👤 Qibing Li

🔍 Search 📋 Browse 🏷️ Tags ❤️ Favourites ➕ Add 🧪 BiometalDB 🧬 Extraction
3991
Articles
2551
Name variants
Also published as: A Li, Ai-Jun Li, Ai-Qin Li, Ailing Li, Aimin Li, Aixin Li, Alexander H Li, Alexander Li, Amy Li, An-Qi Li, AnHai Li, Anan Li, Andrew C Li, Ang Li, Anna Fen-Yau Li, Annie Li, Anqi Li, Anyao Li, Ao Li, Aowen Li, Aoxi Li, Audrey Li, Bai-Qiang Li, Baichuan Li, Baiqiang Li, Baixing Li, Baizhou Li, Bang-Yan Li, Bao Li, Bao-Shan Li, Baoguang Li, Baoguo Li, Baohong Li, Baohua Li, Baolin Li, Baoqi Li, Baoqing Li, Baosheng Li, Baoting Li, Bei Li, Bei-Bei Li, Beibei Li, Beixu Li, Ben Li, Ben-Shang Li, Benyi Li, Biao Li, Bichun Li, Bin Li, Bin-Kui Li, Binbin Li, Bing Li, Bing-Heng Li, Bing-Hui Li, Bing-Mei Li, Bingbing Li, Binghu Li, Binghua Li, Bingjie Li, Bingjue Li, Bingkun Li, Binglan Li, Bingong Li, Bingshan Li, Bingsheng Li, Bingsong Li, Bingxin Li, Binjun Li, Binkui Li, Binru Li, Binxing Li, Biyu Li, Bizhi Li, Bo Li, BoWen Li, Bohao Li, Bohua Li, Bolun Li, Boru Li, Botao Li, Boxuan Li, Boya Li, Boyang Li, Bugao Li, C H Li, C Li, C X Li, C Y Li, Caesar Z Li, Cai Li, Cai-Hong Li, Caihong Li, Caili Li, Caixia Li, Caiyu Li, Caiyun Li, Can Li, Cang Li, Caolong Li, Chang Li, Chang-Da Li, Chang-Ping Li, Chang-Sheng Li, Chang-Yan Li, Chang-hai Li, Changcheng Li, Changgui Li, Changhong Li, Changhui Li, Changjiang Li, Changkai Li, Changqing Li, Changwei Li, Changxian Li, Changyan Li, Changyu Li, Changzheng Li, Chanjuan Li, Chanyuan Li, Chao Bo Li, Chao Li, Chaochen Li, Chaojie Li, Chaonan Li, Chaoqian Li, Chaowei Li, Chaoying Li, Chen Li, Chen-Chen Li, Chen-Lu Li, Chen-Xi Li, Chenfeng Li, Cheng Li, Cheng-Lin Li, Cheng-Tian Li, Cheng-Wei Li, Chengbin Li, Chengcheng Li, Chenghao Li, Chenghong Li, Chengjian Li, Chengjun Li, Chenglan Li, Chenglong Li, Chengnan Li, Chengping Li, Chengqian Li, Chengquan Li, Chengsi Li, Chenguang Li, Chengwen Li, Chengxin Li, Chengyun Li, Chenhao Li, Chenjie Li, Chenli Li, Chenlin Li, Chenlong Li, Chenlu Li, Chenmeng Li, Chenrui Li, Chensheng Li, Chenwen Li, Chenxi Li, Chenxiao Li, Chenxin Li, Chenxuan Li, Chenyang Li, Chenyao Li, Chenyu Li, Cheung Li, Chi-Ming Li, Chi-Yuan Li, Chia Li, Chia-Yang Li, Chien-Feng Li, Chien-Hsiu Li, Chien-Te Li, Chih-Chi Li, Chitao Li, Chiyang Li, Chong Li, Chongyang Li, Chongyi Li, Chris Li, Chu-Qiao Li, Chuan F Li, Chuan Li, Chuan-Hai Li, Chuan-Yun Li, Chuanbao Li, Chuanfang Li, Chuang Li, Chuangpeng Li, Chuanning Li, Chuanyin Li, Chumei Li, Chun Li, Chun-Bo Li, Chun-Lai Li, Chun-Mei Li, Chun-Quan Li, Chun-Xiao Li, Chun-Xu Li, Chung-Hao Li, Chung-I Li, Chunhong Li, Chunhui Li, Chunjie Li, Chunjun Li, Chunlan Li, Chunlian Li, Chunliang Li, Chunlin Li, Chunmei Li, Chunmiao Li, Chunqing Li, Chunqiong Li, Chunshan Li, Chunsheng Li, Chunting Li, Chunxia Li, Chunxiao Li, Chunxing Li, Chunxue Li, Chunya Li, Chunyan Li, Chunyi Li, Chunying Li, Chunyu Li, Chunzhu Li, Chuzhong Li, Cien Li, Cong Li, Congcong Li, Congfa Li, Conghui Li, Congjiao Li, Conglin Li, Congxin Li, Congye Li, Cui Li, Cui-lan Li, Cuicui Li, Cuiguang Li, Cuilan Li, Cuiling Li, Cun Li, Cunxi Li, Cyril Li, D C Li, Da Li, Da-Hong Li, Da-Jin Li, Da-Lei Li, Da-wei Li, DaZhuang Li, Dacheng Li, Dai Li, Daiyue Li, Dalei Li, Dali Li, Dalin Li, Dan C Li, Dan Li, Dan-Dan Li, Dan-Ni Li, Dandan Li, Daniel Tian Li, Danjie Li, Danni Li, Danxi Li, Danyang Li, Daoyuan Li, Dapei Li, Dawei Li, Dayong Li, Dazhi Li, De-Jun Li, De-Tao Li, Dechao Li, Defa Li, Defeng Li, Defu Li, Dehai Li, Deheng Li, Dehua Li, Dejun Li, Demin Li, Deming Li, Dengfeng Li, Dengke Li, Dengxiong Li, Deqiang Li, Desen Li, Desheng Li, Dexiong Li, Deyu Li, Dezhi Li, Di Li, Di-Jie Li, Dianjie Li, Dijie Li, Ding Li, Ding Yang Li, Ding-Biao Li, Ding-Jian Li, Dingchen Li, Dingshan Li, Diyan Li, Dong Li, Dong Sheng Li, Dong-Jie Li, Dong-Ling Li, Dong-Run Li, Dong-Yun Li, Dong-fei Li, Dongbiao Li, Dongdong Li, Dongfang Li, Dongfeng Li, Donghe Li, Donghua Li, Dongliang Li, Dongmei Li, Dongmin Li, Dongnan Li, Dongtao Li, Dongyang Li, Dongye Li, Duan Li, Duanbin Li, Duanxiang Li, Dujuan Li, Duo Li, Duoyun Li, Ellen Li, En Li, En-Min Li, Enhao Li, Enhong Li, Enxiao Li, F Li, Fa-Hong Li, Fa-Hui Li, Fadi Li, Fan Li, Fang Li, Fangqi Li, Fangyan Li, Fangyong Li, Fangyuan Li, Fangzhou Li, Fei Li, Fei-Lin Li, Fei-feng Li, Feifei Li, Feilong Li, Fen Li, Feng Li, Feng-Feng Li, Fengfeng Li, Fengjuan Li, Fengli Li, Fengqi Li, Fengqiao Li, Fengqing Li, Fengxia Li, Fengxiang Li, Fengyi Li, Fengyuan Li, Fu-Rong Li, Fugen Li, Fuhai Li, Fujun Li, Fulun Li, Fuping Li, Fusheng Li, Fuyu Li, Fuyuan Li, G Li, G-P Li, Gaijie Li, Gaizhen Li, Gaizhi Li, Gan Li, Gang Li, Ganggang Li, Gao-Fei Li, Gaoyuan Li, Ge Li, Gen Li, Gen-Lin Li, Gerard Li, Gong-Hua Li, Gongda Li, Guanbin Li, Guandu Li, Guang Li, Guang Y Li, Guang-Li Li, Guang-Xi Li, Guangda Li, Guangdi Li, Guanghua Li, Guanghui Li, Guangjin Li, Guangli Li, Guanglu Li, Guanglve Li, Guangming Li, Guangping Li, Guangpu Li, Guangqiang Li, Guangquan Li, Guangwen Li, Guangxi Li, Guangxiao Li, Guangyan Li, Guangzhao Li, Guangzhen Li, Guannan Li, Guanqiao Li, Guanyu Li, Gui Lin Li, Gui-Bo Li, Gui-Hua Li, Gui-Rong Li, Gui-xing Li, Guigang Li, Guihua Li, Guilan Li, Guisen Li, Guixia Li, Guixin Li, Guiyang Li, Guiying Li, Guiyuan Li, Guo Li, Guo-Chun Li, Guo-Jian Li, Guo-Li Li, Guo-Ping Li, Guo-Qiang Li, Guobin Li, Guoge Li, Guohong Li, Guohua Li, Guohui Li, Guojin Li, Guojun Li, Guoli Li, Guoping Li, Guoqin Li, Guoqing Li, Guowei Li, Guoxi Li, Guoxiang Li, Guoxing Li, Guoyan Li, Guoyin Li, H J Li, H Li, H-F Li, H-H Li, H-J Li, Hai Li, Hai-Yun Li, Haibin Li, Haibo Li, Haifeng Li, Haihong Li, Haihua Li, Haijun Li, Hailong Li, Haimin Li, Haiming Li, Hainan Li, Haipeng Li, Hairong Li, Haitao Li, Haitong Li, Haixia Li, Haiyan Li, Haiyang Li, Haiying Li, Haiyu Li, Han Li, Han-Bing Li, Han-Bo Li, Han-Ni Li, Han-Ru Li, Han-Wei Li, Hanbin Li, Hanbing Li, Hanbo Li, Handong Li, Hang Li, Hangwen Li, Hanjun Li, Hankun Li, Hanlu Li, Hanmei Li, Hanqi Li, Hanqin Li, Hansen Li, Hanting Li, Hanxiao Li, Hanxue Li, Hao Li, Hao-Fei Li, Haojing Li, Haolong Li, Haomiao Li, Haoqi Li, Haoran Li, Haotong Li, Haoxian Li, Haoyu Li, Haying Li, He Li, He-Zhen Li, Hecheng Li, Hegen Li, Hehua Li, Heng Li, Heng-Zhen Li, Hengguo Li, Hengtong Li, Hengyu Li, Hening Li, Hewei Li, Hexin Li, Heying Li, Hong Li, Hong-Chun Li, Hong-Lan Li, Hong-Lian Li, Hong-Mei Li, Hong-Tao Li, Hong-Wen Li, Hong-Yan Li, Hong-Yu Li, Hong-Zheng Li, Hongbo Li, Hongchang Li, Hongde Li, Honggang Li, Hongguo Li, Honghua Li, Honghui Li, Hongjia Li, Hongjiang Li, Hongjuan Li, Honglei Li, Hongli Li, Honglian Li, Hongliang Li, Honglin Li, Hongling Li, Honglong Li, Hongmei Li, Hongmin Li, Hongming Li, Hongqin Li, Hongquan Li, Hongru Li, Hongsen Li, Hongwei Li, Hongxia Li, Hongxin Li, Hongxing Li, Hongxue Li, Hongyan Li, Hongye Li, Hongyi Li, Hongyu Li, Hongyun Li, Hongzhe K Li, Hongzheng Li, Hongzhi Li, Hsiao-Fen Li, Hsiao-Hui Li, Hsin-Hua Li, Hsin-Yun Li, Hu Li, Hua Li, Hua-Zhong Li, Huabin Li, Huafang Li, Huafu Li, Huaixing Li, Huaiyuan Li, Hualian Li, Hualing Li, Huamao Li, Huan Li, Huanan Li, Huang Li, Huangbao Li, Huangyuan Li, Huanhuan Li, Huanjun Li, Huanqing Li, Huanqiu Li, Huaping Li, Huashun Li, Huawei Li, Huayao Li, Huayin Li, Huaying Li, Hui Li, Hui-Jun Li, Hui-Long Li, Hui-Ping Li, Huibo Li, Huifang Li, Huifeng Li, Huihuang Li, Huihui Li, Huijie Li, Huijuan Li, Huijun Li, Huilan Li, Huili Li, Huiliang Li, Huilin Li, Huilong Li, Huimin Li, Huiping Li, Huiqin Li, Huiqing Li, Huiqiong Li, Huiting Li, Huixia Li, Huixue Li, Huiying Li, Huiyou Li, Huiyuan Li, Huizi Li, Hujie Li, Hulun Li, Hung Li, Hung-Yuan Li, Ivan Li, J Li, J T Li, Jason Li, Jen-Ming Li, Jenny J Li, Ji Li, Ji Xia Li, Ji-Cheng Li, Ji-Feng Li, Ji-Liang Li, Ji-Lin Li, Ji-Min Li, Jia Li, Jia Li Li, Jia-Da Li, Jia-Huan Li, Jia-Peng Li, Jia-Ru Li, Jia-Xin Li, Jiabei Li, Jiachen Li, Jiacheng Li, Jiafang Li, Jiafei Li, Jiahao Li, Jiahui Li, Jiajia Li, Jiajie Li, Jiajing Li, Jiajun Li, Jiajv Li, Jiali Li, Jialin Li, Jialing Li, Jialun Li, Jiaming Li, Jian Li, Jian'an Li, Jian-Jun Li, Jian-Mei Li, Jian-Qiang Li, Jian-Shuang Li, Jianan Li, Jianang Li, Jianbin Li, Jianbo Li, Jianchun Li, Jiandong Li, Jianfang Li, Jianfeng Li, Jiang Li, Jiangan Li, Jiangbo Li, Jiangchao Li, Jiangfeng Li, Jianglin Li, Jianglong Li, Jiangtao Li, Jiangui Li, Jianguo Li, Jiangxia Li, Jiangya Li, Jianhai Li, Jianhua Li, Jiani Li, Jianing Li, Jianliang Li, Jianlin Li, Jianmin Li, Jiannan Li, Jianping Li, Jianrong Li, Jianrui Li, Jiansheng Li, Jianshuang Li, Jianwei Li, Jianxin Li, Jianxiong Li, Jianye Li, Jianyi Li, Jianyong Li, Jianyu Li, Jianzhong Li, Jiao Li, Jiao-Jiao Li, Jiaomei Li, Jiaping Li, Jiaqi Li, Jiawei Li, Jiaxi Li, Jiaxin Li, Jiaxuan Li, Jiayan Li, Jiayang Li, Jiayi Li, Jiaying Li, Jiayu Li, Jiayuan Li, Jiazhou Li, Jicheng Li, Jie Li, Jie-Pin Li, Jie-Shou Li, Jiehan