👤 Huazhen Liu

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3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

PNPLA3-I148M is a neomorph that interferes with two primary hepatic triglyceride clearance pathways.

David J Sherman, Lei Liu, Edward L LaGory +8 more · 2025 · Cell reports · Elsevier · added 2026-04-24
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cel Show more
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cells expressing endogenous I148M, we find that the variant impairs cellular secretion of apolipoprotein B (ApoB), the scaffolding protein of very-low-density lipoprotein (VLDL). This is not due to loss-of-function of wild-type PNPLA3. Expression of human I148M in primary hepatocytes and mice also hinders VLDL secretion. Lipidomic profiling reveals a shift from polyunsaturated phosphatidylcholine to polyunsaturated triglycerides in I148M cells, reducing membrane fluidity and, concomitantly, VLDL biogenesis. ApoB secretion is substantially rescued in I148M cells overexpressing ABHD5/CGI-58, an I148M-binding partner that normally activates ATGL/PNPLA2-mediated triglyceride lipolysis. Conversely, knocking down CGI-58 or PNPLA2 mimics I148M. We propose that I148M is a neomorph that exacerbates fatty liver risk by simultaneously impeding two major CGI-58-dependent pathways for liver triglyceride clearance: lipolysis and secretion. Show less
no PDF DOI: 10.1016/j.celrep.2025.116371
APOB

Low physical activity-related disease burden, 1990-2021: assessment of global trends and social determinants based on GBD 2021 data.

Hao Liu, Zhenhao Liu, Yanqing Gong +6 more · 2025 · Journal of global health · added 2026-04-24
Low physical activity (LPA) is associated with cardiovascular and cerebrovascular pathologies. This study aimed to assess the prevalence of several noncommunicable diseases relating to LPA. Using the Show more
Low physical activity (LPA) is associated with cardiovascular and cerebrovascular pathologies. This study aimed to assess the prevalence of several noncommunicable diseases relating to LPA. Using the 2021 Global Burden of Disease data set, we modelled LPA-related disease burdens across 204 countries and territories, quantifying mortality counts, age-standardised mortality rates, and disability-adjusted life years (DALYs) for five noncommunicable diseases. We conducted multivariable stratification analyses to assess variations by gender, age, and sociodemographic index (SDI) quintiles. We used age-period-cohort modelling to project burden trajectories, while applying counterfactual decomposition frameworks to delineate synergistic interactions between LPA and risk factors. We found that LPA accounted for 555 101 related deaths globally in 2021 across the five studied pathologies, mostly among individuals aged 60-94 years. Association between LPA-related disease burden and SDI followed a U-shaped distribution across regions and diseases. Among individuals aged 60-89 years, LPA-related deaths were significantly higher in women than in men, indicating a disproportionate burden on elderly females. Ischaemic heart disease (IHD) trends stabilised in low- and middle-SDI regions but declined significantly in high-SDI regions, underscoring global health disparities. From 2007 to 2011, LPA DALYs and mortality risk ratios for IHD, stroke, and lower extremity peripheral arterial disease declined from >1 to <1, whereas diabetes mellitus exhibited an opposite trend, highlighting LPA's persistent and significant impact on diabetes-related morbidity. Demographic shifts and epidemiological transitions were primary drivers of LPA-related disease burden across five pathologies. In high-SDI regions, epidemiological changes predominated, whereas population growth was a key factor in low- and middle-SDI regions. Synergistic interaction of these factors with LPA is projected to substantially amplify future disease burden. Physical activity should be increased among elderly women to address health risks associated with LPA. Likewise, urgent public health interventions are needed for LPA-related diabetes. As IHD burden rises in low- and middle-SDI regions, vascular disease care strategies require optimisation. Moreover, high-SDI regions should strengthen nationwide physical activity promotion, while low- and middle-SDI areas must enhance healthcare infrastructure and manage population growth to reduce LPA-related disease burdens. Show less
📄 PDF DOI: 10.7189/jogh.15.04314
LPA

Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.

Chengli Yu, Xin Huang, Yating Cao +7 more · 2025 · Frontiers in molecular biosciences · Frontiers · added 2026-04-24
Colorectal cancer (CRC) is one of the leading causes of cancer-related death, and most CRCs arise from colorectal adenomas. Early detection and removal of precancerous lesions during the adenoma-carci Show more
Colorectal cancer (CRC) is one of the leading causes of cancer-related death, and most CRCs arise from colorectal adenomas. Early detection and removal of precancerous lesions during the adenoma-carcinoma sequence can significantly reduce CRC risk. However, current clinical practice lacks rapid, noninvasive screening tools for reliable adenoma detection. Proteomic analysis was performed on serum samples from patients with inflammatory polyps (non-neoplastic), patients with adenomas, and healthy controls to identify key differentially expressed proteins capable of distinguishing adenoma patients. The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma. In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. These candidates were subsequently validated in a third cohort using ELISA. The ELISA results for APOA4 were discordant with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) findings. In contrast, FLNA levels measured by ELISA showed a progressive decrease from healthy controls to patients with inflammatory polyps and further to those with adenomas. We propose FLNA as a potential biomarker for the diagnosis of colorectal adenomas. The areas under the ROC curves exceeded 0.7 for both key clinical comparisons: 0.810 for adenomas versus healthy controls, and 0.734 for adenomas versus inflammatory polyps. Overall, this study not only enhances our understanding of the serum proteome in colorectal adenoma but also identifies FLNA as a promising biomarker for its clinical diagnosis. Show less
📄 PDF DOI: 10.3389/fmolb.2025.1628587
APOA4

Targeting the BCKDK/BCLAF1/MYC/HK2 axis to alter aerobic glycolysis and overcome Trametinib resistance in lung cancer.

Hao Wu, Jiajia Yang, Zixia Yang +8 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The protein branched-chain ketoacid dehydrogenase kinase (BCKDK), which regulates the metabolism of branched-chain amino acids, has recently been implicated in tumor progression. However, the role of Show more
The protein branched-chain ketoacid dehydrogenase kinase (BCKDK), which regulates the metabolism of branched-chain amino acids, has recently been implicated in tumor progression. However, the role of BCKDK in lung cancer remains largely unexplored. In this study, we explored the mechanisms by which BCKDK influences lung cancer progression and contributes to drug resistance. By integrating single-cell RNA and bulk RNA sequencing data from lung cancer patients, we identified BCKDK as a novel gene related to malignant epithelial cells, involved in tumor initiation and associated with poor patient prognosis. Subsequently, through a series of molecular biology experiments, we demonstrated that BCKDK promotes aerobic glycolysis, Trametinib resistance, and tumor progression in lung cancer by upregulating MYC transcription. Mechanistically, BCKDK interacts with BCLAF1 to promote its phosphorylation at the serine 285 site. This modification facilitates BCLAF1 binding to the MYC promoter, thereby enhancing MYC transcription. Subsequently, elevated MYC levels upregulate hexokinase 2, promoting aerobic glycolysis and lung cancer progression. In addition, the elevated glycolysis product, lactate, promotes Trametinib resistance by upregulating the ABC transporters. Taken together, our data identify BCKDK as a novel regulator of aerobic glycolysis that promotes lung cancer progression and Trametinib resistance through the BCKDK/BCLAF1/MYC/HK2 axis. Targeting BCKDK in combination with Trametinib may offer a promising treatment for lung cancer. Graphical representation of the BCKDK/BCLAF1/MYC/HK2 axis and its role in Trametinib resistance and lung cancer progression. Created with BioRender.com. Show less
no PDF DOI: 10.1038/s41418-025-01531-6
BCKDK

Identifying novel drug targets for calcific aortic valve disease through Mendelian randomization.

