👤 Riping Wu

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Also published as: Jiake Wu, Ming-Jiuan Wu, Yijian Wu, Siying Wu, Fong-Li Wu, Chih-Chung Wu, Jin'en Wu, Zixiang Wu, D P Wu, Zhongwei Wu, Haiping Wu, Geyan Wu, Qi-Zhu Wu, Jianjin Wu, Su Wu, Shwu-Yuan Wu, Xiaodi Wu, Changxin Wu, Kuen-Phon Wu, Zhiping Wu, Guofeng Wu, Xiaojun Wu, Qibing Wu, Cheng-Hsin Wu, Junhua Wu, Xiaoting Wu, Wenze Wu, Yandi Wu, Hong Wu, Zhong Wu, An-Chih Wu, Jianhui Wu, Xiaoke Wu, Zhenguo Wu, Jason H Y Wu, Bing-Bing Wu, Selena Meiyun Wu, Yi-Mi Wu, M Wu, Hui-Mei Wu, Danni Wu, Sijie Wu, Minqing Wu, Geng-ze Wu, Cheng-Hua Wu, Kun Wu, Shaofei Wu, Zhaoyang Wu, Qihan Wu, Kunling Wu, R Ryanne Wu, Hao Wu, Pei Wu, Mingxuan Wu, Wendy Wu, Yukang Wu, Douglas C Wu, Jingtao Wu, Guizhen Wu, Zhangjie Wu, Lili Wu, Jianwu Wu, Min-Jiao Wu, Biaoliang Wu, Huan Wu, Shengxi Wu, Fei-Fei Wu, Peih-Shan Wu, Guoqing Wu, Yu-Yuan Wu, Pei-Yu Wu, Jing Wu, Geting Wu, Lun-Gang Wu, Dongzhe Wu, G Wu, Junlong Wu, Jia-Jun Wu, Jiangyue Wu, Muzhou Wu, Junzhu Wu, Ray-Chin Wu, Jian-Qiu Wu, T Wu, Jianxiong Wu, Liping Wu, Haiwei Wu, Yong-Hao Wu, Guoping Wu, Jin-hua Wu, Yi Wu, You Wu, Chongming Wu, Xudong Wu, Qunzheng Wu, Liqiang Wu, Cuiling Wu, Kunfang Wu, Bian Wu, Limeng Wu, Jason Wu, Shuying Wu, Zhibing Wu, Caihong Wu, Naqiong Wu, Joseph C Wu, Huating Wu, Tianhao Wu, Zhi-Hong Wu, Congying Wu, Gaojun Wu, Dongping Wu, Chiao-En Wu, Li Wu, Shaoxuan Wu, Haixia Wu, Yihang Wu, Fanchang Wu, Gen Wu, Xiaorong Wu, Mei Wu, Mingjie Wu, Jiahao Wu, Jiapei Wu, Jia Wu, Lingqian Wu, Fangge Wu, Sen-Chao Wu, Yanhui Wu, Zhiqiang Wu, Sarah Wu, Shugeng Wu, Xuanqin Wu, Dongmei Wu, Caiwen Wu, Junjing Wu, Jiangdong Wu, Guihua Wu, Meini Wu, Yingbiao Wu, Rui Wu, Hua-Yu Wu, Bifeng Wu, Jingwan Wu, Lingling Wu, Junzheng Wu, Xinmiao Wu, Yi-Fang Wu, Yuyi Wu, Qinglin Wu, Yixuan Wu, Leilei Wu, Bin Wu, Tianqi Wu, Shiya Wu, Hui-Chen Wu, Jian Wu, Cong Wu, Sijun Wu, Yiwen Wu, Feng Wu, Xi-Ze Wu, Qiuji Wu, Alexander T H Wu, Qinan Wu, Semon Wu, Lai Man Natalie Wu, Zhuokai Wu, Ran Wu, Panyun Wu, Kui Wu, Yumei Wu, Yueling Wu, Xinrui Wu, Biwei Wu, Xing Wu, Jiayi Wu, Hua Wu, Yuen-Jung Wu, Bingjie Wu, Xiaoliang Wu, Matthew A Wu, Jin Wu, Juanjuan Wu, Qiuhong Wu, Hongfu Wu, Xiaoming Wu, Ming-Sian Wu, Ronghua Wu, Junduo Wu, Dandan Wu, Ming-Shiang Wu, Yuliang Wu, Ying-Ying Wu, Chaoling Wu, Guang-Liang Wu, De Wu, Yihua Wu, Yuanyuan Wu, Tsung-Jui Wu, Yulian Wu, Han Wu, Lipeng Wu, Zhihao Wu, Jiexi Wu, Anna H Wu, Huazhen Wu, Qiu Wu, Yaqin Wu, Shengru Wu, Chieh-Lin Stanley Wu, Xiahui Wu, Xiaoqian Wu, Jianli Wu, Yun-Wen Wu, Jian-Yi Wu, Qiuya Wu, Tsai-Kun Wu, Xinyin Wu, Guoyao Wu, Guoli Wu, Zhenfeng Wu, Bill X Wu, J W Wu, Zujun Wu, Jianliang Wu, Yuanshun Wu, Ling-Ying Wu, Zeng-An Wu, Jianrong Wu, Xue Wu, Ke Wu, Mengxue Wu, Cheng-Yang Wu, Jinghong Wu, Rongrong Wu, Ruolan Wu, Rong Wu, Kevin Zl Wu, Run Wu, Xiaohong Wu, Zaihao Wu, Chaowei Wu, Yu-Ke Wu, Xinjing Wu, Anyue Wu, Meili Wu, Wanxia Wu, Yun Wu, Xuan Wu, Shu Wu, Yi-No Wu, Chao-Liang Wu, Chengwei Wu, Y-W Wu, Pensee Wu, Zhao-Bo Wu, Guangxian Wu, Xiao Wu, Juanli Wu, Xinlei Wu, Sai Wu, Changjie Wu, Jiawei Wu, Yujuan Wu, Haoze Wu, Renlv Wu, Yipeng Wu, Xiaoyang Wu, Yuh-Lin Wu, Yu'e Wu, Dan-Chun Wu, An-Hua Wu, Meng-Chao Wu, Yuanhao Wu, Jer-Yuarn Wu, Qian-Yan Wu, Guangyan Wu, Huisheng Wu, Huijuan Wu, Shuting Wu, Long-Jun Wu, Alice Ying-Jung Wu, Xiru Wu, Zhenfang Wu, Lidi Wu, Yetong Wu, Disheng Wu, Linmei Wu, Huiwen Wu, Zhenzhou Wu, Yuhong Wu, Liang Wu, Liyan Wu, Kuan-Li Wu, Pei-Ting Wu, Xiao-Jin Wu, Terence Wu, Lifeng Wu, Shujuan Wu, Gang Wu, Szu-Hsien Wu, Xue-Mei Wu, Yan-ling Wu, Lingyan Wu, Yih-Jer Wu, Xiaokang Wu, Xinghua Wu, Chunfu Wu, Yingxia Wu, Rongling Wu, Xifeng Wu, Jinhua Wu, Sihan Wu, Ming-Yue Wu, Shiyang Wu, K D Wu, Jinmei Wu, Luyan Wu, Shin-Long Wu, Shuai Wu, Zhipeng Wu, Zhixiang Wu, Guangzhen Wu, Longting Wu, Zhengsheng Wu, Xiaoqiong Wu, Yaoxing Wu, Yuqin Wu, Yudan Wu, Zoe Wu, Hongting Wu, Chi-Jen Wu, R Wu, Meina Wu, Zhongqiu Wu, Dengying Wu, Anke Wu, Cheng-Jang Wu, Hsi-Chin Wu, Shufang Wu, Yongjiang Wu, Yuan-de Wu, Sihui Wu, Qi Wu, Wenhui Wu, Fenfang Wu, K S Wu, Jianzhi Wu, Nana Wu, Lin-Han Wu, Zhen Wu, Jinjun Wu, Chen-Lu Wu, Jing-Fang Wu, Haiyan Wu, Yihui Wu, Qiqing Wu, Dai-Chao Wu, Zhengzhi Wu, Zhenyan Wu, Wen-Jeng Wu, Guanming Wu, Sean M Wu, Yongqun Wu, Hei-Man Wu, Su-Hui Wu, Diana H Wu, Ben J Wu, Pingxian Wu, Chew-Wun Wu, Yillin Wu, Jiang-Bo Wu, Xiaobing Wu, Jerry Wu, Siming Wu, Zijun Wu, Daqing Wu, Yu-Hsuan Wu, Lichao Wu, Zhimin Wu, Qijing Wu, Daxian Wu, Zhaoyi Wu, Z Wu, Tong Wu, Tracy Wu, Shusheng Wu, Cheng-Chun Wu, D Wu, Ting-Ting Wu, Xiao-Yan Wu, Lan Wu, J Wu, Changchen Wu, Qi-Fang Wu, Changwei Wu, Liangyan Wu, Liufeng Wu, Kan Wu, Eugenia Wu, Mingming Wu, Xiaolong Wu, Chunru Wu, Zhaofei Wu, Shenhao Wu, Li-Peng Wu, Yuna Wu, Minna Wu, Justin Che-Yuen Wu, Buling Wu, Wutian Wu, Chengyu Wu, Yuwei Wu, Guixin Wu, Hei Man Wu, Haijing Wu, Qiuchen Wu, Xiao-Hui Wu, Junfei Wu, Xiaofeng Wu, Linyu Wu, Wenda Wu, Yung-Fu Wu, Mengbo Wu, Zhenling Wu, Maoqing Wu, Zuping Wu, Chun-Chieh Wu, Julian Wu, Binbin Wu, Xiaohui Wu, Qian Wu, Xinchun Wu, Shuisheng Wu, Linxiang Wu, Xueqing Wu, Bo Wu, Moxin Wu, Xiao-Cheng Wu, Anzhou Wu, Shuyi Wu, Jiahui Wu, Meiqin Wu, Shihao Wu, Jer-Yuan Wu, Wen-Shu Wu, Wudelehu Wu, Ruonan Wu, Song Wu, De-Fu Wu, Yulin Wu, Hongyu Wu, Yurong Wu, Zixuan Wu, Shih-Ying Wu, Chih-Hsing Wu, Chengrong Wu, Yinghao Wu, Yuanzhao Wu, Baochuan Wu, Wenjie Wu, Ziliang Wu, Liuting Wu, Chia-Ling Wu, Y Q Wu, Man Wu, Na Wu, Wutain Wu, Chenyang Wu, Jinyu Wu, Selwin K Wu, Ping Wu, Lorna Wu, D I Wu, Yi-Cheng Wu, Jianzhong Wu, Xiaoyun Wu, Zhourui Wu, Li-Jun Wu, Xinhe Wu, Zhi-Wei Wu, Yinan Wu, Xinyan Wu, Xin Wu, Ting-Feng Wu, Yawei Wu, Shixin Wu, Hong-Mei Wu, Xiaojin Wu, Yiqun Wu, Tsung-Teh Wu, Jiarui Wu, Qi-Nian Wu, Ju Wu, Kai-Yue Wu, Pengjie Wu, Xi-Chen Wu, Zhe Wu, Shaoping Wu, Han-Jie Wu, Zhou Wu, Haijiang Wu, Weijie Wu, Hongfei Wu, Xiaojie Wu, Yi-Ying Wu, Zhentian Wu, Ze Wu, Kai-Hong Wu, Yuting Wu, Minyao Wu, Xueyan Wu, Shinan Wu, Feifei Wu, Yonghui Wu, Haoxuan Wu, Yanzhi Wu, Yiyi Wu, Dong Wu, Guohao Wu, Wenjing Wu, Shibo Wu, Wenqian Wu, Tian Wu, Tiantian Wu, Hai-Yan Wu, Chong Wu, Hongxian Wu, Daoyuan Wu, Zongfu Wu, Ling Wu, Yuxiang Wu, Xilong Wu, Yuyu Wu, Fengming Wu, Huijian Wu, Zong-Jia Wu, Guorong Wu, Chuanhong Wu, Choufei Wu, Chi-Chung Wu, Junfang Wu, Xingwei Wu, Xiaoqing Wu, Ling-Fei Wu, Xinyang Wu, Xiaomin Wu, Yili Wu, Hong-Fu Wu, Shao-Ming Wu, Thomas D Wu, Lizhen Wu, Yuanming Wu, Hsien-Ming Wu, Jian Hui Wu, Litong Wu, Yuxian Wu, Weihua Wu, Lei Wu, C Wu, Wei Wu, Yu-E Wu, Qiulian Wu, Mei-Hwan Wu, Yuexiu Wu, Shaoze Wu, Zilong Wu, Chi-Hao Wu, Baojin Wu, Chao Wu, Yao Wu, Ya Wu, Do-Bo Wu, Wenjun Wu, Zhongren Wu, Nini Wu, Michael C Wu, Ning Wu, Jie Wu, Ming J Wu, Yi-Syuan Wu, Limei Wu, Zhenzhen Wu, Tianwen Wu, Wen-Chieh Wu, Yunhua Wu, Junfeng Wu, Shunan Wu, Junqi Wu, Jianing Wu, Honglin Wu, Maureen Wu, Yexiang Wu, Yan-Hua Wu, Mengjun Wu, Y H Wu, Mingxing Wu, Liuying Wu, Xiaomeng Wu, Suhua Wu, Shyh-Jong Wu, Tung-Ho Wu, Hongliang Wu, Wenxian Wu, Xuekun Wu, Ed Xuekui Wu, Wenqiang Wu, Chuang Wu, Jingyi Wu, Duojiao Wu, Xueyuan Wu, Ji-Zhou Wu, Lianqian Wu, Gaige Wu, Qing-Qian Wu, Haihu Wu, Xiushan Wu, Xueyao Wu, Tingchun Wu, Yafei Wu, Lingxi Wu, R-J