Li, Jiejia Li, Jiejie Li, Jiejing Li, Jieming Li, Jiequn Li, Jieshou Li, Jiexi Li, Jiexin Li, Jiezhen Li, Jifang Li, Jihua Li, Jin Li, Jin-Jiang Li, Jin-Liang Li, Jin-Long Li, Jin-Mei Li, Jin-Ping Li, Jin-Qiu Li, Jin-Wei Li, Jin-Xiu Li, Jinchen Li, Jinfang Li, Jinfeng Li, Jing Li, Jing-Jing Li, Jing-Ming Li, Jing-Yao Li, Jing-Yi Li, Jing-gao Li, Jingcheng Li, Jingchun Li, Jingfeng Li, Jinghao Li, Jinghui Li, Jingjing Li, Jingke Li, Jinglin Li, Jingmei Li, Jingming Li, Jingping Li, Jingqi Li, Jingshang Li, Jingshu Li, Jingtong Li, Jingui Li, Jingwen Li, Jingxia Li, Jingxiang Li, Jingxin Li, Jingya Li, Jingyi Li, Jingyong Li, Jingyu Li, Jingyun Li, Jinhua Li, Jinhui Li, Jinjie Li, Jinku Li, Jinlan Li, Jinliang Li, Jinlin Li, Jinman Li, Jinming Li, Jinping Li, Jinsong Li, Jinwei Li, Jinxia Li, Jinxin Li, Jinzhi Li, Jiong Li, Jiong-Ming Li, Jipeng Li, Jiqing Li, Jisen Li, Jisheng Li, Jiuke Li, Jiuyi Li, Jiwei Li, Jiwen Li, Jixi Li, Jixuan Li, Jiyang Li, Jiyuan Li, John Zhong Li, Jonathan Z Li, Joyce Li, Ju-Rong Li, Juan Li, Juan-Juan Li, Juanjuan Li, Juanling Li, Juanni Li, Jufang Li, Julia Li, Jun Li, Jun Z Li, Jun-Cheng Li, Jun-Jie Li, Jun-Ling Li, Jun-Ru Li, Jun-Yan Li, Jun-Ying Li, JunBo Li, Junfeng Li, Junhong Li, Junhui Li, Junjie Li, Junjun Li, Junming Li, Junping Li, Junqin Li, Junru Li, Junsheng Li, Juntong Li, Junxian Li, Junxin Li, Junxu Li, Junya Li, Junyi Li, Junying Li, Justin Li, Jutang Li, Juxue Li, K-L Li, Ka Li, Ka Wan Li, Kai Li, Kai-Wen Li, Kaibin Li, Kaibo Li, Kaifeng Li, Kailong Li, Kaimi Li, Kainan Li, Kaiwei Li, Kaixin Li, Kaiyi Li, Kaiyuan Li, Kang Li, Kangli Li, Kangyuan Li, Karen Li, Kathy H Li, Kawah Li, Ke Li, KeZhong Li, Keanning Li, Kecheng Li, Kechun Li, Keguo Li, Kejuan Li, Keke Li, Kening Li, Kenli Li, Kenneth Kai Wang Li, Keqing Li, Keshen Li, Keying Li, Keyuan Li, Kezhen Li, Kongdong Li, Kuan Li, Kui Li, Kuiliang Li, Kun Li, Kun-Peng Li, Kun-Ping Li, Kun-Xin Li, Kunlin Li, Kunlong Li, Kunlun Li, Kunpeng Li, L I Li, L K Li, L Li, L P Li, L-Y Li, Lai K Li, Laiqing Li, Lamei Li, Lan Li, Lan-Juan Li, Lan-Lan Li, Lanfang Li, Lang Li, Lanjuan Li, Lanlan Li, Lanzhou Li, Le Li, Le-Le Li, Le-Ying Li, Lei Li, Leilei Li, Leipeng Li, Letai Li, Leyao Li, Li Li, Li-Min Li, Li-Na Li, Lian Li, Lianbing Li, Liang Li, Liangdong Li, Liangji Li, Liangkui Li, Liangqian Li, Lianhong Li, Lianjian Li, Lianyong Li, Liao-Yuan Li, Lieyou Li, Liguo Li, Lihong Li, Lihua Li, Lijia Li, Lijuan Li, Lijun Li, Lili Li, Liliang Li, Liling Li, Liming Li, Lin Li, Lin-Feng Li, Linchuan Li, Linfeng Li, Ling Li, Ling-Jie Li, Ling-Ling Li, Ling-Zhi Li, Lingjiang Li, Lingjie Li, Lingjun Li, Lingling Li, Lingxi Li, Lingyan Li, Lingyi Li, Lingzhi Li, Linhong Li, Linke Li, Linlin Li, Linqi Li, Linqing Li, Linsheng Li, Linting Li, Linxin Li, Linyan Li, Linying Li, Lipeng Li, Liping Li, Liqin Li, Liqun Li, Lirong Li, Lisha Li, Litao Li, Liuzheng Li, Liwei Li, Lixi Li, Lixia Li, Lixiang Li, Liyan Li, Long Li, Long Shan Li, Long-Yan Li, Longhui Li, Longxuan Li, Longyu Li, Lu Li, Lu-Yun Li, Lucia M Li, Lucy Li, Luhan Li, Lujiao Li, Lujie Li, Lulu Li, Luquan Li, Luxuan Li, Luyao Li, Luying Li, M D Li, M Li, M V Li, M-J Li, Man Li, Man-Xiang Li, Man-Zhi Li, Mangmang Li, Manjiang Li, Manna Li, Manru Li, Manxia Li, Mao Li, Maogui Li, Maolin Li, Maoquan Li, Maosheng Li, Marilyn Li, Mei Li, Mei-Lan Li, Mei-Ya Li, Mei-Zhen Li, Meifang Li, Meifen Li, Meijia Li, Meilan Li, Meiqing Li, Meitao Li, Meiting Li, Meiyan Li, Meiying Li, Meiyue Li, Meizi Li, Melody M H Li, Meng Li, Meng-Hua Li, Meng-Jun Li, Meng-Meng Li, Meng-Miao Li, Meng-Yang Li, Meng-Yao Li, Meng-Yue Li, MengGe Li, Mengfan Li, Menghua Li, Mengjiao Li, Mengjuan Li, Mengling Li, Menglu Li, Mengmeng Li, Mengqing Li, Mengqiu Li, Mengsen Li, Mengshi Li, Mengxi Li, Mengxia Li, Mengxuan Li, Mengyang Li, Mengyao Li, Mengying Li, Mengyuan Li, Mengyun Li, Mengze Li, Mi Li, Mian Li, Miao Li, Miao X Li, Miaoxin Li, Michelle Li, Mimi Li, Min Li, Min-Dian Li, Min-Rui Li, Min-jun Li, Minerva X Li, Ming D Li, Ming Li, Ming V Li, Ming Xing Li, Ming Zhou Li, Ming-Han Li, Ming-Hao Li, Ming-Jiang Li, Ming-Kai Li, Ming-Qing Li, Ming-Wei Li, Ming-Xing Li, Ming-Yang Li, Mingdan Li, Mingfang Li, Mingfei Li, Minghao Li, Minghua Li, Minghui Li, Mingjiang Li, Mingjie Li, Mingjun Li, Mingke Li, Mingkun Li, Mingli Li, Minglong Li, Minglun Li, Mingna Li, Mingqiang Li, Mingquan Li, Mingrui Li, Mingwei Li, Mingxi Li, Mingxia Li, Mingxing Li, Mingxu Li, Mingxuan Li, Mingyang Li, Mingyao Li, Mingyue Li, Mingzhe Li, Mingzhou Li, Minhui Li, Minle Li, Minmin Li, Minqi Li, Minyue Li, Minze Li, Minzhe Li, Miyang Li, Mo Li, Mohan Li, Monica M Li, Moyi Li, Mufan Li, Mulin Jun Li, Muzi Li, N Li, Na Li, Naishi Li, Nan Li, Nan-Nan Li, Nana Li, Nanjun Li, Nanlong Li, Nanxing Li, Nanzhen Li, Ni Li, Nianfu Li, Nianyu Li, Nien Li, Nien-Chen Li, Nien-Chi Li, Ning Li, Ningyan Li, Ningyang Li, Niu Li, Nuomin Li, O Li, P H Li, P Li, Pan Li, Panlong Li, Panyuan Li, Pei Li, Pei-Lin Li, Pei-Qin Li, Pei-Shan Li, Pei-Ying Li, Pei-Zhi Li, PeiQi Li, Peibo Li, Peifen Li, Peifeng Li, Peihong Li, Peihua Li, Peilin Li, Peilong Li, Peining Li, Peipei Li, Peiqin Li, Peiran Li, Peiwu Li, Peixin Li, Peiyu Li, Peiyuan Li, Peiyun Li, Peng Li, Peng Peng Li, Peng-li Li, Pengcui Li, Penghui Li, Pengjie Li, Pengju Li, Pengsong Li, Pengyang Li, Pengyu Li, Pengyun Li, Pik Yi Li, Pilong Li, Pindong Li, Ping Li, Ping'an Li, Pinghua Li, Pingping Li, Pu Li, Pu-Yu Li, Q Li, Qi Li, Qi-Fu Li, Qi-Jing Li, Qian Li, Qian-Qian Li, Qiang Li, Qiang-Ming Li, Qiankun Li, Qianqian Li, Qiao Li, Qiao-Xin Li, Qiaolian Li, Qiaoqiao Li, Qifang Li, Qihang Li, Qihua Li, Qiji Li, Qijun Li, Qilan Li, Qilong Li, Qin Li, Qiner Li, Qing Li, Qing Run Li, Qing-Chang Li, Qing-Fang Li, Qing-Min Li, Qing-Wei Li, Qingchao Li, Qingfang Li, Qingfeng Li, Qinggang Li, Qinghe Li, Qinghong Li, Qinghua Li, Qingjie Li, Qinglan Li, Qingli Li, Qinglin Li, Qingling Li, Qingqin S Li, Qingrun Li, Qingshang Li, Qingsheng Li, Qingxian Li, Qingyang Li, Qingyu Li, Qingyuan Li, Qingyun Li, Qinqin Li, Qinrui Li, Qintong Li, Qiong Li, Qionghua Li, Qipei Li, Qiqiong Li, Qiu Li, Qiufeng Li, Qiuhong Li, Qiusheng Li, Qiuxuan Li, Qiuya Li, Qiuyan Li, Qiwei Li, Qiyong Li, Qizhai Li, Quan Li, Quan-Zhong Li, Quanpeng Li, Quanshun Li, Quanzhang Li, Qun Li, R H L Li, R Li, Ran Li, Ranchang Li, Ranran Li, Ranwei Li, Ren Li, Ren-Ke Li, Rena Li, Roger Li, Ronald Li, Rong Li, Rong-Bing Li, Ronggui Li, Rongkai Li, Rongling Li, Rongqing Li, Rongsong Li, Rongxia Li, Rongyao Li, Rosa J W Li, Ru Li, Ru-Hao Li, Rui Li, Rui-Fang Li, Rui-Han Li, Rui-Jún Eveline Li, Ruibing Li, Ruidong Li, Ruifang Li, Ruihuan Li, Ruijia Li, Ruijin Li, Ruikai Li, Ruitong Li, Ruiwen Li, Ruixi Li, Ruixia Li, Ruixue Li, Ruiyang Li, Rujia Li, Rulin Li, Rumei Li, Runbing Li, Runwen Li, Runzhao Li, Runzhen Li, Runzhi Li, Ruobing Li, Ruolin Li, Ruonan Li, Ruotai Li, Ruotian Li, Ruotong Li, Ruyi Li, Ruyue Li, S A Li, S E Li, S L Li, S Li, S S Li, S-C Li, Sai Li, Saijuan Li, Sainan Li, San-Feng Li, Sanqiang Li, Senlin Li, Senmao Li, Sha Li, Sha-Sha Li, Shan Li, Shan-Shan Li, Shangjia Li, Shanglai Li, Shangming Li, Shanhang Li, Shanpeng Li, Shanshan Li, Shanyi Li, Shao-Dan Li, Shaobin Li, Shaodan Li, Shaofei Li, Shaoguang Li, Shaojian Li, Shaojing Li, Shaoliang Li, Shaomin Li, Shaoqi Li, Shaoyong Li, Shasha Li, Shawn S C Li, Shawn Shun-Cheng Li, Shen Li, Sheng Li, Sheng-Fu Li, Sheng-Jie Li, Sheng-Qing Li, Sheng-Tien Li, Shengbiao Li, Shengbin Li, Shengchao A Li, Shenghao Li, Shengjie Li, Shengli Li, Shengliang Li, Shengsheng Li, Shengwen Li, Shengxian Li, Shengxu Li, Shengze Li, Sherly X Li, Shi Li, Shi-Fang Li, Shi-Guang Li, Shi-Hong Li, Shi-Ying Li, Shibao Li, Shibo Li, Shichao Li, Shigang Li, Shihao Li, Shiheng Li, Shihong Li, Shijie Li, Shijun Li, Shikang Li, Shilan Li, Shili Li, Shiliang Li, Shilin Li, Shilun Li, Shiqi Li, Shiquan Li, Shisheng Li, Shishi Li, Shitao Li, Shiya Li, Shiyan Li, Shiyang Li, Shiyi Li, Shiying Li, Shiyu Li, Shiyue Li, Shiyun Li, Shu Li, Shu-Fang Li, Shu-Fen Li, Shu-Feng Li, Shu-Hong Li, Shu-Qi Li, Shu-Xin Li, Shuai Li, Shuaicheng Li, Shuang Li, Shuang-Ling Li, Shuangding Li, Shuangfei Li, Shuanglong Li, Shuangmei Li, Shuangshuang Li, Shuangxiu Li, Shubo Li, Shude Li, Shufen Li, Shugang Li, Shuguang Li, Shuhao Li, Shuhua Li, Shuhui Li, Shujiao Li, Shujie Li, Shujin Li, Shujing Li, Shulin Li, Shun Li, Shunhua Li, Shunle Li, Shunqin Li, Shunqing Li, Shunwang Li, Shuo Li, Shupeng Li, Shuqiang Li, Shuwei Li, Shuwen Li, Shuying Li, Shuyu D Li, Shuyu Dan Li, Shuyuan Li, Shuyue Li, Si Li, Si-Wei Li, Si-Xing Li, Si-Ying Li, Si-Yuan Li, Sibing Li, Sichen Li, Sichong Li, Side Li, Siguang Li, Sijie Li, Simin Li, Siming Li, Sin-Lun Li, Siqi Li, Sitao Li, Siting Li, Siwen Li, Siyi Li, Siyu Li, Siyue Li, Song Li, Song-Chao Li, Songhan Li, Songlin Li, Songtao Li, Songyu Li, Songyun