👤 Zhongxu Zhang

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Also published as: A-Mei Zhang, Ai Zhang, Ai-Min Zhang, Aiguo Zhang, Aihua Zhang, Aijun Zhang, Aileen Zhang, Ailin Zhang, Aimei Zhang, Aimin Zhang, Aixiang Zhang, Alaina Zhang, Alex R Zhang, Amy L Zhang, An Zhang, An-Qi Zhang, Anan Zhang, Andrew Zhang, Ang Zhang, Anli Zhang, Anqi Zhang, Anwei Zhang, Anying Zhang, Ao Zhang, Bangke Zhang, Bangzhou Zhang, Bao Long Zhang, Bao-Fu Zhang, Bao-Rong Zhang, Baohu Zhang, Baojing Zhang, Baojun Zhang, Baoren Zhang, Baorong Zhang, Baotong Zhang, Bei B Zhang, Bei Zhang, Bei-Bei Zhang, Beiyu Zhang, Ben Zhang, Benjian Zhang, Benyou Zhang, Bi-Tian Zhang, Biao Zhang, Bicheng Zhang, Bikui Zhang, Bin Zhang, Binbin Zhang, Bing Zhang, Bing-Qi Zhang, Bingbing Zhang, Bingkun Zhang, Bingqiang Zhang, Bingxue Zhang, Bingye Zhang, Bixia Zhang, Bo Zhang, Bo-Fei Zhang, Bo-Heng Zhang, Bo-Ya Zhang, Bochuan Zhang, Bofang Zhang, Bohao Zhang, Bohong Zhang, Bohua Zhang, Bojian Zhang, Bolin Zhang, Boping Zhang, Boqing Zhang, Bosheng Zhang, Bowei Zhang, Bowen Zhang, Boxi Zhang, Boxiang Zhang, Boya Zhang, Boyan Zhang, C D Zhang, C H Zhang, C Zhang, Cai Zhang, Cai-Ling Zhang, Caihong Zhang, Caiping Zhang, Caiqing Zhang, Caishi Zhang, Caiyi Zhang, Caiying Zhang, Caiyu Zhang, Can Zhang, Cathy C Zhang, Chan-na Zhang, Chang Zhang, Chang-Hua Zhang, Changhua Zhang, Changhui Zhang, Changjiang Zhang, Changjing Zhang, Changlin Zhang, Changlong Zhang, Changquan Zhang, Changteng Zhang, Changwang Zhang, Channa Zhang, Chao Zhang, Chao-Hua Zhang, Chao-Sheng Zhang, Chao-Yang Zhang, ChaoDong Zhang, Chaobao Zhang, Chaoke Zhang, Chaoqiang Zhang, Chaoyang Zhang, Chaoyue Zhang, Chen Zhang, Chen-Qi Zhang, Chen-Ran Zhang, Chen-Song Zhang, Chen-Xi Zhang, Chen-Yan Zhang, Chen-Yang Zhang, Chenan Zhang, Chenfei Zhang, Cheng Cheng Zhang, Cheng Zhang, Cheng-Lin Zhang, Cheng-Wei Zhang, Chengbo Zhang, Chengcheng Zhang, Chengfei Zhang, Chenggang Zhang, Chengkai Zhang, Chenglong Zhang, Chengnan Zhang, Chengrui Zhang, Chengsheng Zhang, Chengshi Zhang, Chenguang Zhang, Chengwu Zhang, Chengxiang Zhang, Chengxiong Zhang, Chengyu Zhang, Chenhong Zhang, Chenhui Zhang, Chenjie Zhang, Chenlin Zhang, Chenlu Zhang, Chenmin Zhang, Chenming Zhang, Chenrui Zhang, Chenshuang Zhang, Chenxi Zhang, Chenyan Zhang, Chenyang Zhang, Chenyi Zhang, Chenzi Zhang, Chi Zhang, Chong Zhang, Chong-Hui Zhang, Chongguo Zhang, Chonghe Zhang, Chris Zhiyi Zhang, Chu-Yue Zhang, Chuan Zhang, Chuanfu Zhang, Chuankuan Zhang, Chuankuo Zhang, Chuanmao Zhang, Chuantao Zhang, Chuanxin Zhang, Chuanyong Zhang, Chuchu Zhang, Chumeng Zhang, Chun Zhang, Chun-Lan Zhang, Chun-Mei Zhang, Chun-Qing Zhang, Chungu Zhang, Chunguang Zhang, Chunhai Zhang, Chunhong Zhang, Chunhua Zhang, Chunjun Zhang, Chunli Zhang, Chunling Zhang, Chunqing Zhang, Chunxia Zhang, Chunxiang Zhang, Chunxiao Zhang, Chunyan Zhang, Chunying Zhang, Churen Zhang, Chuting Zhang, Chuyue Zhang, Ci Zhang, Claire Y Zhang, Claire Zhang, Clarence K Zhang, Cong Zhang, Congen Zhang, Cuihua Zhang, Cuijuan Zhang, Cuilin Zhang, Cuiping Zhang, Cuiyu Zhang, Cun Zhang, Da Zhang, Da-Qi Zhang, Da-Wei Zhang, Dachuan Zhang, Dadong Zhang, Daguo Zhang, Dai Zhang, Dalong Zhang, Daming Zhang, Dan Zhang, Dan-Dan Zhang, DanDan Zhang, Danfeng Zhang, Danhua Zhang, Danning Zhang, Danyan Zhang, Danyang Zhang, Daolai Zhang, Daoyong Zhang, Dapeng Zhang, David Y Zhang, David Zhang, Dawei Zhang, Daxin Zhang, Dayi Zhang, De-Jun Zhang, Dekai Zhang, Delai Zhang, Deng-Feng Zhang, Dengke Zhang, Deqiang Zhang, Detao Zhang, Deyi Zhang, Deyin Zhang, Di Zhang, Dian Ming Zhang, Dianbo Zhang, Dianzheng Zhang, Ding Zhang, Dingdong Zhang, Dinghu Zhang, Dingkai Zhang, Dingyi Zhang, Dingyu Zhang, Dong Zhang, Dong-Hui Zhang, Dong-Mei Zhang, Dong-Wei Zhang, Dong-Ying Zhang, Dong-cui Zhang, Dong-juan Zhang, Dong-qiang Zhang, Dongdong Zhang, Dongfeng Zhang, Donghua Zhang, Donghui Zhang, Dongjian Zhang, Dongjie Zhang, Donglei Zhang, Dongmei Zhang, Dongsheng Zhang, Dongxin Zhang, Dongyan Zhang, Dongyang Zhang, Dongying Zhang, Donna D Zhang, Donna Zhang, Duo Zhang, Duoduo Zhang, Duowen Zhang, En Zhang, Enhui Zhang, Enming Zhang, Erchen Zhang, F P Zhang, F Zhang, Fa Zhang, Famin Zhang, Fan Zhang, Fang Zhang, Fanghong Zhang, Fangmei Zhang, Fangting Zhang, Fangyuan Zhang, Fei Zhang, Fei-Ran Zhang, Feifei Zhang, Feixue Zhang, Fen Zhang, Feng Zhang, Fengqing Zhang, Fengshi Zhang, Fengshuo Zhang, Fengwei Zhang, Fengxi Zhang, Fengxia Zhang, Fengxu Zhang, Fomin Zhang, Fred Zhang, Fu-Ping Zhang, Fubo Zhang, Fugui Zhang, Fuhan Zhang, Fujun Zhang, Fukang Zhang, Fuming Zhang, Fuqiang Zhang, Fuquan Zhang, Furen Zhang, Fushun Zhang, Fuxing Zhang, Fuyang Zhang, Fuyuan Zhang, G Zhang, G-Y Zhang, Gan Zhang, Gang Zhang, Ganlin Zhang, Gaoxin Zhang, Gary Zhang, Ge Zhang, Geng Zhang, Genglin Zhang, Genxi Zhang, Geyang Zhang, Gong Zhang, Gu Zhang, Guan-Yan Zhang, Guang Zhang, Guang-Qiong Zhang, Guang-Xian Zhang, Guang-Ya Zhang, Guanghui Zhang, Guangji Zhang, Guanglei Zhang, Guangliang Zhang, Guangping Zhang, Guangqiong Zhang, Guangxian Zhang, Guangxin Zhang, Guangye Zhang, Guangyong Zhang, Guangyuan Zhang, Guanqun Zhang, Gui-Ping Zhang, Guicheng Zhang, Guihua Zhang, Guijie Zhang, Guili Zhang, Guiliang Zhang, Guilin Zhang, Guimin Zhang, Guiping Zhang, Guisen Zhang, Guixia Zhang, Guixiang Zhang, Gumuyang Zhang, Guo-Fang Zhang, Guo-Fu Zhang, Guo-Guo Zhang, Guo-Liang Zhang, Guo-Wei Zhang, Guo-Xiong Zhang, Guoan Zhang, Guochao Zhang, Guodong Zhang, Guofang Zhang, Guofeng Zhang, Guofu Zhang, Guoguo Zhang, Guohua Zhang, Guohui Zhang, Guojun Zhang, Guoli Zhang, Guoliang Zhang, Guolong Zhang, Guomin Zhang, Guoming Zhang, Guoping Zhang, Guoqiang Zhang, Guoqing Zhang, Guorui Zhang, Guosen Zhang, Guowei Zhang, Guoxin Zhang, Guoying Zhang, Guozhi Zhang, H D Zhang, H F Zhang, H L Zhang, H P Zhang, H W Zhang, H X Zhang, H Y Zhang, H Zhang, H-F Zhang, Hai Zhang, Hai-Bo Zhang, Hai-Feng Zhang, Hai-Gang Zhang, Hai-Han Zhang, Hai-Liang Zhang, Hai-Man Zhang, Hai-Ying Zhang, Haibei Zhang, Haibing Zhang, Haibo Zhang, Haicheng Zhang, Haifeng Zhang, Haihong Zhang, Haihua Zhang, Haijiao Zhang, Haijun Zhang, Haikuo Zhang, Hailei Zhang, Hailian Zhang, Hailiang Zhang, Hailin Zhang, Hailing Zhang, Hailong Zhang, Hailou Zhang, Haiming Zhang, Hainan Zhang, Haipeng Zhang, Haisan Zhang, Haisen Zhang, Haitao Zhang, Haiwang Zhang, Haiwei Zhang, Haixia Zhang, Haiyan Zhang, Haiyang Zhang, Haiying Zhang, Haiyue Zhang, Han Zhang, Hanchao Zhang, Hang Zhang, Hanqi Zhang, Hanrui Zhang, Hansi Zhang, Hanting Zhang, Hanwang Zhang, Hanwen Zhang, Hanxu Zhang, Hanyin Zhang, Hanyu Zhang, Hao Zhang, Hao-Chen Zhang, Hao-Yu Zhang, Haohao Zhang, Haojian Zhang, Haojie Zhang, Haojun Zhang, Haokun Zhang, Haolin Zhang, Haomin Zhang, Haonan Zhang, Haopeng Zhang, Haoran Zhang, Haotian Zhang, Haowen Zhang, Haoxing Zhang, Haoyu Zhang, Haoyuan Zhang, Haoyue Zhang, Haozheng Zhang, He Zhang, Hefang Zhang, Hejun Zhang, Heng Zhang, Hengming Zhang, Hengrui Zhang, Hengyuan Zhang, Heping Zhang, Hong Zhang, Hong-Jie Zhang, Hong-Sheng Zhang, Hong-Xing Zhang, Hong-Yu Zhang, Hong-Zhen Zhang, Hongbin Zhang, Hongbing Zhang, Hongcai Zhang, Hongfeng Zhang, Hongfu Zhang, Honghe Zhang, Honghong Zhang, Honghua Zhang, Hongjia Zhang, Hongjie Zhang, Hongjin Zhang, Hongju Zhang, Hongjuan Zhang, Honglei Zhang, Hongliang Zhang, Hongmei Zhang, Hongmin Zhang, Hongquan Zhang, Hongrong Zhang, Hongrui Zhang, Hongsen Zhang, Hongtao Zhang, Hongting Zhang, Hongwu Zhang, Hongxia Zhang, Hongxin Zhang, Hongxing Zhang, Hongya Zhang, Hongyan Zhang, Hongyang Zhang, Hongyi Zhang, Hongying Zhang, Hongyou Zhang, Hongyuan Zhang, Hongyun Zhang, Hongzhong Zhang, Hongzhou Zhang, Houbin Zhang, Hu Zhang, Hua Zhang, Hua-Min Zhang, Hua-Xiong Zhang, Huabing Zhang, Huafeng Zhang, Huaiyong Zhang, Huajia Zhang, Huan Zhang, Huan-Tian Zhang, Huanmin Zhang, Huanqing Zhang, Huanxia Zhang, Huanyu Zhang, Huaqi Zhang, Huaqiu Zhang, Huawei Zhang, Huawen Zhang, Huayang Zhang, Huayong Zhang, Huayu Zhang, Hugang Zhang, Huhan Zhang, Hui Hua Zhang, Hui Z Zhang, Hui Zhang, Hui-Jun Zhang, Hui-Wen Zhang, Huibing Zhang, Huifang Zhang, Huihui Zhang, Huijie Zhang, Huijun Zhang, Huili Zhang, Huilin Zhang, Huimao Zhang, Huimin Zhang, Huiming Zhang, Huiping Zhang, Huiqing Zhang, Huiru Zhang, Huiting Zhang, Huixin Zhang, Huiying Zhang, Huiyu Zhang, Huiyuan Zhang, Huize Zhang, Huizhen Zhang, Igor Ying Zhang, J B Zhang, J R Zhang, J Y Zhang, J Zhang, J-Y Zhang, Jamie Zhang, Jason Z Zhang, Jennifer Y Zhang, Jerry Z Zhang, Ji Yao Zhang, Ji Zhang, Ji-Yuan Zhang, Jia Zhang, Jia-Bao Zhang, Jia-Si Zhang, Jia-Su Zhang, Jia-Xuan Zhang, Jiabi Zhang, Jiachao Zhang, Jiachen Zhang, Jiacheng Zhang, Jiahai Zhang, Jiahao Zhang, Jiahe Zhang, Jiajia Zhang, Jiajing Zhang, Jiaming Zhang, Jian Zhang, Jian-Guo Zhang, Jian-Ping Zhang, Jian-Xu Zhang, Jianan Zhang, Jianbin Zhang, Jianbo Zhang, Jianchao Zhang, Jianduan Zhang, Jianeng Zhang, Jianfa Zhang, Jiang Zhang, Jiangang Zhang, Jianghong Zhang, Jianglin Zhang, Jiangmei Zhang, Jiangtao Zhang, Jianguang Zhang, Jianguo Zhang, Jiangyan Zhang, Jianhai Zhang, Jianhong Zhang, Jianhua Zhang, Jianhui Zhang, Jianing Zhang, Jianjun Zhang, Jiankang Zhang, Jiankun Zhang, Jianliang Zhang, Jianling Zhang, Jianmei Zhang, Jianmin Zhang, Jianming Zhang, Jiannan Zhang, Jianping Zhang, Jianqiong Zhang, Jianshe Zhang, Jianting Zhang, Jianwei Zhang, Jianwen Zhang, Jianwu Zhang, Jianxia Zhang, Jianxiang Zhang, Jianxin Zhang, Jianying Zhang, Jianyong Zhang, Jianzhao Zhang, Jiao Zhang, Jiaqi Zhang, Jiasheng Zhang, Jiawei Zhang, Jiawen Zhang, Jiaxin