Dilin Xu, Jin Lu, Yanfang Yang +11 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Calcific aortic valve disease (CAVD) is characterized by progressive leaflet thickening and calcification, with no available pharmacological treatments. Plasma proteins play a pivotal role in disease Show more
Calcific aortic valve disease (CAVD) is characterized by progressive leaflet thickening and calcification, with no available pharmacological treatments. Plasma proteins play a pivotal role in disease regulation. This study aimed to uncover novel therapeutic targets for CAVD using Mendelian randomization (MR) integrated with transcriptomic analysis. Protein quantitative trait loci (pQTL) from the deCODE and UK Biobank Pharma Proteomics Project (UKB-PPP) plasma protein databases were used as exposure data. The FinnGen cohort (9870 cases, 402,311 controls) served as the discovery set, while the TARGET cohort (13,765 cases, 640,102 controls) provided validation. MR and summary data-based Mendelian randomization (SMR) were employed to screen for potential causal targets of CAVD. Colocalization analysis was conducted to assess whether CAVD and target proteins shared common causal SNPs. Additional analyses included trancriptomic profiling at multiple RNA levels. Protein-level validation was conducted via Western blot and immunostaining. Six proteins (ANGPTL4, PCSK9, ITGAV, CTSB, GNPTG, and FURIN) with strong genetic colocalization were identified by MR and SMR analysis. Among these, cellular trancriptomic analysis revealed ANGPTL4 and ITGAV with significantly greater expression in osteogenic group, which was further validated in calcified aortic valves and osteogenic valvular interstitial cells in protein level. This study identified six causal proteins with strong genetic colocalization for CAVD, with ANGPTL4 and ITGAV emerging as the most promising targets for further investigation. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119110
ANGPTL4

Gut microbiota may affect Alzheimer's disease through synaptic function mediated by CAMs pathway: A study combining Mendelian randomization and bioinformatics.

Ji-Yun Liu, Cong-Yan Tan, Li Luo +1 more · 2025 · Journal of Alzheimer's disease reports · SAGE Publications · added 2026-04-24
The association between gut microbes and Alzheimer's disease (AD) has not been entirely elucidated. We aimed to demonstrate the association between gut microbes and AD and to further investigate the p Show more
The association between gut microbes and Alzheimer's disease (AD) has not been entirely elucidated. We aimed to demonstrate the association between gut microbes and AD and to further investigate the pathogenesis of microbes with a causal relationship to AD. Mendelian randomization analyses were used to determine the significant causal relationship between gut microbes and AD. Protein-protein interaction (PPI) network was used to identify the hub genes. Functional enrichment analysis was used to reveal the pathogenesis theoretically between gut microbes and AD. In the present study, a total of 32 microbes were identified that were significantly associated with AD. Subsequently, DLGAP2, NRXN3, NEGR1, NTNAP2, MYH9, and SCN3A were identified as hub genes. The genes NRXN3, NEGR1, and NTNAP2 were enriched in the cell adhesion molecules (CAMs) signaling, and the taxons of gut microbes that corresponded to these were Show less
no PDF DOI: 10.1177/25424823241310719
NRXN3

TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter.

Rongjia Wang, Xunde Dong, Xiuling Liu +5 more · 2025 · Computer methods and programs in biomedicine · Elsevier · added 2026-04-24
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving e Show more
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving encouraging results. However, many existing methods are limited to task-specific model training and require retraining or full fine-tuning when confronted with a new ECG classification task, thus lacking flexibility in clinical applications. In this study, we propose a Task-Adaptive Classification method for ECG (TAC-ECG) based on cross-modal contrastive learning and low-rank convolutional adapters. TAC-ECG comprises two main phases. In the first phase, inspired by the Contrastive Language-Image Pre-training, we design the Contrastive ECG-Text Pre-training (CETP) to pre-train a robust ECG encoder. In the second phase, the pre-trained ECG encoder is frozen and integrated with a lightweight plug-in, the Low-Rank Convolutional Adapter (LRC-Adapter), forming an extensible ECG classification model. The frozen encoder extracts more discriminative features from the ECG signal, while the LRC-Adapter enables task-specific adaptation. For diverse ECG classification tasks, TAC-ECG only requires training the LRC-Adapter. This mechanism enables TAC-ECG to efficiently perform different ECG classification tasks, significantly reducing resource consumption and deployment costs in multi-tasking scenarios compared to traditional fully fine-tuned methods. We conducted extensive experiments using six different network architectures as ECG encoders. Specifically, we performed ECG classification experiments on four datasets: CPSC2018, Cinc2017, PTB-XL, and Chapman, targeting 9-category, 3-category, 5-category, and 4-category classifications respectively. The TAC-ECG achieved highly competitive results using only approximately 3% of the trainable parameters and approximately 25% of the total parameters compared to the fully fine-tuned method. These results demonstrates the effectiveness and practicality of the TAC-ECG method. The TAC-ECG offers a flexible and efficient method for ECG classification, enabling rapid adaptation to diverse tasks and enhancing clinical diagnostic practicality. Show less
no PDF DOI: 10.1016/j.cmpb.2025.108918
CETP

ERK1/2 Inhibition Alleviates Diabetic Cardiomyopathy by Suppressing Fatty Acid Metabolism.