Li, Stephen Li, Su Li, SuYun Li, Suchun Li, Suheng Li, Suhong Li, Suiyan Li, Sujing Li, Suk-Yee Li, Sumei Li, Sunan Li, Sung-Chou Li, Supeng Li, Suping Li, Suran Li, Suwei Li, Suwen Li, Suyan Li, T Li, Taibo Li, Taiwen Li, Taixu Li, Tao Li, Taoyingnan Li, Teng Li, Tengyan Li, Thomas Li, Tian Li, Tian-Yi Li, Tian-chang Li, Tian-wang Li, Tianchang Li, Tiandong Li, Tianfeng Li, Tiange Li, Tianjiao Li, Tianjun Li, Tianming Li, Tiansen Li, Tiantian Li, Tianxiang Li, Tianyao Li, Tianye Li, Tianyi Li, Tianyou Li, Tie Li, Tiegang Li, Tiehua Li, Tiewei Li, Timmy Li, Ting Li, Tingguang Li, Tinghao Li, Tinghua Li, Tingsong Li, Tingting Li, Tong Li, Tong-Ruei Li, Tongyao Li, Tongzheng Li, Tsai-Kun Li, Tuojian Li, Tuoping Li, Vivian Li, Vivian S W Li, W H Li, W J Li, W Li, W W Li, W Y Li, W-B Li, Wan Jie Li, Wan Li, Wan-Hong Li, Wan-Shan Li, Wan-Xin Li, Wang Li, Wanling Li, Wanni Li, Wanqian Li, Wanru Li, Wanshi Li, Wanshun Li, Wanting Li, Wanwan Li, Wanxin Li, Wanyan Li, Wanyi Li, Wei Li, Wei-Bo Li, Wei-Dong Li, Wei-Jun Li, Wei-Li Li, Wei-Ming Li, Wei-Na Li, Wei-Ping Li, Wei-Qin Li, Wei-Yang Li, Weidong Li, Weifeng Li, Weiguang Li, Weiguo Li, Weihai Li, Weiheng Li, Weihua Li, Weijian Li, Weijie Li, Weijun Li, Weike Li, Weiling Li, Weimin Li, Weina Li, Weining Li, Weiping Li, Weiqin Li, Weirong Li, Weisong Li, Weiyang Li, Weiye Li, Weiyong Li, Weizu Li, Wen Lan Li, Wen Li, Wen-Chao Li, Wen-Jie Li, Wen-Ting Li, Wen-Wen Li, Wen-Xi Li, Wen-Xing Li, Wen-Ya Li, Wen-Ying Li, Wen-juan Li, Wenbo Li, Wenchao Li, Wende Li, Wendeng Li, Wenfang Li, Wenfeng Li, Wenge Li, Wenguo Li, Wenhao Li, Wenhong Li, Wenhua Li, Wenhui Li, Wenjia Li, Wenjian Li, Wenjie Li, Wenjing Li, Wenjuan Li, Wenjun Li, Wenke Li, Wenlei Li, Wenli Li, Wenlong Li, Wenming Li, Wenqi Li, Wenqiang Li, Wenqing Li, Wenqun Li, Wenrui Li, Wensheng Li, Wentao Li, Wenwen Li, Wenxi Li, Wenxia Li, Wenxiang Li, Wenxin Li, Wenxiu Li, Wenxue Li, Wenyan Li, Wenyang Li, Wenyi Li, Wenying Li, Wenyong Li, Wenyu Li, Wenzhe Li, Wenzhuo Li, Wu-Jun Li, Wuguo Li, Wulan Li, Wuyan Li, X B Li, X L Li, X Li, X Y Li, X-H Li, X-L Li, Xi Li, Xi-Hai Li, Xi-Xi Li, Xia Li, Xian Li, Xiancheng Li, Xiang Li, Xiang-Dong Li, Xiang-Jun Li, Xiang-Ping Li, Xiang-Yu Li, Xiangcheng Li, Xiangchun Li, Xiangdong Li, Xiangfei Li, Xiangjun Li, Xiangling Li, Xianglong Li, Xiangnan Li, Xiangpan Li, Xiangping Li, Xiangqi Li, Xiangrui Li, Xiangwei Li, Xiangyan Li, Xiangyang Li, Xiangyun Li, Xiangzhe Li, Xiankai Li, Xiankun Li, Xianlin Li, Xianlong Li, Xianlu Li, Xianlun Li, Xianrui Li, Xianyong Li, Xiao Li, Xiao-Cheng Li, Xiao-Dong Li, Xiao-Feng Li, Xiao-Gang Li, Xiao-Guang Li, Xiao-Hong Li, Xiao-Hui Li, Xiao-Jiao Li, Xiao-Jing Li, Xiao-Jun Li, Xiao-Kang Li, Xiao-Li Li, Xiao-Lin Li, Xiao-Long Li, Xiao-Min Li, Xiao-Na Li, Xiao-Qiang Li, Xiao-Qin Li, Xiao-Qiu Li, Xiao-Sa Li, Xiao-Tong Li, Xiao-Yao Li, Xiao-Yun Li, Xiao-kun Li, Xiao-mei Li, Xiao-xu Li, Xiao-yu Li, XiaoQiu Li, Xiaobai Li, Xiaobin Li, Xiaobing Li, Xiaobo Li, Xiaochen Li, Xiaochun Li, Xiaocun Li, Xiaodong Li, Xiaofang Li, Xiaofei Li, Xiaofeng Li, Xiaoguang Li, Xiaohan Li, Xiaoheng Li, Xiaohong Li, Xiaohu Li, Xiaohua Li, Xiaohuan Li, Xiaohui Li, Xiaojiao Li, Xiaojiaoyang Li, Xiaojing Li, Xiaoju Li, Xiaojuan Li, Xiaokun Li, Xiaolei Li, Xiaoli Li, Xiaolian Li, Xiaoliang Li, Xiaolin Li, Xiaoling Li, Xiaolong Li, Xiaoman Li, Xiaomei Li, Xiaomeng Li, Xiaomin Li, Xiaoming Li, Xiaona Li, Xiaonan Li, Xiaoning Li, Xiaopeng Li, Xiaoping Li, Xiaoqi Li, Xiaoqiang Li, Xiaoqin Li, Xiaoqing Li, Xiaoqiong Li, Xiaoquan Li, Xiaoran Li, Xiaorong Li, Xiaotian Li, Xiaoting Li, Xiaotong Li, Xiaowei Li, Xiaoxia Li, Xiaoxiao Li, Xiaoxiong Li, Xiaoxuan Li, Xiaoya Li, Xiaoyan Li, Xiaoyao Li, Xiaoyi Li, Xiaoying Li, Xiaoyong Li, Xiaoyu Li, Xiaoyuan Li, Xiaoyun Li, Xiaozhao Li, Xiaozhen Li, Xiaozheng Li, Xiatian Li, Xiawei Li, Xiaxia Li, Xiayu Li, Xidan Li, Xihao Li, Xihe Li, Xijing Li, Xikun Li, Xiliang Li, Ximei Li, Xin Li, Xin-Chang Li, Xin-Jian Li, Xin-Ping Li, Xin-Tao Li, Xin-Ya Li, Xin-Yu Li, Xin-Yue Li, Xin-Zhu Li, Xinbin Li, Xing Li, Xing-Wang Li, Xingchen Li, Xingcheng Li, Xingfang Li, Xinghuan Li, Xinghui Li, Xingli Li, Xinglong Li, Xingwang Li, Xingxing Li, Xingya Li, Xingye Li, Xingyu Li, Xingyuan Li, Xinhai Li, Xinhua Li, Xinhui Li, Xining Li, Xinjia Li, Xinjian Li, Xinke Li, Xinle Li, Xinli Li, Xinlin Li, Xinmei Li, Xinmiao Li, Xinmin Li, Xinming Li, Xinpeng Li, Xinping Li, Xinrong Li, Xinrui Li, Xinsheng Li, Xinwei Li, Xinxin Li, Xinxiu Li, Xinyan Li, Xinyang Li, Xinyao Li, Xinye Li, Xinyi Li, Xinyu Li, Xinyuan Li, Xinzhi Li, Xinzhong Li, Xiong Bing Li, Xiong Li, Xiongfeng Li, Xionghao Li, Xionghui Li, Xiu-Ling Li, Xiucui Li, Xiufeng Li, Xiujuan Li, Xiuli Li, Xiuling Li, Xiumei Li, Xiuqi Li, Xiurong Li, Xiushen Li, Xiushi Li, Xiuzhen Li, Xixi Li, Xiying Li, Xiyue Li, Xiyun Li, Xu Li, Xu-Bo Li, Xu-Wei Li, Xu-Zhao Li, Xuan Li, Xuan-Ling Li, Xuanfei Li, Xuanxuan Li, Xuanzheng Li, Xudong Li, Xue Cheng Li, Xue Li, Xue-Er Li, Xue-Fei Li, Xue-Hua Li, Xue-Lian Li, Xue-Min Li, Xue-Nan Li, Xue-Peng Li, Xue-Yan Li, Xue-Ying Li, Xue-jing Li, Xue-zhi Li, Xuebiao Li, Xueer Li, Xuefei Li, Xuefeng Li, Xuehua Li, Xuejie Li, Xuejun Li, Xuekun Li, Xuelian Li, Xuelin Li, Xueling Li, Xuemei Li, Xuemin Li, Xuening Li, Xuepeng Li, Xueqin Li, Xueren Li, Xueshan Li, Xuesong Li, Xueting Li, Xuewang Li, Xuewei Li, Xuewen Li, Xueyang Li, Xueyi Li, Xueying Li, Xuezhong Li, Xuhang Li, Xuhong Li, Xuhua Li, Xujun Li, Xun Li, Xunjia Li, Xuri Li, Xutong Li, Xuyi Li, Xuze Li, Y H Li, Y L Li, Y Li, Y M Li, Y X Li, Y-Y Li, Ya Li, Ya-Feng Li, Ya-Ge Li, Ya-Jun Li, Ya-Li Li, Ya-Pei Li, Ya-Qiang Li, Ya-Ting Li, Ya-Zhou Li, YaJie Li, Yadong Li, Yahui Li, Yajiao Li, Yajing Li, Yajuan Li, Yajun Li, Yakui Li, Yalan Li, Yali Li, Yalin Li, Yan Bing Li, Yan Li, Yan Ning Li, Yan-Chun Li, Yan-Guang Li, Yan-Hong Li, Yan-Hua Li, Yan-Li Li, Yan-Nan Li, Yan-Xue Li, Yan-Yan Li, Yan-Yu Li, Yanan Li, Yanbin Li, Yanbing Li, Yanbo Li, Yanchang Li, Yanchuan Li, Yanchun Li, Yandong Li, Yanfeng Li, Yang Li, Yangxue Li, Yangyang Li, Yanhui Li, Yani Li, Yanjiao Li, Yanjie Li, Yanjing Li, Yanjun Li, Yanli Li, Yanlin Li, Yanling Li, Yanlong Li, Yanmei Li, Yanmin Li, Yanming Li, Yanni Li, Yanping Li, Yanqing Li, Yansen Li, Yanshu Li, Yansong Li, Yantao Li, Yanwei Li, Yanwu Li, Yanxi Li, Yanxiang Li, Yanxin Li, Yanyan Li, Yanying Li, Yanze Li, Yanzhong Li, Yao Li, Yaobo Li, Yaochen Li, Yaodong Li, Yaofu Li, Yaojia Li, Yaokun Li, Yaoqi Li, Yaoyao Li, Yaqi Li, Yaqiang Li, Yaqiao Li, Yaqin Li, Yaqing Li, Yaqiong Li, Yarong Li, Yawei Li, Yaxi Li, Yaxian Li, Yaxiong Li, Yaxuan Li, Yaying Li, Yayu Li, Yazhou Li, Ye Li, Yehong Li, Yeshan Li, Yetian Li, Yi Li, Yi-Heng Li, Yi-Ling Li, Yi-Ning Li, Yi-Shuan J Li, Yi-Ting Li, Yi-Wen Li, Yi-Yang Li, Yi-Ying Li, Yi-Yun Li, YiPing Li, YiQing Li, Yibo Li, Yiche Li, Yicun Li, Yifan Li, Yifei Li, Yifeng Li, Yige Li, Yihan Li, Yihao Li, Yiheng Li, Yihong Li, Yijian Li, Yijie Li, Yijing Li, Yiju Li, Yikang Li, Yike Li, Yilang Li, Yiliang Li, Yilong Li, Yimei Li, Yimeng Li, Yiming Li, Yin Li, Yinan Li, Ying Li, Ying-Bo Li, Ying-Lan Li, Ying-Qin Li, Ying-Qing Li, Ying-na Li, Yinggao Li, Yinghao Li, Yinghua Li, Yinghui Li, Yingjian Li, Yingjie Li, Yingjun Li, Yinglin Li, Yingnan Li, Yingpu Li, Yingqin Li, Yingrui Li, Yingshuo Li, Yingxi Li, Yingxia Li, Yingyi Li, Yingying Li, Yinhao Li, Yining Li, Yinliang Li, Yinxiong Li, Yinyan Li, Yinzhen Li, Yipeng Li, Yiqiang Li, Yirun Li, Yitong Li, Yiwei Li, Yiwen Li, Yixi Li, Yixiang Li, Yixiao Li, Yixin Li, Yixing Li, Yixuan Li, Yixue Li, Yiyang Li, Yizhe Li, Yong Li, Yong-Jian Li, Yong-Jun Li, Yong-Liang Li, Yongchao Li, Yonghao Li, Yonghe Li, Yongjia Li, Yongjiang Li, Yongjin Li, Yongjing Li, Yongjun Li, Yongkai Li, Yongle Li, Yongli Li, Yongmei Li, Yongnan Li, Yongpeng Li, Yongping Li, Yongqi Li, Yongqiang Li, Yongqiu Li, Yongsen Li, Yongsheng Li, Yongting Li, Yongxiang Li, Yongxin Li, Yongxue Li, Yongze Li, Yongzhe Li, Yongzhen Li, Yongzheng Li, You Li, You Ran Li, You-Mei Li, Youchen Li, Youjun Li, Youming Li, Youran Li, Yousheng Li, Youwei Li, Yu Li, Yu-Cheng Li, Yu-Chia Li, Yu-Hang Li, Yu-Hao Li, Yu-He Li, Yu-Hui Li, Yu-I Li, Yu-Jin Li, Yu-Jui Li, Yu-Kun Li, Yu-Lin Li, Yu-Sheng Li, Yu-Xiang Li, Yu-Ye Li, Yu-Ying Li, Yu-quan Li, Yuan Hao Li, Yuan Li, Yuan-Hai Li, Yuan-Jing Li, Yuan-Tao Li, Yuan-Yuan Li, Yuan-hao Li, Yuanchang Li, Yuanchuang Li, Yuancong Li, Yuandong Li, Yuanfang Li, Yuanfei Li, Yuanhao Li, Yuanhe Li, Yuanheng Li, Yuanhong Li, Yuanhua Li, Yuanjing Li, Yuanmei Li, Yuanyou Li, Yuanyuan Li, Yuanze Li, Yubin Li, Yubo Li, Yuchan Li, Yuchao Li, Yucheng Li, Yuchuan Li, Yuchun Li, Yudong Li, Yue Li, Yue-Chun Li, Yue-Jia Li, Yue-Ming Li, Yue-Rui Li, Yue-Ting Li, Yue-Ying Li, YueQiang Li, Yuefei Li, Yuefeng Li, Yueguo Li, Yuehua Li, Yuemei Li, Yueping Li, Yueqi Li, Yueting Li, Yuezheng Li, Yufan Li, Yufen