Zhang, Jiaxing Zhang, Jiayan Zhang, Jiayi Zhang, Jiayin Zhang, Jiaying Zhang, Jiayu Zhang, Jiayuan Zhang, Jibin Zhang, Jicai Zhang, Jie Zhang, Jiecheng Zhang, Jiehao Zhang, Jiejie Zhang, Jieming Zhang, Jieping Zhang, Jieqiong Zhang, Jieying Zhang, Jifa Zhang, Jifeng Zhang, Jihang Zhang, Jimei Zhang, Jiming Zhang, Jimmy Zhang, Jin Zhang, Jin-Ge Zhang, Jin-Jing Zhang, Jin-Man Zhang, Jin-Ru Zhang, Jin-Rui Zhang, Jin-Yu Zhang, Jinbiao Zhang, Jinfan Zhang, Jinfang Zhang, Jinfeng Zhang, Jing Jing Zhang, Jing Zhang, Jing-Bo Zhang, Jing-Chang Zhang, Jing-Fa Zhang, Jing-Lve Zhang, Jing-Nan Zhang, Jing-Qiu Zhang, Jing-Zhan Zhang, JingZi Zhang, Jingchuan Zhang, Jingchun Zhang, Jingdan Zhang, Jingdong Zhang, Jingfa Zhang, Jinghui Zhang, Jingjing Zhang, Jinglan Zhang, Jingli Zhang, Jingliang Zhang, Jinglu Zhang, Jingmei Zhang, Jingmian Zhang, Jingning Zhang, Jingping Zhang, Jingqi Zhang, Jingrong Zhang, Jingru Zhang, Jingshuang Zhang, Jingsong Zhang, Jingtian Zhang, Jingting Zhang, Jingwei Zhang, Jingwen Zhang, Jingxi Zhang, Jingxiao Zhang, Jingxuan Zhang, Jingxue Zhang, Jingyao Zhang, Jingyi Zhang, Jingying Zhang, Jingyu Zhang, Jingyuan Zhang, Jingyue Zhang, Jingzhe Zhang, Jinhua Zhang, Jinhui Zhang, Jinjin Zhang, Jinjing Zhang, Jinliang Zhang, Jinlong Zhang, Jinming Zhang, Jinquan Zhang, Jinrui Zhang, Jinsong Zhang, Jinsu Zhang, Jintao Zhang, Jinwei Zhang, Jinxiu Zhang, Jinyi Zhang, Jinying Zhang, Jinyu Zhang, Jinze Zhang, Jinzhou Zhang, Jiqiang Zhang, Jiquan Zhang, Jishou Zhang, Jishui Zhang, Jitai Zhang, Jiuchun Zhang, Jiupan Zhang, Jiuwei Zhang, Jiuxuan Zhang, Jixia Zhang, Jixing Zhang, Jiyang Zhang, Joe Z Zhang, John H Zhang, John Z H Zhang, Joshua Zhang, Joyce Zhang, Juan Zhang, Juan-Juan Zhang, Jue Zhang, Juliang Zhang, Jun Zhang, Jun-Feng Zhang, Jun-Jie Zhang, Jun-Xiao Zhang, Jun-Xiu Zhang, Jun-ying Zhang, June Zhang, Junfeng Zhang, Junhan Zhang, Junhang Zhang, Junhua Zhang, Junhui Zhang, Junjie Zhang, Junjing Zhang, Junkai Zhang, Junli Zhang, Junling Zhang, Junlong Zhang, Junmei Zhang, Junmin Zhang, Junpei Zhang, Junpeng Zhang, Junping Zhang, Junqing Zhang, Junran Zhang, Junru Zhang, Junsheng Zhang, Juntai Zhang, Junwei Zhang, Junxia Zhang, Junxiao Zhang, Junxing Zhang, Junxiu Zhang, Junyan Zhang, Junyi Zhang, Junying Zhang, Junyu Zhang, Junzhi Zhang, Juqing Zhang, K Y Zhang, K Zhang, Kai Zhang, Kai-Jie Zhang, Kai-Qiang Zhang, Kaichuang Zhang, Kaige Zhang, Kaihua Zhang, Kaihui Zhang, Kailin Zhang, Kailing Zhang, Kaiming Zhang, Kainan Zhang, Kaitai Zhang, Kaituo Zhang, Kaiwen Zhang, Kaiyi Zhang, Kan Zhang, Kang Zhang, Kang-Ling Zhang, Kangjun Zhang, Kangning Zhang, Karen Zhang, Ke Zhang, Ke-Wen Zhang, Ke-lan Zhang, Kefen Zhang, Kejia Zhang, Kejian Zhang, Kejin Zhang, Kejun Zhang, Keke Zhang, Keshan Zhang, Kewen Zhang, Keyi Zhang, Keyong Zhang, Keyu Zhang, Kezhong Zhang, Kongyong Zhang, Kui Zhang, Kui-ming Zhang, Kun Zhang, Kunning Zhang, Kunshan Zhang, Kunyi Zhang, Kuo Zhang, L F Zhang, L Zhang, L-S Zhang, Laihong Zhang, Lan Zhang, Lanfang Zhang, Lanju Zhang, Lanjun Zhang, Lanlan Zhang, Lantian Zhang, Lanyue Zhang, Le Zhang, Le-Le Zhang, Lechi Zhang, Lei Zhang, Lei-Lei Zhang, Lei-Sheng Zhang, Leilei Zhang, Leili Zhang, Leitao Zhang, Leiying Zhang, Lele Zhang, Leli Zhang, Leo H Zhang, Li Zhang, Li-Fen Zhang, Li-Jie Zhang, Li-Ke Zhang, Li-ping Zhang, Lian Zhang, Lian-Lian Zhang, Lianbo Zhang, Lianfeng Zhang, Liang Zhang, Liang-Rong Zhang, Liangdong Zhang, Liangliang Zhang, Liangming Zhang, Lianjun Zhang, Lianmei Zhang, Lianqin Zhang, Lianxin Zhang, Libo Zhang, Lichao Zhang, Lichen Zhang, Licheng Zhang, Lichuan Zhang, Licui Zhang, Lida Zhang, Lie Zhang, Lifan Zhang, Lifang Zhang, Liguo Zhang, Lihong Zhang, Lihua Zhang, Lijian Zhang, Lijiao Zhang, Lijie Zhang, Lijuan Zhang, Lijun Zhang, Lilei Zhang, Lili Zhang, Limei Zhang, Limin Zhang, Liming Zhang, Lin Zhang, Lin-Jie Zhang, Lina Zhang, Linan Zhang, Linbo Zhang, Linda S Zhang, Ling Xia Zhang, Ling Zhang, Ling-Yu Zhang, Lingjie Zhang, Lingli Zhang, Lingling Zhang, Lingna Zhang, Lingqiang Zhang, Lingxiao Zhang, Lingyan Zhang, Lingyu Zhang, Lining Zhang, Linjing Zhang, Linli Zhang, Linlin Zhang, Lintao Zhang, Linyou Zhang, Linyuan Zhang, Liping Zhang, Liqian Zhang, Lirong Zhang, Lishuang Zhang, Litao Zhang, Liu Zhang, Liuming Zhang, Liuwei Zhang, Liwei Zhang, Liwen Zhang, Lixia Zhang, Lixing Zhang, Liyan Zhang, Liyi Zhang, Liyin Zhang, Liying Zhang, Liyu Zhang, Liyuan Zhang, Liyun Zhang, Lizhi Zhang, Long Zhang, Longlong Zhang, Longxin Zhang, Longzhen Zhang, Lu Zhang, Lu-Pei Zhang, Lu-Yang Zhang, Luanluan Zhang, Lucia Zhang, Lufei Zhang, Lukuan Zhang, Lulu Zhang, Lun Zhang, Lunan Zhang, Luning Zhang, Luo Zhang, Luo-Meng Zhang, Luoping Zhang, Lupei Zhang, Lusha Zhang, Luwen Zhang, Luyao Zhang, Luyun Zhang, Luzheng Zhang, Lv-Lang Zhang, M H Zhang, M J Zhang, M M Zhang, M Q Zhang, M X Zhang, M Zhang, Man Zhang, Manjin Zhang, Mao Zhang, Maomao Zhang, Mei Zhang, Mei-Fang Zhang, Mei-Ling Zhang, Mei-Qing Zhang, Mei-Ya Zhang, Mei-Zhen Zhang, MeiLu Zhang, Meidi Zhang, Meijia Zhang, Meiling Zhang, Meimei Zhang, Meishan Zhang, Meiwei Zhang, Meixia Zhang, Meixian Zhang, Meiyu Zhang, Melissa C Zhang, Melody Zhang, Meng Zhang, Meng-Jie Zhang, Meng-Wen Zhang, Meng-Ying Zhang, Mengdi Zhang, Mengguo Zhang, Menghao Zhang, Menghuan Zhang, Menghui Zhang, Mengjia Zhang, Mengjie Zhang, Mengliang Zhang, Menglu Zhang, Mengmeng Zhang, Mengmin Zhang, Mengna Zhang, Mengnan Zhang, Mengni Zhang, Mengqi Zhang, Mengqiu Zhang, Mengren Zhang, Mengshi Zhang, Mengxi Zhang, Mengxian Zhang, Mengxue Zhang, Mengying Zhang, Mengyuan Zhang, Mengyue Zhang, Mengzhao Zhang, Mengzhen Zhang, Mi Zhang, Mianzhi Zhang, Miao Zhang, Miao-Miao Zhang, Miaomiao Zhang, Miaoran Zhang, Michael Zhang, Min Zhang, Minfang Zhang, Ming Zhang, Ming-Jun Zhang, Ming-Liang Zhang, Ming-Ming Zhang, Ming-Rong Zhang, Ming-Yu Zhang, Ming-Zhu Zhang, Mingai Zhang, Mingchang Zhang, Mingdi Zhang, Mingfa Zhang, Mingfeng Zhang, Minghang Zhang, Minghao Zhang, Minghui Zhang, Mingjie Zhang, Mingjiong Zhang, Mingjun Zhang, Mingming Zhang, Mingqi Zhang, Mingtong Zhang, Mingxiang Zhang, Mingxiu Zhang, Mingxuan Zhang, Mingxue Zhang, Mingyang A Zhang, Mingyang Zhang, Mingyao Zhang, Mingyi Zhang, Mingying Zhang, Mingyu Zhang, Mingyuan Zhang, Mingyue Zhang, Mingzhao Zhang, Mingzhen Zhang, Minhong Zhang, Minying Zhang, Minyue Zhang, Minzhi Zhang, Minzhu Zhang, Mo Zhang, Mo-Ruo Zhang, Mu Zhang, Muqing Zhang, Muxin Zhang, Muzi Zhang, N Zhang, Na Zhang, Naijin Zhang, Naiqi Zhang, Naisheng Zhang, Naixia Zhang, Nan Yang Zhang, Nan Zhang, Nan-Nan Zhang, Nana Zhang, Nannan Zhang, Nasha Zhang, Ni Zhang, Niankai Zhang, Nianxiang Zhang, Nieke Zhang, Ning Zhang, Ning-Ping Zhang, Ninghan Zhang, Ningkun Zhang, Ningning Zhang, Ningzhen Zhang, Ningzhi Zhang, Nisi Zhang, Nong Zhang, Nu Zhang, P Zhang, Pan Zhang, Pan-Pan Zhang, Panpan Zhang, Pei Zhang, Pei-Weng Zhang, Pei-Zhuo Zhang, PeiFeng Zhang, Peichun Zhang, Peijing Zhang, Peijun Zhang, Peilin Zhang, Peiqin Zhang, Peiwen Zhang, Peiyi Zhang, Peizhen Zhang, Peng Zhang, Peng-Cheng Zhang, Peng-Fei Zhang, Pengbo Zhang, Pengcheng Zhang, Pengfei Zhang, Pengpeng Zhang, Pengwei Zhang, Pengyuan Zhang, Pili Zhang, Ping Zhang, Ping-Fan Zhang, Pingchuan Zhang, Pinggen Zhang, Pingmei Zhang, Pu-Hong Zhang, Pumin Zhang, Q L Zhang, Q Y Zhang, Q Zhang, Q-D Zhang, Qi Zhang, Qi-Ai Zhang, Qi-Lei Zhang, Qi-Min Zhang, QiYue Zhang, Qian Jun Zhang, Qian ZHANG, Qian-Qian Zhang, Qian-Wen Zhang, Qiang Zhang, Qiang-Sheng Zhang, Qiangsheng Zhang, Qiangyan Zhang, Qianhui Zhang, Qianjun Zhang, Qiannan Zhang, Qianqian Zhang, Qianru Zhang, Qiao-Xia Zhang, Qiaofang Zhang, Qiaojun Zhang, Qiaoxuan Zhang, Qifan Zhang, Qiguo Zhang, Qihao Zhang, Qihong Zhang, Qilong Zhang, Qilu Zhang, Qimin Zhang, Qin Zhang, Qing Zhang, Qing-Hui Zhang, Qing-Zhu Zhang, Qingchao Zhang, Qingcheng Zhang, Qingchuan Zhang, Qingfeng Zhang, Qinghong Zhang, Qinghua Zhang, Qingjiong Zhang, Qingjun Zhang, Qingling Zhang, Qingna Zhang, Qingqing Zhang, Qingquan Zhang, Qingrun Zhang, Qingshuang Zhang, Qingtian Zhang, Qingxiu Zhang, Qingxue Zhang, Qingyu Zhang, Qingyue Zhang, Qingyun Zhang, Qinjun Zhang, Qiong Zhang, Qishu Zhang, Qiu Zhang, Qiuting Zhang, Qiuxia Zhang, Qiuyang Zhang, Qiuyue Zhang, Qiwei Zhang, Qiyong Zhang, Quan Zhang, Quan-bin Zhang, Quanfu Zhang, Quanqi Zhang, Quanquan Zhang, Qun Zhang, Qun-Feng Zhang, Qunchen Zhang, Qunfeng Zhang, Qunyuan Zhang, R Zhang, Ran Zhang, Ranran Zhang, Ren Zhang, Renbo Zhang, Renhe Zhang, Renliang Zhang, Renshuai Zhang, Rey M Zhang, Richard Zhang, Rong Zhang, Rong-Kai Zhang, Rongcai Zhang, Rongchao Zhang, Rongguang Zhang, Rongrong Zhang, Rongxin Zhang, Rongxu Zhang, Rongying Zhang, Rongyu Zhang, Ru Zhang, Rugang Zhang, Rui Long Zhang, Rui Xue Zhang, Rui Yan Zhang, Rui Zhang, Rui-Nan Zhang, Rui-Ning Zhang, Rui-fang Zhang, Ruihao Zhang, Ruihong Zhang, Ruikun Zhang, Ruilin Zhang, Ruiling Zhang, Ruimin Zhang, Ruiqi Zhang, Ruiqian Zhang, Ruisan Zhang, Ruixia Zhang, Ruixin Zhang, Ruixue Zhang, Ruiyan Zhang, Ruiyang Zhang, Ruiying Zhang, Ruizhe Zhang, Ruizhi Zhang, Ruizhong Zhang, Rulin Zhang, Run Zhang, Runcheng Zhang, Runxiang Zhang, Runyun Zhang, Runze Zhang, Ruo-Xin Zhang, Ruohan Zhang, Ruoshi Zhang, Ruotian Zhang, Ruoxuan Zhang, Ruoying Zhang, Rusi Zhang, Ruth Zhang, Ruxiang Zhang, Ruxuan Zhang, Ruyi Zhang, S Y Zhang, S Z Zhang, S Zhang, Sai Zhang, Saidan Zhang, Saifei Zhang, Sainan Zhang, Sanbao Zhang, Sen Zhang, Sha Zhang, Shan Zhang, Shan-Shan Zhang, Shanchun Zhang, Shang Zhang, Shangxiong Zhang, Shanhong Zhang, Shanshan Zhang, Shanxiang