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公共数据库方法学Nature genetics

All of Us diversity and scale yield context-dependent improvements in polygenic prediction.

2 次引用趋势数据不足

利用“All of Us”研究计划245,388例全基因组序列及英国生物银行数据,针对32项性状和疾病构建多祖先多基因风险评分,并在“All of Us”多祖先参与者中评估祖先背景、方法和遗传结构对评分性能的影响。结果显示,增加“All of Us”多样性可提高若干性状的评分准确性,尤其在代表性不足人群中;但将“All of Us”与英国生物银行荟萃分析以最大化样本量并非普遍最优,对多基因性较低的性状,仅用“All of Us”训练在非洲祖先参与者中表现最佳,提示祖先富集效应。个体评分准确性随与发现全基因组关联研究祖先分歧增加而线性下降,多祖先训练数据可减弱该衰减。结论强调,更具代表性的生物样本库有助于实现公平的多基因风险评分性能。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Kristin Tsuo、Zhuozheng Shi、Tian Ge、Ravi Mandla、Kangcheng Hou、Yi Ding、Bogdan Pasaniuc、Ying Wang、Alicia R Martin

作者机构

  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. ktsuo@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ktsuo@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ktsuo@broadinstitute.org.
  • Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. yiwang@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. yiwang@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. yiwang@broadinstitute.org.
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. armartin@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. armartin@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. armartin@broadinstitute.org.

技术路线

数据/样本:All of Us研究项目245,388例全基因组序列与UK Biobank数据;分析:开发32个性状/疾病的多祖先多基因风险评分(PRS),评估祖先背景、方法学和遗传结构对AoU参与者PRS性能的影响。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/30 06:04
临床队列临床研究BMJ (Clinical research ed.)

Simultaneous derivation, validation, and comparison of predictor hazard ratios for cardiovascular risk prediction equations in patients with diabetes from high versus non-high income countries: cohort study.

0 次引用趋势数据不足

一项基于人群的队列研究,比较新西兰(高收入国家)与中国(非高收入国家)30~74岁、无心血管疾病(CVD)的糖尿病患者,采用标准化方法分别建立两国5年CVD风险预测方程,并比较预测因子风险比。2004—2018年新西兰47 958名参与者中发生5 622例(11.7%)首次CVD事件;2010—2018年中国46 558名参与者中发生3 650例(7.8%)首次CVD事件。性别特异性方程包含16个预测因子,多数预测因子风险比相近,但年龄风险比差异明显(女性每增加10岁:新西兰1.61,95%CI 1.51~1.71;中国2.51,95%CI 2.32~2.72)。将新西兰方程应用于中国队列,标准重校准未改善校准(期望/观察比:男性0.938,女性0.809);以中国年龄系数替换新西兰年龄系数后校准明显改善(期望/观察比:男性1.027,女性1.025)。结论认为,将高收入国家CVD风险方程应用于非高收入国家糖尿病人群可能引入系统性误差,主要由年龄效应差异驱动;除简单重校准外,应考虑更新关键预测因子系数(尤其年龄),以提高风险分层准确性。

为什么值得关注

编辑短评

高收入国家开发的糖尿病心血管风险评分直接在其他国家使用时,仅靠简单重校准不够;年龄的风险效应在人群间差异很大,更新年龄这类关键预测因子的 HR,才能显著改善风险标定。

查看扩展信息

完整作者

Jingyuan Liang、Yeunhyang Choi、Peng Shen、Susan Wells、Katrina Poppe、Zhangping Fu、Matire Harwood、Xiaofei Liu、Sue Crengle、Ting Wang、Corina Grey、Hongbo Lin、Suneela Mehta、Xun Tang、Rod Jackson、Pei Gao

作者机构

  • Section of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
  • Department of Chronic Diseases and Health Promotion, Yinzhou District Centre for Disease Control and Prevention, Ningbo, China.
  • Department of General Practice and Primary Health Care, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Department of Medicine, School of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Ngāi Tahu Māori Health Research Unit, Division of Health Sciences, University of Otago, Dunedin, New Zealand.
  • Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
  • Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China peigao@bjmu.edu.cn.
  • Peking University Clinical Research Institute, Peking University, Beijing, China.

