πŸ‘€ Xiaozhun Huang

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Also published as: Ai-Chun Huang, Ai-long Huang, Aijie Huang, Ailong Huang, Aimin Huang, Alden Y Huang, An-Fang Huang, Annie Huang, Aohuan Huang, Ariane Huang, Baihai Huang, Baisong Huang, Bao-Hua Huang, Bao-Yi Huang, Baoqin Huang, Baoying Huang, Benjamin J Huang, Benlin Huang, Bevan E Huang, Bi Huang, Biao Huang, Bin Huang, Binfang Huang, Bing Huang, Bingcang Huang, Bingkun Huang, Bizhi Huang, Bo Huang, Bo-Shih Huang, Bor-Ren Huang, Bowen Huang, Boyue Huang, C Y Huang, Caihong Huang, Caiyun Huang, Can Huang, Canhua Huang, Caoxin Huang, Cathelin Huang, Catherine Huang, Chang Ming Huang, Chang X Huang, Chang-Jen Huang, Changjiang Huang, Chao Huang, Chao Wei Huang, Chao-Wei Huang, Chao-Yuan Huang, Chaolin Huang, Chaoqun Huang, Chaowang Huang, Chaoyang Huang, Chen Huang, Chen-Na Huang, Chen-Ping Huang, Cheng Huang, Chengcheng Huang, Chengrui Huang, Chenshen Huang, Chenxiao Huang, Chi-Cheng Huang, Chi-Shuan Huang, Chia-Chang Huang, Chia-Wei Huang, Chieh-Cheng Huang, Chieh-Liang Huang, Chien-Hsun Huang, Chih-Chun Huang, Chih-Hsiang Huang, Chih-Jen Huang, Chih-Ting Huang, Chih-Yang Huang, Chin-Chang Huang, Chin-Chou Huang, Ching-Shan Huang, Ching-Shin Huang, Ching-Tang Huang, Ching-Wei Huang, Chiu-Ju Huang, Chiu-Jung Huang, Chiun-Sheng Huang, Chong Huang, Chongbiao Huang, Christine S Huang, Chuan Huang, Chuanbing Huang, Chuanhong Huang, Chuanjiang Huang, Chuanjun Huang, Chuansheng Huang, Chuiguo Huang, Chun Huang, Chun-Mei Huang, Chun-Yao Huang, Chun-Yin Huang, Chunfan Huang, Chung-Hsiung Huang, Chunhong Huang, Chunjian Huang, Chunkai Huang, Chunlan Huang, Chunling Huang, Chunshuai Huang, Chunxia Huang, Chunyao Huang, Chunyi Huang, Chunying Huang, Chunyu Huang, Chuxin Huang, Chuying Huang, Congcong Huang, Cuiyu Huang, Da Huang, Dajun Huang, Dan Huang, Dane Huang, Danqing Huang, Dantong Huang, David Huang, David J Huang, De Huang, De-Jun Huang, Dejia Huang, Dengjun Huang, Dianhua Huang, Dishu Huang, Dong Huang, Donglan Huang, Dongmei Huang, Dongni Huang, Dongqin Huang, Dongqing Huang, Dongsheng Huang, Dongyu Huang, Du-Juan Huang, Emily C Huang, Enhao Huang, Enping Huang, Eric Huang, Erya Huang, F Huang, Fan Huang, Fang Huang, Fang-Ling Huang, Fangling Huang, Fei Huang, Fei Wan Huang, Feiruo Huang, Feiteng Huang, Feizhou Huang, Feng Huang, Fengxian Huang, Fengyu Huang, Franklin W Huang, Fu-Chen Huang, Fu-Mei Huang, Fubiao Huang, Fude Huang, Fuhao Huang, Furong Huang, G Huang, Gairong Huang, Gang Huang, Gao-Zhong Huang, Gaoxingyu Huang, Ge Huang, Guang-Jian Huang, Guang-Yun Huang, Guangjian Huang, Guangming Huang, Guangqian Huang, Guangrui Huang, Guanhong Huang, Guanling Huang, Guanning Huang, Guanqun Huang, Guanrong Huang, Guicheng Huang, Guodong Huang, Guohong Huang, Guoping Huang, Guoqian Huang, Guowei Huang, Guoxing Huang, Guoying Huang, Guoyong Huang, Guoyuan Huang, H Huang, H S Huang, Hai Huang, Haigang Huang, Haihong Huang, Hailin Huang, Haimiao Huang, Haixin Huang, Haiyan Huang, Han-Chang Huang, Hanxia Huang, Hao Huang, Hao-Fei Huang, Haobo Huang, Haochu Huang, Haomin Huang, Haoyu Huang, Haoyue Huang, Haozhang Huang, Haozhong Huang, He Huang, Hefeng Huang, Heguang Huang, Helen Huang, Heming Huang, Hengbin Huang, Heqing Huang, Hete Huang, Hong Huang, Hongbiao Huang, Hongcan Huang, Hongda Huang, Hongfei Huang, Hongfeng Huang, Honghui Huang, Hongou Huang, Hongqiang Huang, Hongyan Huang, Hongyang Huang, Hongyi Huang, Hongying Huang, Hongyu Huang, Hongyun Huang, Hsi-Yuan Huang, Hsien-Da Huang, Hsing-Yen Huang, Hsu Chih Huang, Hsuan-Cheng Huang, Hsuan-Ying Huang, Hu Huang, Hua Huang, Huafei Huang, Huaju Huang, Huan Huang, Huanhuan Huang, Huanliang Huang, Huapin Huang, Huashan Huang, Huayun Huang, Hui Huang, Hui-Huang Huang, Hui-Kuang Huang, Hui-Yu Huang, Huibin Huang, Huifen Huang, Huiling Huang, Huimin Huang, Huina Huang, Huiqiao Huang, Huixian Huang, Huixin Huang, Huiyan Huang, Huiyu Huang, Huizhe Huang, Huizhen Huang, Hy Huang, I-Chieh Huang, J V Huang, Janice J Huang, Jasmin Huang, Jeffrey K Huang, Jia Huang, Jia-Jia Huang, Jiaan Huang, Jiahui Huang, Jiajin Huang, Jiajun Huang, Jian Huang, Jian-Dong Huang, Jiana Huang, Jianbiao Huang, Jianbing Huang, Jianfang Huang, Jianfeng Huang, Jiangfeng Huang, Jiangtao Huang, Jiangwei Huang, Jianhua Huang, Jianlu Huang, Jianmin Huang, Jianming Huang, Jiansheng Huang, Jianzhen Huang, Jiao-Qian Huang, Jiaoti Huang, Jiaotian Huang, Jiaqi Huang, Jiawen Huang, Jiaxing Huang, Jiayu Huang, Jiayue Huang, Jie Huang, Jie Qi Huang, Jiechun Huang, Jieli Huang, Jieling Huang, Jieping Huang, Jin Huang, Jin-Di Huang, Jin-Feng Huang, Jin-Hong Huang, Jin-Yan Huang, Jinbao Huang, Jinfang Huang, Jing Huang, Jing-Fei Huang, Jingang Huang, Jinghan Huang, Jingjing Huang, Jingkun Huang, Jinglong Huang, Jingtao Huang, Jingxian Huang, Jingyong Huang, Jingyuan Huang, Jingyue Huang, Jinhua Huang, Jinling Huang, Jinlu Huang, Jinshu Huang, Jinxing Huang, Jinyan Huang, Jinzhou Huang, Jiuhong Huang, Jiyu Huang, Ju Huang, Juan Huang, Jucun Huang, Jun Huang, Jun-Hua Huang, Jun-You Huang, Junhao Huang, Junhua Huang, Junjie Huang, Junming Huang, Junning Huang, Junqi Huang, Junwen Huang, Junyuan Huang, Junyun Huang, Juxiang Huang, K Huang, K N Huang, Kai Huang, Kaipeng Huang, Kang Huang, Kangbo Huang, Kate Huang, Katherine Huang, Ke Huang, Ke-Ke Huang, Ke-Pu Huang, Kevin Huang, Kevin Y Huang, Kuan-Chun Huang, Kui-Yuan Huang, Kuiyuan Huang, Kun Huang, Kuo-Hsiang Huang, Kuo-Hung Huang, L Huang, L-B Huang, Laiqiang Huang, Lan Huang, Lanlan Huang, Lei Huang, Leijuan Huang, Li Huang, Li-Hao Huang, Li-Jiang Huang, Li-Juan Huang, Li-Jun Huang, Li-Ping Huang, Li-Rung Huang, Li-Wei Huang, Li-Yun Huang, Lian Huang, Liang Huang, Liang-Yu Huang, Liangchong Huang, Lianggui Huang, Libin Huang, Lige Huang, Lihua Huang, Lijia Huang, Lijiang Huang, Lijuan Huang, Lijun Huang, Lili Huang, Limin Huang, Liming Huang, Lin Huang, Linchen Huang, Ling Huang, Ling-Chun Huang, Ling-Jin Huang, Lingling Huang, Lining Huang, Linjing Huang, Linsheng Huang, Linxue Huang, Linyuan Huang, Liping Huang, Liqiong Huang, Lixia Huang, Lixiang Huang, Lixuan Huang, Lixue Huang, Lizhen Huang, Longfei Huang, Lu Huang, Lu-Jie Huang, Lu-Qi Huang, Luanluan Huang, Luqi Huang, Luyang Huang, Luyao Huang, Lvzhen Huang, M C Huang, Man Huang, Manning Y Huang, Manyun Huang, Mao-Mao Huang, Mei Huang, Meihua Huang, Meina Huang, Meixiang Huang, Melissa Y Huang, Meng-Chuan Huang, Meng-Fan Huang, Meng-Na Huang, MengQian Huang, Menghao Huang, Mengjie Huang, Mengjun Huang, Mengnan Huang, Mengting Huang, Mengzhen Huang, Mia L Huang, Miao Huang, Min Huang, Ming-Lu Huang, Ming-Shyan Huang, Mingjian Huang, Mingjun Huang, Minglei Huang, Mingrui Huang, Mingwei Huang, Mingxuan Huang, Mingyu Huang, Mingyuan Huang, Minjun Huang, Minqi Huang, Minxuan Huang, Minyuan Huang, N Huang, Na Huang, Nian Huang, Nianyuan Huang, Ning-Na Huang, Ning-Ping Huang, Ninghao Huang, Nongyu Huang, Pan Huang, Pang-Shuo Huang, Paul L Huang, Pei Huang, Pei-Chi Huang, Pei-Ying Huang, Peiying Huang, Peng Huang, Peng-Fei Huang, Pengyu Huang, Piao-Piao Huang, Piaopiao Huang, Pin-Rui Huang, Ping Huang, Pingping Huang, Pintong Huang, Po-Hsun Huang, Po-Jung Huang, Poyao Huang, Qi Huang, Qi-Tao Huang, Qian Huang, Qiang Huang, Qianqian Huang, Qiaobing