Wu, Weidong Wu, Re-Wen Wu, Zhidan Wu, Peiyao Wu, Xuemei Wu, Chen Wu, Yiting Wu, Kerui Wu, Lihong Wu, Shiqi Wu, Liren Wu, Xiuhua Wu, Beili Wu, Yongqi Wu, Ruihong Wu, Huini Wu, Guang-Long Wu, Lingyun Wu, Po-Chang Wu, Qinghua Wu, Ru-Zi Wu, Wenxue Wu, Wenlin Wu, Changjing Wu, Xiexing Wu, J Y Wu, Jianping Wu, Guanggeng Wu, W J Wu, Zhichong Wu, Di Wu, Shaoyu Wu, Xiaotong Wu, Junyong Wu, Hui Wu, Shengde Wu, Hongyan Wu, Mengyuan Wu, Yutong Wu, Zheming Wu, Yiping Wu, Guiping Wu, Wen-Hui Wu, Dapeng Wu, Bing Wu, Wen-Sheng Wu, Yunpeng Wu, Li-Ling Wu, Xiao-Yuan Wu, Baiyan Wu, Qiu-Li Wu, Ying Wu, Xiao-Ye Wu, Da-Hua Wu, Hsing-Chieh Wu, Hui-Xuan Wu, Chieh-Jen Wu, Pengning Wu, Sichen Wu, S F Wu, Mengying Wu, Jia-En Wu, Ming-Der Wu, Qi-Jun Wu, Guo-Chao Wu, Weida Wu, Zhenyong Wu, Qi-Biao Wu, Yangfeng Wu, Lijie Wu, Zhiye Wu, Jihui Wu, JieQian Wu, Qianqian Wu, Zhengliang L Wu, Jingyun Wu, Xiaoman Wu, Ruohao Wu, Yiyang Wu, Zhengfeng Wu, Xiao-Jun Wu, Lizi Wu, Qiang Wu, J-Z Wu, Guangjie Wu, Pengfei Wu, Jundong Wu, Beier Wu, Jianying Wu, Meng-Ling Wu, Lingxiang Wu, Jamie L Y Wu, Keija Wu, Xilin Wu, Yanhua Wu, An-Li Wu, Yi-Ming Wu, Chengbiao Wu, Huanghui Wu, Dong-Feng Wu, Kunsheng Wu, Zhengcan Wu, Yuxin Wu, Kun-Rong Wu, Dong-Fang Wu, Guanxian Wu, Sensen Wu, Guifen Wu, Yifeng Wu, Tzu-Chun Wu, Pin Wu, Qingping Wu, R M Wu, Mian Wu, S J Wu, Senquan Wu, Haisu Wu, Jingjing Wu, Cheng Wu, Meng Wu, Geping Wu, Yu Wu, Yumin Wu, Xia Wu, William Ka Kei Wu, Xian-Run Wu, Juan Wu, Pei-Ei Wu, Meng-Hsun Wu, Yingying Wu, S M Wu, Xiangwei Wu, Guangrun Wu, Liuxin Wu, Yangyu Wu, Jia-Hui Wu, Jin-Zhen Wu, S L Wu, Shaohuan Wu, June K Wu, Yanli Wu, Haishan Wu, H Wu, Zhou-Ming Wu, Deqing Wu, Tao Wu, Dong-Bo Wu, Binxin Wu, Yalan Wu, Xiangxin Wu, Xueji Wu, Hongxi Wu, Zhonghui Wu, Jiaxi Wu, Tianzhi Wu, Meiqi Wu, Weiwei Wu, Yan-Jun Wu, Lijuan Wu, Jianming Wu, Tingqin Wu, P L Wu, Yih-Ru Wu, Lanlan Wu, Jianjun Wu, Jianguang Wu, An-Xin Wu, Xingjie Wu, Jianzhang Wu, Xianan Wu, Wei-Ping Wu, Haoan Wu, Fang-Tzu Wu, Wenwen Wu, Zhongjun Wu, Xi Wu, Teng Wu, Xiaoling Wu, Mengjuan Wu, Wen Wu, Yifan Wu, Yang Wu, Qianhu Wu, Wu-Tian Wu, Shenyue Wu, Qianwen Wu, Ye Wu, Lixing Wu, Gui-Qin Wu, Grace F Wu, Xing-Ping Wu, Ming Wu, Lisha Wu, Yanchuan Wu, Siqi Wu, Yuming Wu, Yuan Wu, I H Wu, Yu-Ting Wu, Hailong Wu, Minghua Wu, Zhenlong Wu, B Wu, Fang Wu, Guanzhong Wu, Liqun Wu, Guifu Wu, Zhikang Wu, Chris Y Wu, Qi-Yong Wu, Qingshi Wu, Zhao-Yang Wu, Man-Jing Wu, Chih-Ching Wu, Jun Wu, Jinhui Wu, Jincheng Wu, Linhong Wu, Hung-Tsung Wu, Tangchun Wu, Xinglong Wu, Zhen-Yang Wu, Ma Wu, Yin Wu, Dongyan Wu, Jiu-Lin Wu, Yong Wu, Yan Wu, Weizhen Wu, Changyu Wu, Fanggeng Wu, Dishan Wu, Ge-ru Wu, Yue Wu, Yi-Long Wu, Jinqiao Wu, Jing-Wen Wu, Zhongyang Wu, Lifang Wu, Sheng-Li Wu, Songfen Wu, Jia-Wei Wu, Yihan Wu, Kebang Wu, Wenyong Wu, Cai-Qin Wu, Yilong Wu, Yanan Wu, Hsiu-Chuan Wu, Xueqian Wu, Yen-Wen Wu, Paul W Wu, Xing-De Wu, Ying-Ting Wu, Mingfu Wu, Yucan Wu, Na-Qiong Wu, Linzhi Wu, Jinze Wu, Xuhan Wu, H J Wu, Ruize Wu, Dirong Wu, Yaohong Wu, Chung-Yi Wu, Jianyi Wu, Jugang Wu, Jiao Wu, Liang-Huan Wu, Xueling Wu, Ruying Wu, Gen Sheng Wu, Zhaoyuan Wu, Shiwen Wu, Andong Wu, Yu-Ling Wu, Hsan-Au Wu, Jia-Qi Wu, Yanting Wu, Xihai Wu, Lulu Wu, Xuxian Wu, Xiaomei Wu, Jingyue Wu, Ren Wu, Shuihua Wu, S Wu, Yupeng Wu, Haoming Wu, Samuel M Wu, Fan Wu, Yuesheng Wu, Tiange Wu, Yihe Wu, Shuang Wu, Jiayu Wu, Chia-Lung Wu, Shengnan Wu, Yaojiong Wu, Y Wu, Y Y Wu, Zhuoze Wu, Zimu Wu, Depei Wu, Yi-Hua Wu, Haiyun Wu, Yanyan Wu, Min Wu, Wenjuan Wu, Jinfeng Wu, Guangxi Wu, Junjie Wu, Yawen Wu, Pinglian Wu, Hui-Hui Wu, Xunwei Wu, Xuefeng Wu, Depeng Wu, Constance Wu, Dianqing Wu, Qibiao Wu, Nan Wu, Hao-Tian Wu, Hanyu Wu, Xiaojiang Wu, San-pin Wu, Cheng-Jun Wu, Xiaofan Wu, Xiwei Wu, Shi-Xin Wu, Shao-Guo Wu, Sunyi Wu, Yueheng Wu, Chengqian Wu, Kuixian Wu, Xin-Xi Wu, Guanyi Wu, Qiuxia Wu, Danhong Wu, He Wu, Zhong-Jun Wu, Siyi Wu, Xiangsheng Wu, Liting Wu, Lanxiang Wu, Kaili Wu, Ping-Hsun Wu, Zheng Wu, Wen-Ling Wu, Jiang-Nan Wu, Huanlin Wu, Yongfei Wu, Catherine A Wu, Leslie Wu, Shuo Wu, Peng-Fei Wu, Cho-Kai Wu, Meng-Han Wu, Hon-Yen Wu, Anguo Wu, Yuguang Philip Wu, Hai-Yin Wu, Yicheng Wu, Xiaolang Wu, Qing Wu, Yujie Wu, V C Wu, Haomin Wu, Xingdong Wu, Hengyu Wu, Jiang Wu, Xiaoli Wu, Chengxi Wu, Junyi Wu, Ling-qian Wu, William K K Wu, Chun Wu, Lesley Wu, Niting Wu, Jiayuan Wu, Xueying Wu, Yingning Wu, S-F Wu, David Wu, Mei-Na Wu, Joshua L Wu, Jin-Shang Wu, Guanzhao Wu, Jianqiang Wu, Runda Wu, Li-Hsien Wu, Rongjie Wu, June-Hsieh Wu, Huazhang Wu, Huanwen Wu, Xiu-Zhi Wu, Xianfeng Wu, Yanran Wu, Weibin Wu, Xuanshuang Wu, Yan Yan Wu, G X Wu, Jiaqi Wu, Runpei Wu, Chien-Ting Wu, Li-Na Wu, Qinfeng Wu, Chia-Chang Wu, Yueming Wu, Renhai Wu, Siyu Wu, Baojian Wu, Yi-Xia Wu, Wei-Yin Wu, C-H Wu, Renrong Wu, Chuan-Ling Wu, Xinran Wu, Fengying Wu, Qiuliang Wu, Guanhui Wu, Jinjie Wu, Wei-Chi Wu, Wei-Xun Wu, Meng-Na Wu, Lin Wu, Wan-Fu Wu, Jiajing Wu, Colin Chih-Chien Wu, Yajie Wu, Yaru Wu, Qiaowei Wu, Xiaoping Wu, Xue-Yan Wu, Mengchao Wu, Weijun Wu, Boquan Wu, Chunyan Wu, Zelai Wu, Pei-Wen Wu, Guojun Wu, Yichen Wu, Ming-Tao Wu, Hsueh-Erh Wu, Guang-Bo Wu, Zhi-Yong Wu, Chia-Zhen Wu, Kay L H Wu, Yong-Hong Wu, Anping Wu, Jiahang Wu, Xiaobin Wu, Ching-Yi Wu, Linzhen Wu, Xiaoxing Wu, Haidong Wu, Zhen-Qi Wu, Mark N Wu, Jianmin Wu, Guanrong Wu, Xianpei Wu, Yanchun Wu, Dongsheng Wu, An-Dong Wu, Ren-Chin Wu, Yuchen Wu, Mengna Wu, Lijun Wu, Zhuanbin Wu, Yanjing Wu, Haodi Wu, Lun Wu, Si-Jia Wu, Yongfa Wu, Ximei Wu, Hai-Ping Wu, Wenyu Wu, Xiangping Wu, L-F Wu, Yixia Wu, Yiran Wu, Haiying Wu, Yanhong Wu, Xiayin Wu, Yushun Wu, Yali Wu, Qitian Wu, Xiaofu Wu, Qin Wu, Jiamei Wu, Xiaoyong Wu, Qiong Wu, Wujun Wu, Xiaoying Wu, Peiyi Wu, N Wu, Yongmei Wu, Xiaojing Wu, Yizhou Wu, Dan Wu, Wen-Qiang Wu, Anshi Wu, Junqing Wu, Xiao-Yang Wu, Zhaoxia Wu, Liyang Wu, Hongke Wu, Mengqiu Wu, Peng Wu, Haibin Wu, Ding Lan Wu, Yingzhi Wu, Lecheng Wu, Kejia Wu, Anyi Wu, Junshu Wu, Jianxin Wu, Deguang Wu, Jiaxuan Wu, W Wu, Justin C Y Wu, Jiong Wu, Yu-Chih Wu, Qinglan Wu, Xinyi Wu, Diana Wu, Xuefen Wu, Zhongluan Wu, Yanqiong Wu, Shengming Wu, Jian-Lin Wu, Donglin Wu, Daren Wu, Lintao Wu, Xiaodong Wu, Chang-Jiun Wu, Irene X Y Wu, Chunshuai Wu, Yaping Wu, Yangna Wu, Xiping Wu, Zongheng Wu, Chia-Chen Wu, Wenyi Wu, Yansheng Wu, Shaojun Wu, Aimin Wu, Caisheng Wu, Xu Wu, Zhongchan Wu, Fei Wu, Yaohua Wu, Qinyi Wu, Yibo Wu, Zhengyu Wu, Yadi Wu, Hang Wu, L Wu, Mingjun Wu, Yuetong Wu, Wen-Juan Wu, Guangming Wu, Lingzhi Wu, Tingting Wu, Zhong-Yan Wu, Zhuzhu Wu, Yuanbing Wu, Cuiyan Wu, Baoqin Wu, Colin O Wu, Shuyan Wu, Hongmei Wu, Guangsen Wu, Xiaolin Wu, An Guo Wu, Kailang Wu, Chien-Sheng Wu, Chun-Hua Wu, Jemma X Wu, Wenqi Wu, Quanhui Wu, Qing-Wu Wu, Yanxiang Wu, Jiajin Wu, Qiao Wu, Yuan Kai Wu
articles
Yuwen Guo, Huai Bai, Linbo Guan +4 more · 2025 · Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · added 2026-04-24
To assess the association between the single nucleotide polymorphisms (SNP) rs174575 and rs2845574 of the fatty acid desaturase 2 (FADS2) gene and gestational diabetes mellitus (GDM). A total of 1 514 Show more