Erin McLean, Caroline De Roo, Annabel Maag +3 more · 2025 · Frontiers in bioscience (Landmark edition) · added 2026-04-24
Diabetes mellitus is associated with morphological and functional impairment of the heart primarily due to lipid toxicity caused by increased fatty acid metabolism. Extracellular signal-regulated prot Show more
Diabetes mellitus is associated with morphological and functional impairment of the heart primarily due to lipid toxicity caused by increased fatty acid metabolism. Extracellular signal-regulated protein kinases 1 and 2 (ERK1/2) have been implicated in the metabolism of fatty acids in the liver and skeletal muscles. However, their role in the heart in diabetes remains unclear. In this study, we tested our hypothesis that pharmacological inhibition of ERK1/2 alleviates cardiac remodeling in diabetic mice through a reduction in fatty acid metabolism. ERK1/2 phosphorylation in diabetes was determined both ERK1/2 phosphorylation was significantly increased in diabetic conditions. Inhibition of ERK1/2 by U0126 in both streptozotocin-induced diabetic mice and ERK1/2 are potential therapeutic targets for diabetic cardiomyopathy by modulating fatty acid metabolism in the heart. Show less
no PDF DOI: 10.31083/FBL26700
DUSP6

Dietary Supplementation with Whole-Fat or Defatted Antarctic Krill Powder Improves the Growth Performance, Body Coloration, and Immune Capability of Red-White Koi Carp (

Hongmei Song, Yixin Liang, Yexin Yang +4 more · 2025 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
This study was conducted to investigate the effects of replacing fish meal with either whole-fat or defatted krill powder on the growth, body color, immunity, and related gene expression of red-white Show more
This study was conducted to investigate the effects of replacing fish meal with either whole-fat or defatted krill powder on the growth, body color, immunity, and related gene expression of red-white koi carp. A total of 630 red-white koi carp with an initial body mass of 13.5 ± 0.05 g were randomly divided into seven groups with three replicates per group and 30 fish per replicate. The control group was fed a basic diet (C0). The other six diets were supplemented with different levels of whole krill meal or defatted krill meal as replacements (10% whole fat, 20% whole fat, 30% whole fat, 10% defatted, 20% defatted, and 30% defatted) in the experimental groups, named W10, W20, W30, D10, D20, and D30, respectively, for a total duration of 60 days. The growth, body color, immunity and gene expression indexes were measured in the koi after completion. The results indicate the following. (1) Compared with C0, the experimental groups of koi showed a significant increase in the specific growth rate (SGR) ( Show less
📄 PDF DOI: 10.3390/ani15111561
LPL

Emerging roles of angiopoietin‑like 4 in human tumors (Review).

Ruyi Liu, Miaomiao Fu, Pengxiang Chen +6 more · 2025 · International journal of oncology · added 2026-04-24
Angiopoietin‑like 4 (ANGPTL4), a member of the angiopoietin family, plays critical roles in angiogenesis, lipid metabolism and inflammation. It has been demonstrated that ANGPTL4 has significant influ Show more
Angiopoietin‑like 4 (ANGPTL4), a member of the angiopoietin family, plays critical roles in angiogenesis, lipid metabolism and inflammation. It has been demonstrated that ANGPTL4 has significant influence on various diseases. Accumulating evidence has highlighted the impacts of ANGPTL4 on human malignancies. ANGPTL4 is commonly overexpressed in various types of cancer, such as breast, non‑small cell lung, gastric and colorectal cancer. Its upregulation promotes tumor growth, invasion, metastasis and angiogenesis, as well as metabolic reprogramming and resistance to programmed cell death, radiotherapy and chemotherapy. However, ANGPTL4 has also exhibited antitumor effects under certain conditions, indicating its complex roles in tumor biology. The transcriptional regulation of ANGPTL4 is influenced by multiple factors, such as HIF‑1, PPARs, TGF‑β and long non‑coding RNAs. In terms of signaling pathways, STATs, PI3K/AKT and COX-2/PGE2 are important in regulating cellular processes. The present review summarizes the biological functions of ANGPTL4 in tumors and its association with patient prognosis. Furthermore, the key molecular mechanisms and potential reasons for its dual roles in cancer are also discussed. In conclusion, ANGPTL4 is a valuable diagnostic biomarker and a potential therapeutic target for human cancers. Show less
📄 PDF DOI: 10.3892/ijo.2024.5715
ANGPTL4

First report of B-lymphoblastic leukemia harboring LRRFIP1::FGFR1 fusion: expanding the clinical spectrum of myeloid/lymphoid neoplasms with tyrosine kinase gene fusions.

Xue Chen, Yuyue Ren, Yinglan Jin +7 more · 2025 · Annals of hematology · Springer · added 2026-04-24
Myeloid/lymphoid neoplasms with eosinophilia and tyrosine kinase gene fusions (MLN-TK) are rare hematologic malignancies defined by recurrent kinase gene rearrangements.
📄 PDF DOI: 10.1007/s00277-025-06481-0
FGFR1

TMEM106A mediates atherosclerosis progression through macrophage-centered immune responses and chemokine signaling.

Menglong Gao, Xingbang Liu, Zhen Fang +5 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identify Show more
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identifying key mediators in its pathogenesis is critical for improving diagnostics and therapies. This study focuses on Transmembrane Protein 106A (TMEM106A) to clarify its role and clinical relevance in AS progression. Public transcriptomic datasets (GSE43292, GSE100927, GSE28829) were analyzed to assess TMEM106A expression and diagnostic value; single-cell RNA-seq data (GSE159677) defined its cellular localization. Immune infiltration (ssGSEA, Cibersort, xCell) and CellChat (intercellular communication) analyses explored its immune associations. TMEM106A was significantly upregulated in AS samples across datasets, with strong diagnostic efficacy (AUC 0.80-0.95). Single-cell analysis confirmed its specific enrichment in macrophages, with functional links to immune-related pathways. TMEM106A promoted macrophage infiltration, foam cell formation, oxidative stress, and inflammatory responses, while regulating PLCB2 in chemokine signaling; silencing TMEM106A alleviated these pro-atherosclerotic effects. TMEM106A contributes to AS progression by modulating macrophage-mediated immune responses and chemokine signaling, as validated in experimental models. These findings support its potential as a clinically relevant biomarker and promising therapeutic target for AS intervention. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1681645
APOE

Sleep Deprivation Induces Anxiety-like Behaviors in Mice by Impairing ApoE/AMPK/mTOR-Mediated Autophagy.

Xianbing Bai, Hongmei Du, Xiangxuan Liu +9 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Sleep Deprivation (SD) severely disrupts emotional regulation, predisposing individuals to mood disturbances and anxiety. However, the precise mechanisms underlying anxiety triggered by sleep loss rem Show more
Sleep Deprivation (SD) severely disrupts emotional regulation, predisposing individuals to mood disturbances and anxiety. However, the precise mechanisms underlying anxiety triggered by sleep loss remain elusive. In this study, a mouse model of chronic SD was established using a continuously running treadmill paradigm for 28 days. SD induced anxiety-like behaviors and hippocampal ApoE downregulation. Furthermore, SD downregulated the expression of the autophagy-related protein ATG5 and upregulated p62. In addition, SD inhibited AMPK phosphorylation and induced mTOR phosphorylation. Levels of pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-18, were markedly increased. Immunofluorescence staining revealed a notable increase in the activation of microglia and astrocytes in the hippocampi of SD mice. Either hippocampal overexpression of ApoE via bilateral AAV injection or rapamycin treatment significantly alleviated anxiety-like behaviors, enhanced autophagy, and reduced neuroinflammation in SD mice. Thus, SD induces anxiety by suppressing autophagy level. This effect is mediated through the inhibition of ApoE-dependent AMPK phosphorylation and the concomitant promotion of mTOR phosphorylation, revealing a potential therapeutic target. Show less
no PDF DOI: 10.1007/s12035-025-05610-0
APOE

Integrated network pharmacology and metabolomics analysis to reveal the potential mechanism of Ershen Wan in ameliorating ulcerative colitis.