Zhang, Shao Kang Zhang, Shao Zhang, Shao-Qi Zhang, Shaochuan Zhang, Shaochun Zhang, Shaofei Zhang, Shaofeng Zhang, Shaohua Zhang, Shaojun Zhang, Shaoyang Zhang, Shaozhao Zhang, Shaozhen Zhang, Shasha Zhang, Shen Zhang, Sheng Zhang, Sheng-Dao Zhang, Sheng-Hong Zhang, Sheng-Qiang Zhang, Sheng-Xiao Zhang, Shengchi Zhang, Shengding Zhang, Shengkun Zhang, Shenglai Zhang, Shenglan Zhang, Shenglei Zhang, Shengli Zhang, Shengming Zhang, Shengnan Zhang, Shengye Zhang, Shenqi Zhang, Shenqian Zhang, Shi Zhang, Shi-Han Zhang, Shi-Jie Zhang, Shi-Meng Zhang, Shi-Qian Zhang, Shi-Yao Zhang, ShiSong Zhang, Shichao Zhang, Shihan Zhang, Shijun Zhang, Shikai Zhang, Shilei Zhang, Shimao Zhang, Shining Zhang, Shiping Zhang, Shiqi Zhang, Shiquan Zhang, Shiti Zhang, Shitian Zhang, Shiwen Zhang, Shiwu Zhang, Shiyao Zhang, Shiyi Zhang, Shiyu Zhang, Shiyun Zhang, Shou-Mei Zhang, Shou-Peng Zhang, Shouyue Zhang, Shu Zhang, Shu-Dong Zhang, Shu-Fan Zhang, Shu-Fang Zhang, Shu-Min Zhang, Shu-Ming Zhang, Shu-Yang Zhang, Shu-Zhen Zhang, Shuai Zhang, Shuai-Nan Zhang, Shuaishuai Zhang, Shuang Zhang, Shuangjie Zhang, Shuanglu Zhang, Shuangxin Zhang, Shubing Zhang, Shuchen Zhang, Shucong Zhang, Shuer Zhang, Shuge Zhang, Shuhong Zhang, Shuijun Zhang, Shujun Zhang, Shuli Zhang, Shulong Zhang, Shun Zhang, Shun-Bo Zhang, Shunfen Zhang, Shunming Zhang, Shuo Zhang, Shupeng Zhang, Shuran Zhang, Shurui Zhang, Shushan Zhang, Shuwan Zhang, Shuwei Zhang, Shuxia Zhang, Shuya Zhang, Shuyan Zhang, Shuyang Zhang, Shuye Zhang, Shuyi Zhang, Shuyuan Zhang, Si Zhang, Si-Zhong Zhang, Sibin Zhang, Sifan Zhang, Sihe Zhang, Simeng Zhang, Simin Zhang, Siqi Zhang, Sisi Zhang, Sixue Zhang, Siyuan Zhang, Siyue Zhang, Sizhong Zhang, Song Zhang, Song-Yang Zhang, Songlin Zhang, Songying Zhang, Sophia L Zhang, Stanley Weihua Zhang, Stephen X Zhang, Su Zhang, Sujiang Zhang, Sulin Zhang, Sumei Zhang, Suming Zhang, Suping Zhang, Susie Zhang, Suya Zhang, Suyang Zhang, Suzhen Zhang, T Zhang, Tangjuan Zhang, Tao Zhang, Tao-Lan Zhang, Taojun Zhang, Taoyuan Zhang, Teng Zhang, Tengfang Zhang, Terry Jianguo Zhang, Ti Zhang, Tian Zhang, Tian-Guang Zhang, Tian-Yu Zhang, Tiane Zhang, Tianfeng Zhang, Tianliang Zhang, Tianlong Zhang, Tianpeng Zhang, Tianshu Zhang, Tiantian Zhang, Tianxi Zhang, Tianxiao Zhang, Tianxin Zhang, Tianyang Zhang, Tianye Zhang, Tianyi Zhang, Tianyu Zhang, Tie-mei Zhang, Tiefeng Zhang, Tiehua Zhang, Tiejun Zhang, Ting Ting Zhang, Ting Zhang, Ting-Ting Zhang, Tinghu Zhang, Tingting Zhang, Tingxue Zhang, Tingying Zhang, Tong Xuan Zhang, Tong Zhang, Tong-Cun Zhang, Tongcun Zhang, Tongfu Zhang, Tonghan Zhang, Tonghua Zhang, Tonghui Zhang, Tongran Zhang, Tongshuo Zhang, Tongtong Zhang, Tongwu Zhang, Tongxin Zhang, Tongxue Zhang, Tuo Zhang, Vita Zhang, W G Zhang, W X Zhang, W Zhang, Wancong Zhang, Wang-Dong Zhang, Wangang Zhang, Wangping Zhang, Wanjiang Zhang, Wanjun Zhang, Wannian Zhang, Wanqi Zhang, Wanting Zhang, Wanying Zhang, Wanyu Zhang, Wei Zhang, Wei-Jia Zhang, Wei-Na Zhang, Wei-Yi Zhang, Weibo Zhang, Weichen Zhang, Weifeng Zhang, Weiguo Zhang, Weihua Zhang, Weijian Zhang, Weikang Zhang, Weili Zhang, Weilin Zhang, Weiling Zhang, Weilong Zhang, Weimin Zhang, Weina Zhang, Weipeng Zhang, Weiping J Zhang, Weiqin Zhang, Weisen Zhang, Weiwei Zhang, Weixia Zhang, Weiyi Zhang, Weiyu Zhang, Weizheng Zhang, Weizhou Zhang, Wen Jun Zhang, Wen Zhang, Wen-Hong Zhang, Wen-Jie Zhang, Wen-Jing Zhang, Wen-Xin Zhang, Wen-Xuan Zhang, Wenbin Zhang, Wenbo Zhang, Wenchao Zhang, Wencheng Zhang, Wencong Zhang, Wendi Zhang, Wenguang Zhang, Wenhao Zhang, Wenhong Zhang, Wenhua Zhang, Wenhui Zhang, Wenji Zhang, Wenjia Zhang, Wenjing Zhang, Wenjuan Zhang, Wenjun Zhang, Wenkai Zhang, Wenkui Zhang, Wenli Zhang, Wenlong Zhang, Wenlu Zhang, Wenming Zhang, Wenqian Zhang, Wenru Zhang, Wentao Zhang, Wenting Zhang, Wenwen Zhang, Wenxi Zhang, Wenxiang Zhang, Wenxin Zhang, Wenxue Zhang, Wenya Zhang, Wenyang Zhang, Wenyi Zhang, Wenyuan Zhang, Wenzhong Zhang, Wuhu Zhang, X N Zhang, X X Zhang, X Y Zhang, X Zhang, X-T Zhang, X-Y Zhang, Xi Zhang, Xi'an Zhang, Xi-Feng Zhang, XiHe Zhang, Xia Zhang, Xian Zhang, Xian-Bo Zhang, Xian-Li Zhang, Xian-Man Zhang, Xiang Yang Zhang, Xiang Zhang, Xiangbin Zhang, Xiangfei Zhang, Xianglian Zhang, Xiangsong Zhang, Xiangwu Zhang, Xiangyang Zhang, Xiangyu Zhang, Xiangzheng Zhang, Xianhong Zhang, Xianhua Zhang, Xianjing Zhang, Xianpeng Zhang, Xianxian Zhang, Xiao Bin Zhang, Xiao Min Zhang, Xiao Yu Cindy Zhang, Xiao Zhang, Xiao-Chang Zhang, Xiao-Cheng Zhang, Xiao-Chong Zhang, Xiao-Feng Zhang, Xiao-Hong Zhang, Xiao-Hua Zhang, Xiao-Jun Zhang, Xiao-Lei Zhang, Xiao-Lin Zhang, Xiao-Ling Zhang, Xiao-Meng Zhang, Xiao-Ming Zhang, Xiao-Qi Zhang, Xiao-Qian Zhang, Xiao-Shuo Zhang, Xiao-Wei Zhang, Xiao-Xuan Zhang, Xiao-Yong Zhang, Xiao-Yu Zhang, Xiao-bo Zhang, Xiao-yan Zhang, XiaoLin Zhang, XiaoPing Zhang, XiaoYi Zhang, Xiaobao Zhang, Xiaobiao Zhang, Xiaobo Zhang, Xiaochang Zhang, Xiaochen Zhang, Xiaochun Zhang, Xiaocong Zhang, Xiaocui Zhang, Xiaodan Zhang, Xiaodong Zhang, Xiaofan Zhang, Xiaofang Zhang, Xiaofei Zhang, Xiaofeng Zhang, Xiaogang Zhang, Xiaohan Zhang, Xiaohong Zhang, Xiaohui Zhang, Xiaojia Zhang, Xiaojian Zhang, Xiaojie Zhang, Xiaojin Zhang, Xiaojing Zhang, Xiaojun Zhang, Xiaokui Zhang, Xiaolan Zhang, Xiaolei Zhang, Xiaoli Zhang, Xiaoling Zhang, Xiaolong Zhang, Xiaomei Zhang, Xiaomeng Zhang, Xiaomin Zhang, Xiaoming Zhang, Xiaoning Zhang, Xiaonyun Zhang, Xiaopei Zhang, Xiaopo Zhang, Xiaoqi Zhang, Xiaoqing Zhang, Xiaorong Zhang, Xiaosheng Zhang, Xiaotian Michelle Zhang, Xiaotian Zhang, Xiaotong Zhang, Xiaotun Zhang, Xiaowan Zhang, Xiaowei Zhang, Xiaoxi Zhang, Xiaoxia Zhang, Xiaoxian Zhang, Xiaoxiao Zhang, Xiaoxin Zhang, Xiaoxue Zhang, Xiaoyan Zhang, Xiaoying Zhang, Xiaoyu Zhang, Xiaoyuan Zhang, Xiaoyue Zhang, Xiaoyun Zhang, Xiaozhe Zhang, Xiayin Zhang, Xibo Zhang, Xieyi Zhang, Xijiang Zhang, Xilin Zhang, Xiling Zhang, Ximei Zhang, Xin Zhang, Xin-Hui Zhang, Xin-Xin Zhang, Xin-Yan Zhang, Xin-Ye Zhang, Xin-Yuan Zhang, Xinan Zhang, Xinbao Zhang, Xinbo Zhang, Xincheng Zhang, Xindang Zhang, Xindong Zhang, Xinfeng Zhang, Xinfu Zhang, Xing Yu Zhang, Xing Zhang, Xingan Zhang, Xingang Zhang, Xingcai Zhang, Xingen Zhang, Xinglai Zhang, Xingong Zhang, Xingwei Zhang, Xingxing Zhang, Xingxu Zhang, Xingyi Zhang, Xingyu Zhang, Xingyuan Zhang, Xinhai Zhang, Xinhan Zhang, Xinhe Zhang, Xinheng Zhang, Xinhong Zhang, Xinhua Zhang, Xinjiang Zhang, Xinjing Zhang, Xinjun Zhang, Xinke Zhang, Xinlei Zhang, Xinlian Zhang, Xinlin Zhang, Xinling Zhang, Xinlong Zhang, Xinlu Zhang, Xinmin Zhang, Xinping Zhang, Xinqiao Zhang, Xinquan Zhang, Xinran Zhang, Xinrui Zhang, Xinruo Zhang, Xintao Zhang, Xinwei Zhang, Xinwu Zhang, Xinxin Zhang, Xinyao Zhang, Xinye Zhang, Xinyi Zhang, Xinyu Zhang, Xinyue Zhang, Xiong Zhang, Xiongjun Zhang, Xiongze Zhang, Xipeng Zhang, Xiping Zhang, Xiu Qi Zhang, Xiu-Juan Zhang, Xiu-Li Zhang, Xiu-Peng Zhang, Xiujie Zhang, Xiujun Zhang, Xiulan Zhang, Xiuming Zhang, Xiupeng Zhang, Xiuping Zhang, Xiuqin Zhang, Xiuqing Zhang, Xiuse Zhang, Xiushan Zhang, Xiuwen Zhang, Xiuxing Zhang, Xiuxiu Zhang, Xiuyin Zhang, Xiuyue Zhang, Xiuyun Zhang, Xiuzhen Zhang, Xixi Zhang, Xixun Zhang, Xiyu Zhang, Xu Dong Zhang, Xu Zhang, Xu-Chao Zhang, Xu-Jun Zhang, Xu-Mei Zhang, Xuan Zhang, Xudan Zhang, Xudong Zhang, Xue Zhang, Xue-Ping Zhang, Xue-Qin Zhang, Xue-Qing Zhang, XueWu Zhang, Xuebao Zhang, Xuebin Zhang, Xuefei Zhang, Xueguang Zhang, Xuehai Zhang, Xuehong Zhang, Xuehui Zhang, Xuejiao Zhang, Xuejun C Zhang, Xueli Zhang, Xuelian Zhang, Xuelong Zhang, Xueluo Zhang, Xuemei Zhang, Xuemin Zhang, Xueming Zhang, Xuening Zhang, Xueping Zhang, Xueqia Zhang, Xueqian Zhang, Xueqin Zhang, Xueting Zhang, Xuewei Zhang, Xuewen Zhang, Xuexi Zhang, Xueya Zhang, Xueyan Zhang, Xueyi Zhang, Xueying Zhang, Xuezhi Zhang, Xufang Zhang, Xuhao Zhang, Xujun Zhang, Xunming Zhang, Xuting Zhang, Xutong Zhang, Xuxiang Zhang, Y H Zhang, Y L Zhang, Y Y Zhang, Y Zhang, Y-H Zhang, Ya Zhang, Ya-Juan Zhang, Ya-Li Zhang, Ya-Long Zhang, Ya-Meng Zhang, Yachen Zhang, Yadi Zhang, Yadong Zhang, Yafang Zhang, Yafei Zhang, Yafeng Zhang, Yaguang Zhang, Yahua Zhang, Yajie Zhang, Yajing Zhang, Yajun Zhang, Yakun Zhang, Yalan Zhang, Yali Zhang, Yaling Zhang, Yameng Zhang, Yamin Zhang, Yaming Zhang, Yan Zhang, Yan-Chun Zhang, Yan-Ling Zhang, Yan-Min Zhang, Yan-Qing Zhang, Yanan Zhang, Yanbin Zhang, Yanbing Zhang, Yanchao Zhang, Yandong Zhang, Yanfei Zhang, Yanfen Zhang, Yanfeng Zhang, Yang Zhang, Yang-Yang Zhang, Yangfan Zhang, Yanghui Zhang, Yangqianwen Zhang, Yangyang Zhang, Yangyu Zhang, Yanhong Zhang, Yanhua Zhang, Yani Zhang, Yanjiao Zhang, Yanju Zhang, Yanjun Zhang, Yanli Zhang, Yanlin Zhang, Yanling Zhang, Yanman Zhang, Yanmin Zhang, Yanming Zhang, Yanna Zhang, Yannan Zhang, Yanping Zhang, Yanqiao Zhang, Yanquan Zhang, Yanru Zhang, Yanting Zhang, Yanxia Zhang, Yanxiang Zhang, Yanyan Zhang, Yanyi Zhang, Yanyu Zhang, Yao Zhang, Yao-Hua Zhang, Yaodong Zhang, Yaoxin Zhang, Yaoyang Zhang, Yaoyao Zhang, Yaozhengtai Zhang, Yaping Zhang, Yaqi Zhang, Yaru Zhang, Yashuo Zhang, Yating Zhang, Yawei Zhang, Yaxin Zhang, Yaxuan Zhang, Yayong Zhang, Yazhuo Zhang, Ye Zhang, Yefan Zhang, Yeqian Zhang, Yerui Zhang, Yeting Zhang, Yexiang Zhang, Yi J Zhang, Yi Ping Zhang, Yi Zhang, Yi-Chi Zhang, Yi-Feng Zhang, Yi-Ge Zhang, Yi-Hang Zhang, Yi-Hua Zhang, Yi-Min Zhang, Yi-Ming Zhang, Yi-Qi Zhang, Yi-Wei Zhang, Yi-Wen Zhang, Yi-Xuan Zhang, Yi-Yue Zhang, Yi-yi Zhang, YiJie Zhang, YiPei Zhang, Yibin Zhang, Yibo Zhang, Yichen Zhang, Yichi Zhang, Yidan Zhang, Yidong Zhang, Yifan