Huang, Qibin Huang, Qidi Huang, Qin Huang, Qing Huang, Qing-yong Huang, Qingjiang Huang, Qingke Huang, Qingling Huang, Qingqing Huang, Qingsong Huang, Qingxia Huang, Qingxing Huang, Qingyu Huang, Qingzhi Huang, Qinlou Huang, Qiong Huang, Qiubo Huang, Qiumin Huang, Qiuming Huang, Qiuru Huang, Qiuyin Huang, Qiuyue Huang, Qizhen Huang, Quanfang Huang, Qun Huang, R H Huang, R Stephanie Huang, Rae-Chi Huang, Ran Huang, Renbin Huang, Renhua Huang, Renli Huang, Richard Huang, Richard S P Huang, Riqing Huang, Ritai Huang, Robert J Huang, Rong Huang, Rong Stephanie Huang, Ronghua Huang, Ronghui Huang, Rongjie Huang, Rongrong Huang, Rongxiang Huang, Ru-Ting Huang, Ruby Yun-Ju Huang, Rui Huang, Ruihua Huang, Ruijin Huang, Ruina Huang, Ruiyan Huang, Ruizhen Huang, Runyue Huang, Ruo-Hui Huang, S Huang, S Y Huang, S Z Huang, Saisai Huang, San-Yuan Huang, See-Chang Huang, Sen Huang, Serina Huang, Shan Huang, Shang-Ming Huang, Shanhe Huang, Shanshan Huang, Shaojun Huang, Shaoxin Huang, Shaoze Huang, Shau Ku Huang, Shau-Ku Huang, Shenan Huang, Sheng-He Huang, Shengfeng Huang, Shengjie Huang, Shengnan Huang, Shengyan Huang, Shengyun Huang, Shi-Feng Huang, Shi-Shi Huang, Shi-Ying Huang, Shiang-Suo Huang, Shichao Huang, Shih-Chiang Huang, Shih-Wei Huang, Shih-Yi Huang, Shihao Huang, Shijing Huang, Shilu Huang, Shixia Huang, Shiya Huang, Shiying Huang, Shiyun Huang, Shoucheng Huang, Shu Huang, Shu-Pang Huang, Shu-Pin Huang, Shu-Qiong Huang, Shu-Wei Huang, Shu-Yi Huang, Shu-ying Huang, Shuai Huang, Shuang Huang, Shungen Huang, Shuo Huang, Shushu Huang, Shutong Huang, Shuwen Huang, Si-Yang Huang, Sidong Huang, Sihua Huang, Sijia Huang, Sinchun Huang, Sisi Huang, Sixiu Huang, Song Bin Huang, Song-Mei Huang, Songmei Huang, Songming Huang, Songqian Huang, Steven Huang, Steven Kuan-Hua Huang, Suli Huang, Sung-Ying Huang, Susan M Huang, Suwen Huang, Taiqi Huang, Tang-Hsiu Huang, Tao Huang, Te-Hsuan Huang, Tengda Huang, Tengfei Huang, Tian Hao Huang, Tianhao Huang, Tianpu Huang, Tiantian Huang, Tieqiu Huang, Tim H Huang, Ting Huang, Tinghua Huang, Tingping Huang, Tingqin Huang, Tingting Huang, Tingxuan Huang, Tingyun Huang, Tong Huang, Tongsheng Huang, Tongtong Huang, Tony T Huang, Tse-Shun Huang, Tseng-Yu Huang, Tsung-Wei Huang, Tzu-Rung Huang, Wan-Ping Huang, Way-Ren Huang, Wei Huang, Wei-Chi Huang, Weibin Huang, Weicheng Huang, Weifeng Huang, Weihua Huang, Weijun Huang, Weiqi Huang, Weisu Huang, Weiwei Huang, Weixue Huang, Weizhen Huang, Wen Huang, Wen-yu Huang, Wenbin Huang, Wenda Huang, Wenfang Huang, Wenfeng Huang, Wenhua Huang, Wenji Huang, Wenjie Huang, Wenjun Huang, Wenqiao Huang, Wenqing Huang, Wenqiong Huang, Wenshan Huang, Wentao Huang, Wenxin Huang, Wenya Huang, Wenying Huang, Wunan Huang, Wuqing Huang, X F Huang, X Huang, Xi Huang, Xian-sheng HUANG, Xiang Huang, Xianghua Huang, Xianglong Huang, Xiangming Huang, Xianping Huang, Xianqing Huang, Xiansheng Huang, Xianwei Huang, Xianxi Huang, Xianxian Huang, Xianying Huang, Xianzhang Huang, Xiao Huang, Xiao-Fang Huang, Xiao-Fei Huang, Xiao-Ming Huang, Xiao-Song Huang, Xiao-Yan Huang, Xiao-Yong Huang, Xiao-Yu Huang, XiaoFang Huang, Xiaochun Huang, Xiaofei Huang, Xiaofeng Huang, Xiaohong Huang, Xiaohua Huang, Xiaojie Huang, Xiaojing Huang, Xiaojuan Huang, Xiaolan Huang, Xiaoli Huang, Xiaolin Huang, Xiaoman Huang, Xiaomin Huang, Xiaoqing Huang, Xiaoshuai Huang, Xiaowen Huang, Xiaowu Huang, Xiaoxia Huang, Xiaoyan Huang, Xiaoying Huang, Xiaoyu Huang, Xiaoyuan Huang, Xiaoyun Huang, Xiayang Huang, Xichang Huang, Xie-Lin Huang, Xin Huang, Xin-Di Huang, Xinen Huang, Xinfeng Huang, Xingguo Huang, Xingming Huang, Xingqin Huang, Xingru Huang, Xingxu Huang, Xingya Huang, Xingzhen Huang, Xinwen Huang, Xinyi Huang, Xinying Huang, Xinyue Huang, Xinzhu Huang, Xiongfeng Huang, Xionggao Huang, Xiuju Huang, Xiuyun Huang, Xiuzhen Huang, Xiwen Huang, Xu Huang, Xu-Feng Huang, Xuan Huang, Xuanzhang Huang, Xucong Huang, Xudong Huang, Xue-Ying Huang, Xue-shuang Huang, Xuehong Huang, Xuejie Huang, Xuejing Huang, Xuejun Huang, Xuemei Huang, Xueming Huang, Xueqi Huang, Xuewei Huang, Xuezhe Huang, Xuhui Huang, Xuliang Huang, Xun Huang, Xuxiong Huang, Y Huang, Y Joyce Huang, Y S Huang, Ya-Chih Huang, Ya-Dong Huang, Ya-Fang Huang, Ya-Ru Huang, Yabo Huang, Yadong Huang, Yafang Huang, Yajiao Huang, Yajuan Huang, Yali Huang, Yamei Huang, Yan Huang, Yan-Lin Huang, Yan-Qing Huang, Yan-Ting Huang, Yang Huang, Yang Zhong Huang, Yangqing Huang, Yangyang Huang, Yanhao Huang, Yani Huang, Yanjun Huang, Yanlong Huang, Yanna Huang, Yanping Huang, Yanqin Huang, Yanqing Huang, Yanqun Huang, Yanru Huang, Yanshan Huang, Yansheng Huang, Yanxia Huang, Yanyan Huang, Yanyao Huang, Yao Huang, Yao-Kuang Huang, Yaowei Huang, Yatian Huang, Yating Huang, Ye Huang, Yechao Huang, Yen-Chu Huang, Yen-Ning Huang, Yen-Tsung Huang, Yeqing Huang, Yewei Huang, Yi Huang, Yi-Chun Huang, Yi-Jan Huang, Yi-Jia Huang, Yi-Wen Huang, Yi-ping Huang, Yichao Huang, Yichuan Huang, Yicong Huang, Yifan Huang, Yihao Huang, Yiheng Huang, Yihong Huang, Yikeng Huang, Yilin Huang, Yin Huang, Yin-Tsen Huang, Ying Huang, Ying-Hsuan Huang, Ying-Jung Huang, Ying-Zhi Huang, Yinghua Huang, Yingying Huang, Yingzhen Huang, Yingzhi Huang, Yiping Huang, Yiquan Huang, Yishan Huang, Yiwei Huang, Yixian Huang, Yizhou Huang, Yong Huang, Yong-Fu Huang, Yongbiao Huang, Yongcan Huang, Yongjie Huang, Yongqi Huang, Yongsheng Huang, Yongtong Huang, Yongye Huang, Yongyi Huang, Yongzhen Huang, Youheng Huang, Youyang Huang, Yu Huang, Yu-Ching Huang, Yu-Chu Huang, Yu-Chuen Huang, Yu-Chyi Huang, Yu-Fang Huang, Yu-Han Huang, Yu-Jie Huang, Yu-Lei Huang, Yu-Ren Huang, Yu-Shu Huang, Yu-Ting Huang, Yuan Huang, Yuan-Lan Huang, Yuan-Li Huang, Yuan-Lu Huang, Yuancheng Huang, Yuanpeng Huang, Yuanshuai Huang, Yuanyu Huang, Yuanyuan Huang, Yue Huang, Yue-Hua Huang, Yuedi Huang, Yueh-Hsiang Huang, Yuehong Huang, Yuejun Huang, Yueye Huang, Yuezhen Huang, Yufang Huang, Yufen Huang, Yuguang Huang, Yuh-Chin T Huang, Yuhong Huang, Yuhua Huang, Yuhui Huang, Yujia Huang, Yujie Huang, Yulin Huang, Yumei Huang, Yumeng Huang, Yun Huang, Yun-Juan Huang, Yunchao Huang, Yung-Hsin Huang, Yung-Yu Huang, Yunmao Huang, Yunpeng Huang, Yunru Huang, Yunyan Huang, Yuping Huang, Yuqi Huang, Yuqiang Huang, Yuqiong Huang, Yusi Huang, Yutang Huang, Yuting Huang, Yutong Huang, Yuxian Huang, Yuxin Huang, Yuxuan Huang, Yuyang Huang, Yuying Huang, Z Huang, Z Z Huang, Z-Y Huang, Zebin Huang, Zebo Huang, Zehua Huang, Zeling Huang, Zengwen Huang, Zhang Huang, Zhao Huang, Zhaoxia Huang, Zhe Huang, Zhen Huang, Zhenfei Huang, Zheng Huang, Zheng-Xiang Huang, Zhengwei Huang, Zhengxian Huang, Zhengxiang Huang, Zhengyang Huang, Zhenlin Huang, Zhenrui Huang, Zhenyao Huang, Zhenyi Huang, Zhi Huang, Zhi-Ming Huang, Zhi-Qiang Huang, Zhi-Xin Huang, Zhi-xiang Huang, Zhican Huang, Zhicong Huang, Zhifang Huang, Zhifeng Huang, Zhigang Huang, Zhihong Huang, Zhilin Huang, Zhilong Huang, Zhipeng Huang, Zhiping Huang, Zhiqi Huang, Zhiqiang Huang, Zhiqin Huang, Zhiqing Huang, Zhitong Huang, Zhiwei Huang, Zhixiang Huang, Zhiying Huang, Zhiyong Huang, Zhiyu Huang, Zhongbin Huang, Zhongcheng Huang, Zhongfeng Huang, Zhonglu Huang, Zhouyang Huang, Zi-Xin Huang, Zi-Ye Huang, Zicheng Huang, Zichong Huang, Zihan Huang, Zihao Huang, Ziheng Huang, Ziling Huang, Zini Huang, Zirui Huang, Zizhan Huang, Zongjian Huang, Zongliang Huang, Zunnan Huang, Zuotian Huang, Zuxian Huang, Zuyi Huang