To assess the association between the single nucleotide polymorphisms (SNP) rs174575 and rs2845574 of the fatty acid desaturase 2 (FADS2) gene and gestational diabetes mellitus (GDM). A total of 1 514 pregnant women who visited West China Second University Hospital of Sichuan University between January 1, 2013 and December 31, 2021 were enrolled in this study. Among them, 583 were diagnosed with gestational diabetes mellitus (GDM group), and 931 had normal pregnancies (control group). The SNPs rs174575 and rs2845574 of the FADS2 gene were analyzed using Sanger DNA sequencing. Plasma levels of insulin (INS), apolipoprotein A1 (apoA1) and apolipoprotein B (apoB) were measured using enzymatic methods, chemiluminescence and immunoturbidimetry. This study was approved by the Medical Ethics Committee of the West China Second University Hospital of Sichuan University (Ethics No.: 2020-036). The main genotype at the rs174575 C/G and rs2845574 C/T loci were CC in both GDM and control groups. No significant difference was found between the GDM and control groups regarding the genotypic or allelic frequencies of rs174575 and rs2845574 sites (P > 0.05). Among the GDM group, individuals with the GG genotype at the rs174575 site had lower plasma HDL-C levels compared to those with the CC genotype (P < 0.05), and had higher atherogenic indices (AI) compared with the CC and CG genotype (P < 0.05; P < 0.05). Individuals with the TT genotype at the rs2845574 site had higher AI compared with the CT genotype (P < 0.05). Among the control group, individuals with the GG genotype had lower diastolic blood pressure (DBP) compared to those with the CC genotype (P < 0.05). Additional subgroup analysis demonstrated that the rs174575 polymorphism was associated with AI levels in obesity subgroup of GDM, TG levels in non-obese subgroup of control and DBP levels in the obese subgroup of control (P < 0.05; P < 0.05; P < 0.05). The FADS2 rs174575 and rs2845574 polymorphisms in GDM patients are associated wit HDL-C and AI levels, and the FADS2 rs174575 polymorphisms was also associated with DBP levels in normal pregnant women. The AI and DBP levels have a BMI-dependent effect. Show less
no PDF DOI: 10.3760/cma.j.cn511374-20221221-00866
APOB
Guoping Wu, Zhe Dong, Zhongcai Li +12 more · 2025 · Schizophrenia (Heidelberg, Germany) · Nature · added 2026-04-24
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause Show more
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research. Show less
📄 PDF DOI: 10.1038/s41537-025-00646-6
APOB
Yuhang Wang, Shuang Shi, Xinghua Wei +3 more · 2025 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term Show more
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term risks of cardiometabolic diseases. Machine learning (ML) offers an effective framework for constructing efficient risk prediction models in pediatric populations. This study aimed to develop and evaluate two ML models-Random Forest (RF) and Support Vector Classification (SVC)-to predict the risk of childhood obesity and hyperuricemia by integrating clinical and biochemical variables. A total of 101 children were enrolled, including 60 with obesity and 41 with obesity plus hyperuricemia. Data preprocessing involved recursive feature elimination (RFE), ROSE-based oversampling, and feature standardization. Both RF and SVC models were trained and evaluated using area under the ROC curve (AUC), precision-recall curves, and calibration curves. SHAP (Shapley Additive Explanations) analysis was conducted to interpret feature contributions. Both models demonstrated strong predictive performance, with AUCs reaching 0.96. The SVC model achieved slightly higher average precision and recall, making it more suitable for community- or school-based screening of high-risk children. In contrast, the RF model exhibited superior calibration, suggesting its greater utility in clinical decision-making where probabilistic risk estimation guides personalized follow-up or intervention planning. SHAP analysis identified glomerular filtration rate (GFR), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB) as key predictors, some exhibiting nonlinear associations with disease risk. RF and SVC models offer reliable tools for early risk prediction of obesity and hyperuricemia in children, each tailored to distinct clinical scenarios. These findings support early identification and targeted intervention. Future studies will explore the integration of metabolomic data and ensemble approaches to further enhance model performance and clinical applicability. Show less
📄 PDF DOI: 10.2147/DMSO.S519284
APOB
Payel Roy, Anusha Bellapu, Sujit Silas Armstrong Suthahar +11 more · 2025 · Nature cardiovascular research · Nature · added 2026-04-24
Atherosclerosis underlies most coronary artery disease (CAD). It involves a significant autoimmune component against apolipoprotein B (APOB). In this study, we used short activation-induced marker (AI Show more
Atherosclerosis underlies most coronary artery disease (CAD). It involves a significant autoimmune component against apolipoprotein B (APOB). In this study, we used short activation-induced marker (AIM) assays to characterize APOB-reactive CD4 Show less
no PDF DOI: 10.1038/s44161-025-00671-9
APOB
Robert N Helsley, Mikala M Zelows, Victoria P Noffsinger +11 more · 2025 · Arteriosclerosis, thrombosis, and vascular biology · added 2026-04-24
Genome- and epigenome-wide association studies have associated variants and methylation status of CPT1a (carnitine palmitoyltransferase 1a) to reductions in VLDL (very low-density lipoprotein) cholest Show more
Genome- and epigenome-wide association studies have associated variants and methylation status of CPT1a (carnitine palmitoyltransferase 1a) to reductions in VLDL (very low-density lipoprotein) cholesterol and triglyceride levels. The objective of this study was to determine the mechanisms by which CPT1a-dependent mitochondrial fatty acid oxidation influences hepatic and lipoprotein metabolism. Eight-week-old male and female We report significant associations between the presence of These studies provide mechanistic insight linking genetic variants and methylation status of Show less
no PDF DOI: 10.1161/ATVBAHA.125.322473
APOB
Yayu Wang, Yue Chang, Pei Zhang +4 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the d Show more
