For patients with nonmild stenosis, sdLDL-C and ApoB levels were higher in the PCS group than in the ACS group (P < 0.05). SdLDL-C (P < 0.001) and ApoB (P < 0.05) were independent risk factors for increased intracranial artery stenosis in the posterior circulation group. Binary logistic regression analysis showed that sdLDL-C (P < 0.05) and ApoB (P < 0.05) were independent risk factors for non-mild stenosis of the intracranial arteries in patients with PCS after correction for confounders. In the posterior circulation group, there was an interaction between the effects of sdLDL and ApoB on intracranial artery stenosis, P < 0.05. Plotting the ROC curve showed that the AUC of the combined detection of sdLDL-C and ApoB was 0.791, which was better than that of the single index. We built nomogram model, the DCA curves, calibration curves, NRI index, and IDI index of both the modeling and validation groups indicated that the diagnostic efficacy and clinical benefit of the combined sdLDL-C and ApoB assay were greater than those of single-indicator assays for cerebral artery stenosis in posterior circulation stroke. Risk factors contributing to the increased degree of intracranial arterial stenosis in ACS and PCS vary somewhat. SdLDL-C and ApoB may be of value in clinical decision making as predictors of cerebral arterial stenosis in patients with PCS. Show less
📄 PDF DOI: 10.1038/s41598-025-93074-6
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Tongxue Zhang, Yajing Li, Xiaoyu Liu +8 more · 2025 · Kardiologiia · added 2026-04-24
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneu Show more
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneurysm remains unclear. This study used a Mendelian randomization (MR) approach to explore the causal relationships between ApoB, aortic aneurysm, and potential mediators.Material and methods    Single nucleotide polymorphism (SNP) data related to ApoB, apolipoprotein A1 (ApoA1), triglycerides, frailty index, and aortic aneurysm were obtained from large-scale genome-wide association studies. MR analysis was conducted to evaluate causal relationships, using inverse variance weighting (IVW) as the primary statistical method. Additionally, we assessed whether the frailty index mediates the relationship between ApoB and aortic aneurysm.Results    Univariate MR analysis revealed that ApoB is significantly associated with aortic aneurysm (IVW odds ratio (OR) = 1.443, 95 % confidence interval (CI) = 1.273-1.637, p < 0.001). Multivariable MR (MVMR) analysis, adjusted for ApoA1 and triglycerides, confirmed these results. In mediation analysis, the frailty index was found to partially mediate the effect of ApoB on aortic aneurysm (mediation contribution: 20.1 %-23.1 %). The ORs for ApoB and the frailty index with respect to aortic aneurysm were 1.325 (95 % CI = 1.168-1.505) and 4.188 (95 % CI = 1.859-9.435), respectively.Conclusion    ApoB has a causal relationship with aortic aneurysm, with the frailty index acting as a partial mediator in this pathway. Show less
no PDF DOI: 10.18087/cardio.2025.2.n2796
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Jingjing Guo, Haifan Qiu, Jianping Wang +3 more · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregn Show more
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregnancy women and 317 healthy non-pregnant women. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) [Lp(a)], and hypersensitive C-reactive protein (hs-CRP) were measured in both groups. The mean and standard deviation of each index were calculated to establish the reference range of normal serum lipid levels in pregnant women in mid-to-late pregnancy. The associations between serum lipid levels and perinatal outcomes were assessed statistically. There were no significant differences in age, pregnancy, or parity between the adverse outcome and normal delivery groups, but the caesarean section rate was significantly higher in the adverse outcome group. The levels of hs-CRP, TG, TC, HDL-C, LDL-C, and ApoA1 were significantly higher in the adverse outcome group. Elevated hs-CRP, TG, and HDL-C levels were risk factors for adverse pregnancy outcomes. According to the receiver operating characteristic curve, the optimal threshold of the combined diagnosis of these three indicators to predict adverse pregnancy outcomes was 0.534, and the area under the curve was 0.822. The establishment of lipid reference intervals in the second and third trimesters of pregnancy can effectively evaluate lipid metabolism in pregnant women, and the measurement of lipid metabolism in pregnant women is helpful in predicting adverse pregnancy outcomes. Show less
📄 PDF DOI: 10.3389/fmed.2025.1530525
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Pengfei Zhang, Wenting Wang, Qian Xu +5 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. Show more
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC). We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated. MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity. Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119136
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Juan Zhou, Shanshan Wang, Qiang Wang +11 more · 2025 · Food & function · Royal Society of Chemistry · added 2026-04-24
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycati Show more