articles
Yiliang Zhang, Shengyang Zhou, Runming Zhao +2 more Β· 2025 Β· Life metabolism Β· Oxford University Press Β· added 2026-04-24
Graphical Abstract Lipoprotein lipase (LPL) mediates peripheral tissue triglyceride (TG) uptake. Hepatic ANGPTL3 (A3) and ANGPTL8 (A8) form a complex and inhibit LPL activity in the white adipose tiss Show more
Graphical Abstract Lipoprotein lipase (LPL) mediates peripheral tissue triglyceride (TG) uptake. Hepatic ANGPTL3 (A3) and ANGPTL8 (A8) form a complex and inhibit LPL activity in the white adipose tissue (WAT) via systematic circulation. ANGPTL4 (A4) isΒ expressedΒ in WAT and inhibits LPL activityΒ locally. Feeding increases hepatic A8 expression and increases its inhibition for WAT LPL activityΒ together with A3, while feeding suppresses WAT A4 expression and releases its inhibition on LPL. At room temperature, the feeding-suppressed A4 overrides the feeding-increased A3/A8, resulting in increased LPL activity in WAT by food intake. Browning improves hepatic insulin sensitivity and increases postprandial A8 expression. The feeding-increased A3/A8 overrides the feeding-suppressed A4, resulting in suppressed LPL activity in WAT by food intake. This reprogrammed LPL regulation plays an important role in reprogramming TG metabolism during adipose tissue browning. Show less
πŸ“„ PDF DOI: 10.1093/lifemeta/loae037
ANGPTL4
Yi Sun, Yan Peng, Zezhuo Su +10 more Β· 2025 Β· Bone research Β· Nature Β· added 2026-04-24
Fibrotic remodeling of nucleus pulposus (NP) leads to structural and mechanical anomalies of intervertebral discs that prone to degeneration, leading to low back pain incidence and disability. Emergen Show more
Fibrotic remodeling of nucleus pulposus (NP) leads to structural and mechanical anomalies of intervertebral discs that prone to degeneration, leading to low back pain incidence and disability. Emergence of fibroblastic cells in disc degeneration has been reported, yet their nature and origin remain elusive. In this study, we performed an integrative analysis of multiple single-cell RNA sequencing datasets to interrogate the cellular heterogeneity and fibroblast-like entities in degenerative human NP specimens. We found that disc degeneration severity is associated with an enrichment of fibrocyte phenotype, characterized by CD45 and collagen I dual positivity, and expression of myofibroblast marker Ξ±-smooth muscle actin. Refined clustering and classification distinguished the fibrocyte-like populations as subtypes in the NP cells - and immunocytes-clusters, expressing disc degeneration markers HTRA1 and ANGPTL4 and genes related to response to TGF-Ξ². In injury-induced mouse disc degeneration model, fibrocytes were found recruited into the NP undergoing fibrosis and adopted a myofibroblast phenotype. Depleting the fibrocytes in CD11b-DTR mice in which myeloid-derived lineages were ablated by diphtheria toxin could markedly attenuate fibrous modeling and myofibroblast formation in the NP of the degenerative discs, and prevent disc height loss and histomorphological abnormalities. Marker analysis supports that disc degeneration progression is dependent on a function of CD45 Show less
πŸ“„ PDF DOI: 10.1038/s41413-024-00372-2
ANGPTL4
Weifeng Huang, Liqing Jiang, Yingsong Jiang +6 more Β· 2025 Β· Scientific reports Β· Nature Β· added 2026-04-24
Acute rejection (AR) is a significant complication in liver transplantation, impacting graft function and patient survival. Kupffer cells (KCs), liver-specific macrophages, can polarize into pro-infla Show more
Acute rejection (AR) is a significant complication in liver transplantation, impacting graft function and patient survival. Kupffer cells (KCs), liver-specific macrophages, can polarize into pro-inflammatory M1 or anti-inflammatory M2 phenotypes, both of which critically influence AR outcomes. Angiopoietin-like 4 (ANGPTL4), a secretory protein, is recognized for its function in regulating inflammation and macrophage polarization. This study investigates the effects of ANGPTL4 on KC polarization through cellular interactions between hepatocytes (HCs) and KCs. Using a rat orthotopic liver transplantation model, we observed reduced ANGPTL4 expression during AR, whereas increased ANGPTL4 levels were linked to immune tolerance. Administration of ANGPTL4 recombinant protein improved liver function, suppressed inflammation, and promoted M2 polarization of KCs. Co-culture experiments demonstrated that hepatocyte-derived ANGPTL4 significantly modulates KC polarization and inflammatory responses, mainly by inhibiting the NF-ΞΊB signaling pathway. The results emphasize the promise of ANGPTL4 as a therapeutic target to reduce AR and improve liver transplant outcomes by influencing hepatocyte-KC interactions. Show less
πŸ“„ PDF DOI: 10.1038/s41598-024-81832-x
ANGPTL4
Ruyi Liu, Miaomiao Fu, Pengxiang Chen +6 more Β· 2025 Β· International journal of oncology Β· 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
πŸ“„ PDF DOI: 10.3892/ijo.2024.5715
ANGPTL4
Zhi Xiong, Rui-Lin Zhuang, Shun-Li Yu +8 more Β· 2025 Β· Journal of advanced research Β· Elsevier Β· added 2026-04-24
Cancer-associated fibroblasts (CAFs) are a critical component of the tumor microenvironment, being implicated in enhancing tumor growth and fostering drug resistance. Nonetheless, the mechanisms under Show more
Cancer-associated fibroblasts (CAFs) are a critical component of the tumor microenvironment, being implicated in enhancing tumor growth and fostering drug resistance. Nonetheless, the mechanisms underlying their function in prostate cancer (PCa) remain incompletely understood, which is essential for devising effective therapeutic strategies. The main objective of this study was to explore the mechanisms by which CAFs mediate PCa growth and chemoresistance. We validated through data analysis and experimentation that CAFs significantly impact PCa cell proliferation and chemoresistance. Subsequently, we conducted a comprehensive proteomic analysis of the conditioned media from CAFs and PCa cells and identified angiopoietin-like protein 4 (ANGPTL4) as a key factor. We employed ELISA and multiplex immunofluorescence assays, all of which indicated that ANGPTL4 was primarily secreted by CAFs.Next, we conducted metabolomics analysis, GST pull-down assays, Co-IP, and other experiments to explore the specific molecular mechanisms of ANGPTL4 and its precise effects on PCa cells. Through drug screening, we identified Quercetin 3-O-(6'-galactopyranosyl)-Ξ²-D-galactopyranoside (QGGP) as an effective inhibitor of CAFs function. Finally, we thoroughly assessed the therapeutic potential of QGGP both as a monotherapy and in combination with docetaxel in PCa cells. We discovered that the extracrine factor ANGPTL4 is primarily expressed in CAFs in PCa. When ANGPTL4 binds to IQ motif-containing GTPase-activating protein 1 (IQGAP1) on the PCa cell membrane, it activates the Raf-MEK-ERK-PGC1Ξ± axis, promoting mitochondrial biogenesis and OXPHOS metabolism, and thereby facilitating PCa growth and chemoresistance. Furthermore, virtual and functional screening strategies identified QGGP as a specific inhibitor of IQGAP1 that promotes its degradation. Combined with docetaxel treatment, QGGP can reverse the effects of CAFs and improve the responsiveness of PCa to chemotherapy. This study uncovers a paracrine mechanism of chemoresistance in PCa and proposes that targeting the stroma could be a therapeutic choice. Show less