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the dystrophin gene (DMD) encoding dystrophin. DMD is a primary muscle disorder that often presents with secondary abnormalities in lipid metabolism and decreased bone mineral density. Although disturbances in circulating lipid profiles and skeletal health have been observed in individuals with DMD, their relationship remains underexplored.This study aimed to investigate the potential association between lipid metabolic disturbances and spinal bone mineral density in patients with DMD by combining clinical lipid levels and bone density with transcriptomic pathway analysis of DMD muscle tissue. Retrospective analysis was performed on 219 genetically confirmed DMD patients and 99 age-matched healthy controls. Healthy controls with a family history of genetic disorders were excluded. Clinical data included lipid profiles (triglycerides [TGs], remnant cholesterol [RC]); bone mineral density of the lumbar spine was evaluated using Dual-energy X-ray absorptiometry (DXA); and corticosteroid use, including treatment status, dose, and duration. Patients were stratified by corticosteroid exposure. Restricted cubic splines and multivariable regression models were applied to explore potential relationships between lipid parameters and bone mineral density. Bioinformatic analyses were performed on RNA sequencing data from muscle biopsy samples from patients with DMD (GSE38417 dataset) and an independent validation cohort (GSE6011 dataset), focusing on pathways related to lipid metabolism and osteoclast differentiation. Patients with DMD had higher TG, RC, and apolipoprotein B (ApoB) levels and lower high-density lipoprotein cholesterol (HDL-C) levels than healthy controls (P < 0.05). Elevated TG and RC levels were associated with reduced spine bone mineral density, independent of corticosteroid use. The bioinformatic analyses identified key pathways, including sphingolipid metabolism and osteoclast differentiation, as well as hub genes such as FCGR2B, C1QA, which are involved in lipid regulation and bone remodeling. Lipid abnormalities, particularly elevated TG and RC levels, were significantly associated with lower bone mineral density in patients with DMD. These findings suggest that lipid abnormalities are involved in bone health impairment in DMD, warranting further studies to confirm the association. Show less
📄 PDF DOI: 10.1186/s12944-025-02628-0
APOB
Aochuan Sun, Yiduo Chen, Yang Wu +3 more · 2025 · Reviews in cardiovascular medicine · added 2026-04-24
Previous studies have indicated that blood lipids can influence skeletal health. However, limited research exists on the impact of serum apolipoprotein B (ApoB) on bone mineral density (BMD); meanwhil Show more
Previous studies have indicated that blood lipids can influence skeletal health. However, limited research exists on the impact of serum apolipoprotein B (ApoB) on bone mineral density (BMD); meanwhile, it remains unclear to what extent cardiovascular disease plays in mediating this process. Therefore, we conducted a cross-sectional analysis involving 2930 participants from the National Health and Nutrition Examination Survey (NHANES) database to explore the relationship between serum ApoB and total body BMD (TB-BMD) and lumbar spine BMD (LS-BMD). We employed a two-step, two-sample Mendelian randomization (MR) analysis using genetic instruments to investigate causality and assess the mediating effects of six cardiovascular diseases. Multivariable linear regression models demonstrated an inverse linear association between serum ApoB and TB-BMD (β = -0.26, 95% confidence interval (CI): -0.41 to -0.12, The results of this study support that lowering serum ApoB levels could enhance BMD while preventing the occurrence of heart failure might reduce the harm caused by the decrease in BMD due to elevated ApoB levels. Show less
📄 PDF DOI: 10.31083/RCM31395
APOB
Hui Wang, Sensen Wu, Dikang Pan +6 more · 2025 · Nutrition & diabetes · Nature · added 2026-04-24
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic re Show more
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic regression to assess the relationship between ApoB and DN risk, utilizing data from the National Health and Nutrition Examination Survey spanning 2007-2016. Then, we used restricted cubic splines (RCS) to flexibly model and visualize the relation of predicted ApoB levels with DN risk. Subsequently, a bidirectional two-sample Mendelian randomization study using genome-wide association study summary statistics was performed. The primary Inverse Variance Weighted method, along with supplementary MR approaches, was employed to verify the causal link between ApoB and DN. Sensitivity analyses were conducted to confirm the robustness of the results. Our observational study enrolled 2242 participants with diabetes mellitus from NHANES. The multivariable logistic regression model indicated that elevated ApoB levels (>1.2 g/L), compared to low levels (<0.8 g/L), were significantly associated with DN risk (P < 0.05). The RCS model revealed a positive linear association with the risk of DN when ApoB levels exceeded 1.12 g/L (OR = 1.29, 95% CI: 1.07-1.57, P = 0.008). However, the MR IVW method did not reveal a direct causal effect of DN on ApoB (OR: 0.976; 95% CI: 0.950-1.004; P = 0.095), nor a direct causal effect of ApoB on DN (OR: 0.837; 95% CI: 0.950-1.078; P = 0.428). The evidence from observational studies indicates a positive correlation between ApoB levels exceeding 1.12 g/L and the onset of DN. However, the causal effects of ApoB on DN and vice versa were not supported by the MR analysis. Show less
📄 PDF DOI: 10.1038/s41387-025-00370-1
APOB
Liubo Xiang, Huan Wu, Zhihao Zhao +6 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
This study aimed to evaluate the impact of combining high-intensity statins with CETP inhibitors on lipid levels, as well as to explore their potential clinical significance. We conducted a comprehens Show more
This study aimed to evaluate the impact of combining high-intensity statins with CETP inhibitors on lipid levels, as well as to explore their potential clinical significance. We conducted a comprehensive search of relevant studies in the PubMed, Embase, Cochrane Library, and Web of Science databases. The Cochrane Risk of Bias Tool RoB 2.0 was employed to evaluate the quality of the included studies. Statistical analyses were carried out using STATA 15 software, with primary outcomes being high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). Out of 2,552 records, 7 studies were included in the final analysis. The findings revealed that the combination of high-intensity statins with CETP inhibitors significantly raised HDL-C levels (SMD 2.47 [1.77, 3.18], p < 0.001) and lowered LDL-C levels (SMD -1.75 [-2.19, -1.31], p < 0.001). Compared to statin monotherapy, the combination of high-intensity statins and CETP inhibitors resulted in a more pronounced increase in HDL-C and ApoAI, while reducing LDL-C, triglycerides (TG), and ApoB levels, without increasing the incidence of adverse events. Show less
📄 PDF DOI: 10.3389/fendo.2025.1512670
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
Ruibing Li, Jinyang Wang, Jianan Wang +7 more · 2025 · Journal of inflammation research · added 2026-04-24
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in s Show more