Zhang, Yifang Zhang, Yige Zhang, Yiguo Zhang, Yihan Zhang, Yihang Zhang, Yihao Zhang, Yiheng Zhang, Yihong Zhang, Yihui Zhang, Yijing Zhang, Yikai Zhang, Yikun Zhang, Yili Zhang, Yiliang Zhang, Yilin Zhang, Yimei Zhang, Yimeng Zhang, Yimin Zhang, Yiming Zhang, Yin Jiang Zhang, Yin Zhang, Yin-Hong Zhang, Yina Zhang, Yinci Zhang, Ying E Zhang, Ying Zhang, Ying-Jun Zhang, Ying-Lin Zhang, Ying-Qian Zhang, Yingang Zhang, Yingchao Zhang, Yinghui Zhang, Yingjie Zhang, Yingli Zhang, Yingmei Zhang, Yingna Zhang, Yingnan Zhang, Yingqi Zhang, Yingqian Zhang, Yingyi Zhang, Yingying Zhang, Yingze Zhang, Yingzi Zhang, Yinhao Zhang, Yinjiang Zhang, Yintang Zhang, Yinzhi Zhang, Yinzhuang Zhang, Yipeng Zhang, Yiping Zhang, Yiqian Zhang, Yiqing Zhang, Yiren Zhang, Yirong Zhang, Yitian Zhang, Yiting Zhang, Yiwan Zhang, Yiwei Zhang, Yiwen Zhang, Yixia Zhang, Yixin Zhang, Yiyao Zhang, Yiyi Zhang, Yiyuan Zhang, Yizhe Zhang, Yizhi Zhang, Yong Zhang, Yong-Guo Zhang, Yong-Liang Zhang, Yong-hong Zhang, Yongbao Zhang, Yongchang Zhang, Yongchao Zhang, Yongci Zhang, Yongfa Zhang, Yongfang Zhang, Yongfeng Zhang, Yonggang Zhang, Yonggen Zhang, Yongguang Zhang, Yongguo Zhang, Yongheng Zhang, Yonghong Zhang, Yonghui Zhang, Yongjie Zhang, Yongjiu Zhang, Yongjuan Zhang, Yonglian Zhang, Yongliang Zhang, Yonglong Zhang, Yongpeng Zhang, Yongping Zhang, Yongqiang Zhang, Yongsheng Zhang, Yongwei Zhang, Yongxiang Zhang, Yongxing Zhang, Yongyan Zhang, Yongyun Zhang, You-Zhi Zhang, Youjin Zhang, Youmin Zhang, Youti Zhang, Youwen Zhang, Youyi Zhang, Youying Zhang, Youzhong Zhang, Yu Chen Zhang, Yu Zhang, Yu-Bo Zhang, Yu-Chi Zhang, Yu-Fei Zhang, Yu-Hui Zhang, Yu-Jie Zhang, Yu-Jing Zhang, Yu-Qi Zhang, Yu-Qiu Zhang, Yu-Yu Zhang, Yu-Zhe Zhang, YuHang Zhang, YuHong Zhang, Yuan Zhang, Yuan-Wei Zhang, Yuan-Yuan Zhang, Yuanchao Zhang, Yuanhao Zhang, Yuanhui Zhang, Yuanping Zhang, Yuanqiang Zhang, Yuanqing Zhang, Yuansheng Zhang, Yuanxi Zhang, Yuanxiang Zhang, Yuanyi Zhang, Yuanyuan Zhang, Yuanzhen Zhang, Yuanzhuang Zhang, Yubin Zhang, Yucai Zhang, Yuchao Zhang, Yuchen Zhang, Yuchi Zhang, Yue Zhang, Yue-Bo Zhang, Yue-Ming Zhang, Yuebin Zhang, Yuebo Zhang, Yuehong Zhang, Yuehua Zhang, Yuejuan Zhang, Yuemei Zhang, Yueqi Zhang, Yueru Zhang, Yuetong Zhang, Yufang Zhang, Yufeng Zhang, Yuhan Zhang, Yuhao Zhang, Yuheng Zhang, Yuhua Zhang, Yuhui Zhang, Yujia Zhang, Yujiao Zhang, Yujie Zhang, Yujin Zhang, Yujing Zhang, Yujuan Zhang, Yuke Zhang, Yukun Zhang, Yulin Zhang, Yuling Zhang, Yulong Zhang, Yumei Zhang, Yumeng Zhang, Yumin Zhang, Yun Zhang, Yun-Feng Zhang, Yun-Lin Zhang, Yun-Mei Zhang, Yun-Sheng Zhang, Yun-Xiang Zhang, Yunfan Zhang, Yunfei Zhang, Yunfeng Zhang, Yunhai Zhang, Yunhang Zhang, Yunhe Zhang, Yunhui Zhang, Yuning Zhang, Yunjia Zhang, Yunli Zhang, Yunmei Zhang, Yunpeng Zhang, Yunqi Zhang, Yunqiang Zhang, Yunqing Zhang, Yunsheng Zhang, Yunxia Zhang, Yupei Zhang, Yupeng Zhang, Yuping Zhang, Yuqi Zhang, Yuqing Zhang, Yurou Zhang, Yuru Zhang, Yusen Zhang, Yushan Zhang, Yutian Zhang, Yuting Zhang, Yutong Zhang, Yuwei Zhang, Yuxi Zhang, Yuxia Zhang, Yuxin Zhang, Yuxuan Zhang, Yuyan Zhang, Yuyanan Zhang, Yuyang Zhang, Yuying Zhang, Yuyu Zhang, Yuyuan Zhang, Yuzhe Zhang, Yuzhi Zhang, Yuzhou Zhang, Yuzhu Zhang, Yvonne Zhang, Z Zhang, Z-K Zhang, Zai-Rong Zhang, Zaifeng Zhang, Zaijun Zhang, Zaiqi Zhang, Zebang Zhang, Zekun Zhang, Zemin Zhang, Zeming Zhang, Zeng Zhang, Zengdi Zhang, Zengfu Zhang, Zenglei Zhang, Zengli Zhang, Zengqiang Zhang, Zengrong Zhang, Zengtie Zhang, Zepeng Zhang, Zewei Zhang, Zewen Zhang, Zeyan Zhang, Zeyuan Zhang, Zhan-Xiong Zhang, Zhangjin Zhang, Zhanhao Zhang, Zhanjie Zhang, Zhanjun Zhang, Zhanming Zhang, Zhanyi Zhang, Zhao Zhang, Zhao-Huan Zhang, Zhao-Ming Zhang, Zhaobo Zhang, Zhaocong Zhang, Zhaofeng Zhang, Zhaohua Zhang, Zhaohuai Zhang, Zhaohuan Zhang, Zhaohui Zhang, Zhaomin Zhang, Zhaoping Zhang, Zhaoqi Zhang, Zhaotian Zhang, Zhaoxue Zhang, Zhe Zhang, Zhehua Zhang, Zhemei Zhang, Zhen Zhang, Zhen-Dong Zhang, Zhen-Jie Zhang, Zhen-Shan Zhang, Zhen-Tao Zhang, Zhen-lin Zhang, Zhenfeng Zhang, Zheng Zhang, Zhengbin Zhang, Zhengfen Zhang, Zhenglang Zhang, Zhengliang Zhang, Zhengxiang Zhang, Zhengxing Zhang, Zhengyu Zhang, Zhengyun Zhang, Zhenhao Zhang, Zhenhua Zhang, Zhenlin Zhang, Zhenqiang Zhang, Zhentao Zhang, Zhenyang Zhang, Zhenyu Zhang, Zhenzhen Zhang, Zhenzhu Zhang, Zhewei Zhang, Zhewen Zhang, Zheyuan Zhang, Zhezhe Zhang, Zhi Zhang, Zhi-Chang Zhang, Zhi-Jie Zhang, Zhi-Jun Zhang, Zhi-Peng Zhang, Zhi-Qing Zhang, Zhi-Shuai Zhang, Zhi-Shuo Zhang, Zhi-Xin Zhang, Zhibo Zhang, Zhicheng Zhang, Zhicong Zhang, Zhifei Zhang, Zhigang Zhang, Zhiguo Zhang, Zhihan Zhang, Zhihao Zhang, Zhihong Zhang, Zhihua Zhang, Zhihui Zhang, Zhijian Zhang, Zhijiao Zhang, Zhijing Zhang, Zhijun Zhang, Zhikun Zhang, Zhimin Zhang, Zhiming Zhang, Zhiping Zhang, Zhiqian Zhang, Zhiqiang Zhang, Zhiqiao Zhang, Zhiru Zhang, Zhishang Zhang, Zhishuai Zhang, Zhiwang Zhang, Zhiwen Zhang, Zhixia Zhang, Zhixin Zhang, Zhiyan Zhang, Zhiyao Zhang, Zhiye Zhang, Zhiyi Zhang, Zhiyong Zhang, Zhiyu Zhang, Zhiyuan Zhang, Zhiyun Zhang, Zhizhong Zhang, Zhong Zhang, Zhong-Bai Zhang, Zhong-Yi Zhang, Zhong-Yin Zhang, Zhong-Yuan Zhang, Zhongheng Zhang, Zhongjie Zhang, Zhonglin Zhang, Zhongqi Zhang, Zhongwei Zhang, Zhongxin Zhang, Zhongyang Zhang, Zhongyi Zhang, Zhou Zhang, Zhu Zhang, Zhu-Qin Zhang, Zhuang Zhang, Zhuo Zhang, Zhuo-Ya Zhang, Zhuohua Zhang, Zhuojun Zhang, Zhuorong Zhang, Zhuoya Zhang, Zhuqin Zhang, Zhuqing Zhang, Zhuzhen Zhang, Zi-Feng Zhang, Zi-Jian Zhang, Zian Zhang, Zicheng Zhang, Ziding Zhang, Ziguo Zhang, Zihan Zhang, Ziheng Zhang, Zijian Zhang, Zijiao Zhang, Zijing Zhang, Zikai Zhang, Zilong Zhang, Zilu Zhang, Ziping Zhang, Ziqi Zhang, Zishuo Zhang, Zixiong Zhang, Zixu Zhang, Zixuan Zhang, Ziyang Zhang, Ziyi Zhang, Ziyin Zhang, Ziyu Zhang, Ziyue Zhang, Zizhen Zhang, Zongping Zhang, Zongquan Zhang, Zongwang Zhang, Zongxiang Zhang, Zu-Xuan Zhang, Zufa Zhang, Zuoyi Zhang
articles
Chunhui He, Xingming Song, Ting He +9 more · 2025 · Reviews in cardiovascular medicine · added 2026-04-24
Coronary heart disease (CHD) arises from a complex interplay of genetic and environmental factors. This study examines the influence of This retrospective case-control study enrolled 900 CHD patients Show more
Coronary heart disease (CHD) arises from a complex interplay of genetic and environmental factors. This study examines the influence of This retrospective case-control study enrolled 900 CHD patients and 900 control subjects. We evaluated associations between conventional cardiovascular risk factors and polymorphisms at the No significant differences were observed in the distribution of The Show less
📄 PDF DOI: 10.31083/RCM37356
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Yuying Li, Weiquan Liao, Ying'ao Guo +4 more · 2025 · Current pharmaceutical biotechnology · Bentham Science · added 2026-04-24
Primary Sjögren's Syndrome (pSS) is a chronic autoimmune condition affecting lacrimal and salivary glands. While previous studies suggest potential associations between dyslipidemia and autoimmune dis Show more
Primary Sjögren's Syndrome (pSS) is a chronic autoimmune condition affecting lacrimal and salivary glands. While previous studies suggest potential associations between dyslipidemia and autoimmune diseases, the causal relationship between lipid-lowering medications and pSS remains unclear. This study employed drug-targeted Mendelian randomization (MR) analysis to assess the impact of lipid-lowering drugs on pSS risk, focusing on genetic targets including HMGCR, PCSK9, NPC1L1, APOB, CETP, and LDLR. Data were sourced from the Global Lipids Genetics Consortium and UK Biobank. Significant single-nucleotide polymorphisms linked to LDL cholesterol were utilized as instrumental variables. Causal effects were estimated using Inverse Variance Weighted, Weighted Median, MR Egger, Simple Mode, and Weighted Mode methods. Robustness was ensured through heterogeneity and sensitivity analyses. The inhibition of HMGCR and CETP genes was found to be significantly associated with an increased risk of developing pSS (HMGCR: OR = 3.602, 95% CI [1.051, 12.344], p = 0.041; CETP: OR = 12.251, 95% CI [2.599, 57.743], p = 0.002). HMGCR and CETP may affect pSS risk via non-lipid pathways, suggesting distinct mechanisms among different lipid-lowering drug targets. This study provides compelling evidence suggesting that lipid-lowering drugs may contribute to the risk of pSS, thus offering new insights for clinical intervention strategies. Show less
no PDF DOI: 10.2174/0113892010387265250730110805
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Guoping Wu, Zhe Dong, Zhongcai Li +12 more · 2025 · Schizophrenia (Heidelberg, Germany) · Nature · added 2026-04-24
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause Show more
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research. Show less
📄 PDF DOI: 10.1038/s41537-025-00646-6
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Guangyu Gao, Tianci Yao, Chengyun Liu +10 more · 2025 · Food science & nutrition · Wiley · added 2026-04-24
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have show Show more