Li, Yufeng Li, Yuguang Li, Yuhan Li, Yuhang Li, Yuhong Li, Yuhua Li, Yuhuang Li, Yuhui Li, Yujie Li, Yujun Li, Yukun Li, Yuli Li, Yulin Li, Yuling Li, Yulong Li, Yumao Li, Yumei Li, Yumiao Li, Yumin Li, Yun Li, Yun-Da Li, Yun-Lin Li, Yun-Peng Li, Yun-tian Li, Yuna Li, Yunan Li, Yunchu Li, Yunfeng Li, Yunjiu Li, Yunlong Li, Yunlun Li, Yunman Li, Yunmin Li, Yunpeng Li, Yunqi Li, Yunrui Li, Yunshen Li, Yunsheng Li, Yunting Li, Yunxi Li, Yunxiao Li, Yunxu Li, Yunyun Li, Yunze Li, Yuping Li, Yuqi Li, Yuqian Li, Yuqing Li, Yuqiu Li, Yuquan Li, Yushan Li, Yutang Li, Yutian Li, Yuting Li, Yutong Li, Yuwei Li, Yuxi Li, Yuxiang Li, Yuxin Li, Yuxiu Li, Yuxuan Li, Yuyan Li, Yuying Li, Yuyun Li, Yuzhe Li, Yvonne Li, Z Li, Z-H Li, Zaibo Li, Ze Li, Ze-An Li, Zecai Li, Zechuan Li, Zehan Li, Zehua Li, Zejian Li, Zemin Li, Zengyang Li, Zequn Li, Zesong Li, Zexu Li, Zeyu Li, Zeyuan Li, Zezhi Li, Zhan Li, Zhandong Li, Zhang Li, Zhanjun Li, Zhankui Li, Zhanquan Li, Zhantao Li, Zhao Li, Zhao-Cong Li, Zhao-Yang Li, Zhaobing Li, Zhaohan Li, Zhaojin Li, Zhaoliang Li, Zhaolun Li, Zhaoping Li, Zhaosha Li, Zhaoshui Li, Zhaoyong Li, Zhe Li, Zhehui Li, Zhen Li, Zhen-Hua Li, Zhen-Jia Li, Zhen-Li Li, Zhen-Xi Li, Zhen-Yu Li, Zhen-Yuan Li, Zhenbei Li, Zhencheng Li, Zhencong Li, Zhenfei Li, Zhenfen Li, Zheng Li, Zheng-Dao Li, Zhengda Li, Zhenghao Li, Zhenghui Li, Zhengjie Li, Zhengliang Li, Zhenglong Li, Zhengnan Li, Zhengpeng Li, Zhengrui Li, Zhenguang Li, Zhengwei Li, Zhengyang Li, Zhengyao Li, Zhengying Li, Zhengyu Li, Zhenhao Li, Zhenhua Li, Zhenhui Li, Zhenjia Li, Zhenjun Li, Zhenli Li, Zhenlu Li, Zhenming Li, Zhenshu Li, Zhenyan Li, Zhenyu Li, Zhenzhe Li, Zhenzhou Li, Zheyun Li, Zhi Li, Zhi-Bin Li, Zhi-Gang Li, Zhi-Jian Li, Zhi-Peng Li, Zhi-Wei Li, Zhi-Xing Li, Zhi-Yong Li, Zhi-Yuan Li, Zhi-qiang Li, Zhibin Li, Zhichao Li, Zhifan Li, Zhifei Li, Zhigang Li, Zhigao Li, Zhihao Li, Zhihong Li, Zhihua Li, Zhihui Li, Zhijia Li, Zhijie Li, Zhijun Li, Zhilei Li, Zhimei Li, Zhiming Li, Zhipeng Li, Zhiping Li, Zhiqiang Li, Zhiqiong Li, Zhiquan Li, Zhirong Li, Zhisheng Li, Zhiwei Li, Zhixiong Li, Zhixuan Li, Zhiyang Li, Zhiyi Li, Zhiyong Li, Zhiyu Li, Zhiyuan Li, Zhizhong Li, Zhizong Li, Zhong Li, Zhong-Xin Li, Zhongcai Li, Zhongding Li, Zhonggen Li, Zhonghua Li, Zhongjie Li, Zhonglian Li, Zhonglin Li, Zhongwen Li, Zhongxia Li, Zhongxian Li, Zhongxuan Li, Zhongyu Li, Zhongzhe Li, Zhou Li, Zhouhua Li, Zhouxiang Li, Zhu Li, Zhuang Li, Zhuangzhuang Li, Zhuanjian Li, Zhuo Li, Zhuo-Rong Li, Zhuoran Li, Zhuorong Li, Zi-Zhan Li, Zichao Li, Zihai Li, Zihan Li, Zihao Li, Zihua Li, Zihui Li, Zijian Li, Zijing Li, Zili Li, Ziliang Li, Zilin Li, Zilu Li, Zimeng Li, Ziming Li, Zipeng Li, Ziqi Li, Ziqiang Li, Ziqing Li, Ziru Li, Zirui Li, Ziwen Li, Zixiao Li, Ziyang Li, Ziyu Li, Ziyue Li, Ziyun Li, Zizhuo Li, Zong-Xue Li, Zongchao Li, Zongdi Li, Zongfang Li, Zonghong Li, Zonghua Li, Zongjun Li, Zonglin Li, Zongyi Li, Zongyu Li, Zongyun Li, Zongzhe Li, Zu-Ling Li, Zu-guo Li, Zulong Li, Zunjiang Li, Zuo-Lin Li
articles
Q Zang, F Li, Y Ju +6 more · 2025 · Scandinavian journal of rheumatology · Taylor & Francis · added 2026-04-24
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA Show more
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA disease activity remains unclear, which is the focus of this study. We evaluated dyslipidaemia and atherosclerosis in a cohort of untreated female patients. Fifty untreated female patients with TA (median age 30 years) and 98 healthy controls matched for age and body mass index (median age 30 years) were assessed for lipid profiles [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), ApoB, ApoE, lipoprotein(a)], inflammatory markers [C-reactive protein (CRP), erythrocyte sedimentation rate (ESR)], and atherosclerotic plaque frequency. TA patients exhibited significantly higher levels of TG and the non-HDL-C/HDL-C ratio than the control group, whereas TC, HDL-C, LDL-C, and ApoA1 levels were significantly lower. Pearson's correlation analysis indicated a positive correlation between CRP and ApoB, as well as the non-HDL-C/HDL-C ratio, and negative correlations with TG, HDL-C, and ApoA1. Atherosclerotic plaques were detected in 14.3% of the TA patients. Multivariate regression analysis revealed that the presence of atherosclerotic plaques was associated only with age, independent of inflammatory markers and lipoprotein levels. The results of this study indicate that untreated female TA patients exhibit a markedly dysregulated serum lipid profile. Atherosclerosis in early TA was not related to lipids or markers of inflammation. Show less
no PDF DOI: 10.1080/03009742.2025.2488096
APOB
Mimi Li, Lichao Ye, Chunnuan Chen · 2025 · Scientific reports · Nature · added 2026-04-24
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contr Show more
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contributing to stroke remains poorly understood. This study aims to investigate the association between the apoB/apoA1 ratio and intracranial or extracranial atherosclerosis. We enrolled 408 patients with acute ischemic stroke who had never been treated with statins or fibrates. Based on the images from computed tomography angiography (CTA), the patients were categorized into four groups: intracranial atherosclerosis stenosis (ICAS, n = 136), extracranial carotid atherosclerosis stenosis (ECAS, n = 45), combined intracranial and extracranial atherosclerosis stenosis (COAS, n = 73), and non-cerebral atherosclerosis stenosis (NCAS, n = 154). Demographic characteristics, clinical factors, and serum lipid levels were collected and then compared across groups. The apoB/apoA1 ratio was significantly higher in patients with ICAS, ECAS and COAS compared to those in the NCAS group. Multivariable logistic regression analysis demonstrated that the ApoB/ApoA1 ratio was independently associated with ICAS, but not with ECAS. ROC curve analysis showed that the ApoB/ApoA1 ratio had a good diagnostic ability for ICAS, with an area under the curve (AUC) of 0.764, an optimal cut-off value of 0.8122, a sensitivity of 81.3%, and a specificity of 59.8%. An higher apoB/apoA1 ratio is associated with ICAS in ischemic stroke patients. Show less
📄 PDF DOI: 10.1038/s41598-025-97625-9
APOB
Feixiang He, Qifang Chen, Peilin Gu +4 more · 2025 · Ophthalmology science · Elsevier · added 2026-04-24
To identify the connections between lipid biomarkers and the anti-VEGF therapy response in patients with neovascular age-related macular degeneration (nAMD). A bidirectional and multivariable Mendelia Show more
To identify the connections between lipid biomarkers and the anti-VEGF therapy response in patients with neovascular age-related macular degeneration (nAMD). A bidirectional and multivariable Mendelian randomization study. The summary statistics for anti-VEGF nAMD treatment response included a total of 128 responders, 51 nonresponders, and 6 908 005 genetic variants available for analysis. The sample size of lipid biomarkers is 441 016 and 12 321 875 genetic variants available for analysis. Two-sample Mendelian randomization (MR) method was conducted to exhaustively appraise the causalities among 13 lipid biomarkers and the risk of different anti-VEGF treatment responses (including visual acuity [VA] and central retinal thickness [CRT]) for nAMD subtypes. Thirteen lipid biomarkers, VA, and CRT. A positive causal relationship was identified between triglycerides (TGs), apolipoproteins (Apos) E2, ApoE3, total cholesterol (TC), and VA response to anti-VEGF therapy in patients with nAMD, as confirmed by MR-Egger, weighted median, and weighted mode models. The MR-Egger model yielded statistically significant results for TC, ApoA-I, ApoB, and ApoA-V in relation to the CRT response to anti-VEGF treatment in patients with nAMD. In the reverse MR, the MR-Egger model identified significant causal relationships between ApoA-I, low-density lipoprotein cholesterol (LDL-c), ApoE3, and ApoF and the VA response. However, this was not the case in the weighted median and weighted mode models. In the MR-Egger model, ApoB, LDL-c, ApoE3, and ApoM were identified as significantly influencing the CRT response. In the multisample MR analysis, TC, high-density lipoprotein cholesterol, LDL-c, and TG were found to be causally related to VA response, and TC was also identified as being causally related to the CRT response to anti-VEGF therapy in patients with nAMD. This MR study suggests unidirectional causality between TG and ApoE3 and the response to anti-VEGF treatment in patients with nAMD. The author(s) have no proprietary or commercial interest in any materials discussed in this article. Show less
📄 PDF DOI: 10.1016/j.xops.2025.100711
APOB
Jiaqing Song, Ying Jin, Qinghong Yu +5 more · 2025 · Translational cancer research · added 2026-04-24