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in seronegative cases, highlighting the need for reliable biomarkers to enhance clinical management. This study aimed to identify serum lipid biomarkers for the diagnosis and monitoring of NMOSD and to assess their potential to improve clinical decision-making. We conducted a comprehensive serum proteomic analysis in a discovery cohort of NMOSD patients and controls to identify lipid-related proteins associated with NMOSD. Subsequently, we validated the candidate biomarkers in the retrospective cohort and developed diagnostic models using a random forest algorithm. The association between these lipid biomarkers and disease activity was further evaluated in longitudinal analysis. Our analysis identified a panel of serum lipid-related biomarkers that demonstrated significant differences between NMOSD patients and controls. The diagnostic models achieved the impressive accuracy of 72% for the full NMOSD spectrum, 72% for AQP4-IgG+ NMOSD, and 68% for double seronegative NMOSD. Importantly, these biomarkers showed a correlation with disease activity, with levels changing from relapse to remission. Additionally, a combination of these lipid biomarkers was found to predict relapse with the AUC of 0.861. A user-friendly smartphone application was developed to facilitate the straightforward "input-index, output-answer" screening process, enhancing both clinical decision-making and patient care. The diagnostic model based on the serum lipid-related indexes (TC, TG, LDL, HDL, ApoA1, and ApoB) may be the useful tool for NMOSD in diagnosis and monitoring of disease stage, thereby improving the treatment outcome for patients. Future studies should focus on integrating these biomarkers into routine clinical practice to realize their full potential in enhancing NMOSD management. Show less
📄 PDF DOI: 10.2147/JIR.S496018
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Yuexin Xu, Yingzi Pan, Chengqian Wu +3 more · 2025 · American journal of reproductive immunology (New York, N.Y. : 1989) · Blackwell Publishing · added 2026-04-24
Pre-eclampsia (PE) is a common complication of pregnancy and there is an urgent need for new drug targets. We performed whole proteome-wide Mendelian randomisation (MR) and colocalisation analyses to Show more
Pre-eclampsia (PE) is a common complication of pregnancy and there is an urgent need for new drug targets. We performed whole proteome-wide Mendelian randomisation (MR) and colocalisation analyses to identify potential therapeutic targets for PE. A two-sample MR study was conducted using summary-level statistics of 734 plasma proteins retrieved from large genome-proteome-wide association studies. The summary statistics of PE or eclampsia were obtained from the FinnGen consortium. Wald ratio and Inverse variance weighted (IVW) were used to assess the causal association between proteins and PE. Colocalisation analyses were conducted to examine whether the identified proteins and PE shared incidental variants. Genetically predicted circulating levels of 42 proteins were associated with PE risk after Benjamini-Hochberg correction. Nineteen of the gene-predicted proteins showed evidence of increased PE risk (CRELD1, CPA4, AHSG, NFASC, QDPR, NTM, PZP, FAM171B, RTN4R, FLRT2, ADH4, ADM, SPINK5, LGALS4, CKM, SPON2, UROS, CXCL10 and APOBEC3G); 23 proteins reduced the risk of PE (CLIC5, NEO1, SWAP70, KLK8, VWA2, FSTL1, CXCL11, APOB, NPPB, CNTN4, IL12B, ACHE, TCN1, GFRA2, GNMT, HPGDS, DPT, MANBA, SPARCL1, ACE, FUT8, BST1 and ACP1). Bayesian colocalisation indicated that six proteins (VWA2, ACHE, CXCL10, PZP, AHSG and UROS) and PE, which were identified as high evidence of colocalisation with PE. This study provides evidence of the causal association between genetically predicted 42 proteins associated with PE risk, which might be promising drug targets for PE. Show less
no PDF DOI: 10.1111/aji.70063
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Junhua Wu, Ming Qin, Yue Gao +5 more · 2025 · International journal of environmental health research · Taylor & Francis · added 2026-04-24
Previous studies found relationship between fluoride exposure and lipid metabolism. In present study, a cross-sectional study was conducted. Urinary fluoride concentrations and lipid metabolism indica Show more
Previous studies found relationship between fluoride exposure and lipid metabolism. In present study, a cross-sectional study was conducted. Urinary fluoride concentrations and lipid metabolism indicators were tested. Single nucleotide polymorphisms of ATP2B1 were sequenced. The median of urinary fluoride was 1.32 mg/L. Urinary fluoride was positively associated with the decrease in serum ApoA1 (OR = 1.48 [95% CI, 1.27-1.72]), inversely with ApoB elevation (OR = 0.69 [95% CI, 0.59-0.80]). Rs12817819 with carriers of T allele was associated with the decrease in serum ApoA1 (OR = 0.46 [95% CI, 0.26-0.81]), but inversely in rs17249754 with carriers for A allele (OR = 1.48 [95% CI, 1.07-2.06]) and rs7136259 with carriers for T allele (OR = 1.70 [95% CI, 1.22-2.37]). There was an interaction between urinary fluoride which was lower than 0.9 mg/L and rs7136259 for carriers of T allele (OR = 2.67 [95% CI, 1.34-5.31]) in serum ApoA1 decrease. It indicated fluoride exposure might be associated with the alteration of serum ApoA1 and ApoB in adults. Show less
no PDF DOI: 10.1080/09603123.2025.2466674
APOB
Shuai Wang, Hanshen Zhou, Kaili Cai +4 more · 2025 · World journal of surgical oncology · BioMed Central · added 2026-04-24
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression o Show more
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression on the long-term survival after surgery. Between December 2015 and December 2019, a cohort of 509 patients diagnosed with PDAC and undergoing resection at our hospital was analyzed. They were stratified into two groups, Control group (Control) and study group (PPTDM), depending on the onset of postoperative diabetes mellitus. We analyzed the survival rates at 6 months, 12 months and 24 months post-operation in the two groups. We use univariate and logostic multivariate regressions to analyze the risk factors for PPTDM. ROC curve analysis was conducted to assess the diagnostic significance of perioperative FBG levels regarding patients' long-term survival rates. The Kaplan-Meier method was employed to assess the impact of both preoperative and postoperative FBG levels on the survival rates within 24 months for each patient group. The comparison of general clinical data between the two groups shows marginal differences without statistical significance(P > 0.05); Patients in PPTDM group had significantly higher BMI, preoperative jaundice proportion, larger tumor diameter, higher TNM stage and higher proportion of distal pancreatectomy (DP), with P values of 0.023, 0.010, 0.040, 0.012 and 0.005, respectively. The levels of preoperative FBG and postoperative FBG in PPTDM patients exhibited statistically significant elevation compared to the control group (P < 0.05). There were no significant differences in surgery-related indicators between the two groups in operative time, number of dissected positive lymph nodes, total number of dissected lymph nodes, intraoperative blood loss and other related data (P > 0.05). Hospitalization duration of PPTDM patients was longer than control group (P = 0.047). PPTDM group had significantly higher expression concentrations of BUN, Cr, TG, LDL and Apo-B factors (P = 0.023, 0.024, 0.013, 0.045 and 0.017). 17 patients (5.03%) died in the PPTDM group and 4 patients (2.35%) in control group which had significantly difference (P = 0.020). In univariate and logostic multivariate regression analysis indicated tumor size, jaundice, BUN, Cr, TG, LDL, Apo-B concentrations and DP approach were significantly correlated to the risk for PPTDM (P < 0.05). ROC curve analysis results showed combining of preoperative and postoperation FBG showed the highest diagnostic efficacy, followed by postoperation FBG and preoperative FBG. The AUC areas of the three groups were 0.745, 0.623 and 0.588, respectively, and the critical values of the three groups were 9.81/9.95 mmol/L, 10.18 mmol/L and 10.23 mmol/L, respectively, with statistical significance (P < 0.05). Results were considered statistically significant if the p-value was less than 0.05. PPTDM stands as a significant postoperative complication following pancreatic cancer surgery, characterized by a high incidence and severity. Several risk factors have garnered considerable attention among clinical surgeon. PPTDM may be an influential factor in postoperative prognosis of pancreatic cancer. The expression levels of preoperative and postoperative blood glucose hold diagnostic value for the long-term prognosis of pancreatic cancer patients. Early regulation and intervention by surgeons concerning perioperative FBG could potentially mitigate the risk of PPTDM. Show less