Mingyang Wang, Meiyu Wan, Meijuan Liu +7 more · 2025 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
Ershen Wan (ESW), a classic traditional Chinese medicine (TCM) prescription composed of Psoralea corylifolia Linn. and Myristica fragrans Houtt., has been applied to treat gastrointestinal disorders i Show more
Ershen Wan (ESW), a classic traditional Chinese medicine (TCM) prescription composed of Psoralea corylifolia Linn. and Myristica fragrans Houtt., has been applied to treat gastrointestinal disorders in clinical practices for thousands of years. However, its potential molecular mechanism in alleviating ulcerative colitis (UC) remains to be elusive. The purpose of the study is to explore the underlying mechanism of ESW in treating UC. The protective effect of ESW on dextran sodium sulfate (DSS)-induced UC mice was assessed by body weight, disease activity index (DAI), colon length, colon tissue pathology, and colonic inflammatory factors. Furthermore, network pharmacology was applied to dissect the possible targets and biological pathways regulated by ESW. The plasma and fecal metabolomics were comprehensively analyzed by UPLC-Q-TOF/MS. Subsequently, an efficient and feasible approach integrating network pharmacology, metabolomics, and molecular docking was used to explore the key targets obtained from the metabolite-reaction-enzyme-gene network. And the effect of ESW on the MAPK signaling mediated intestinal epithelial cell apoptosis was further investigated by in vitro and in vivo experiments. ESW could notably alleviate colon injury and inflammation of UC mice. Network pharmacology suggested that the bioactive components of ESW could mainly modulate signaling pathways associated with inflammation and metabolism. Consistently, plasma and fecal metabolomics further indicated that ESW could regulate the metabolic pathways of arachidonic acid, linoleic acid, sphingolipid, tryptophan, and glycerophospholipid. And the combined analysis of network pharmacology and metabolomics revealed that 14 pivotal targets were modulated by ESW, including PTGS1, PTGS2, CYP1A1, FADS1, CBR1, ALOX5, EPHX1, EPHX2, HPGD, PLA2G1B, PLA2G7, MGLL, ACHE, and SPHK1. Additionally, molecular docking suggested that bioactive components of ESW could bind well to these potential targets. And in vitro and in vivo experiments further verified that ESW could markedly ameliorate pathological symptoms of UC mice through inhibiting MAPK signaling mediated colonic epithelial cell apoptosis. Collectively, these findings indicated that ESW could effectively alleviate the pathological symptoms of UC mice, mainly involving in the modulation of lipid and amino acid metabolism pathways, and the suppression of MAPK signaling-mediated apoptosis. In this study, the potential mechanism of ESW for the treatment of UC was first clarified, which provided a solid scientific foundation for its clinical application. Notably, the proposed strategy facilitated a comprehensive prediction and validation of the efficacy and molecular mechanism of TCMs, and also provided a novel approach for revealing the intricate biological pathogenesis of diseases. Show less
no PDF DOI: 10.1016/j.jep.2025.119690
FADS1

Identification of diagnostic and prognostic biomarkers for tuberculosis based on plasma proteomics.

Yan Hu, Chao Quan, Yuanyuan Zhou +6 more · 2025 · PloS one · PLOS · added 2026-04-24
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic method Show more
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic methods. Furthermore, distinguishing between Active Tuberculosis (ATB) and Latent Tuberculosis Infection (LTBI) poses significant challenges. This study aims to develop a molecular differentiation system for ATB, LTBI, and NTM by integrating plasma proteomics with multi-dimensional analytical techniques, while also exploring key biomarkers associated with disease progression and treatment response. Using label-free quantitative technology, we conducted a plasma proteomics analysis across five groups: ATB, LTBI, NTM, Cured Patients (CPs), and Healthy Donors (HD). Differentially Expressed Proteins (DEPs) were identified through screening (FC > 1.5 or <0.67, P < 0.05), followed by Gene Ontology/KEGG pathway enrichment, STRING interaction network, and Mfuzz dynamic clustering analysis to systematically elucidate molecular characteristics. Experimental data were validated through a multidimensional quality control system (Pearson correlation coefficient, peptide distribution, molecular weight distribution, etc.). Enzyme-linked immunosorbent assay (ELISA) was employed to detect the plasma expression levels of target proteins across the groups and to facilitate comparisons. This study identified 1,338 non-redundant proteins across five cohorts. Comparative analysis revealed 142 DEPs across the three comparative groups (ATB, LTBI, and NTM), which were primarily localized in the extracellular domain. Key findings include: 27 DEPs in the ATB-LTBI group, primarily enriched in inflammatory responses (such as A2M, IL-1R2) and epithelial barrier functions (TGM3, KRT3); 69 DEPs in the ATB-NTM group, characterized by significant changes in immunoglobulin light chains (IGLV2-11) and innate immune effector molecules (S100A8); 46 DEPs in the NTM-LTBI group, closely related to lipid metabolism (APOC3) and extracellular matrix remodeling (FN1). KEGG pathway analysis revealed that DEPs in the ATB-LTBI group were enriched in nitrogen metabolism pathways, those in the ATB-NTM group were associated with thyroid hormone synthesis, and the NTM-LTBI group was involved in phagosome function. Dynamic clustering results showed six treatment response modules: Cluster 1/2 (riboflavin metabolism, complement coagulation pathway) were activated post-treatment, Cluster 3/4 (proteasome, cardiac signaling pathway) exhibited partial reversal in expression, and Cluster 5/6 (platelet activation, cytoskeleton) showed delayed regression. Research confirmed 10 differential proteins between the ATB-CPs and ATB-HD groups, including S100A8, LTA4H, and DEFA1B, which constitute a molecular fingerprint specific to ATB. ELISA validation confirmed significantly elevated S100A8 and GPX3 in ATB group, while NTM group showed higher FGB and lower ATRN levels. This study systematically reveals the plasma proteomic characteristics under infection statuses caused by different mycobacteria. A discrimination framework for ATB/LTBI/NTM was constructed based on disease-specific differential proteins, overcoming the limitations of traditional diagnostic techniques in distinguishing infection states. Through dynamic analysis of six temporal therapeutic modules, the reprogramming patterns of the host protein network during tuberculosis treatment were elucidated. This research lays a multidimensional molecular foundation for the precise typing, personalized treatment, and prognostic evaluation of mycobacterial infections. Show less
📄 PDF DOI: 10.1371/journal.pone.0339558
APOC3

Apolipoprotein A1 and Lipoprotein(a) as Biomarkers for the "Penumbra Freezing" in Acute Ischemic Stroke: Insights From a Case-Control and Mendelian Randomization Study.