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have shown that genetic proxied total blood phytosterol affects the development of cardiovascular disease through non-HDL-c and apolipoprotein B mediation, which makes phytosterol an underlying risk factor for frailty. The aim of this Mendelian randomization (MR) study was to investigate the genetic associations between phytosterols and frailty. We used univariate Mendelian randomization (UVMR) to assess the causal effects of blood phytosterols on the Frailty Index (FI) and Fried Frailty Score (FFS). We also employed multivariate Mendelian randomization (MVMR) and Two-step MR (TSMR) to evaluate the mediating role of blood lipids in the relationship between blood phytosterols and FI. We used the product of coefficients method to calculate the mediating effect. The inverse-variance weighted method was used as the primary analysis. Genetically proxied higher levels of blood total sitosterol were significantly associated with a higher risk of Frailty Index (OR = 1.035, 95% CI = 1.009-1.061, Show less
📄 PDF DOI: 10.1002/fsn3.70616
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Li Han, Qijun Li, Lifan Zhang +7 more · 2025 · Diabetes research and clinical practice · Elsevier · added 2026-04-24
To investigate the relation of glycemic and lipid metabolism with brain structure and cognitive function in people with diabetes, so as to improve cognitive function in these individuals. Based on the Show more
To investigate the relation of glycemic and lipid metabolism with brain structure and cognitive function in people with diabetes, so as to improve cognitive function in these individuals. Based on the UK Biobank, 26,394 patients, who were diagnosed with diabetes by doctors between 2006 and 2010, were included in the study. The demographic information, clinical data of glycemic and lipid metabolism and cognitive function (brain MRI and cognitive function scores) were collected. Multiple linear regression and non-restricted cubic spline analyses were used to investigate the relations of glycemic and lipid metabolism with brain structure and cognitive function. In this study, the mean age of people with diabetes (containing 39 % females) was 59.58 ± 7.21 years. Higher random blood glucose (β = -0.116, p < 0.001) and glycosylated hemoglobin (HbA1c) (β = -0.062, p = 0.051) were associated with a smaller brain volume. Higher HbA1c (β = 0.036, p < 0.001; β = 0.023, p = 0.021) was related with worse cognitive function. Further analysis showed that HbA1c < 6.5 % had a protective effect on cognitive function, and HbA1c = 6.5 %∼8.5 % and >8.5 % was unrelated and negatively related with cognitive function, respectively. Different types of lipids had varying effects on cognitive function. Higher total cholesterol (TC) (β = 0.125, p = 0.008), low density lipoprotein-cholesterol (LDL-C) (β = 0.086, p = 0.025), and ApoB (β = 0.092, p = 0.026) were associated with more significant brain structural abnormalities. Conversely, triglyceride (TG) = 0.75∼8.0 mmol/L was positively correlated with cognitive function (β = -0.036, p < 0.001; β = -0.044, p < 0.001; β = 0.058, p = 0.001), and higher ApoA (β = -0.032, p < 0.001; β = -0.033, p < 0.001; β = 0.047, p = 0.004) was associated with better cognitive function. The age-stratified analysis revealed that the impact of lipids on cognitive function was age-dependent. TC and LDL-C were related to brain structural abnormalities in the 55-60 age group, while TG had a stronger protective effect on cognitive function in older adults, particularly those aged 65-70 years. In people with diabetes, higher HbA1c (>8.5 %), as well as elevated TC, LDL-C, and ApoB, are associated with worse brain structure and cognitive function. Conversely, HbA1c < 6.5 % and elevated TG within the range of 0.75∼8.0 mmol/L have a protective effect on cognitive function, and the later exhibited more evident impact in older adults. To prevent or delay the onset of dementia in people with diabetes, it may be necessary to intensify glycemic control, targeting an HbA1c level of <6.5 %. Additionally, the age-specific lipid-lowering strategies shall be considered, with more flexible triglyceride-lowering goals for elderly patients. Show less
no PDF DOI: 10.1016/j.diabres.2025.112366
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Litong Qi, Hua Shen, Meng Chai +11 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
This study evaluated the efficacy and safety of tafolecimab in patients with type 2 diabetes (T2D) and hypercholesterolemia by a post-hoc analysis of pooled data from three phase 3 trials. Data from u Show more
This study evaluated the efficacy and safety of tafolecimab in patients with type 2 diabetes (T2D) and hypercholesterolemia by a post-hoc analysis of pooled data from three phase 3 trials. Data from up to 12 weeks were analyzed to assess the effects of tafolecimab 450 mg every four weeks (Q4W) in patients with T2D and hypercholesterolemia. The primary endpoint was the percentage change in low-density lipoprotein cholesterol (LDL-C) levels from baseline to week 12. Secondary endpoints included the proportion of participants achieving LDL-C levels below 1.8 mmol/L at weeks 12, the proportion of patients achieving LDL-C ≥ 50% reduction and LDL-C < 1.4 mmol/L, as well as percentage changes from baseline to week 12 in non-high-density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (apo B), lipoprotein(a) [Lp(a)], and triglyceride (TG) levels. The reduction in LDL-C from baseline was significantly greater in patients receiving tafolecimab than in those receiving placebo (estimated treatment difference: - 64.02%, 95% confidence interval: [- 68.08%, - 59.96%], P < 0.0001). The proportion of patients achieving a reduction of over 50% and an absolute LDL-C value below 1.4 mmol/L was significantly higher in the tafolecimab group than that in the placebo group (P < 0.0001). Furthermore, a significantly greater proportion of patients in the tafolecimab group achieved LDL-C levels below 1.8 mmol/L at week 12 compared to the placebo group (P < 0.0001). The tafolecimab group also showed significant reductions in TG, non-HDL-C, apo B, and Lp(a) from baseline to week 12 compared to the placebo group (all P < 0.001). The incidence of adverse events was generally similar between the two groups. Tafolecimab 450 mg Q4W demonstrated a superior lipid-lowering efficacy and favorable safety profile compared to placebo. This suggests it could be a promising new treatment option for Chinese patients with T2D and hypercholesterolemia. Show less
📄 PDF DOI: 10.1186/s12933-025-02816-3
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Wen Long, Hongdong Ou, Jiajie Luo +3 more · 2025 · Journal of the science of food and agriculture · Wiley · added 2026-04-24
Citrus pulp (CP) is rich in pectin, which exhibits anti-inflammatory, antioxidant, and hypolipidemic properties. Despite these advantages, the application of CP in aquafeed remains limited. This study Show more
Citrus pulp (CP) is rich in pectin, which exhibits anti-inflammatory, antioxidant, and hypolipidemic properties. Despite these advantages, the application of CP in aquafeed remains limited. This study investigated the effects of dietary CP inclusion on the glucolipid metabolism of largemouth bass (Micropterus salmoides). Juveniles were fed diets containing varying levels of CP (0%, 3%, 6%, 9%, 12%, or 15%) for 58 days. Adding 3-6% CP in feed has no adverse effect on growth performance. Dietary CP had direct effects on lipid and glucose metabolism. For lipid metabolism, the inclusion of 3-12% CP resulted in reduced serum complement 3, complement 4, total cholesterol and triglyceride levels, as well as low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C) ratio. Additionally, the inclusion upregulated the relative expression levels of lipid metabolism-related genes such as ppar-α, cpt-1α, and apoa1, but downregulated the relative expression level of apob in liver. However, higher doses (>12%) of CP led to increased serum LDL-C and HDL-C levels. Regarding glucose metabolism, the inclusion of 3-12% CP enhanced hepatic glucose-6-phosphate dehydrogenase (G6PDH), phosphoenolpyruvate carboxykinase (PEPCK), hexokinase and phosphofructokinase-6 (PFK-6) activities as well as serum insulin, insulin-like growth factor 1, growth hormone, G6PDH, PEPCK, hexokinase and PFK-6 levels. Additionally, the inclusion upregulated the relative expression levels of glucose metabolism-related genes such as glut2, glut4, gk, hk, pk, pfk, pepck, g6pase, fbp1, ir, irs1, and pik3r1 in liver. However, higher doses (>12%) of CP did not improve the indicators of glucose metabolism and even downregulated the relative expression level of irs1. In summary, the recommended dietary inclusion of CP is between 3% and 12%, as this range can enhance lipid and glucose metabolism in largemouth bass, and the addition of 6% CP had the most beneficial effect on the glucolipid metabolism of largemouth bass. © 2025 Society of Chemical Industry. Show less
no PDF DOI: 10.1002/jsfa.70029
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Xinya Jia, Keke Du, Yuanting Zhu +6 more · 2025 · Human mutation · added 2026-04-24
Cardiac arrest (CA) prevention continues to be a substantial hurdle for global public health. Although dyslipidemia and 25-hydroxyvitamin D (25(OH)D) insufficiency are recognized contributing factors Show more