📄 PDF DOI: 10.1186/s12957-025-03705-5
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Robert S Rosenson, J Antonio G López, Daniel Gaudet +14 more · 2025 · JAMA cardiology · added 2026-04-24
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering Show more
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering RNA, blocks Lp(a) production by inducing degradation of apolipoprotein(a) messenger RNA. Olpasiran's effects on OxPL and systemic markers of inflammation are not well described. To assess the effects of olpasiran on OxPL, high-sensitivity interleukin 6 (hs-IL-6), and hs-C-reactive protein (hs-CRP) in the OCEAN(a)-DOSE randomized clinical trial. OCEAN(a)-DOSE was an international, multicenter, placebo-controlled, phase 2, dose-finding randomized clinical trial conducted between July 2020 and November 2022. A total of 281 patients with atherosclerotic cardiovascular disease and Lp(a) levels greater than 150 nmol/L were included. Participants were randomized to receive 1 of 4 active subcutaneous doses of olpasiran vs placebo: (1) 10 mg, administered every 12 weeks (Q12W); (2) 75 mg, Q12W; (3) 225 mg, Q12W; or (4) 225 mg, administered every 24 weeks (Q24W). OxPL on apolipoprotein B (OxPL-apoB), hs-CRP, and hs-IL-6 were assessed at baseline, week 36, and week 48 in 272 patients. The primary outcome was placebo-adjusted change in OxPL-apoB from baseline to week 36. Among 272 participants, median (IQR) age was 62 years (56-69), and 86 participants (31.6%) were female. Baseline median (IQR) Lp(a) concentration was 260.3 nmol/L (198.1-352.4) and median (IQR) OxPL-apoB concentration was 26.5 nmol/L (19.7-33.9). The placebo-adjusted mean percentage change in OxPL-apoB from baseline to week 36 was -51.6% (95% CI, -64.9% to -38.2%) for the 10-mg Q12W dose, -89.7% (95% CI, -103.0% to -76.4%) for the 75-mg Q12W dose, -92.3% (95% CI, -105.6% to -78.9%) for the 225-mg Q12W dose, and -93.7% (95% CI, -107.1% to -80.3%) for the Q24W dose (P < .001 for all). These effects were maintained to week 48 (-50.8%, -100.2%, -104.7%, and -85.8%, respectively; P < .001 for all). There was a strong correlation between percentage reduction in Lp(a) and OxPL-apoB for patients treated with olpasiran (r = 0.79; P < .001). Olpasiran did not significantly impact hs-CRP or hs-IL-6 compared with placebo to weeks 36 or 48 (P > .05). In the OCEAN(a)-DOSE multicenter randomized clinical trial, olpasiran led to a significant and sustained reduction in OxPL-apoB but no significant effects on hs-CRP or hs-IL-6. Show less
no PDF DOI: 10.1001/jamacardio.2024.5433
APOB
Zhipeng Gong, Dongsheng Wu, Yin Ku +4 more · 2025 · BMC pulmonary medicine · BioMed Central · added 2026-04-24
Observational studies have identified a possible connection between lipid-lowering medications and respiratory illnesses. However, it remains unclear whether lipid-lowering drugs is causative for resp Show more
Observational studies have identified a possible connection between lipid-lowering medications and respiratory illnesses. However, it remains unclear whether lipid-lowering drugs is causative for respiratory diseases, and we aimed to answer this question. We performed Mendelian randomization (MR) analyses by integrating data from genome-wide association studies (GWAS). Three statistical approaches were employed for MR analysis: inverse variance weighting (IVW), MR-Egger, and weighted median. The purpose was to evaluate the causal relationships between 10 drug targets that lower lipid levels and the likelihood of developing 7 respiratory diseases. Additional sensitivity analyses were conducted to ensure the robustness and validity of the results. After adjusting for multiple testing, our MR analysis identified APOB (odd ratios [OR]: 0.86; 95% confidence interval [CI]: 0.77 to 0.97; P Our findings suggest a likely causal relationship between respiratory diseases and lipid-lowering drug targets. Further mechanistic and clinical research is needed to confirm and validate these findings. Show less
📄 PDF DOI: 10.1186/s12890-025-03527-x
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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
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Xiying Ding, Yongxing Zhang, Yang Chen +5 more · 2025 · Journal of shoulder and elbow surgery · Elsevier · added 2026-04-24
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complicatio Show more
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complications following ARCR is retear. This study aims to evaluate the correlation between systemic lipid metabolism and retear occurrence after ARCR through a retrospective analysis of postoperative patients. This retrospective study reviewed consecutive patients of a single surgeon who underwent ARCR from January 2021 to January 2022. Eligibility for inclusion required complete sequential follow-up data, encompassing preoperative laboratory tests and a series of postoperative magnetic resonance imaging (MRI) evaluations at 1, 2, 3, and 6 months. Exclusion criteria included patients with incomplete laboratory tests, a history of tumors, prior shoulder surgeries, isolated subscapularis tendon tears, the rotator cuff related muscles are not clearly or completely displayed in MRI, absence of follow-up MRI, or those under treatment with lipid-lowering medications. Logistic regression analysis was employed to identify preoperative factors associated with retear, with statistical significance adjudged at P < .05. From the initial cohort of 400 patients who underwent ARCR during the study period, 202 met both inclusion and exclusion criteria. These patients were subsequently divided into a training group (n = 122) and a test group (n = 80), maintaining a ratio of 6:4. Statistical analysis revealed significant risk factors for post-ARCR retear including high body mass index (>27.1; odds ratio (OR): 5.994, 95% confidential interval (CI): 1.762-13.980; P = .042), subscapularis muscle fatty infiltration of Grades 3 and 4 (OR: 8.509, 95%CI: 3.811-17.702; P = .009), serum apolipoprotein B (ApoB) levels exceeding 1.4 g/L (OR: 9.658, 95%CI: 3.520-21.753; P = .028), and an ApoB/A1 ratio greater than 1.8 (OR: 5.098, 95%CI: 1.787-10.496; P = .016). Conversely, the serum high-density lipoprotein level above 1.2 mmol/L (OR: -3.342, 95%CI: -7.466 to 0.659; P = .039) served as a protective factor. The model incorporating these 5 factors predicted retear with a sensitivity of 78.3% and specificity of 98.0% (area under the curve = 0.924, accuracy = 90.3%). Moreover, a new model comprising 3 lipid metabolism-related factors including high-density lipoprotein, ApoB and the ApoB/A1 ratio showed a sensitivity of 80.5% and specificity of 83.2% (area under the curve = 0.866, accuracy = 85.8%) for predicting retear after ARCR. A predictive model utilizing key systemic lipid metabolism markers including HDL, ApoB, and the ApoB/A1 ratio, demonstrates effective forecasting of retear incidence following ARCR. Show less