Jianyu Liu, Zhiyao Xu, Yang Wen +5 more · 2025 · Current medicinal chemistry · Bentham Science · added 2026-04-24
"Penumbra freezing" aims to extend vascular recanalization treatment to acute ischemic stroke (AIS) patients beyond the standard time window by preserving the ischemic penumbra. Efficient biomarkers a Show more
"Penumbra freezing" aims to extend vascular recanalization treatment to acute ischemic stroke (AIS) patients beyond the standard time window by preserving the ischemic penumbra. Efficient biomarkers are crucial for identifying patients eligible for AIS treatment. This study enrolled 141 AIS patients who exceeded the conventional treatment window. Using CT perfusion imaging, patients were categorized into "penumbra freezing" and "non-penumbra freezing" groups based on the EXTEND criteria. Multiple regression analysis assessed the association of nine baseline factors and five blood lipid indicators with "penumbra freezing." Diagnostic accuracy was evaluated using ROC curves. Mendelian randomization (MR) analysis validated these findings using blood lipid indicators as exposures and penumbra biomarkers as outcomes. Among AIS patients beyond the treatment window, males exhibited better penumbra preservation (OR=0.243, 95% CI=0.072-0.813, p=0.022), while those with hyperlipidemia showed poorer preservation (OR=2.429, 95% CI=1.027-7.747, p=0.043). In the "penumbra freezing" group, ApoA1 levels were significantly lower (1.29 ± 0.03 g/L) compared to the "non-penumbra freezing" group (1.42 ± 0.06 g/L, p=0.034). Conversely, Lp(a) levels were significantly higher in the "penumbra freezing" group (304.63 ± 52.44 mg/L) than in the "non-penumbra freezing" group (110.26 ± 40.71 mg/L, p=0.034). Higher ApoA1 levels increased the likelihood of "non-penumbra freezing" beyond the time window (OR=3.206, 95% CI=1.034-9.938, p=0.044), while elevated Lp(a) levels reduced this likelihood (OR=0.075, 95% CI=0.007-0.848, p=0.036). MR analysis confirmed genetic associations of ApoA1 and Lp(a) with penumbra biomarkers. ApoA1 and Lp(a) may be linked to ischemic penumbra status, but further validation is needed due to limitations in sample size and study methodology. ApoA1 and Lp(a) are promising biomarkers for identifying AIS patients eligible for "penumbra freezing," suggesting the potential to extend the treatment window. Show less
no PDF DOI: 10.2174/0109298673374444250901100551
LPA

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Fanqi Liang, Man Zheng, Jingjiu Lu +2 more · 2025 · Scientific reports · Nature · added 2026-04-24
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminatin Show more
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets. Show less
📄 PDF DOI: 10.1038/s41598-024-82998-0
ADCY3

Genetics of urinary bladder paragangliomas: a multi-center study of a Chinese cohort.

Jingjing Jiang, Yingxian Pang, Rongkui Luo +24 more · 2025 · Journal of endocrinological investigation · Springer · added 2026-04-24
Pheochromocytomas and paragangliomas (PPGLs) exhibit the highest degree of heritability among all human tumors, yet the genetics of urinary bladder paragangliomas (UBPGLs) remains poorly understood. T Show more
Pheochromocytomas and paragangliomas (PPGLs) exhibit the highest degree of heritability among all human tumors, yet the genetics of urinary bladder paragangliomas (UBPGLs) remains poorly understood. The present study aims to examine the characteristics of a cohort of Chinese patients with UBPGLs, focusing particularly on genetics. The study included 70 Chinese patients with UBPGLs from 15 centers in China, 240 patients with non-head and neck PGLs (non-HNPGLs) outside the urine bladder, and 16 Caucasian patients with UBPGLs. Tumor DNA samples were sequenced by next generation sequencing. All identified pathogenic variants (PVs) were confirmed by Sanger sequencing. Among the 70 Chinese patients, PVs were identified in 38 cases: 23 in cluster 1 A (13 SDHB, 1 SDHD, 1 SDHA, 4 IDH1, 2 SLC25A11, and 2 FH), 4 in cluster 1B (3 EPAS1 and 1 EGLN1), and 11 in cluster 2 genes (7 HRAS, 1 FGFR1, 2 NF1, and 1 H3F3A). Compared with other non-HNPGLs, UBPGLs had more PVs in cluster 1 A genes (32.9% vs. 14.2%, p < 0.001), but fewer in cluster 1B (5.7% vs. 19.2%, p = 0.002) and cluster 2 genes (15.7% vs. 42.5%, p < 0.001). PVs in SDHB (18.6%) was the most common in Chinese patients with UBPGLs, followed by HRAS (10.0%). No PVs was found in 45.7% of all UBPGLs. PVs in HRAS, SLC25A11, EPAS1, and FH were also identified in Caucasians with UBPGLs. Chinese patients with UBPGLs have a diverse genetic profile. PVs in cluster 1 A genes underlie nearly 1/3 of patients, highlighting the importance of genetic testing. Diverse germline and somatic PVs are also present in Caucasian patients with UBPGLs. Show less
📄 PDF DOI: 10.1007/s40618-024-02509-w
FGFR1

Association between dyslipidemia and neovascular age-related macular degeneration: a case-control study.