Cardiac arrest (CA) prevention continues to be a substantial hurdle for global public health. Although dyslipidemia and 25-hydroxyvitamin D (25(OH)D) insufficiency are recognized contributing factors for cardiovascular disease (CVD), their causal relationship with CA risk is still uncertain. Here, we explored these correlations and pinpointed possible therapeutic targets for CA prevention though Mendelian randomization (MR). Both two-sample and multivariable MR analysis methods were conducted to assess how serum lipid traits and 25(OH)D influence the susceptibility to develop CA. Nine thousand nine hundred eighty-eight participants in total from the National Health and Nutrition Examination Survey (NHANES) engaged in validating the relationship between the concentrations of 25(OH)D and cardiovascular mortality in individuals with dyslipidemia. The integration of MR with expression quantitative trait locus (eQTL) analysis enabled the identification of druggable targets, and molecular docking was used to screen small molecules, which were subsequently validated in animal models. The MR results revealed that both elevated levels of low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (ApoB), as well as triglycerides (TGs), significantly contributed to an increased CA risk ( Show less
📄 PDF DOI: 10.1155/humu/5536318
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Chunli Shao, Shu Zhang, Zhifeng Cheng +17 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none Show more
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none have been verified in Chinese patients. This study aimed to evaluate the efficacy and safety of ongericimab, a novel PCSK9 monoclonal antibody, in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. This was a randomized, multicenter, double-blind, placebo-controlled phase 3 study designed to enroll 120 statin-intolerant adult patients. Eligible patients were randomly assigned in a 2:1 ratio to receive ongericimab 150 mg or placebo subcutaneously every 2 weeks for 12 weeks in the double-blind treatment period, followed by 40 weeks of ongericimab treatment during the open-label period. The primary endpoint was a percentage change in LDL-C from baseline to week 12. The key secondary endpoints included percentage change from baseline to week 12 in non-high density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (ApoB), total cholesterol (TC), and lipoprotein(a) [Lp(a)]. From February 6, 2023, to September 23, 2024, a total of 139 patients were enrolled. The least-squares (LS) mean difference between ongericimab and placebo groups in LDL-C from baseline to week 12 was -66.2 % (95 % CI: 74.2 %, -58.2 %; p < 0.0001), with reductions sustained up to week 52. Ongericimab also significantly reduced levels of non-HDL-C, ApoB, TC, and Lp(a). The overall incidence of treatment-emergent adverse events was comparable between the ongericimab and placebo groups. Ongericimab significantly reduced LDL-C as well as other atherogenic lipid levels and was well tolerated in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. http://www. gov; Unique Identifier: NCT05621070. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.120408
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Yayu Wang, Yue Chang, Pei Zhang +4 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the d Show more
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the dystrophin gene (DMD) encoding dystrophin. DMD is a primary muscle disorder that often presents with secondary abnormalities in lipid metabolism and decreased bone mineral density. Although disturbances in circulating lipid profiles and skeletal health have been observed in individuals with DMD, their relationship remains underexplored.This study aimed to investigate the potential association between lipid metabolic disturbances and spinal bone mineral density in patients with DMD by combining clinical lipid levels and bone density with transcriptomic pathway analysis of DMD muscle tissue. Retrospective analysis was performed on 219 genetically confirmed DMD patients and 99 age-matched healthy controls. Healthy controls with a family history of genetic disorders were excluded. Clinical data included lipid profiles (triglycerides [TGs], remnant cholesterol [RC]); bone mineral density of the lumbar spine was evaluated using Dual-energy X-ray absorptiometry (DXA); and corticosteroid use, including treatment status, dose, and duration. Patients were stratified by corticosteroid exposure. Restricted cubic splines and multivariable regression models were applied to explore potential relationships between lipid parameters and bone mineral density. Bioinformatic analyses were performed on RNA sequencing data from muscle biopsy samples from patients with DMD (GSE38417 dataset) and an independent validation cohort (GSE6011 dataset), focusing on pathways related to lipid metabolism and osteoclast differentiation. Patients with DMD had higher TG, RC, and apolipoprotein B (ApoB) levels and lower high-density lipoprotein cholesterol (HDL-C) levels than healthy controls (P < 0.05). Elevated TG and RC levels were associated with reduced spine bone mineral density, independent of corticosteroid use. The bioinformatic analyses identified key pathways, including sphingolipid metabolism and osteoclast differentiation, as well as hub genes such as FCGR2B, C1QA, which are involved in lipid regulation and bone remodeling. Lipid abnormalities, particularly elevated TG and RC levels, were significantly associated with lower bone mineral density in patients with DMD. These findings suggest that lipid abnormalities are involved in bone health impairment in DMD, warranting further studies to confirm the association. Show less
📄 PDF DOI: 10.1186/s12944-025-02628-0
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Aochuan Sun, Yiduo Chen, Yang Wu +3 more · 2025 · Reviews in cardiovascular medicine · added 2026-04-24
Previous studies have indicated that blood lipids can influence skeletal health. However, limited research exists on the impact of serum apolipoprotein B (ApoB) on bone mineral density (BMD); meanwhil Show more
Previous studies have indicated that blood lipids can influence skeletal health. However, limited research exists on the impact of serum apolipoprotein B (ApoB) on bone mineral density (BMD); meanwhile, it remains unclear to what extent cardiovascular disease plays in mediating this process. Therefore, we conducted a cross-sectional analysis involving 2930 participants from the National Health and Nutrition Examination Survey (NHANES) database to explore the relationship between serum ApoB and total body BMD (TB-BMD) and lumbar spine BMD (LS-BMD). We employed a two-step, two-sample Mendelian randomization (MR) analysis using genetic instruments to investigate causality and assess the mediating effects of six cardiovascular diseases. Multivariable linear regression models demonstrated an inverse linear association between serum ApoB and TB-BMD (β = -0.26, 95% confidence interval (CI): -0.41 to -0.12, The results of this study support that lowering serum ApoB levels could enhance BMD while preventing the occurrence of heart failure might reduce the harm caused by the decrease in BMD due to elevated ApoB levels. Show less
📄 PDF DOI: 10.31083/RCM31395
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Mei-Zhen Zhang, Jing Zheng, Li-Ting Cai +9 more · 2025 · Comparative biochemistry and physiology. Part D, Genomics & proteomics · Elsevier · added 2026-04-24
Scatophagus argus is a highly valuable aquaculture fish. Its artificial breeding faces problems in the induction of high quality eggs, thus necessitating studies on the regulation of ovarian developme Show more
Scatophagus argus is a highly valuable aquaculture fish. Its artificial breeding faces problems in the induction of high quality eggs, thus necessitating studies on the regulation of ovarian development. As the centre of nutrient metabolism in fish, the liver provides the material basis for ovarian development. However, the molecular mechanism of the liver in ovarian development in S. argus is still unclear. In this study, a transcriptome analysis of adult S. argus livers at different stages of ovarian development (stages II, III and IV) was performed. 410, 1025 and 1867 differentially expressed genes (DEGs) were obtained between stages II and III, stages II and IV and stages III and IV, respectively. In GO and KEGG analyses, DEGs were mostly involved in vitellogenesis and egg envelope formation (e.g., erα, erβ1, vtga, vtgb, vtgc, zp3, zp4a and zp4b), lipid metabolism and energy metabolism (e.g., dagt1, dagt2, lpl, apob, hk1, acly, ogdh, pc, and fbp1), and hormone signaling (e.g., lepa and igfbp1). Additionally, genes that were significantly upregulated in the liver at stage IV of ovarian development, compared to stages II and III, were markedly enriched in steroid biosynthesis and metabolism pathways. These findings provide clues to understanding the mechanisms of liver action in teleost ovarian development. Show less
no PDF DOI: 10.1016/j.cbd.2025.101550
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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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Tao Yang, Xiaohu Hu, Fei Cao +15 more · 2025 · Nature · Nature · added 2026-04-24
The mammalian gut harbours trillions of commensal bacteria that interact with their hosts through various bioactive molecules
📄 PDF DOI: 10.1038/s41586-025-08990-4
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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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QianKun Yang, XianJie Zhu, Li Zhang +1 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