Breast cancer is the second most common cancer worldwide. Chemotherapy often causes dyslipidemia and obesity in breast cancer patients. Monitoring lipid profiles and body mass index (BMI) is crucial t Show more
Breast cancer is the second most common cancer worldwide. Chemotherapy often causes dyslipidemia and obesity in breast cancer patients. Monitoring lipid profiles and body mass index (BMI) is crucial to evaluate chemotherapy's metabolic side effects, identify interventions to mitigate them, and understand health risks linked to weight changes during treatment. Shenling Baizhu Powder (SLBZP), a traditional Chinese medicine (TCM), treats spleen-stomach ailments by boosting spleen function, enhancing qi, and reducing dampness. SLBZP has potential benefits in managing chemotherapy-induced dyslipidemia and improving overall metabolic health in cancer patients. This study retrospectively examined the effects of SLBZP on blood lipid levels and BMI in breast cancer patients undergoing adjuvant chemotherapy. This study reviewed the medical records of patients who were diagnosed with breast cancer at the Breast Surgery Department of Zhejiang Provincial Hospital of Traditional Chinese Medicine from January 2022 to December 2023. Based on the inclusion criteria, a total of 180 eligible patients were included and divided into an observational group (which received SLBZP) and a control group (which did not receive SLBZP) during chemotherapy. Patients' clinical data, including age at diagnosis, menopausal status, tumor location, smoking and drinking habits, tumor molecular type, tumor node metastasis (TNM) stage, chemotherapy drugs, targeted therapy, lipid levels, and BMI before and after chemotherapy, were collected. Statistical analyses were conducted using SPSS 25.0. After chemotherapy, the control group showed significant increases in total cholesterol (TC) (P=0.03), triglyceride (TG) (P=0.001), low-density lipoprotein cholesterol (LDL-C) (P=0.02), and apolipoprotein B (ApoB) (P=0.01) levels. In the observational group, the TC, TG, and LDL-C levels remained stable (P>0.05), but the high-density lipoprotein cholesterol (HDL-C) (P=0.001) and apolipoprotein A1 (ApoA1) (P<0.001) levels significantly decreased, and BMI (P=0.02) significantly increased. The subgroup analysis revealed that the taxane followed by anthracycline subgroup showed significant increases in BMI (P=0.007) and significant decreases in the HDL-C (P=0.007) and ApoA1 (P<0.001) levels, while the taxane subgroup showed a significant decrease in the HDL-C level post-chemotherapy (P=0.003). In the control group, the TG (P=0.002) and LDL-C (P=0.02) levels were significantly elevated in the taxane followed by anthracycline subgroup post-chemotherapy. No significant changes were observed in BMI or the other lipid indexes in the remaining chemotherapy drug regime subgroups (P>0.05). Chemotherapy increased the TC, TG, LDL-C, and ApoB levels in breast cancer patients, but SLBZP mitigated dyslipidemia. The patients who received SLBZP also showed increased BMI post-chemotherapy, which was likely due to reduced gastrointestinal side effects. Taxane-based chemotherapy drugs had greater effects on blood lipids and BMI, while anthracycline-based drugs did not significantly affect blood lipids and BMI. Show less
📄 PDF DOI: 10.21037/tcr-2024-2658
APOB
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
APOB
Hui-Hui Liu, Sha Li, Jian-Jun Li · 2025 · Drugs · Springer · added 2026-04-24
Tafolecimab, a fully human monoclonal antibody targeting proprotein convertase subtilisin/kexin type 9 (PCSK9), represents a significant therapeutic advancement in the management of hypercholesterolem Show more
Tafolecimab, a fully human monoclonal antibody targeting proprotein convertase subtilisin/kexin type 9 (PCSK9), represents a significant therapeutic advancement in the management of hypercholesterolemia and has been approved for use in the Chinese population. Elevated low-density lipoprotein cholesterol (LDL-C) is a major risk factor for atherosclerotic cardiovascular disease, and traditional treatments often fall short due to challenges such as statin intolerance. Clinical trials have demonstrated that tafolecimab can effectively reduce LDL-C levels, achieving reductions of over 60% in many patients. It also improves other lipid parameters, including lipoprotein(a) [Lp(a)], non-high-density lipoprotein cholesterol (non-HDL-C), and apolipoprotein B (ApoB). It has a favorable safety profile, primarily characterized by mild to moderate adverse events. The long-acting formulation of tafolecimab enables less frequent dosing, thereby promoting compliance. As cardiovascular diseases continue to escalate globally, tafolecimab holds promise not only for patients in China but also for broader international applications, representing a critical advancement in the evolving landscape of lipid-lowering therapies. Show less
📄 PDF DOI: 10.1007/s40265-025-02167-z
APOB
Xuliang Luo, Yan Guo, Xuelian Li +6 more · 2025 · BMC genomics · BioMed Central · added 2026-04-24
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) Show more
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) are nanoscale vesicles that carry and transmit signaling molecules, thereby affecting semen quality. Currently it is still unclear whether SPEV are involved in the process of that aromatase affects the quality semen in chicken. To clarify this issue, lentivirus carrying Cyp19a1 (LV-CYP19A1) for over-expression of aromatase was constructed and injected to testis of 35-week-old roosters. Semen quality and seminal plasma hormone were measured, and SPEV were also extracted and proteome sequencing was performed after treatment of LV-CYP19A1. The results indicated that semen volume, fertility, sperm motility, testosterone (T) levels were significantly decreased, and estradiol (E Our results reveal that aromatase can down-regulate the protein expression related to regulation of ATP synthesis and metabolism, and sperm motility in SPEV, thereby reducing semen quality in roosters. Show less
📄 PDF DOI: 10.1186/s12864-025-11500-5
APOB
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
APOB
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
APOB
J J Da, R Dong, Q Li +4 more · 2025 · Zhonghua yi xue za zhi · added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112137-20241111-02522
APOB
Wenxiu Wang, Rui Li, Zimin Song +4 more · 2025 · JAMA cardiology · added 2026-04-24
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lower Show more
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lowering triglycerides. Multiple clinical trials of small-interfering RNA therapeutics targeting APOC3 are currently underway. To investigate whether genetically predicted lower APOC3 is associated with a reduction in cardiovascular risk and if the combined exposure to APOC3 and LDL-C-lowering variants is associated with a reduction in the risk of coronary heart disease (CHD). This was a population-based genetic association study with 2 × 2 factorial mendelian randomization. Included were participants of European ancestry in the UK Biobank. Data were analyzed from November 2023 to July 2024. Genetic scores were constructed to mimic the effects of APOC3, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors. Plasma lipid and lipoprotein levels, CHD, and type 2 diabetes (T2D). This study included 401 548 UK Biobank participants (mean [SD] age, 56.9 [8.0] years; 216 901 female [54.0%]). Genetically predicted lower APOC3 was associated with a lower risk of CHD (odds ratio [OR], 0.96; 95% CI, 0.93-0.98) and T2D (0.97; 95% CI, 0.95-0.99). Genetically lower APOC3 and PCSK9 were associated with a similar magnitude of risk reduction in CHD per 10-mg/dL decrease in apolipoprotein B (ApoB) level (APOC3: 0.70; 95% CI, 0.59-0.83; PCSK9: 0.71; 95% CI, 0.65-0.77). Combined exposure to genetically lower APOC3 and PCSK9 was associated with an additive lower risk of CHD (APOC3: 0.96; 95% CI, 0.92-0.99; PCSK9: 0.93; 95% CI, 0.90-0.97; combined: 0.90; 95% CI, 0.86-0.93). Genetically lower HMGCR was also associated with a lower risk of CHD, and the risk was further reduced when combined with APOC3 (0.93; 95% CI, 0.90-0.97). Genetically predicted lower APOC3 was associated with a reduced risk of CHD that is comparable with that associated with lower PCSK9 per unit decrease in ApoB. Combined exposure to APOC3 and LDL-C-lowering variants was associated with an additive reduction in CHD risk. Future studies are warranted to investigate the therapeutic potential of these combined therapies, particularly among high-risk patients who cannot achieve therapeutic targets with existing lipid-lowering therapies. Show less
no PDF DOI: 10.1001/jamacardio.2025.0195
APOB
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