技术路线

电子健康记录队列数据(新西兰47,958人、中国46,558人)→按标准化方法分别推导两国性别特异性5年心血管风险预测方程并比较预测因子风险比→将新西兰方程应用于中国队列,评估标准化再校准前后的性能,再更新风险比存在显著差异的预测因子(如年龄系数)并验证校准改善

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:53
公共数据库临床研究Cell

Advancing cancer detection and treatment using longitudinal routine clinical data.

2 次引用趋势数据不足

肿瘤管理在诊疗连续过程中仍较分散,从晚期诊断、挽救治疗到非个体化监测均存在碎片化问题。为此,研究提出统一多模态Transformer模型Oncoformer,基于中国肿瘤多模态预测与监测研究(COMPASS)队列(367万人、1770万次就诊)训练,并在包括英国生物样本库在内的独立外部队列中验证。该模型整合纵向电子健康记录与胸部X线影像,可完成泛癌诊断(AUROC=0.956)、诊断前1年内未来癌症预测(AUROC=0.869)、肿瘤分期推断(平均AUROC>0.90)、患者特异性治疗反应预测及10种癌症的无复发生存分层(均p<0.01)。分期预测经术后病理终点独立验证,并显示集中于核心癌症基因组通路。Oncoformer通过将常规临床数据转化为癌症动态演化视图,为基于常规临床数据的风险知情癌症预测与治疗分层提供了框架。

为什么值得关注

编辑短评

癌症是动态演化疾病,碎片化、单任务模型不匹配临床需求;Oncoformer 证明整合纵向常规临床 + 影像时序信息,能用一套模型实现提前预警、诊断、分期、疗效与复发风险分层,把静态筛查升级成动态个体化肿瘤全程监测。

查看扩展信息

完整作者

Fei Liu、Kai Wang、Hui Xu、Cheng Tang、Xian Shen、Meihao Wang、Lei Yang、Li Yang、Li Liu、Changxi Hu、Gen Li、Wei Wu、Zixing Zou、Bingzhou Li、Sian Liu、Jin Kang、Jungho Kong、Ting Li、Io Nam Wong、Xiaoying Huang、Gang Chen、Wenyang Lu、Ian Ziyar、Charlotte L Zhang、Yiwen Sun、Weihong Lin、Caiwen Ou、Manson Fok、Taiwa Hou、Winston Wang、Kanmin Xue、Yun Yin、Hao Zhu、Jonathan Gootenberg、Omar O Abudayyeh、Michael Karin、Alexandre Loupy、John E J Rasko、Trey Ideker、Huiyan Luo、Eric Oermann、Kang Zhang、International Consortium of Digital Twins in Healthcare and Medicine

作者机构

  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of Urology, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of General Surgery, Department of Hepatobiliary Surgery, Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Orthopedics, Key Laboratory of Hepatosplenic Surgery of Ministry of Education, the First Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Department of Respiratory and Critical Care Medicine, Center for Big Data and Intelligent Medicine, the First Affiliated Hospital of Chongqing Medical University, Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing Translational Medicine Center, Chongqing, China.
  • Department of Health Management, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA.
  • Division of Pulmonary Medicine, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou Key Laboratory of Interdisciplinary and Translational Medicine, Wenzhou Key Laboratory of Heart and Lung, Wenzhou, Zhejiang, China.
  • Nepean Hospital, Sydney, Australia.
  • Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Dongguan Hospital, Southern Medical University, Guangzhou, China.
  • Conde S. Januario Hospital, Macau, China.
  • Department of Internal Medicine, Mayo Clinic, Arizona, USA.
  • Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Faculty of Health and Wellness, Faculty of Business, City University of Macau, Taipa, Macau SAR, China.
  • Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Gene and Cell Therapy Institute, Mass General Brigham, Cambridge, MA, USA.
  • Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015 Paris, France; Department of Kidney Transplantation, Assistance Publique - Hôpitaux de Paris, Paris, France.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA; Big Data Institute, Oxford University, Oxford, UK.
  • Department of Medical Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
  • Departments of Neurosurgery, Radiology, and Data Science, Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY, USA.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China. Electronic address: kang.zhang@gmail.com.

技术路线

3.67 million individuals/17.7 million clinical visits (COMPASS cohort, longitudinal EHR + chest X-ray) → Oncoformer unified multimodal transformer analysis → validation on independent external cohorts including UK Biobank, with staging independently validated against postoperative pathological endpoints and convergence on core cancer genomic pathways

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 06:32