Dyslipidemia has been proved to play a pivotal role in biological aging. Atherogenic Index of Plasma (AIP), derived from serum triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), is an Show more
Dyslipidemia has been proved to play a pivotal role in biological aging. Atherogenic Index of Plasma (AIP), derived from serum triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), is an effective biomarker of dyslipidemia. However, whether AIP can be used as an indicator of biological aging remains unclear. This study aims to investigate the relationship between AIP and biological aging in the US adult population. 4,471 American adults with age over 20 years from the National Health and Nutrition Examination Survey (NHANES) database were included in this study. Biological aging was assessed by phenotypic age acceleration (PhenoAgeAccel). Multivariable linear regression models, subgroup analyses and interaction tests were employed to explore the association between AIP and PhenoAgeAccel. Furthermore, adjusted restricted cubic spline (RCS) analyses were employed to assess potential nonlinear relationships, while mediation analysis was utilized to identify the mediating effects of homeostatic model assessment of insulin resistance (HOMA-IR). Besides, network pharmacology was performed to determine the potential mechanisms underlying dyslipidemia-related aging acceleration. A total of 4,471 participants were included in this study, the median chronological age, PhenoAge and PhenoAgeAccel for the overall population were 49 (35-64) years, 42.85 (27.30-59.68) years, and - 6.92 (- 10.52 to -2.46) years, respectively. In the fully adjusted model, one unit increase of AIP was correlated with 1.820-year increase in PhenoAgeAccel (β = 1.820, 95% CI: 1.085-2.556), which was more pronounced among individuals being female, diabetic and hypertensive. Furthermore, RCS analysis revealed a nonlinear relationship between AIP and PhenoAgeAccel, with an inflection point identified at -0.043 for AIP via threshold and saturation effect analysis. AIP demonstrated a positive correlation with PhenoAgeAccel both before (β = 6.550, 95% CI: 5.070-8.030) and after (β = 3.898, 95% CI: 2.474-5.322) this inflection point. Additionally, HOMA-IR was found to mediate 39.21% of the association between AIP and PhenoAgeAccel. Finally, network pharmacology analysis identified INS, APOE, APOB, IL6, IL10, PPARG, MTOR, ACE, PPARGC1A, and SERPINE1 as core targets in biological aging, which were functionally linked to key signaling pathways like AMPK, apelin, JAK-STAT, FoxO, etc. CONCLUSIONS: An elevated AIP was notably and positively correlated with accelerated aging, suggesting that AIP may serve as an effective predictor to evaluate accelerated aging. Show less
📄 PDF DOI: 10.1186/s12933-025-02695-8
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Weiyan Feng, Ying Lin, Ling Zhang +1 more · 2025 · Clinical proteomics · BioMed Central · added 2026-04-24
Exosomes play important role in biological functions, including both normal and disease process. Multiple cell types can secret exosomes, which act as message carriers. Increased evidences reveal that Show more
Exosomes play important role in biological functions, including both normal and disease process. Multiple cell types can secret exosomes, which act as message carriers. Increased evidences reveal that exosomes are promising diagnosis biomarkers in malignant tumors. In this study, we enrolled 78 participants, including 20 lung adenocarcinoma (LUAD), 18 lung squamous carcinoma (LUSC), 20 lung benign diseases (LUBN) and 20 healthy controls (NL) and we performed parallel reaction-monitoring (PRM)-mass spectrometry to screening the proteomic variation by label free analysis in exosomes from all groups, which has been widely used to quantify and detect target proteins. Total 14 protein were identified as candidate biomarkers, complement components C9, apolipoprotein B (APOB), filamin A (FLNA), guanine nucleotide binding protein G subunit 2 (GNB2), fermitin family homolog 3 (FERMT3) showed significantly differentiation in total lung cancer (LUAD and LUSC together), we then obtained combination analysis of 5 proteins and the area under the curve (AUC), sensitivity (SN) and specificity (SP) were 63.0%, 65.0%, and 75.0%, respectively, in comparison to NL group. And the LUAD combination panel, peroxiredoxin 6 (PRDX6), integrin alpha-IIb (ITGA2B) and hemoglobin subunit delta (HBD) revealed AUC was 95.0%, SN was 90.0% and SP was 95.0% in comparison to NL controls. In LUSC analysis, combination analysis of fibronectin 1 (FN1), pregnancy zone protein (PZP) and complement C1q tumor necrosis factor related protein 3 (C1QTNF3) showed that AUC was 88.1%, SN was 75.0%, SP was 100% in paralleled with NL group. Finally C9, FLNA, PZP were overexpressed in lung cancer H1299 and A549 cell lines and the results indicated that C9 acted as oncogenic role by increasing proliferation, migration and invasion of lung cancer cells, while FLNA and PZP played tumor-suppression by inhibition biological functions of lung cancer cells. Taken together, our study revealed multiple exosomal proteins which could be applied as candidate biomarkers in diagnosis of lung cancer. Show less
📄 PDF DOI: 10.1186/s12014-025-09535-7
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Kathryn N Porter Starr, Margery A Connelly, Jessica Wallis +11 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu17071200
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Dan-Dan Kuang, Ting Zhang, Xiao-Yu Guo +5 more · 2025 · Journal of agricultural and food chemistry · ACS Publications · added 2026-04-24
Hepatic VLDL overproduction, tightly modulated by insulin signaling, plays a pivotal role in the progression of atherosclerosis (AS). The present study aimed to investigate whether inhibition of hepat Show more
Hepatic VLDL overproduction, tightly modulated by insulin signaling, plays a pivotal role in the progression of atherosclerosis (AS). The present study aimed to investigate whether inhibition of hepatic VLDL overproduction is a novel therapeutic strategy for the homogeneous tea polysaccharide (TPS3A) to ameliorate AS under insulin resistance (IR) conditions and the potential molecular basis involved. Results showed that TPS3A supplementation effectively alleviated systemic IR and delayed atherosclerotic plaque progression in HFD-exposed ApoE Show less
no PDF DOI: 10.1021/acs.jafc.4c11144
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Kangshou Ji, Meizi Han, Mingqian Yang +2 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Zhigancao Decoction (ZGCD) is derived from "Treatise on Febrile Diseases" and is traditionally prescribed for treating a variety of cardiovascular conditions. As of now, there are no data to support i Show more
Zhigancao Decoction (ZGCD) is derived from "Treatise on Febrile Diseases" and is traditionally prescribed for treating a variety of cardiovascular conditions. As of now, there are no data to support its use as a treatment for diabetic cardiomyopathy (DCM) and the mechanism behind the effect is unclear as well. In the present study, clinical evidence for the efficacy of ZGCD in patients with DCM was examined using a meta-analysis and its underlying anti-DCM molecular mechanisms were explored via network pharmacology. The current study utilized an extensive search strategy encompassing various domestic and foreign databases databases to retrieve pertinent articles published up to June 2024. In light of this, a thorough evaluation of the benefits and safety of Zhigancao decoction (ZGCD) was conducted in this study using RevMan and Stata. Subsequently, a number of active compounds and target genes for ZGCD were gathered from the TCMSP and BATMAN-TCM databases, while the main targets for DCM were obtained from databases such as GenCards, OMIM, TTD, and DrugBank. To select core genes, protein-protein interaction networks were generated using the STRING platform, and enrichment analyses were completed using the Metascape platform. Meta-analysis results were ultimately derived from 9 studies involving 661 patients in total. In comparison with WM therapy alone, the pooled results showed that ZGCD significantly enhanced overall effectiveness. Additionally, the utilization of ZGCD was leading to a reduction in LVEDV, LVESV and LVDD, also a greater increase in LVEF. Meanwhile, the utilization of ZGCD during intervention was more effective in reducing SBP, and DBP. In addition, the ZGCD showed potential in reducing the occurrence of adverse events. In the context of network pharmacology, five constituents of ZGCD-namely lysine, quercetin, gamma-aminobutyric acid, stigmasterol, and beta-sitosterol-are posited to exert anti-diabetic cardiomyopathy (anti-DCM) effects through interactions with the molecular targets ASS1, SERPINE1, CACNA2D1, AVP, APOB, ICAM1, EGFR, TNNC1, F2, F10, IGF1, TNNI2, CAV1, INSR, and INS. The primary mechanisms by which ZGCD may achieve its anti-DCM effects are likely mediated via the AGEs/RAGE signaling pathway, as well as through pathways related to lipid metabolism and atherosclerosis. In comparison to WM therapy alone, ZGCD demonstrates greater efficacy and safety in the management of DCM. ZGCD not only significantly reduces blood pressure, but also enhances cardiac function while producing fewer adverse effects. The therapeutic effects of ZGCD on DCM can likely be ascribed to its capacity to modulate the AGEs-RAGE signaling pathway, as well as its efficacy in enhancing lipid metabolism and mitigating atherosclerosis. identifier (INPLASY202430133). Show less