no PDF DOI: 10.1016/j.jse.2024.12.031
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Fengwu Chen, Aizhen Yang, Yue Lu +7 more · 2025 · Nature communications · Nature · added 2026-04-24
Saturated fatty acid (SFA) and unsaturated fatty acid (UFA) have distinct impacts on health. Whether SFA and UFA are differentially transported in liver remains elusive. Here, we find the secretion of Show more
Saturated fatty acid (SFA) and unsaturated fatty acid (UFA) have distinct impacts on health. Whether SFA and UFA are differentially transported in liver remains elusive. Here, we find the secretion of UFA but not SFA esters is retarded in a male mouse hepatic endoplasmic reticulum (ER) stress model. Among 13 members of protein disulfide isomerase (PDI) family, only PDIA1 (PDI) deficiency leads to hepatosteatosis and hypolipidemia. In PDI-deficient male mouse liver, there is a severe accumulation but secretory blockade of UFA esters, whereas the accumulation and secretion of SFA esters remain normal. PDI catalyzes the oxidative folding of microsomal triglyceride transfer protein (MTP). In addition, PDI deficiency in hepatocytes abolishes Apolipoprotein B-100 (ApoB-100) very low-density lipoprotein (VLDL) secretion while maintaining partial ApoB-48 VLDL secretion. In summary, we find that the secretion of UFA esters is PDI-MTP indispensable, while SFA esters could be transferred out of liver via ApoB-48 VLDL through a PDI-MTP-independent pathway. Show less
📄 PDF DOI: 10.1038/s41467-025-56620-4
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
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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
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Yuhui Lai, Shaozhao Zhang, Yue Guo +11 more · 2025 · American heart journal · Elsevier · added 2026-04-24
Elevated lipoprotein(a) (Lp[a]) and apolipoprotein B (apoB) are individually associated with the risk of atherosclerotic cardiovascular disease (ASCVD). Moreover, previous basic research has implicate Show more
Elevated lipoprotein(a) (Lp[a]) and apolipoprotein B (apoB) are individually associated with the risk of atherosclerotic cardiovascular disease (ASCVD). Moreover, previous basic research has implicated the potential interaction between apoB and Lp(a) in the atherogenic process. We aimed to determine whether apoB levels significantly modulate ASCVD risk associated with Lp(a) in a large community-based population without baseline cardiovascular disease. Plasma Lp(a) and apoB were measured in the Atherosclerosis Risk in Communities (ARIC) study. Elevated Lp(a) was defined as the highest race-specific quintile, and elevated apoB was defined as ≥89 mg/dl (median value). The modifying effect of apoB on the Lp(a)-related risk of ASCVD and coronary heart disease (CHD) was determined using Cox regression models adjusted for cardiovascular risk factors. Among 12,988 ARIC participants, 3,888 ASCVD events and 1754 CHD events were observed. Elevated apoB (≥89 mg/dl) and elevated Lp(a) (race-specific quintile 5) were independently associated with ASCVD (hazard ratio [HR]: 1.19; 95% CI: 1.08-1.30; P <0.001; HR: 1.27; 95% CI: 1.16-1.40; P < .001, respectively). Lp(a)-by-apoB interaction was noted [Lp(a) (quintile 1-4 or quintile 5) * apoB (<89 or ≥89 mg/dl) = 0.002]. Compared to the concordantly low Lp(a) group, the individuals with high Lp(a) had a greater ASCVD risk only when apoB was elevated (HR: 1.48; 95% CI: 1.34-1.63; P < .001). In the context of primary prevention, ASCVD risk associated with Lp(a) was observed only when apoB was elevated. The measurement of apoB can further refine and contextualize the ASCVD risk associated with Lp(a). Show less
no PDF DOI: 10.1016/j.ahj.2024.11.014
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Lathan Liou, Judit García-González, Hei Man Wu +5 more · 2025 · Arteriosclerosis, thrombosis, and vascular biology · added 2026-04-24
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alt Show more
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine. Show less
no PDF DOI: 10.1161/ATVBAHA.124.321846
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Qiaofang Yan, Yuanyuan Du, Fei Huang +9 more · 2025 · PeerJ · added 2026-04-24
Diabetic nephropathy (DN) is the most intractable complication of diabetes. Despite decades of research, accurate diagnostic markers and effective therapeutic drugs are still elusive. Abnormal copper Show more
Diabetic nephropathy (DN) is the most intractable complication of diabetes. Despite decades of research, accurate diagnostic markers and effective therapeutic drugs are still elusive. Abnormal copper metabolism is also implicated in diabetes and its complications. This study aims to identify copper metabolism-related biomarkers and potential drugs for DN. DN datasets and copper metabolism-related genes (CMGs) were obtained from Gene Expression Omnibus (GEO) and GeneCards. Differentially expressed CMGs (DE-CMGs) were identified using the limma package and the Venn algorithm. Functional enrichment analysis and protein-protein interaction (PPI) network were performed to identify candidate hub genes. The single gene with an area under the receiver operating characteristic (ROC) curve > 0.7 was identified as a potential diagnostic biomarker of DN. Finally, these biomarkers were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. These validated hub genes were used to construct a combined prediction model, confirmed by additional GSE30528 and GSE30529 datasets. The correlation analysis between the expression level of the hub genes and the estimated glomerular filtration rate (eGFR) was carried out. Additionally, immune cell infiltration and potential target drugs were investigated for these biomarkers. Five hub genes associated with copper metabolism, namely CD36, CCL2, CASP3, LPL, and APOC3, were identified as biomarkers for the early diagnosis of DN. Utilizing multiple biomarkers enhanced diagnostic accuracy and specificity. CD36, CCL2, and CASP3 correlated negatively with eGFR levels, while LPL and APOC3 correlated positively. Additionally, these hub genes were significantly linked to various immune cell types, including macrophages M1 and M2, T cells, gamma delta resting dendritic cells, neutrophils, and NK cells. Furthermore, 15 agents targeting these biomarkers were retrieved from the DrugBank database. Our study identified key genes possibly related to copper metabolism in the pathological mechanism of DN that could serve as novel targets for the diagnosis and therapy of DN. Show less