πŸ“„ PDF DOI: 10.1016/j.jare.2024.12.003
ANGPTL4
Binzhen Chen, Jia Liu, Yaoxin Zhang +10 more Β· 2025 Β· Advanced science (Weinheim, Baden-Wurttemberg, Germany) Β· Wiley Β· added 2026-04-24
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevale Show more
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevalent in cancer genomes of both coding and non-coding regions. However, the role of non-coding eccDNA regions that serve as enhancers has been largely overlooked. Here, genome-wide profiling of serum eccDNAs from donors and MM patients who responded well or poorly to bortezomib-lenalidomide-dexamethasone (VRd) therapy is characterized. A high copy number of eccDNA ANKRD28 (eccANKRD28) predicts poor therapy response and prognosis but enhanced transcriptional activity. Established VRd-resistant MM cell lines exhibit a higher abundance of eccANKRD28, and CRISPR/Cas9-mediated elevation of eccANKRD28 desensitizes bortezomib and lenalidomide treatment both in vitro and in vivo. Integrated multi-omics analysis (H3K27ac ChIP-seq, scRNA-seq, scATAC-seq, CUT&Tag, etΒ al.) identifies eccANKRD28 as an active enhancer involved in drug resistance driven by the key transcription factor, POU class 2 homeobox 2 (POU2F2). POU2F2 interacts with sequence-specific eccANKRD28 as well as RUNX1 and RUNX2 motifs to form the protein complex, which activates the promoter of oncogenes, including IRF4, JUNB, IKZF3, RUNX3, and BCL2. This study elucidates the potential transcriptional network of enhancer eccANKRD28 in MM drug resistance from a previously unrecognized epigenetic perspective. Show less
πŸ“„ PDF DOI: 10.1002/advs.202415695
ANKRD28
Chengli Yu, Xin Huang, Yating Cao +7 more Β· 2025 Β· Frontiers in molecular biosciences Β· Frontiers Β· 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
πŸ“„ PDF DOI: 10.3389/fmolb.2025.1628587
APOA4
Yuan Cai, Rong Huang, Tianfeng Lin +6 more Β· 2025 Β· Molecules (Basel, Switzerland) Β· MDPI Β· added 2026-04-24
πŸ“„ PDF DOI: 10.3390/molecules30183727
APOA4
Yang Wei, Ting Zhang, Yingying Jin +4 more Β· 2025 Β· Acta biochimica et biophysica Sinica Β· added 2026-04-24
Obesity-induced metabolic inflammation is a key driver of chronic kidney disease (CKD), with immune dysregulation, particularly among lymphocytes, contributing to early disease pathology. To explore t Show more
Obesity-induced metabolic inflammation is a key driver of chronic kidney disease (CKD), with immune dysregulation, particularly among lymphocytes, contributing to early disease pathology. To explore the role of apolipoprotein A4 (Apoa4) in regulating immune cell metabolism and function, we establish high-fat diet-induced obese (DIO) models using wild-type and Show less
πŸ“„ PDF DOI: 10.3724/abbs.2025171
APOA4
Yali Zhang, Xiaoli Gao, Chao Liu +4 more Β· 2025 Β· Journal of proteomics Β· Elsevier Β· added 2026-04-24
Cold stress poses a significant challenge to pig farming in northern China, leading to reduced productivity and, in severe cases, even mortality. However, the mechanisms underlying cold resistance in Show more
Cold stress poses a significant challenge to pig farming in northern China, leading to reduced productivity and, in severe cases, even mortality. However, the mechanisms underlying cold resistance in pigs are not well understood. To explore the genetic mechanism of cold resistance in pigs under low-temperature conditions, the cold-tolerant Hezuo pig was selected as a model. DIA proteomics analysis was performed on liver tissues from Hezuo pigs after 24Β h of exposure to low-temperature treatments. The results showed that approximately 149 differential abundance proteins (DAPs) were detected (95 up-regulated and 54 down-regulated). GO analysis showed that these DAPs were mainly associated with lipid metabolism, vesicle fusion, and membrane function. KEGG analysis showed that these DAPs were primarily enriched in lipid metabolism-related pathways such as cholesterol metabolism and vitamin digestion and absorption. Comprehensive analysis identified APOA4, APOA2, SREBF2, ATP23, STX2, USO1, ETFA, RAB11FIP1, ETNPPL, and SGMS1 as potential key proteins involved in cold resistance mechanisms. The mRNA expression of the genes for two key candidate proteins (APOA4 and SREBF2), which are involved in lipid metabolism, was analyzed using qRT-PCR, revealing a significant up-regulation after low-temperature treatment. These findings provide significant insights into the mechanisms of cold resistance in animals and may serve as candidate markers for further studies on cold tolerance. SIGNIFICANCE: Cold resistance is one of the key traits in pigs and involves multiple complex coordinated regulatory mechanisms. However, its genetic mechanisms are not completely understood. In this study, a DIA proteomics approach was used to identify proteins and pathways associated with cold resistance in the liver of low-temperature-treated Hezuo pigs. These findings offer novel candidate proteins and key pathways for investigating the molecular mechanisms of cold resistance in Hezuo pigs, providing a base for further elucidating the mechanisms of cold tolerance in pigs. Show less
no PDF DOI: 10.1016/j.jprot.2025.105420
APOA4
Sijing Shi, Kaikai Lu, Yijun Tao +6 more Β· 2025 Β· MedComm Β· Wiley Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1002/mco2.70555
APOA5
Zehua Huang, Li Wen, Chunlan Huang +12 more Β· 2025 Β· Chinese medical journal Β· added 2026-04-24
no PDF DOI: 10.1097/CM9.0000000000003663
APOA5
Jia-Xuan Zhang, Zhi-Qiang Huang, Jian-Ming Yang +2 more Β· 2025 Β· Neuropsychiatric disease and treatment Β· added 2026-04-24
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation a Show more
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation antipsychotics (SGAs). Medical records of patients hospitalized between March 2019 and March 2025 were retrospectively reviewed. The optimal cut-off points for baseline serum ApoB levels and the ApoB/ApoA1 ratio were identified using a maximally selected log-rank statistic analysis. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (95% CIs). The Kaplan-Meier method with Log rank testing was used to compare the cumulative incidence of dyslipidemia between groups defined by these cut-off points. Of 311 enrolled patients, 33 (10.6%) lacking baseline ApoA1 measurements were excluded from ApoB/ApoA1 ratio analyses. The optimal cut-off points were 0.70 g/L for baseline ApoB and 0.45 for the ApoB/ApoA1 ratio. Multivariable Cox proportional hazards models, fully adjusted for covariates, demonstrated significantly elevated dyslipidemia risk for patients exceeding these thresholds vs low-risk groups: adjusted HR 2.98 (95% CI: 2.05-4.32, p < 0.001) for high ApoB and 3.17 (95% CI: 1.62-6.22, p = 0.001) for high ApoB/ApoA1 ratio. Continuous analysis showed each 0.1 g/L ApoB increase conferred a 34% higher risk (adjusted HR 1.34, 95% CI: 1.21-1.48, p < 0.001), while each 0.1-unit ApoB/ApoA1 ratio increase conferred a 20% higher risk (adjusted HR 1.20, 95% CI: 1.10-1.30, p < 0.001). Kaplan-Meier curves confirmed significantly higher cumulative dyslipidemia incidence in high vs low groups for both markers (Log rank test, both p < 0.001). Baseline serum ApoB levels and the ApoB/ApoA1 ratio are valuable risk markers for dyslipidemia in patients treated with SGAs. Show less