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycation end products (AGEs), but data on its effects in centrally obese individuals are sparse. Thus, we aimed to investigate the effects of lutein supplementation in subjects with central obesity. A double-blind, randomized controlled trial was conducted involving patients with central obesity. Anthropometric indices, dietary intake, metabolic parameters, carotenoid and AGEs levels were compared between those receiving a 32-week intervention of 10 mg d Show less
no PDF DOI: 10.1039/d4fo05578k
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Shuai Wang, Hanshen Zhou, Kaili Cai +4 more · 2025 · World journal of surgical oncology · BioMed Central · added 2026-04-24
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression o Show more
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression on the long-term survival after surgery. Between December 2015 and December 2019, a cohort of 509 patients diagnosed with PDAC and undergoing resection at our hospital was analyzed. They were stratified into two groups, Control group (Control) and study group (PPTDM), depending on the onset of postoperative diabetes mellitus. We analyzed the survival rates at 6 months, 12 months and 24 months post-operation in the two groups. We use univariate and logostic multivariate regressions to analyze the risk factors for PPTDM. ROC curve analysis was conducted to assess the diagnostic significance of perioperative FBG levels regarding patients' long-term survival rates. The Kaplan-Meier method was employed to assess the impact of both preoperative and postoperative FBG levels on the survival rates within 24 months for each patient group. The comparison of general clinical data between the two groups shows marginal differences without statistical significance(P > 0.05); Patients in PPTDM group had significantly higher BMI, preoperative jaundice proportion, larger tumor diameter, higher TNM stage and higher proportion of distal pancreatectomy (DP), with P values of 0.023, 0.010, 0.040, 0.012 and 0.005, respectively. The levels of preoperative FBG and postoperative FBG in PPTDM patients exhibited statistically significant elevation compared to the control group (P < 0.05). There were no significant differences in surgery-related indicators between the two groups in operative time, number of dissected positive lymph nodes, total number of dissected lymph nodes, intraoperative blood loss and other related data (P > 0.05). Hospitalization duration of PPTDM patients was longer than control group (P = 0.047). PPTDM group had significantly higher expression concentrations of BUN, Cr, TG, LDL and Apo-B factors (P = 0.023, 0.024, 0.013, 0.045 and 0.017). 17 patients (5.03%) died in the PPTDM group and 4 patients (2.35%) in control group which had significantly difference (P = 0.020). In univariate and logostic multivariate regression analysis indicated tumor size, jaundice, BUN, Cr, TG, LDL, Apo-B concentrations and DP approach were significantly correlated to the risk for PPTDM (P < 0.05). ROC curve analysis results showed combining of preoperative and postoperation FBG showed the highest diagnostic efficacy, followed by postoperation FBG and preoperative FBG. The AUC areas of the three groups were 0.745, 0.623 and 0.588, respectively, and the critical values of the three groups were 9.81/9.95 mmol/L, 10.18 mmol/L and 10.23 mmol/L, respectively, with statistical significance (P < 0.05). Results were considered statistically significant if the p-value was less than 0.05. PPTDM stands as a significant postoperative complication following pancreatic cancer surgery, characterized by a high incidence and severity. Several risk factors have garnered considerable attention among clinical surgeon. PPTDM may be an influential factor in postoperative prognosis of pancreatic cancer. The expression levels of preoperative and postoperative blood glucose hold diagnostic value for the long-term prognosis of pancreatic cancer patients. Early regulation and intervention by surgeons concerning perioperative FBG could potentially mitigate the risk of PPTDM. Show less
📄 PDF DOI: 10.1186/s12957-025-03705-5
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Ling-Ling Wang, Zi-Xiang Xu, Bo-Qian Sun +3 more · 2025 · Angiology · SAGE Publications · added 2026-04-24
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol ( Show more
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol (non-HDL-C)/high-density lipoprotein cholesterol (HDL-C), remnant cholesterol (RC)/HDL-C, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1), low-density lipoprotein cholesterol (LDL-C)/HDL-C, ApoB/HDL-C, total cholesterol (TC)/HDL-C, triglycerides (TG)/HDL-C, were included and analyzed. Sex differences in the relationship between lipid ratios and carotid plaque were discussed. The risk of carotid plaque was found to be significantly associated with the Non-HDL-C /HDL-C, RC/HDL-C, ApoB/ApoA1, LDL-C /HDL-C, ApoB/HDL-C, TC/HDL-C in females but not in males. The ApoB/HDL risk presented the highest relationship with carotid plaque in females only. The predictive value of the aforementioned lipid ratios for carotid plaque was observed in females only. Show less
no PDF DOI: 10.1177/00033197251316624
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Robert S Rosenson, J Antonio G López, Daniel Gaudet +14 more · 2025 · JAMA cardiology · added 2026-04-24
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering Show more