Jinghong Yao, Yan Liu, Jiusheng Zheng +2 more · 2025 · International journal of clinical and experimental pathology · added 2026-04-24
Neovascular age-related macular degeneration (nAMD) is an advanced stage of AMD and is associated with an increased risk of visual impairment. Disturbances in lipid metabolism have been proposed as a Show more
Neovascular age-related macular degeneration (nAMD) is an advanced stage of AMD and is associated with an increased risk of visual impairment. Disturbances in lipid metabolism have been proposed as a major contributing factor to the pathogenesis of AMD. This study aims to investigate whether lipid profiles in the serum and components of dyslipidemia can be used as indicators for predicting progression to nAMD. A retrospective analysis was conducted involving 125 participants with nAMD. 125 non-AMD controls, matched by age, sex, and BMI, were incorporated into the study. The comparative analysis between the groups involved six lipid biomarkers in the serum: HDL-C, LDL-C TG, TC, ApoA1, and ApoB. Moreover, the existence of dyslipidemia and its constituents was assessed through t-tests, as well as univariate and multivariable logistic regression models. Individuals with nAMD exhibited significantly higher serum HDL-C (P = 0.02) compared to the controls without AMD. Furthermore, the concentrations of ApoB were significantly less in the nAMD cohort (P < 0.01) when compared to the control group. During the investigation of the correlation between levels of serum HDL-C (P < 0.01) and serum ApoB (P < 0.01) with nAMD through logistic regression analysis, notable findings indicated a significant association between both variables and nAMD. However, by multivariate logistic regression analysis, neither serum HDL-C nor serum ApoB was an independent risk factor for nAMD. While individuals with nAMD demonstrated elevated serum HDL-C and reduced serum ApoB levels, these lipid markers may not be suitable as biomarkers for monitoring or preventing nAMD. Show less
no PDF DOI: 10.62347/QJPQ2923
APOB

Associations among melanocortin-4 receptor rs17782313 gene polymorphisms, 12-week aerobic exercise combined with low-fat diet and degree of reduction in blood lipids in adults with obesity.

Dong Bai, Xiaoyan Hao, Fei Wang +4 more · 2025 · Postgraduate medicine · Taylor & Francis · added 2026-04-24
This study investigates the relationships between melanocortin-4 receptor (MC4R) rs17782313 gene polymorphisms, low-fat diet, aerobic exercise, and the reduction in blood lipid levels in individuals w Show more
This study investigates the relationships between melanocortin-4 receptor (MC4R) rs17782313 gene polymorphisms, low-fat diet, aerobic exercise, and the reduction in blood lipid levels in individuals with obesity. A total of 240 adults living with obesity were enrolled to take part in a 12-week program that combined exercises with dietary interventions. Measurements taken included body weight, body mass index (BMI), plasma lipids, fasting insulin (FIN), and insulin resistance (Homeostasis Model Assessment, HOMA-IR). All participants underwent exercise intervention and genotyping. Our findings revealed significant interactions between genotype, sex, and diet in modulating lipid metabolism. Specifically, after the exercise intervention, the mean reduction in BMI in was: CC+CT with low-fat diet: -2.56 ± 1.98 kg/m The CC+CT genotype group, particularly males on a low-fat diet, showed robust improvements in TG, LDL-C, and insulin resistance markers. However, HDL-C responses were inconsistent across subgroups. Notably, males with the CC+CT allele exhibited the most pronounced benefits in LDL-C reduction and HOMA-IR improvement with a low-fat diet. Show less
no PDF DOI: 10.1080/00325481.2025.2552640
MC4R

Neuroprotective Effects of Tuina in CP Rats Are Associated With Gut Microbiota Remodeling and Intestinal Barrier Restoration.

Chenqin Si, Rui Qiao, Yu Liu +5 more · 2025 · Brain and behavior · Wiley · added 2026-04-24
Cerebral palsy (CP) is a neurodevelopmental disorder that has been linked to gut microbiota dysbiosis. Although Tuina has shown neuroprotective effects, it remains unclear whether these benefits invol Show more
Cerebral palsy (CP) is a neurodevelopmental disorder that has been linked to gut microbiota dysbiosis. Although Tuina has shown neuroprotective effects, it remains unclear whether these benefits involve regulation of the gut-brain axis. This study aimed to evaluate the therapeutic effects of Tuina in CP rats, with emphasis on its potential regulation of the gut-brain axis. CP was induced in 7-day-old Sprague-Dawley rats through hypoxia-ischemia. Beginning on postnatal day 8 (P8), the Tuina group received daily Tuina therapy for 32 consecutive days. Motor function was assessed using the negative geotaxis test (P6-P12), the beam balance test (P36-P39), and the modified neurological severity score on P40. Gut microbiota composition was analyzed using 16S rRNA sequencing. Brain and intestinal histopathology were evaluated histologically via hematoxylin-eosin and Luxol fast blue staining. Protein expression of BDNF, Nrf2, GPX4, ZO-1, and occludin was assessed via western blotting and immunofluorescence. Serum short-chain fatty acids (SCFAs) were measured by mass spectrometry, whereas oxidative stress and intestinal barrier markers (superoxide dismutase, malondialdehyde, glutathione peroxidase, lipopolysaccharide [LPS], diamine oxidase [DAO], and D-lactate [D-LA]) were detected using enzyme-linked immunosorbent assay. In CP models induced by hypoxic-ischemic encephalopathy, significant brain injury and motor dysfunction were observed, accompanied by gut microbiota dysbiosis and impaired intestinal barrier function. Tuina intervention improved motor function and growth, regulated gut microbiota, and increased serum SCFA levels. It also enhanced intestinal barrier proteins (occludin, ZO-1), reduced serum levels of LPS, DAO, and D-LA, and increased the expression of brain-derived BDNF, Nrf2, and GPX4. Tuina significantly alleviated brain injury and improved motor function in CP rats. These effects were associated with modulation of the gut microbiota and restoration of intestinal barrier integrity, suggesting that the gut-brain axis may mediate the neuroprotective effects of Tuina. Show less
📄 PDF DOI: 10.1002/brb3.71136
BDNF

BCAA metabolism: the Achilles' heel of ovarian cancer, polycystic ovary syndrome, and premature ovarian failure.

Tian Zeng, Yitong Liu, Xing Tang +7 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine, are essential nutrient signals that influence mammalian animal metabolism. Many enzymes are involved in the metabolism of Show more
Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine, are essential nutrient signals that influence mammalian animal metabolism. Many enzymes are involved in the metabolism of BCAAs, such as branched-chain amino acid transaminases (BCATs), branched-chain α-keto acid dehydrogenase (BCKDH), and BCKDH kinase (BCKDK). The aberrant expression of enzymes involved in BCAA metabolism and an imbalance in BCAA amino acid intake can lead to disordered metabolism. Aberrant BCAA metabolism can lead to several diseases, such as human ovarian disease, including ovarian cancer (OC), polycystic ovary syndrome (PCOS), and premature ovarian failure (POF), which are common gynaecological diseases. The overexpression of BCATs is found in OC, which promotes BCAA catalysis to provide a large amount of energy for tumorigenesis. However, BCKDK is overexpressed in epithelial ovarian cancer (EOC), which promotes proliferation and migration via MEK-ERK. In addition, several studies have reported that high levels of BCAAs are increased in the plasma of PCOS and POF patients. This review focuses on the role of BCAA metabolism and potential management methods for OC, PCOS and POF. Show less
📄 PDF DOI: 10.3389/fendo.2025.1579477
BCKDK

Atf3 + senescent chondrocytes mediate meniscus degeneration in aging.