πŸ“„ PDF DOI: 10.2147/NDT.S564450
APOB
Petnamnueng Dettipponpong, Mei-Ying Sin, Yu-Hui Chen +5 more Β· 2025 Β· Journal of thermal biology Β· Elsevier Β· added 2026-04-24
By various assessments, the previous study has unequivocally concluded functional apoB and MTTP (microsomal triglyceride transfer protein) for VLDL production in chicken ovaries. The present study sou Show more
By various assessments, the previous study has unequivocally concluded functional apoB and MTTP (microsomal triglyceride transfer protein) for VLDL production in chicken ovaries. The present study sought to use whole tissue culture to define the role of VLDL secretion by small yellow follicles (SYFs) along their development under normal and heat stress (HS) conditions. Under thermoneutral conditions (39Β Β°C), chicken SYFs increased MTTP activity, apoB expression and VLDL secretion, while underwent cell apoptosis along the time course. Despite relieved ER stress and protein ubiquitinylation, inhibition of VLDL secretion by Lomitapide and Mipomersen greatly increased triglyceride accumulation, impaired estradiol production and cell proliferation, and accelerated cell apoptosis in accordance with upregulated caspase 3/7 activity, JNK activation, protein carbonylation, and MDA accumulation. Exposure to HS at 44Β Β°C boosted cell apoptosis in a duration-dependent manner. Acute HS for 3Β h enhanced VLDL secretion, impaired estradiol production and cell proliferation, and promoted IL-1b production, oxidative damages, and cell apoptosis, whereas except MDA content and cell proliferation, the detrimental effects were halted after 13Β h recovery. Lomitapide and Mipomersen augmented lipid accumulation, oxidative stress, inflammatory response, and exacerbated transient impairment of estradiol secretion and cell proliferation in SYFs under 3Β h HS and after recovery, but failed to rescue cell viability despite relieved ER and proteostatic stress. In conclusion, routine secretion of VLDL by SYFs serves as an intrinsic mechanism to sustain cell viability and functions to support the whole program required for follicle development, while under HS, this mechanism provisionally rescues steroidogenesis and cell proliferation. Show less
no PDF DOI: 10.1016/j.jtherbio.2025.104298
APOB
Helen Williams, Habib Francis, Jasmin Huang +4 more Β· 2025 Β· Atherosclerosis plus Β· Elsevier Β· added 2026-04-24
Familial Hypercholesterolaemia (FH) is characterised by high cholesterol and premature cardiovascular disease. While hypercholesterolaemia and inflammation are both key drivers in the formation of ath Show more
Familial Hypercholesterolaemia (FH) is characterised by high cholesterol and premature cardiovascular disease. While hypercholesterolaemia and inflammation are both key drivers in the formation of atherosclerotic plaques, inflammation remains understudied in FH. Inflammatory (M1) macrophages contribute to plaque destabilisation and macrophage precursors, monocytes, can be skewed towards an inflammatory state. Aims: Determine; whether monocytes of FH individuals are inflammatory, if they readily form inflammatory macrophages, and whether this remains so in statin-treated individuals. Blood samples were collected from people with FH (statin-treated and untreated) and healthy controls. Lipid profile was obtained and monocyte inflammatory marker expression was determined by whole blood flow cytometry. Monocytes were cultured with autologous serum and resultant macrophage profile determined by flow cytometry. Total cholesterol and low-density lipoprotein cholesterol (LDL-C) were higher in the Untreated-FH group compared to the Treated-FH group and controls. In both Treated-FH and Untreated-FH groups, monocytes were inflammatory with high CD86 (M1). The ratio of inflammatory/anti-inflammatory markers (CD86/CD163) significantly correlated with LDL-C and ApoB/ApoA1 ratio across the cohort, indicating the high LDL-C of FH may promote an inflammatory monocyte profile. Monocyte-derived-macrophages from (Treated) FH individuals also had a more inflammatory profile (CD86 and CD86/CD163). Overall, monocytes show inflammatory skewing in FH individuals, even those with moderately-reduced cholesterol levels. These monocytes readily become inflammatory macrophages. This, along with subsequent inflammatory macrophage formation, could contribute to plaque destabilisation and downstream clinical events. This supports inflammatory monocyte targeting as a potential approach to reduce residual risk in FH individuals. Show less
πŸ“„ PDF DOI: 10.1016/j.athplu.2025.09.002
APOB
Lili Zhou, Wei Cheng, Dan Luo +10 more Β· 2025 Β· Frontiers in cell and developmental biology Β· Frontiers Β· added 2026-04-24
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and Show more
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and underlying molecular mechanisms of cholesterol metabolism alterations in diffuse large B-cell lymphoma (DLBCL) remain poorly understood. We retrospectively analyzed clinical data from 200 DLBCL patients and 185 healthy controls, focusing on lipid and lipoprotein levels, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and apolipoprotein E (ApoE). Univariate and multivariate Cox proportional hazard models were used to evaluate the prognostic value of these markers, and Kaplan-Meier analysis assessed their associations with overall survival (OS). Bioinformatics analysis predicted associations between lipid markers and cholesterol metabolism. Cellular experiments further investigated the expression of cholesterol metabolism-related proteins and the effect of the cholesterol-depleting agent Methyl-Ξ²-cyclodextrin (MΞ²CD) on DLBCL cells. We confirmed significant alterations in metabolic markers (such as TC and ApoA1) between the healthy control group and patients, which were significantly associated with patient prognosis and overall OS. Bioinformatics analysis revealed a strong correlation between these markers and elevated CD36 expression. In addition, DLBCL cells exhibited increased expression of cholesterol uptake and synthesis proteins (CD36, SREBP2, and HMGCR) and decreased expression of efflux proteins (APOA1, NR1H2 and ABCG1), consistent with cholesterol metabolic reprogramming. Treatment with MΞ²CD disrupted CD36 expression and cholesterol metabolism, leading to reduced DLBCL cell survival. These findings underscore the pivotal role of cholesterol metabolic reprogramming in DLBCL progression. CD36 and related metabolic markers represent promising therapeutic targets, opening novel avenues for the treatment of this malignancy. Show less
πŸ“„ PDF DOI: 10.3389/fcell.2025.1585521
APOB
S Y Huang, F Y Song, X O Wang +6 more Β· 2025 Β· Zhonghua er ke za zhi = Chinese journal of pediatrics Β· added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112140-20250531-00465
APOB