APOB
Xiaoting Pei, Xi Yao, Di Qi +3 more · 2025 · Scientific reports · Nature · added 2026-04-24
Diabetic retinopathy (DR) is a common complication of type II diabetes mellitus (T2DM) and a leading cause of blindness in the working population. Apolipoprotein levels have been reported to be associ Show more
Diabetic retinopathy (DR) is a common complication of type II diabetes mellitus (T2DM) and a leading cause of blindness in the working population. Apolipoprotein levels have been reported to be associated with the risk of DR. This study aimed to develop a predictive model for DR based on apolipoproteins (apoA and apoB) and menopausal status in Chinese Han women with T2DM and to evaluate the model's effectiveness. Data from 2339 T2DM women were collected between January 2018 and June 2022. Multilevel regression was used to explore the independent effect of apolipoproteins and interaction between apolipoproteins and menopausal status on DR and proliferative diabetic retinopathy (PDR). Receiver operating characteristic (ROC) analysis was performed to compare the fitting degree and predictive efficiency of different models. Results showed that both apoA and apoB were independent influencing factors for DR and PDR and interacted significantly with menopausal status. The interaction between apoA and menopausal status had a protective effect on DR [OR (95% CI) = 0.925 (0.858-0.996), P = 0.040] and PDR [OR (95% CI) = 0.937 (0.895-0.981), P = 0.006]. In contrast, the interaction between apoB and menopausal status was a risk factor for DR [OR (95% CI) = 1.684 (1.141-2.379), P = 0.008)] and PDR [OR (95% CI) = 3.377 (1.148-9.937), P = 0.027]. ROC analysis demonstrated that the interaction model outperformed models without interaction terms (P < 0.01). The area under the curve for the interaction model was 0.879 (95% CI 0.864-0.893) for DR and 0.930 (95% CI 0.915-0.945) for PDR. These findings suggest that the interaction model is highly efficient in predicting DR, particularly PDR, in Chinese Han women with T2DM. Show less
📄 PDF DOI: 10.1038/s41598-025-93161-8
APOB
Tongxue Zhang, Yajing Li, Xiaoyu Liu +8 more · 2025 · Kardiologiia · added 2026-04-24
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneu Show more
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneurysm remains unclear. This study used a Mendelian randomization (MR) approach to explore the causal relationships between ApoB, aortic aneurysm, and potential mediators.Material and methods    Single nucleotide polymorphism (SNP) data related to ApoB, apolipoprotein A1 (ApoA1), triglycerides, frailty index, and aortic aneurysm were obtained from large-scale genome-wide association studies. MR analysis was conducted to evaluate causal relationships, using inverse variance weighting (IVW) as the primary statistical method. Additionally, we assessed whether the frailty index mediates the relationship between ApoB and aortic aneurysm.Results    Univariate MR analysis revealed that ApoB is significantly associated with aortic aneurysm (IVW odds ratio (OR) = 1.443, 95 % confidence interval (CI) = 1.273-1.637, p < 0.001). Multivariable MR (MVMR) analysis, adjusted for ApoA1 and triglycerides, confirmed these results. In mediation analysis, the frailty index was found to partially mediate the effect of ApoB on aortic aneurysm (mediation contribution: 20.1 %-23.1 %). The ORs for ApoB and the frailty index with respect to aortic aneurysm were 1.325 (95 % CI = 1.168-1.505) and 4.188 (95 % CI = 1.859-9.435), respectively.Conclusion    ApoB has a causal relationship with aortic aneurysm, with the frailty index acting as a partial mediator in this pathway. Show less
no PDF DOI: 10.18087/cardio.2025.2.n2796
APOB
Jingjing Guo, Haifan Qiu, Jianping Wang +3 more · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregn Show more
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregnancy women and 317 healthy non-pregnant women. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) [Lp(a)], and hypersensitive C-reactive protein (hs-CRP) were measured in both groups. The mean and standard deviation of each index were calculated to establish the reference range of normal serum lipid levels in pregnant women in mid-to-late pregnancy. The associations between serum lipid levels and perinatal outcomes were assessed statistically. There were no significant differences in age, pregnancy, or parity between the adverse outcome and normal delivery groups, but the caesarean section rate was significantly higher in the adverse outcome group. The levels of hs-CRP, TG, TC, HDL-C, LDL-C, and ApoA1 were significantly higher in the adverse outcome group. Elevated hs-CRP, TG, and HDL-C levels were risk factors for adverse pregnancy outcomes. According to the receiver operating characteristic curve, the optimal threshold of the combined diagnosis of these three indicators to predict adverse pregnancy outcomes was 0.534, and the area under the curve was 0.822. The establishment of lipid reference intervals in the second and third trimesters of pregnancy can effectively evaluate lipid metabolism in pregnant women, and the measurement of lipid metabolism in pregnant women is helpful in predicting adverse pregnancy outcomes. Show less
📄 PDF DOI: 10.3389/fmed.2025.1530525
APOB
Pengfei Zhang, Wenting Wang, Qian Xu +5 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. Show more
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC). We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated. MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity. Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119136
APOB
Jimmy H Mo, Chao Zhai, Kwangsek Jung +4 more · 2025 · iScience · Elsevier · added 2026-04-24
Vitellogenin is thought to share a common ancestor with human apolipoprotein B (ApoB) for systemic lipid transport. In
📄 PDF DOI: 10.1016/j.isci.2025.111860
APOB
Jiawei Li, Ximei Li, Jiamin Tian +5 more · 2025 · Frontiers in veterinary science · Frontiers · added 2026-04-24
Lower intramuscular fat (IMF) and excessive abdominal fat reduce carcass quality in broilers. The study aimed to investigate the effects of dietary VD
📄 PDF DOI: 10.3389/fvets.2025.1542637
APOB
Xiangliang Liu, Yuguang Li, Wang Yang +5 more · 2025 · Journal of affective disorders · Elsevier · added 2026-04-24
Growing evidence suggests that lipid metabolism may play a crucial role in mood disorder pathophysiology, and the correlation between blood lipids and mood disorder remains further clarified. This pro Show more
Growing evidence suggests that lipid metabolism may play a crucial role in mood disorder pathophysiology, and the correlation between blood lipids and mood disorder remains further clarified. This prospective, population-based cohort study utilized data from the UK Biobank. The study included 268,098 and 292,121 participants who had never been diagnosed with depression or bipolar disorder and who had complete data at both the baseline and follow-up points. A principal component analysis (PCA) was conducted on seven blood lipids, and the first three principal components (PCs) were derived. Cox regression analysis was employed to examine the correlation between the risk of mood disorders and the PCs. Multiplicative interaction and sensitivity analyses were also conducted. The relationship between blood lipids and neurological biomarkers was explored using Spearman's analysis. PC1, primarily reflecting levels of Apolipoprotein B (ApoB), cholesterol, and low-density lipoprotein cholesterol (LDL-C), showed a protective effect against depression, with HRs of 0.98 (95 % CI: 0.96,1.00) in the fully adjusted Cox regression model. In contrast, PC2, characterized by opposite loadings for triglycerides and high-density lipoprotein cholesterol (HDLC), was positively associated with the risk of depression and bipolar disorder.(HR = 1.03,95 % CI: 1.01,1.06; HR = 1.11, 95 % CI: 1.01,1.23). Increased PC2 level was related to a significant increase in bipolar disorder risk among participants with high genetic risk (genetic risk score > 90 %, HR = 1.22, 95 % CI: 1.02,1.46). Complicated correlations between blood lipids and serum neuroproteins were detected. These findings suggest complex associations between blood lipid profiles and the risk of depression and bipolar disorder. Show less
no PDF DOI: 10.1016/j.jad.2025.02.040
APOB
Yuetong Wu, Li Zhang, Jing Li +3 more · 2025 · Frontiers in physiology · Frontiers · added 2026-04-24
To evaluate the impact of maximal fat oxidation intensity exercise combined with calorie restriction intervention on lipid-related parameters in a hypercholesterolemic population, and to determine if Show more