📄 PDF DOI: 10.7717/peerj.20468
APOC3
Bo Wang, Li Qiang, Geng Zhang +6 more · 2025 · Medicine · added 2026-04-24
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is Show more
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is insensitive or limited. This study aimed to analyze differentially expressed proteins in the plasma of patients with HBV-ACLF using data-independent acquisition mass spectrometry to provide a reference for short-term prognosis. Fifty HBV-ACLF patients and 15 healthy controls were enrolled in this study. Of these, 10 patients with HBV-ACLF and 5 healthy volunteers participated in data-independent acquisition-based proteomics and the potential core proteins were screened out via bioinformatics. Apolipoprotein C3 (APOC3) was selected and quantified by enzyme linked immunosorbent assays in all patients. And the area under the curve (AUC) was calculated to evaluate the value of APOC3 in the diagnosis and prognosis of patients with HBV-ACLF. A total of 247 differentially expressed proteins were identified in the serum of patients in the HBV-ACLF and normal control groups. A total of 148 proteins were upregulated and 99 proteins were downregulated in the HBV-ACLF group compared with those in the normal group. The expression level of APOC3 was 1.65 ± 0.44 mg/mL in patients with HBV-ACLF, which was obviously lower than the normal controls (2.04 ± 0.22 mg/mL) (P < .001) (AUC was 0.766, with a sensitivity of 62%, and specificity of 93.3%). The expression level of APOC3 was 1.38 ± 0.44 mg/mL in the non-survival group, which was obviously lower than the survival group (1.83 ± 0.35 mg/mL) (P < .0001) (AUC was 0.780, with a sensitivity of 50%, and specificity of 96.7%). APOC3 is associated with short-term prognosis of patients with HBV-ACLF and can be used as a potential prognostic biomarker in patients with HBV-ACLF. Show less
📄 PDF DOI: 10.1097/MD.0000000000041503
APOC3
Lei Wu, Zhong Zhuang, Wenqian Jia +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an exp Show more
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an experimental facility until they were culled at 42 d of age. Thirty high RFI (HRFI) and 30 low RFI (LRFI) birds were selected to evaluate their growth performance, carcass characteristics, and muscle development. Transcriptome and weighted gene co-expression correlation network analyses of pectoral muscles were conducted on six LRFI and six HRFI ducks. The results revealed that selecting for LRFI significantly reduced feed consumption (P < 0.05) and improved feed efficiency without affecting the growth performance, slaughter rate, or meat quality of ducks (P > 0.05). Moreover, compared with HRFI ducks, LRFI ducks had a lower pectoral muscle fat content (P < 0.05), larger muscle fiber diameter and area (P < 0.05), and lower muscle fiber density (P < 0.05). There were significant differences in gene expression between LRFI and HRFI ducks, with 102 upregulated and 258 downregulated genes, which were enriched in the PPAR signaling pathway, adipocytokine signaling pathway, actin cytoskeleton regulation, ECM-receptor interaction, and focal adhesion. The expression of genes associated with fat and energy metabolism, including ACSL6, PCK1, APOC3, HMGCS2, PRKAG3, and G6PC1, was downregulated in LRFI ducks, and weighted gene co-expression correlation network analysis identified PRKAG3 as a hub gene. Our findings indicate that reduced mitochondrial energy metabolism may contribute to the RFI of slow-growing ducks, with PRKAG3 playing a pivotal role in this biological process. These findings provide novel insights into the molecular changes underlying RFI variation in slow-growing ducks. Show less
📄 PDF DOI: 10.1016/j.psj.2024.104613
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Xiangyang Cheng, Chong Bian, Yiming Zhang +3 more · 2025 · Archives of medical science : AMS · added 2026-04-24
Osteosarcoma (OS) is a highly malignant bone tumor with limited treatment options. The role of apolipoprotein E (
📄 PDF DOI: 10.5114/aoms/203990
APOE
Weibin Wu, Zheng Peng, Yi Yu +5 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Increasing evidence suggests that familial hypercholesterolemia (FHC) exacerbates myocardial infarction (MI). This study aimed to identify possible candidate biomarkers for patients with FHC and MI. T Show more
Increasing evidence suggests that familial hypercholesterolemia (FHC) exacerbates myocardial infarction (MI). This study aimed to identify possible candidate biomarkers for patients with FHC and MI. The data were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using Limma, while module genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA) in GSE48060. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, protein-protein interaction (PPI) network and CIBERSORT methods were performed to explore the intersection genes. A receiver operating characteristic (ROC) curve were employed to evaluate the diagnostic effectiveness, with validation conducted using datasets GSE61144 and RT-qPCR. The FHC datasets included 656 DEGs, while there were 956 DEGs and 90 module genes in MI datasets. There were 49 overlapping DEGs between FHC and MI, which were associated with immune functions. Additionally, immune infiltration analysis revealed disturbances in immune cell populations. There were 13 candiate hub genes were screen after PPI network analysis. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1715216
APOE
Andong Wu, Jiayi Dong, Jiankun Liu +10 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu18010021
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Shaoshen Wang, Xiangxiang Shi, Xiaoqi Li +9 more · 2025 · International journal of nanomedicine · added 2026-04-24
The early, precise, and safe management of vulnerable atherosclerotic plaques (VAPs) remains a formidable clinical challenge. Here, we present a targeted nanotherapeutic approach in which osteopontin- Show more
The early, precise, and safe management of vulnerable atherosclerotic plaques (VAPs) remains a formidable clinical challenge. Here, we present a targeted nanotherapeutic approach in which osteopontin-targeted nanoparticles encapsulate luteolin (NPs-Lut) for the precise delivery and treatment of VAPs. This engineered system enables site-specific accumulation and sustained release of luteolin at plaque sites. We innovatively constructed an osteopontin-targeted drug delivery system designed for vulnerable atherosclerotic plaques, in which luteolin and atorvastatin were successfully encapsulated. The system demonstrated sustained-release capability in vitro, and its biosafety and histocompatibility were comprehensively evaluated both in vitro and in vivo. Moreover, therapeutic efficacy was further assessed in ApoE In vivo evaluation in ApoE This work provides a robust and translationally promising nanoplatform for the precision treatment of VAPs, offering a novel strategy for safe and effective intervention in atherosclerotic cardiovascular disease. Show less
📄 PDF DOI: 10.2147/IJN.S566896
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