📄 PDF DOI: 10.3389/fcvm.2025.1454647
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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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Xiaolong Li, Fan Ding, Lu Zhang +7 more · 2025 · BMC public health · BioMed Central · added 2026-04-24
The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern Show more
The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning methods have proven effective in enhancing prediction accuracy. However, existing approaches may lack interpretability regarding underlying mechanisms. Therefore, we aim to employ an interpretable machine learning approach utilizing nationwide cross-sectional data to predict pre-diabetic risk and quantify the impact of potential risks. The LASSO regression algorithm was used to conduct feature selection from 30 factors, ultimately identifying nine non-zero coefficient features associated with pre-diabetes, including age, TG, TC, BMI, Apolipoprotein B, TP, leukocyte count, HDL-C, and hypertension. Various machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANNs), Decision Trees (DT), and Logistic Regression (LR), were employed to compare predictive performance. Employing an interpretable machine learning approach, we aimed to enhance the accuracy of pre-diabetes risk prediction and quantify the impact and significance of potential risks on pre-diabetes. From the China Health and Nutrition Survey (CHNS) data, a cohort of 8,277 individuals was selected, exhibiting a disease prevalence of 7.13%. The XGBoost model demonstrated superior performance with an AUC value of 0.939, surpassing RF, SVM, DT, ANNs, Naive Bayes, and LR models. Additionally, Shapley Additive Explanation (SHAP) analysis indicated that age, BMI, TC, ApoB, TG, hypertension, TP, HDL-C, and WBC may serve as risk factors for pre-diabetes. The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM. Show less
📄 PDF DOI: 10.1186/s12889-025-22419-7
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Qinghua Fang, Hongdan Fan, Qiaoqiao Li +4 more · 2025 · Journal of the American Heart Association · added 2026-04-24
Genome-wide association studies have revealed numerous loci associated with coronary artery disease (CAD). However, some potential causal/risk genes remain unidentified, and causal therapies are lacki Show more
Genome-wide association studies have revealed numerous loci associated with coronary artery disease (CAD). However, some potential causal/risk genes remain unidentified, and causal therapies are lacking. We integrated multi-omics data from gene methylation, expression, and protein levels using summary data-based Mendelian randomization and colocalization analysis. Candidate genes were prioritized based on protein-level associations, colocalization probability, and links to methylation and expression. Single-cell RNA sequencing data were used to assess differential expression in the coronary arteries of patients with CAD. Our findings provide multi-omics evidence suggesting that Show less
📄 PDF DOI: 10.1161/JAHA.124.037203
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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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Chunming Cao, Qiyuan Hu, Xinyue Hu +6 more · 2025 · Journal of cardiothoracic surgery · BioMed Central · added 2026-04-24
The objective was to assess the clinical efficacy of long non-coding RNA (lncRNA) alpha-2-macroglobulin-antisense 1 (A2M-AS1) in acute myocardial infarction (AMI). One hundred patients with AMI and ei Show more
The objective was to assess the clinical efficacy of long non-coding RNA (lncRNA) alpha-2-macroglobulin-antisense 1 (A2M-AS1) in acute myocardial infarction (AMI). One hundred patients with AMI and eighty patients with chest pain were recruited in the case-control study. A2M-AS1 expression was examined by quantitative real-time polymerase chain reaction (qRT-PCR). Receiver operating characteristic (ROC) analysis was utilized for evaluating the diagnostic value. Pearson's correlation analysis was used to analyze the correlation between A2M-AS1 and conventional AMI biomarkers. AMI-associated risk indicators were identified using logistic regression analysis. A significant reduction of serum A2M-AS1 was measured in AMI patients relative to chest pain patients. A2M-AS1 had an area under the curve (AUC) of 0.927 to distinguish AMI patients from those with chest pain. Pearson's correlation analysis showed that A2M-AS1 was adversely correlated with white blood cell (WBC) (r=-0.6682, P < 0.001), low density lipoprotein cholesterol (LDL-C) (r=-0.5795, P < 0.001), creatine kinase MB (CK-MB) (r=-0.6022, P < 0.001) and cTnl (r=-0.5473; P < 0.001), while positively correlated with high density lipoprotein cholesterol (HDL-C) (r = 0.6445, P < 0.001). Relative to non-Major Adverse Cardiovascular Events (non-MACE) group, serum A2M-AS1 was obviously declined in the MACE group of AMI patients with high capacity to distinguish the MACE group from the non-MACE patients (AUC = 0.802). Additionally, A2M-AS1 (P = 0.013; OR = 0.268; 95%CI = 0.095-0.760) was a risk indicator for predicting MACE with AMI patients, as well as age (P = 0.014; OR = 3.478; 95%CI = 1.285-9.414). A reduction in A2M-AS1 expression was observed in AMI patients, suggesting its potential as an underlying indicator for AMI diagnosis. Show less
📄 PDF DOI: 10.1186/s13019-025-03381-2
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Boyan Zhang, Antoine Rimbert, Antoine Lainé +6 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
G-protein coupled receptor 146 (GPR146)-deficient mice exhibit a moderate 21 % reduction in plasma cholesterol. This is associated with decreased phosphorylation of ERK1/2 and reduced SREBP2 activity Show more
G-protein coupled receptor 146 (GPR146)-deficient mice exhibit a moderate 21 % reduction in plasma cholesterol. This is associated with decreased phosphorylation of ERK1/2 and reduced SREBP2 activity in the liver, which leads to lower VLDL secretion. Insight into the role of GPR146 in humans is however limited. We therefore set out to study rare genetic variants in GPR146 to improve our understanding of this new player in lipid metabolism. We used whole genome sequencing data from UK Biobank participants to search for rare coding variants in GPR146. We first carried out gene-based burden tests (using SAIGE-GENE-framework) and examined the association of individual variants with plasma cholesterol levels. One of the variants (P62L) was also studied using the Global Lipids Genetics Consortium (GLGC) data set and in a knock-in mouse model. We found that the combination of rare genetic variants identified in GPR146 is significantly associated with plasma cholesterol levels. Three rare variants, i.e. P62L, I129I, and A175T were individually associated with reduced plasma cholesterol. In the GLGC cohort, the P62L variant was associated with reductions in both HDL and LDL cholesterol. Follow-up experiments show lower plasma cholesterol levels in GPR146 This study shows that rare GPR146 gene variants are associated with lower plasma cholesterol levels in humans. One of these variants, P62L is associated with reductions of HDL cholesterol and LDL cholesterol in humans while the ortholog in mice confers a loss of GPR146 function leading to only reduced HDL cholesterol. How GPR146 affects HDL metabolism in humans and mice remains to be resolved. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119137
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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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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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Lili Yang, Jingjing Zhang, Jiangyan Han +1 more · 2025 · Clinical and experimental hypertension (New York, N.Y. : 1993) · Taylor & Francis · added 2026-04-24
Contributing factors for the development of heart failure (HF) involve both apolipoprotein B (ApoB) and coronary microvascular dysfunction (CMD). Although ApoB has been linked to diverse cardiovascula Show more
Contributing factors for the development of heart failure (HF) involve both apolipoprotein B (ApoB) and coronary microvascular dysfunction (CMD). Although ApoB has been linked to diverse cardiovascular risks, its association with CMD remains unclear. A total of 145 patients undergoing cardiac single-photon emission computed tomography (SPECT) scan was enrolled into this retrospective study. Based on ApoB serum level, all subjects were classified into three groups (Group 1-3). Myocardial flow reserve (MFR) was calculated using myocardial blood flow (MBF) tested in different contexts. ApoB serum level was positively correlated to rest MBF but inversely associated with stress MBF and MFR. Following adjustment for covariates, a significant relationship was observed between increased ApoB and decreased MFR. The predictive value of ApoB was test by Receiver Operating Characteristic Curve (ROC) analysis, showing an area under curve (AUC) of 0.87. The findings indicated that a higher level of ApoB correlated with the severity of CMD. Show less
no PDF DOI: 10.1080/10641963.2025.2477651
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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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