Jing-Yi Wang, Yao-Hui Liu, Xiao Wang +7 more · 2025 · Arthritis research & therapy · BioMed Central · added 2026-04-24
Meniscus degeneration contributes to knee arthritis progression, but the cellular and molecular mechanisms of meniscus aging remain poorly understood. We aimed to characterize age-related changes in t Show more
Meniscus degeneration contributes to knee arthritis progression, but the cellular and molecular mechanisms of meniscus aging remain poorly understood. We aimed to characterize age-related changes in the rat meniscus using single-cell RNA sequencing (scRNA-seq) and identify key pathogenic cell populations and pathways. Meniscal tissues from young (12 weeks) and aged (24 months) rats were processed for histology, flow cytometry, and scRNA-seq. Bioinformatics tools, including Seurat, Monocle 2, and CellChat, were used to analyze cellular composition, pseudotime trajectories, and intercellular communication. Senescence-related features and signaling pathways were evaluated. Knee joint of aged rats exhibited higher Osteoarthritis Research Society International (OARSI) scores and synovial inflammation. scRNA-seq revealed three major chondrocyte subpopulations: Sox9 + stable chondrocytes, Fndc1 + fibrochondrocytes, and Atf3 + senescent chondrocytes. Aging caused a significant increase in Atf3 + senescent chondrocytes, characterized by the expression of senescence markers (Cdkn1a/Cdkn2a) and activation of inflammatory pathways such as tumor necrosis factor (TNF) and nuclear factor-κB (NF-κB). These cells were predominantly located at the endpoint of differentiation trajectories. CellChat analysis identified the ANGPTL4-SDC4 axis as a key signaling pathway mediated by Atf3 + cells. Immunostaining confirmed elevated Angiopoietin-Like Protein 4 (ANGPTL4) expression in aged menisci. We identified Atf3 + senescent chondrocytes as a key pathogenic population in the aging meniscus, driving degeneration via the ANGPTL4 pathway. Targeting Atf3 + cells or ANGPTL4 signaling may offer new therapeutic strategies for age-related meniscus degeneration and arthritis. Show less
📄 PDF DOI: 10.1186/s13075-025-03566-z
ANGPTL4

Latent profiles of death anxiety among young adults: associations with self-esteem, security, and perceived social support.

Jingxian Yu, Mingjie Wu, Yongqi Liang +3 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Death anxiety is a critical mental-health concern among young adults; however, its heterogeneity and underlying psychological mechanisms remain understudied. This study aimed to identify latent profil Show more
Death anxiety is a critical mental-health concern among young adults; however, its heterogeneity and underlying psychological mechanisms remain understudied. This study aimed to identify latent profiles of death anxiety in Chinese youth and examine the predictive roles of self-esteem, perceived social support, and security. We conducted a cross-sectional survey of 623 young adults ( Three latent death anxiety profiles emerged, High Death Anxiety (56.2%), Moderate Cognition and Low Death Anxiety (8.8%), and Low Cognition and Moderate Death Anxiety (35%). Higher self-esteem ( Death anxiety among young adults is heterogeneous, influenced by distinct psychological profiles and demographic factors. Interventions should prioritize enhancing self-esteem, social support networks, and security to mitigate death anxiety, especially in high-risk subgroups. Future research should employ longitudinal designs and cross-cultural samples to validate causal pathways and refine targeted strategies. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1594720
LPA

Senescence Alters Antimicrobial Peptide Expression and Induces Amyloid-β Production in Retinal Pigment Epithelial Cells.

Jian Liu, Caijiao Yi, Jinyan Qi +5 more · 2025 · Aging cell · Blackwell Publishing · added 2026-04-24
Age-related retinal degeneration, such as diabetic retinopathy and age-related macular degeneration, are major causes of blindness in modern society. Recent studies suggest that dysbiosis and intraocu Show more
Age-related retinal degeneration, such as diabetic retinopathy and age-related macular degeneration, are major causes of blindness in modern society. Recent studies suggest that dysbiosis and intraocular translocation of bacteria from the blood circulation are critically involved in retinal degeneration. We hypothesise that the blood-retinal barrier (BRB) cells can protect the neuroretina from blood-borne pathogens by producing antimicrobial peptides (AMPs). The antimicrobial activity may decline during ageing, putting the retina at risk of low-degree chronic inflammation and degeneration. Here, we found that the retinal pigment epithelial (RPE) cells, which form the outer BRB, express a variety of AMPs/AMP precursors, including APP, RARRES2, FAM3A, HAMP, CAMP, GNLY, and PI3. Senescent RPE cells expressed lower levels of APP and RARRES2 mRNA, accompanied by increased intracellular retention of E. coli in a bactericidal assay. Silencing APP, not RARRES2, with shRNA reduced the antibacterial activity of RPE cells. Senescent RPE cells had lower levels of α-secretase and higher levels of β-secretase (BACE1) and γ-secretase (PS1), accompanied by reduced soluble APPα and increased amyloid beta (Aβ) production, particularly the Aβ42 isoform. Eyes from aged donors showed a higher Aβ accumulation within RPE cells. Our results suggest that while RPE cells possess antimicrobial activity, this ability declines with age and is impaired in senescent cells. The impaired antimicrobial activity and augmented Aβ deposition in senescent RPE cells may contribute to age-related retinal para-inflammation and neurodegeneration. Show less
📄 PDF DOI: 10.1111/acel.70161
BACE1

Circular RNA CLASP1 modulates the GLI1/SNAIL axis and enhances macrophage polarization in breast cancer.

Lijun Zhou, Mei Liu, Fujun Liu +10 more · 2025 · Oncogene · Nature · added 2026-04-24
Breast cancer (BC) is the most prevalent malignancy among women worldwide. Growing evidence highlights the crucial role of circular RNAs (circRNAs) in BC carcinogenesis; however, their underlying mech Show more
Breast cancer (BC) is the most prevalent malignancy among women worldwide. Growing evidence highlights the crucial role of circular RNAs (circRNAs) in BC carcinogenesis; however, their underlying mechanisms remain largely unknown. In this study, we identify circCLASP1, which is significantly upregulated in BC tissues (n = 65) and serum samples (n = 61). Its expression correlates with lymph node metastasis, ki67 expression, and tumor size. Receiver operation characteristic (ROC) curve analysis reveals area under the curve (AUC) values of 0.8196 (BC tissues) and 0.8902 (BC serum), respectively. Functionally, circCLASP1 knockdown significantly suppresses BC cell proliferation, migration, and invasion. Mechanistically, circCLASP1 prevents the ubiquitin-mediated degradation of GLI1 protein by facilitating its interaction with CCT2, thereby stabilizing GLI1. Moreover, circCLASP1 enhances the nuclear accumulation of GLI1, leading to increased SNAIL expression and thereby upregulating the expression of CCL2 and CCL5, which in turn promotes macrophage M2 polarization, ultimately resulting in BC progression and subsequent lung metastasis. Further analysis reveals that U2AF2 regulates circCLASP1 biogenesis. Collectively, these findings demonstrate that circCLASP1 promotes BC progression and an immunosuppressive microenvironment via the CCT2/GLI1/SNAIL axis, highlighting its potential as a prognostic biomarker and therapeutic target for BC. Show less
no PDF DOI: 10.1038/s41388-025-03627-2
SNAI1

Endothelial cells-derived SEMA3G suppresses glioblastoma stem cells by inducing c-Myc degradation.