Fujia Guo, Min Xu, Qingxian Tu +6 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
Coronary artery disease (CAD) is showing a trend toward earlier onset. Premature CAD (PCAD) is clinically defined as CAD with onset before the age of 55 in males and 65 in females. Notably, many young Show more
Coronary artery disease (CAD) is showing a trend toward earlier onset. Premature CAD (PCAD) is clinically defined as CAD with onset before the age of 55 in males and 65 in females. Notably, many young patients subsequently hospitalized with acute cardiovascular events had undergone annual physical examinations before hospitalization, yet were not identified as high-risk by current risk stratification guidelines or traditional risk assessment tools. This study aims to investigate the diagnostic capacity of novel inflammatory biomarkers (including the monocyte-to-high-density lipoprotein cholesterol ratio (MHR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), apolipoprotein B to apolipoprotein A-1 ratio (apoB/apoA-1), and low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (LDL-c/HDL-c)) for PCAD, thereby providing the evidence-based foundation for PCAD screening. A total of 1,012 young subjects (male<55 years, female<65 years) undergoing diagnostic coronary angiography (CAG) at the Third Affiliated Hospital of Zunyi Medical University (from January 2022 to February 2023) were retrospectively analyzed. We stratified 1,012 eligible participants into two groups: 521 angiographically confirmed PCAD cases and 491 controls with normal coronary arteries. Comprehensive baseline characteristics, including cardiovascular risk profiles and core laboratory-measured inflammatory markers, were recorded. The Mann-Whitney U test and binary logistic regression analysis were employed to assess the associations between inflammatory biomarkers and PCAD. The areas under the receiver operating characteristic (ROC) curves (AUCs) were calculated to evaluate their diagnostic performance for PCAD. The odds ratio (OR) values for MHR, NLR, LDL-c/HDL-c, and apoB/apoA-1 were 5.592 (95% CI: 2.886-7.836), 1.671 (95% CI: 1.500-1.861), 1.663 (95% CI: 1.419-1.950), and 6.268 (95% CI: 2.765-8.213), respectively (all The apoB/apoA-1 outperformed MHR, NLR, and LDL-c/HDL-c as an inflammatory biomarker in PCAD. Its diagnostic capacity was notably enhanced in ACS subgroups. A comprehensive model combining apoB/apoA-1 with traditional risk factors demonstrated exceptional accuracy. Incorporating this biomarker into routine screening protocols could significantly strengthen preventive strategies. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1646944
APOB
Yan Yang, Hao-Fei Huang, Kun-Lin Pu Β· 2025 Β· Medicine Β· added 2026-04-24
An increasing body of research indicates an association between lipid-lowering medications and sensorineural hearing loss (SNHL), although there is still controversy. Therefore, the aim of this study Show more
An increasing body of research indicates an association between lipid-lowering medications and sensorineural hearing loss (SNHL), although there is still controversy. Therefore, the aim of this study is to investigate the genetic correlation between different lipid-lowering therapeutic gene targets and SNHL. The genetic association between lipids, lipid-lowering drug target genes, and SNHL was analyzed using a 2-sample Mendelian randomization approach. The exposures included 5 circulating lipid levels (triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, apolipoprotein A-I, and apolipoprotein B) and 10 target genes simulating the effects of lipid-lowering drugs (HMGCR, PCSK9, Niemann-Pick C1-like 1 [NPC1L1], LDLR, APOB, CETP, LPL, ANGPTL3, APOC3, and PPARA). Summary data from a large-scale genome-wide association study on SNHL from the Finnish database were used as the outcome. The inverse variance-weighted method was employed as the primary approach, with sensitivity tests conducted to evaluate heterogeneity and pleiotropy in the results. The genetic prediction of lipid levels was not significantly associated with SSNL. However, genetic proxies for lowering low-density lipoprotein cholesterol, specifically variants in NPC1L1 (ORβ€…=β€…1.943 [95% CI 1.116-3.383]; Pβ€…=β€….018) and LDL receptor (LDLR) (ORβ€…=β€…1.279 [95% CI 1.107-1.477]; Pβ€…<β€….001), were associated with an increased risk of SNHL. Similarly, a genetic proxy for lowering triglycerides, the apoprotein C-III (APOC3) variant (ORβ€…=β€…1.174 [95% CI 1.054-1.307]; Pβ€…=β€….003), was associated with an increased risk of SNHL. After Bonferroni correction, the genetic variants for LDLR and APOC3 remained significantly associated with an increased risk of SNHL, while the association with the NPC1L1 lipid-lowering variant was no longer significant. This study suggests that lipid-lowering medications potentially have a causal impact on increasing the risk of SNHL through the LDLR and APOC3 pathways. LDLR and APOC3 show potential as candidate drug targets for the prevention of SNHL. However, the results of the study and the potential mechanism of action require further experimental validation and evaluation. Show less
πŸ“„ PDF DOI: 10.1097/MD.0000000000044174
APOB
Sihua Huang, Yan Lan, Cheng Zheng +3 more Β· 2025 Β· BMC neurology Β· BioMed Central Β· added 2026-04-24
As inflammatory processes may be involved in the pathogenesis of diabetic distal sensorimotor polyneuropathy (DSPN), the first aim of the present study was to determine the clinical characteristics of Show more
As inflammatory processes may be involved in the pathogenesis of diabetic distal sensorimotor polyneuropathy (DSPN), the first aim of the present study was to determine the clinical characteristics of type 2 diabetes mellitus (T2DM) with distal sensorimotor polyneuorpathy (DSPN). Next goal was to investigate inflammatory biomarkers, insulin-like growth factor- 1 and lipid profile in these patients. Finally, we aimed to compare the renal function in these patients. In a cross-sectional study, we included 160 patients diagnosed with T2DM. The control group was included 22 non-diabetic healthy subjects (HC). The patients with diabetes were divided into four groups, absent (n = 74), mild (n = 38), moderate (n = 24), and severe (n = 24) using a nomogram based on the MNSI features for a DSPN severity grading probability. Patients with moderate and severe DSPN were a little older and had longer duration of diabetes compared to patients with absent and mild DSPNS (p < 0.05). Serum levels of interferon-gamma (INF-Ξ³), interleukin (IL)-1Ξ², IL-4, IL- 6 levels in patients with severe DSPN were significantly higher than HC, absent, mild and moderate of DSPN (p < 0.05). The circulating levels of insulin-like growth factor-1 (IGF-1) were significantly lower in patients with severe DSPN (p < 0.05) compared to absent, mild and moderate of DSPN and HC. Diabetic patients with moderate DSPN showed increased circulating levels of TC, LDL-C, APOB (p < 0.05) compared to HC and patients with absent, mild and severe DSPN. Moreover, APO-A1/APOB was significantly lower in patients with diabetes compared to HC. In addition, patients with severe DSPN showed increased Cystatin C (p < 0.05) compared to HC and absent, mild, and moderate DSPN. Multivariate