To evaluate the impact of maximal fat oxidation intensity exercise combined with calorie restriction intervention on lipid-related parameters in a hypercholesterolemic population, and to determine if an optimal range of calorie restriction exists for effectively enhancing blood lipid profiles. A 4-week intervention study combined exercise and calorie restriction for 64 patients aged 18-60 with secondary hypercholesterolemia. Ultimately, 43 participants completed the study. The dietary intervention adhered to the principles of a balanced diet, with meal plans designed to provide three meals per day for the duration of the study. Each subject's daily calorie intake was set to match their individual resting energy expenditure (REE) plus varying proportions of physical activity (PA) calories. Participants were divided into four groups based on these proportions: REE only, REE + PA33%, REE + PA67%, and REE + PA100%. FATmax exercises were conducted 5 times per week, lasting 1 h each. 1) Compared with baseline, subjects' body weight, fat mass and body fat rate decreased significantly; fat-free mass also decreased significantly in the REE, REE + PA33%, and REE + PA67% groups. 2) Subjects' serum TC decreased significantly; serum LDL-C and ApoB decreased significantly in the REE, REE + PA33%, and REE + PA67% groups; there were no significant changes in serum HDL-C and ApoA1. 3) Serum PCSK9 was significantly decreased in the REE and the REE + PA 67% groups; serum LDLR was significantly decreased in all groups of subjects. 4) Between the groups, the rate of change in serum LDL-C was significantly different. FATmax exercise combined with proper proportions of calorie restriction can significantly decrease serum cholesterol levels and fat mass in hypercholesterolemic patients. Nevertheless, it is misleading to assume that a drastic reduction in calorie intake invariably results in superior outcomes. Optimal cost-effectiveness may be achieved within a calorie restriction range of REE + PA33-67%. Show less
📄 PDF DOI: 10.3389/fphys.2025.1510949
APOB
Yuanlong Hu, Xinhai Cui, Mengkai Lu +11 more · 2025 · Mayo Clinic proceedings · Elsevier · added 2026-04-24
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was p Show more
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was performed to investigate the causal effect of lipid-modifying drug targets on type 2 diabetes (T2D) outcomes and the proportion of this association that is mediated through ectopic fat accumulation traits. Specific sets of variants in or near genes that encode 11 lipid-modifying drug targets (LDLR, HMGCR, NPC1L1, PCSK9, APOB, ABCG5/ABCG8, LPL, PPARA, ANGPTL3, APOC3, and CETP; for expansion of gene symbols, use search tool at www.genenames.org) were extracted. Random effects inverse variance weighted were performed to evaluate the causal effects among outcomes. Mediation analyses were performed to identify the mediators of the association between lipid-modifying drugs and T2D. The study was conducted from November 10, 2023, to April 2, 2024 RESULTS: The genetic mimicry of HMGCR and APOB inhibition was associated with an increased T2D risk, whereas the genetic mimicry of LPL enhancement was linked to a lower T2D risk. Gluteofemoral adipose tissue volume was a mediator for explaining 9.52% (P=.002), 16.90% (P=.03), and 10.50% (P=.003) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Liver fat was a mediator for explaining 21.12% (P=.005), 12.28% (P=.03), and 9.84% (P=.005) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Our findings support the hypothesis that liver fat and gluteofemoral adipose tissue play a mediating role in the prodiabetic effects of HMGCR and APOB inhibition, as well as in the antidiabetic effects of LPL enhancement. Show less
no PDF DOI: 10.1016/j.mayocp.2024.10.018
APOB
Ze-Yuan Yin, Shi-Min He, Xin-Yuan Zhang +16 more · 2025 · Acta pharmacologica Sinica · Nature · added 2026-04-24
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagi Show more
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagic pathway has emerged as an avenue for cancer therapy. In the present study, we identify apolipoprotein B100 (ApoB100), a key modulator of lipid metabolism, as a potential prognostic biomarker of ovarian cancer. ApoB100 functioned as a tumor suppressor in ovarian cancer, and the knockdown of ApoB100 promoted ovarian cancer progression in vivo. Moreover, ApoB100 blocked autophagic flux, which was dependent on interfering with the lipid accumulation/endoplasmic reticulum (ER) stress axis. The effects of LFG-500, a novel synthetic flavonoid, on ApoB100 induction were confirmed using proteomics and lipidomics analyses. Herein, LFG-500 induced lipid accumulation and ER stress and subsequently blocked autophagy by upregulating ApoB100. Moreover, data from in vivo experiments further demonstrated that ApoB100, as well as the induction of the lipid/ER stress axis and subsequent blockade of autophagy, were responsible for the anti-tumor effects of LFG-500 on ovarian cancer. Hence, our findings support that ApoB100 is a feasible target of ovarian cancer associated with lipid-regulated autophagy and provide evidence for using LFG-500 for ovarian cancer treatment. Show less
no PDF DOI: 10.1038/s41401-024-01470-x
APOB
Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang +6 more · 2025 · Reviews in cardiovascular medicine · added 2026-04-24
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limi Show more
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso-logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients. In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making. Show less
📄 PDF DOI: 10.31083/RCM26102
APOB
Yuting Li, Mingrui Wang, Na Zhang +3 more · 2025 · Ginekologia polska · added 2026-04-24
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glu Show more
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT). A prospective cohort of 150 diet-controlled GDM patients and 150 pregnant women with NGT, all delivering at our hospital, were selected based on predefined criteria. Data on demographics, physical parameters, and perinatal outcomes were compiled. Blood samples for fasting plasma glucose (FPG), homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), and apolipoprotein A1 (apoA1) were collected before delivery. GDM patients exhibited higher levels of FPG, Hcy, and the apoB/apoA1 ratio, but lower HDL-C and apoA1 levels compared to the NGT group. Adverse outcomes such as macrosomia, premature rupture of membranes, and postpartum hemorrhage were more prevalent in the GDM group. In GDM patients, neonatal birth weight positively correlated with FPG and TG levels. Stratified Hcy analysis in GDM showed no significant differences in perinatal outcomes. However, the third quartile of the apoB/apoA1 ratio had a lower incidence of macrosomia compared to the first quartile, and the second quartile showed a higher incidence of birth asphyxia. GDM patients demonstrated increased levels of Hcy, FPG, and the apoB/apoA1 ratio, correlating with more adverse perinatal outcomes than healthy pregnant individuals. The relationships between Hcy, lipids, and these outcomes remain inconclusive, highlighting the need for further research. Show less
no PDF DOI: 10.5603/gpl.101475
APOB
Ping Huang, Yong Zhao, Haiyan Wei +8 more · 2025 · International journal of chronic obstructive pulmonary disease · added 2026-04-24
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian random Show more
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian randomization (MR) analysis to investigate the potential causal connection between blood lipids and COPD. A genome-wide association study (GWAS) on COPD was conducted, encompassing a total of 112,583 European participants from the MRC-IEU. Additionally, extensive UK Biobank data pertaining to blood lipid profiles within European cohorts included measurements for low-density lipoprotein cholesterol (LDL-C) with 440,546 individuals, high-density lipoprotein cholesterol (HDL-C) with 403,943 individuals, triglycerides (TG) with 441,016 individuals, total cholesterol (TC) with 187,365 individuals, apolipoprotein A-I (apoA-I) with 393,193 individuals, and apolipoprotein B (apoB) with 439,214 individuals. Then, MR analyses were performed for lipids and COPD, respectively. The primary analytical technique employed was the inverse-variance weighted (IVW) approach, which included a 95% confidence interval (CI) to calculate the odds ratio (OR). Additionally, a sensitivity analysis was conducted to assess the dependability of the MR analysis outcomes. MR analysis was primarily based on IVW, unveiled a causal link between COPD and LDL-C (OR=0.994, 95% CI (0.989, 0.999), P=0.019), TG (OR=1.005, 95% CI (1.002, 1.009), P=0.006), and apoA-I (OR=0.995, 95% CI (0.992, 0.999), P=0.008), in addition, no causal link was found with HDL-C, TC, apoB. Sensitivity analysis demonstrated the robustness of these causal relationships. However, through multivariate MR(MVMR) and multiple testing correction, LDL-C and TG had no causal effect on the outcome. ApoA-I remained a protective factor for the risk of COPD (OR=0.994, 95% CI (0.990-0.999), P=0.008). Through MR analysis, this study offers evidence of a causal link between apoA-I with COPD. This further substantiates the potential role of lipid metabolism in COPD, and has significant clinical implications for the prevention and management of COPD. Show less