Peng-Xiang Min, Li-Li Feng, Yi-Xuan Zhang +12 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascu Show more
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascular niche to maintain stemness. However, the effect of abnormal communication between endothelial cells (ECs) and GSCs on GBM progression remains unknown. Here, we reveal that ECs-derived SEMA3G, which is aberrantly expressed in GBM patients, impairs GSCs by inducing c-Myc degradation. SEMA3G activates NRP2/PLXNA1 in a paracrine manner, subsequently inducing the inactivation of Cdc42 and dissociation of Cdc42 and WWP2 in GSCs. Once released, WWP2 interacts with c-Myc and mediates c-Myc degradation via ubiquitination. Genetic deletion of Sema3G in ECs accelerates GBM growth, whereas SEMA3G overexpression or recombinant SEMA3G protein prolongs the survival of GBM bearing mice. These findings illustrate that ECs play an intrinsic inhibitory role in GSCs stemness via the SMEA3G-c-Myc distal regulation paradigm. Targeting SEMA3G signaling may have promising therapeutic benefits for GBM patients. Show less
no PDF DOI: 10.1038/s41418-025-01534-3
WWP2

AMPK/SIRT1/PGC-1α Signaling Pathway: Molecular Mechanisms and Targeted Strategies From Energy Homeostasis Regulation to Disease Therapy.

Junyang Chen, Boya Liu, Xinlei Yao +8 more · 2025 · CNS neuroscience & therapeutics · Blackwell Publishing · added 2026-04-24
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunc Show more
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunction is implicated in the pathogenesis of a wide spectrum of complex modern diseases, spanning neurodegeneration, metabolic syndromes, and chronic inflammatory conditions. This review examines the pathway's role as an integrative hub and its potential as a therapeutic target. We synthesize current mechanistic evidence from molecular, cellular, and preclinical studies to elucidate the pathway's operational logic and the consequences of its dysregulation. The analysis is structured around key disease paradigms-including Alzheimer's disease, Parkinson's disease, diabetes, cardiovascular injury, stroke, and chronic kidney disease-to dissect its tissue-specific pathophysiological impacts. The AMPK/SIRT1/PGC-1α axis operates through a core positive feedback loop: AMPK activation elevates NAD+, thereby activating SIRT1, which in turn deacetylates and activates PGC-1α to drive mitochondrial biogenesis and function, further reinforcing SIRT1 activity. Disruption of this cascade manifests in disease-specific mechanisms: promoting Aβ production via BACE1/γ-secretase in Alzheimer's; impairing α-synuclein clearance in Parkinson's; disrupting GLUT4 translocation and insulin signaling in diabetes; exacerbating oxidative damage and mitochondrial dysfunction in cardiovascular and neuronal injury; and accelerating fibrosis and sustained inflammation in renal and pulmonary diseases via NLRP3 and TGF-β/Smad3 signaling. The AMPK/SIRT1/PGC-1α pathway represents a cornerstone target at the intersection of metabolism, aging, and disease. Current therapeutic strategies-including pharmacological activators (e.g., metformin, SRT1720), natural compounds (e.g., resveratrol), lifestyle interventions (e.g., exercise, caloric restriction), and emerging technologies (e.g., gene editing, exosomal miRNAs)-offer multidimensional avenues for intervention. Future research must prioritize elucidating tissue-specific regulatory mechanisms, such as AMPK isoform diversity and PGC-1α interactome dynamics, to enable precision therapeutics and successful clinical translation for a range of complex disorders. Show less
📄 PDF DOI: 10.1111/cns.70657
BACE1

FGFR1 inhibition by carvacrol: A novel strategy for oral squamous cell carcinoma therapy.

Shuzhen Xiang, Hongyan Zhang, Qian Wang +7 more · 2025 · Genes & diseases · Elsevier · added 2026-04-24
📄 PDF DOI: 10.1016/j.gendis.2024.101479
FGFR1

Association between red blood cell distribution width and genetic risk in patients with rheumatoid arthritis: a prospective cohort study and Mendelian randomization analysis.

Mingyang Chen, Jing Lei, Zhenqiu Liu +6 more · 2025 · BMC rheumatology · BioMed Central · added 2026-04-24
Elevated red blood cell distribution width (RDW) is associated with increased risk of rheumatoid arthritis (RA), but the potential interactions of RDW with genetic risk of incident RA remain unclear. Show more
Elevated red blood cell distribution width (RDW) is associated with increased risk of rheumatoid arthritis (RA), but the potential interactions of RDW with genetic risk of incident RA remain unclear. This study aimed to investigate the associations between RDW, genetics, and the risk of developing RA. We analysed data from 145,025 healthy participants at baseline in the UK Biobank. The endpoint was diagnosed rheumatoid arthritis (ICD-10 codes M05 and M06). Using previously reported results, we constructed a polygenic risk score for RA to evaluate the joint effects of RDW and RA-related genetic risk. Two-sample mendelian randomization and bayesian colocalization were used to infer the causal relation between them. A total of 675 patients with RA were enrolled and had a median followed up of 5.1 years, with an incidence rate of 0.57/1000 person-years. The hazard ratio of RA was 1.89 (95% CI: 1.45, 2.47) in highest RDW quartile group compared with the lowest RDW quartile group. Individuals within the top quintile of PRS showed a significantly high risk of RA. Moreover, Participants with high genetic risk and those in highest RDW group exhibited a significantly elevated hazard ratio (7.67, 95% CI: 3.98, 14.81), as opposed to participants with low genetic risk and those in lowest RDW group. Interactions between PRS and RDW on the multiplicative and additive scale were observed. Mendelian randomization provided suggestive evidence of a bi-directional causal relationship between RDW and RA. Loci near IL6R, IL1RN, FADS1/FADS2, UBE2L3 and HELZ2 showed colocalization. Increased RDW is associated with elevated risk of incident RA especially in the high genetic risk populations, but only suggestive evidence supports a causal relationship between them. Show less
📄 PDF DOI: 10.1186/s41927-024-00451-1
FADS1