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering RNA, blocks Lp(a) production by inducing degradation of apolipoprotein(a) messenger RNA. Olpasiran's effects on OxPL and systemic markers of inflammation are not well described. To assess the effects of olpasiran on OxPL, high-sensitivity interleukin 6 (hs-IL-6), and hs-C-reactive protein (hs-CRP) in the OCEAN(a)-DOSE randomized clinical trial. OCEAN(a)-DOSE was an international, multicenter, placebo-controlled, phase 2, dose-finding randomized clinical trial conducted between July 2020 and November 2022. A total of 281 patients with atherosclerotic cardiovascular disease and Lp(a) levels greater than 150 nmol/L were included. Participants were randomized to receive 1 of 4 active subcutaneous doses of olpasiran vs placebo: (1) 10 mg, administered every 12 weeks (Q12W); (2) 75 mg, Q12W; (3) 225 mg, Q12W; or (4) 225 mg, administered every 24 weeks (Q24W). OxPL on apolipoprotein B (OxPL-apoB), hs-CRP, and hs-IL-6 were assessed at baseline, week 36, and week 48 in 272 patients. The primary outcome was placebo-adjusted change in OxPL-apoB from baseline to week 36. Among 272 participants, median (IQR) age was 62 years (56-69), and 86 participants (31.6%) were female. Baseline median (IQR) Lp(a) concentration was 260.3 nmol/L (198.1-352.4) and median (IQR) OxPL-apoB concentration was 26.5 nmol/L (19.7-33.9). The placebo-adjusted mean percentage change in OxPL-apoB from baseline to week 36 was -51.6% (95% CI, -64.9% to -38.2%) for the 10-mg Q12W dose, -89.7% (95% CI, -103.0% to -76.4%) for the 75-mg Q12W dose, -92.3% (95% CI, -105.6% to -78.9%) for the 225-mg Q12W dose, and -93.7% (95% CI, -107.1% to -80.3%) for the Q24W dose (P < .001 for all). These effects were maintained to week 48 (-50.8%, -100.2%, -104.7%, and -85.8%, respectively; P < .001 for all). There was a strong correlation between percentage reduction in Lp(a) and OxPL-apoB for patients treated with olpasiran (r = 0.79; P < .001). Olpasiran did not significantly impact hs-CRP or hs-IL-6 compared with placebo to weeks 36 or 48 (P > .05). In the OCEAN(a)-DOSE multicenter randomized clinical trial, olpasiran led to a significant and sustained reduction in OxPL-apoB but no significant effects on hs-CRP or hs-IL-6. Show less
no PDF DOI: 10.1001/jamacardio.2024.5433
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Zeyu Wang, Zixiao Yin, Guangyong Sun +2 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
The liver‒brain axis is critical in neurodegenerative diseases (NDs), with lipid metabolism influencing neuroinflammation and microglial function. A systematic investigation of the genetic relationshi Show more
The liver‒brain axis is critical in neurodegenerative diseases (NDs), with lipid metabolism influencing neuroinflammation and microglial function. A systematic investigation of the genetic relationship between lipid metabolism abnormalities and ND, namely, Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), is lacking. To assess potential causal links between ND and six lipid parameters, two-sample Mendelian randomization (MR) was used. Large-scale European ancestry GWAS data for lipid parameters and ND (AD, ALS, PD, and MS) were used. Genetic variants demonstrating significant correlations (P < 5 × 10 MR via the inverse-variance weighted method revealed causal effects of cholesterol (CHOL, OR = 1.10, 95% CI: 1.03-1.18, P = 4.23 × 10⁻ Higher CHOL and LDLC levels were associated with increased ALS risk, suggesting a potential causal link, and supporting the liver‒brain axis hypothesis in ND. Current genetic evidence does not support a significant role for lipid metabolism in PD and MS etiology, suggesting the relationship between lipid metabolism and other NDs may be more complex and warrants further investigation. Show less
📄 PDF DOI: 10.1186/s12944-025-02455-3
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Yuanlong Hu, Xinhai Cui, Mengkai Lu +11 more · 2025 · Mayo Clinic proceedings · Elsevier · added 2026-04-24
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was p Show more
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was performed to investigate the causal effect of lipid-modifying drug targets on type 2 diabetes (T2D) outcomes and the proportion of this association that is mediated through ectopic fat accumulation traits. Specific sets of variants in or near genes that encode 11 lipid-modifying drug targets (LDLR, HMGCR, NPC1L1, PCSK9, APOB, ABCG5/ABCG8, LPL, PPARA, ANGPTL3, APOC3, and CETP; for expansion of gene symbols, use search tool at www.genenames.org) were extracted. Random effects inverse variance weighted were performed to evaluate the causal effects among outcomes. Mediation analyses were performed to identify the mediators of the association between lipid-modifying drugs and T2D. The study was conducted from November 10, 2023, to April 2, 2024 RESULTS: The genetic mimicry of HMGCR and APOB inhibition was associated with an increased T2D risk, whereas the genetic mimicry of LPL enhancement was linked to a lower T2D risk. Gluteofemoral adipose tissue volume was a mediator for explaining 9.52% (P=.002), 16.90% (P=.03), and 10.50% (P=.003) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Liver fat was a mediator for explaining 21.12% (P=.005), 12.28% (P=.03), and 9.84% (P=.005) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Our findings support the hypothesis that liver fat and gluteofemoral adipose tissue play a mediating role in the prodiabetic effects of HMGCR and APOB inhibition, as well as in the antidiabetic effects of LPL enhancement. Show less
no PDF DOI: 10.1016/j.mayocp.2024.10.018
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