ordered logistic regression analysis showed that the levels of IL-6 (OR = 3.166, 95%CI 1.461-6.860, p = 0.003, IL-1Ξ²(OR = 1.148, 95%CI 1.070-2.232; p = 0.000), TC (OR = 1.174, 95%CI 1.011-1.364; p = 0.035), LDL-C (OR = 1.246, 95%CI 1.098-3.618; p = 0.003), Cystatin C (OR = 1.867, 95%CI 1.245-3.434; p = 0.004), ages (OR = 1.043, 95%CI 1.009-1.078; p = 0.012), and duration of diabetes (OR = 1.157, 95%CI 1.049-1.277; p = 0.004) were positively associated with increasing the odds ration of DSPN in T2DM. Conversely, the level of IGF-1 (OR = 0.922, 95%CI 0.961-0.982; p = 0.000) and ratio of APO-A1/APOB (OR = 0.212, 95%CI 0.078-0.567; p = 0.002) were significantly associated with decreasing the odds ratio of DSPN in T2DM. The levels of inflammatory biomarkers such as INF-Ξ³, IL-1Ξ², IL-4, IL- 6 were increased in patients with severe DSPN in T2DM. Ages, duration of diabetes as well as high circulating levels of IL-6, IL-1Ξ², TC, LDL-C and Cystatin C were positively associated with DSPN in T2DM. Conversely, the level of IGF-1 and the ratio of APOA1/APOB were independent protective factors for DSPN in T2DM. Our results emphasize the importance of addressing issues related to inflammatory biomarkers, lipids and early impaired renal function in T2DM with DSPN, as these may be of potential relevance for deteriorating DSPN. Show less
πŸ“„ PDF DOI: 10.1186/s12883-025-04379-y
APOB
Guoping Wu, Zhe Dong, Zhongcai Li +12 more Β· 2025 Β· Schizophrenia (Heidelberg, Germany) Β· Nature Β· 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
πŸ“„ PDF DOI: 10.1038/s41537-025-00646-6
APOB
Chi Chen, Yimeng Gu, Junfei Xu +9 more Β· 2025 Β· Scientific reports Β· Nature Β· added 2026-04-24
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg Show more
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg) fat deposits are associated with apoB levels in general US adults. 4585 participants were enrolled from the US National Health and Nutritional Surveys from 2011 to 2016. Overall and regional body fat were measured using dual-energy X-ray absorptiometry. The associations of total and regional fat with apoB levels were evaluated using linear regression models. Following adjustment for demographic, lifestyle, and clinical risk factors, whole-body fat percentage was positively associated with apoB levels. Additionally, percent trunk fat was positively associated (highest vs. lowest tertile beta = 17.73 for men and 14.89 for women, respectively), whereas percent leg fat was inversely associated (highest vs. lowest tertile beta = - 4.84 for men and - 6.55 for women, respectively) with apoB levels in both sexes. The association for trunk fat and leg fat remained significant after further adjustment for body mass index or waist circumference. Higher percent trunk fat combined with lower percent leg fat was associated with particularly higher apoB. In conclusion, among general US adults, both elevated trunk fat and reduced leg fat are associated with higher levels of apoB. Further research is required to elucidate the underlying pathophysiological mechanisms. Show less
πŸ“„ PDF DOI: 10.1038/s41598-025-10502-3
APOB
Liubo Xiang, Huan Wu, Zhihao Zhao +6 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· 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
πŸ“„ PDF DOI: 10.3389/fendo.2025.1512670
APOB
Qinghua Fang, Hongdan Fan, Qiaoqiao Li +4 more Β· 2025 Β· Journal of the American Heart Association Β· added 2026-04-24
Genome-wide association studies have revealed numerous loci associated with coronary artery disease (CAD). However, some potential causal/risk genes remain unidentified, and causal therapies are lacki Show more
Genome-wide association studies have revealed numerous loci associated with coronary artery disease (CAD). However, some potential causal/risk genes remain unidentified, and causal therapies are lacking. We integrated multi-omics data from gene methylation, expression, and protein levels using summary data-based Mendelian randomization and colocalization analysis. Candidate genes were prioritized based on protein-level associations, colocalization probability, and links to methylation and expression. Single-cell RNA sequencing data were used to assess differential expression in the coronary arteries of patients with CAD. Our findings provide multi-omics evidence suggesting that Show less
πŸ“„ PDF DOI: 10.1161/JAHA.124.037203
APOB
Pengfei Xie, Weinan Xie, Zhaobo Wang +8 more Β· 2025 Β· Diabetology & metabolic syndrome Β· BioMed Central Β· added 2026-04-24
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relat Show more
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relationships. This study employed bidirectional Mendelian randomization (MR) to explore these links. Using genome-wide association study (GWAS) data, the primary analysis used the inverse-variance weighted (IVW) method, which was supported by MR-Egger regression and a weighted median estimator (WME). Sensitivity analyses, including heterogeneity, pleiotropy tests, leave-one-out, and reverse causality analyses, were conducted. The IVW model revealed the following: (1) causal relationships between triglycerides (TG) (OR: 1.5807, 95% CI: 1.2578-1.9865, P = 0.0001), high-density lipoprotein cholesterol (HDL-C) (OR: 0.7342, 95% CI: 0.5729-0.9409, P = 0.0146), and apolipoprotein A1 (ApoA1) (OR: 0.6506, 95% CI: 0.5190-0.8156, P = 0.0002) and DN; (2) causal relationships between TG (OR: 1.0607, 95% CI: 1.0143-1.1093, P = 0.0098), HDL-C (OR: 0.9453, 95% CI: 0.9053-1.9871, P = 0.0109), and apolipoprotein B (ApoB) (OR: 1.0672, 95% CI: 0.0070-1.1310, P = 0.0280) and the urinary albumin-creatinine ratio (UACR); (3) no causal relationship between total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), ApoB and DN, or between TC, LDL-C, ApoA1 and UACR; (4) none of the results showed reverse causality. TG is a risk factor for DN and UACR; HDL-C is protective for both; ApoA1 protects against DN; and ApoB is a risk factor for UACR. To further explore the underlying mechanisms between TG, HDL-C, ApoA1, ApoB, and their associations with DN and UACR, and to provide reference for the selection of lipid management and treatment strategies for clinical DN patients. This study demonstrated that causal relationships between TG, HDL-C, and ApoA1 with DN and between TG, HDL-C, and ApoB with the UACR. Show less
πŸ“„ PDF DOI: 10.1186/s13098-025-01641-8
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Wenxiu Wang, Rui Li, Zimin Song +4 more Β· 2025 Β· JAMA cardiology Β· added 2026-04-24
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lower Show more