📄 PDF DOI: 10.2147/COPD.S476833
APOB
Shuqi Cao, Xia Fu, Wenjing Li +3 more · 2025 · Parkinsonism & related disorders · Elsevier · added 2026-04-24
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipopr Show more
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipoproteins in NDDs is currently lacking. Using Cox proportional hazards regression analysis based on data from UK Biobank, we examined the association between baseline serum levels of apolipoprotein A (ApoA) and apolipoprotein B (ApoB) and risk of Parkinson's disease (PD), Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and multiple sclerosis. Elevated baseline levels of serum ApoA (HR = 0.84, 95 % CI: 0.71-0.99, P = 0.047) and ApoB (HR = 0.67, 95 % CI: 0.57-0.78, P = 3.18E-07) were associated with a reduced risk of incident PD. Subgroup analyses suggested the protective effect of serum ApoA was more significant for older participants and those with lower alcohol consumption, while higher serum ApoB was a more significant protective factor in males and those without stroke. No significant associations were found between apolipoproteins and other NDDs. Increased baseline levels of serum ApoA and ApoB are linked to a lower risk of PD. These findings enhance understanding of the role of apolipoproteins in PD, and have implications for the development of therapeutic strategies in clinical trials. Show less
no PDF DOI: 10.1016/j.parkreldis.2025.107266
APOB
Yong Tan, Zixiong Zhang, Jinru Yang +8 more · 2025 · Ecotoxicology and environmental safety · Elsevier · added 2026-04-24
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nona Show more
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nonalcoholic fatty liver (NAFLD) remains unclear. We used serum untargeted metabolomics analysis to evaluate whether high-Se exposure is cross-sectionally associated with lipid metabolism in adults from high-Se exposure area (n = 112) and control area (n = 101) in Hubei Province, China. An untargeted liquid chromatography/mass spectrometry (LC/MS)-based metabolomic analysis identified 144 differential pathways and yielded 204 differentially abundant metabolites, including 32 lipid metabolites associated with lipids profiles. To further explore the correlation between Se exposure and serum lipid metabolism, we measured serum levels of lipid profiles among all the people, including serum cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (APOB). The average serum Se level of the high-Se exposure group was 537.18 μg/L, significantly higher than 72.98 μg/L in the control group (p < 0.0001). The measurement levels of serum TG, LDL-C, HDL-C, and APOB in the high-Se exposure group were 1.03 (0.76, 1.34) mmol/L, 2.25 ± 0.48 mmol/L, 1.12 ± 0.24 mmol/L, and 0.77 ± 0.15 g/L, respectively, while the control group were 1.13 (0.84, 1.80) mmol/L, 2.56 ± 0.61 mmol/L, 1.02 ± 0.22 mmol/L, and 0.83 ± 0.16 g/L, respectively (all p values <0.05). Correlation analysis showed a significant negative correlation between serum Se and CHOL (r = -0.201, p < 0.01), serum Se is also associated with metabolomics markers, the negative correlation includes glyceric acid and ect., the positive correlation includes phosphorylcholine and ect. Our study suggests that high-Se exposure is negatively associated with serum lipid profiles and decreases the risk of high-TC and HDL-C dyslipidemia. Show less
no PDF DOI: 10.1016/j.ecoenv.2025.117677
APOB
Xiangyong Kong, Yanchen Cai, Yuwei Li +1 more · 2025 · Health information science and systems · Springer · added 2026-04-24
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and i Show more
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and in the optimal LDL-C scenario, apolipoprotein B (ApoB) has a more predictive value of ASCVD risk. The study is a genome-wide association study (GWAS) based on a European population. A large GWAS dataset for atherosclerotic cardiovascular diseases was targeted, including coronary heart disease (CHD), ischemic stroke (IS), large-artery atherosclerotic stroke (ISL), small-vessel stroke (ISS), and myocardial infarction (MI). Univariate two-sample mendelian randomization (MR) analyses of ApoB and the above cardiovascular diseases were performed separately, and the association was assessed mainly using the inverse variance weighted (IVW) method, with confidence intervals for the superiority ratios set at 95%. In addition, the experiment was supplemented using MR-Egger, weighted model and weighted median (WM). Based on the results of univariate two-sample mendelian randomisation analysis, it was shown that there was a causal relationship between ApoB and CHD (OR = 1.710, 95% CI 1.529-1.912, P = 0.010), ISL (OR = 1.430, 95% CI 1.231-1.661, P = 2.714E-06), ISS (OR = 1.221, 95% CI 1.062-1.405, P = 0.005) were causally related to each other and the disease prevalence ratio was positively correlated with ApoB concentration. This MR analysis demonstrated a causal relationship between ApoB and CHD, ISL, ISS, but not with the risk of developing IS and MI, which further validated the relationship between ApoB and the risk of ASCVD, and contributed to a better understanding of the genetic impact of ApoB on ASCVD, and to a certain extent, could improve the management of ApoB and reduce the prevalence of ASCVD. Show less
no PDF DOI: 10.1007/s13755-024-00323-5
APOB
Zhiling Cheng, Meiling Gao, Yang Liu +4 more · 2025 · Nutrition, metabolism, and cardiovascular diseases : NMCD · Elsevier · added 2026-04-24
To evaluate the efficacy and safety of inclisiran in the treatment of hypercholesterolemia. Randomized controlled trials comparing inclisiran with a placebo were searched until April 2024. Overall, 8 Show more
To evaluate the efficacy and safety of inclisiran in the treatment of hypercholesterolemia. Randomized controlled trials comparing inclisiran with a placebo were searched until April 2024. Overall, 8 studies involving 4947 patients were included. Inclisiran reduced low-density lipoprotein cholesterol (mean difference [MD]: -46.95 %; 95 % confidence interval [CI]: -53.26 to -40.46; P < 0.05), proprotein convertase subtilisin/kexin type 9 (MD: -70.80 %; 95 % CI: -76.52 to -65.08; P < 0.05), serum total cholesterol (MD: -29.47 %; 95 % CI: -32.56 to -26.39; P < 0.05), non-high-density lipoprotein cholesterol (MD: -40.46 %; 95 % CI: -45.24 to -35.68; P < 0.05), apolipoprotein B (MD: -36.77 %; 95 % CI: -40.94 to -32.61; P < 0.05), and lipoprotein(a) (MD: -20.04 %; 95 % CI: -24.2 to -15.87; P < 0.05) levels but increased high-density lipoprotein cholesterol level (MD: 6.09 %; 95 % CI: 3.63 to 8.55; P < 0.05). The incidences of adverse events, serious adverse events, headache, nasopharyngitis, and muscular adverse reactions were not significantly different between the inclisiran and placebo groups. Inclisiran reduced the incidence of cardiovascular adverse reactions (odds ratio [OR] = 0.79; 95 % CI: 0.65 to 0.96; P = 0.02) and increased the incidence of injection-site reactions (OR = 4.79; 95 % CI: 2.18 to 10.52; P < 0.05). Inclisiran is effective in treating hypercholesterolemia and has a good safety profile. Show less
no PDF DOI: 10.1016/j.numecd.2024.10.017
APOB
Chenxi Li, Xuhui Yang, Yan Zhong +4 more · 2025 · Translational oncology · Elsevier · added 2026-04-24
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis i Show more
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis in patients with pancreatic cancer. A retrospective study was performed on 286 pancreatic cancer patients who admitted to our hospital from January 1, 2017 to December 31, 2021. Serum lipids level were recorded. Clinical-pathological characteristics, oncologic outcomes, progression free survival (PFS) and overall survival (OS) were collected. The prognostic significance was determined by Kaplan-Meier analysis and Cox proportional hazards regression model. Regarding serum lipids level, compared to normal apolipoprotein B/ apolipoprotein A (ApoB/ApoA1), high ApoB/ApoA1 level indicated a shorter OS (HR:2.028, 95% CI: 1.174-2.504, P = 0.011) and a shorter PFS (HR:1.800, 95% CI: 1.076-3.009, P = 0.025). Other serum lipid molecules were not associated with PFS and OS. ApoB/ApoA1 might be an independent prognostic factor of pancreatic cancer. Show less
📄 PDF DOI: 10.1016/j.tranon.2024.102208
APOB