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lowering triglycerides. Multiple clinical trials of small-interfering RNA therapeutics targeting APOC3 are currently underway. To investigate whether genetically predicted lower APOC3 is associated with a reduction in cardiovascular risk and if the combined exposure to APOC3 and LDL-C-lowering variants is associated with a reduction in the risk of coronary heart disease (CHD). This was a population-based genetic association study with 2 × 2 factorial mendelian randomization. Included were participants of European ancestry in the UK Biobank. Data were analyzed from November 2023 to July 2024. Genetic scores were constructed to mimic the effects of APOC3, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors. Plasma lipid and lipoprotein levels, CHD, and type 2 diabetes (T2D). This study included 401β€―548 UK Biobank participants (mean [SD] age, 56.9 [8.0] years; 216β€―901 female [54.0%]). Genetically predicted lower APOC3 was associated with a lower risk of CHD (odds ratio [OR], 0.96; 95% CI, 0.93-0.98) and T2D (0.97; 95% CI, 0.95-0.99). Genetically lower APOC3 and PCSK9 were associated with a similar magnitude of risk reduction in CHD per 10-mg/dL decrease in apolipoprotein B (ApoB) level (APOC3: 0.70; 95% CI, 0.59-0.83; PCSK9: 0.71; 95% CI, 0.65-0.77). Combined exposure to genetically lower APOC3 and PCSK9 was associated with an additive lower risk of CHD (APOC3: 0.96; 95% CI, 0.92-0.99; PCSK9: 0.93; 95% CI, 0.90-0.97; combined: 0.90; 95% CI, 0.86-0.93). Genetically lower HMGCR was also associated with a lower risk of CHD, and the risk was further reduced when combined with APOC3 (0.93; 95% CI, 0.90-0.97). Genetically predicted lower APOC3 was associated with a reduced risk of CHD that is comparable with that associated with lower PCSK9 per unit decrease in ApoB. Combined exposure to APOC3 and LDL-C-lowering variants was associated with an additive reduction in CHD risk. Future studies are warranted to investigate the therapeutic potential of these combined therapies, particularly among high-risk patients who cannot achieve therapeutic targets with existing lipid-lowering therapies. Show less
no PDF DOI: 10.1001/jamacardio.2025.0195
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Tyler A Jacobson, Kian J Rahbari, William A Schwartz +14 more Β· 2025 Β· Journal of the American Heart Association Β· added 2026-04-24
Dried blood spot sampling offers a scalable strategy to close diagnostic gaps and improve global surveillance for cardiovascular-kidney-metabolic syndrome. However, assay performance and the extent of Show more
Dried blood spot sampling offers a scalable strategy to close diagnostic gaps and improve global surveillance for cardiovascular-kidney-metabolic syndrome. However, assay performance and the extent of validity vary widely between biomarkers used in cardiovascular-kidney-metabolic health assessment under different settings and have not been well described. To fill this gap, we conducted a systematic search of the literature and a narrative synthesis through April 2024 and included reports with laboratory or field validation measuring biomarkers that can be used in cardiovascular-kidney-metabolic health assessment. We categorized assays into categories based on laboratory validation: excellent performance (r>0.95 with gold standard methods and coefficients of variation <5%), very good performance (r>0.90 and coefficients of variation <10%), reasonable performance (r>0.80 and coefficients of variation <15%), and poor performance (r<0.80 or coefficients of variation >15%). The extent of validation was determined by the total number of field validation studies with strong agreement. Hemoglobin A1c has strong laboratory and field validation and should be considered for expansion into clinical testing in low-resource settings. Traditional lipid biomarkers showed poor performance in field validation studies, but apoB (apolipoprotein B), creatinine, cystatin C, and NT-proBNP (N-terminal prohormone of brain natriuretic peptide) showed promising initial laboratory validation results and deserve greater attention in field validation studies. High-sensitivity C-reactive protein has strong laboratory and field validation but has limited clinical utility. Dried blood spot assays have been developed for biomarkers that offer mechanistic insights including inflammatory and vascular injury markers, fatty acids, malondialdehyde, asymmetric dimethylarginine, trimethylamine N-oxide, carnitines, and omics. Show less
πŸ“„ PDF DOI: 10.1161/JAHA.124.037454
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Yu-Hui Chen, Petnamnueng Dettipponpong, Mei-Ying Sin +6 more Β· 2025 Β· Poultry science Β· Elsevier Β· added 2026-04-24
In mammals, tissues other than liver and intestine are known to possess functional MTTP (microsomal triglyceride transfer protein) and apoB (apolipoprotein B) capable of VLDL (very low-density lipopro Show more
In mammals, tissues other than liver and intestine are known to possess functional MTTP (microsomal triglyceride transfer protein) and apoB (apolipoprotein B) capable of VLDL (very low-density lipoprotein) assembly. Birds are oviparous and possess unique capabilities in lipid biology to accommodate yolk formation through massive deposition of hepatically assembled yolk-targeted VLDLy into ovarian follicles. Following identifications of MTTP and ApoB expression within chicken ovarian stroma, granulosa, theca, and epithelial cells of various classes of follicles, we sought to define the functionality of ovarian MTTP and ApoB in VLDL assembly. In situ hybridization analysis found that ApoB transcripts are most abundant in thecal layers, whereas immunohistochemistry showed that MTTP predominates in the granulosa layers. MTTP lipid transfer activity was greater in small yellow follicles than in hierarchical follicles. Metabolic labeling, electron microscopy, and Western blot studies confirmed the functionality of ovarian apoB and MTTP as newly assembled VLDL around 50-200 nm in diameter and lacking ApoVLDL-II dissimilar to VLDLy, were secreted from cultured follicular cells. Lomitapide and the ApoB-antisense oligonucleotide Mipomersen dose-dependently decreased MTTP activity and VLDL-apoB secretion from cultured follicular cells, while oleate addition or acute heat stress enhanced VLDL-apoB secretion. Ultrastructural images showed VLDL assembly and trafficking toward the secretion route. The findings support the notion that VLDL assembly and secretion within avian ovarian tissues functions as a protective mechanism against fuel and physical stressors to secure follicle development and/or nutritional quality control of yolk for embryo development. Show less
πŸ“„ PDF DOI: 10.1016/j.psj.2025.104993
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Juan Zhou, Shanshan Wang, Qiang Wang +11 more Β· 2025 Β· Food & function Β· Royal Society of Chemistry Β· added 2026-04-24
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycati Show more
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycation end products (AGEs), but data on its effects in centrally obese individuals are sparse. Thus, we aimed to investigate the effects of lutein supplementation in subjects with central obesity. A double-blind, randomized controlled trial was conducted involving patients with central obesity. Anthropometric indices, dietary intake, metabolic parameters, carotenoid and AGEs levels were compared between those receiving a 32-week intervention of 10 mg d Show less
no PDF DOI: 10.1039/d4fo05578k
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Ping Huang, Yong Zhao, Haiyan Wei +8 more Β· 2025 Β· International journal of chronic obstructive pulmonary disease Β· 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
πŸ“„ PDF DOI: 10.2147/COPD.S476833
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