Predictors and prescribing patterns of definite anticholinergic medication use among community-dwelling older adults in Saudi Arabia: a retrospective multicenter cross-sectional study.
Development and validation of a simple clinical prediction model for post-stroke seizures in critically ill patients: a multicenter retrospective cohort study.
Venise Jan Castillon、Amy X Xie、Sonia Boscenco、Ethan Tse、Neil Ruthen、Jamie Wang、Ziad Bakouny、Michele Waters、Chenlian Fu、Christopher Fong、Mustafa Saleh、Tarek Baroud、Wungki Park、Urvi A Shah、A Ari Hakimi、Puneeth Iyengar、Justin Jee、Eileen White、Alexander Gusev、Tobias Janowitz、Marcus D Goncalves、Ed Reznik
作者机构
Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Weill Cornell Medical College, New York, NY, USA.
Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Rutgers Cancer Institute, Rutgers University, New Brunswick, NJ, USA.
Ludwig Princeton Branch, Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA.
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Harvard Medical School, Boston, MA, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Northwell Health Cancer Institute, Northwell Health, New Hyde Park, NY, USA.
Holman Division of Endocrinology, Diabetes & Metabolism, Department of Medicine and Radiation Oncology, NYU Langone Health, New York, NY, USA.
Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. reznike@mskcc.org.
David M van den Berg、Daniel S Malawsky、Wei Huang、Petr Danecek、Klaudia Walter、Georgios Kalantzis、Isaac Wade、Duncan Palmer、Ewan Birney、Karin J H Verweij、Dirk J A Smit、Sarah J Lindsay、Matthew E Hurles、Abdel Abdellaoui、Hilary C Martin
作者机构
Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
Department of Statistics, University of Oxford, Oxford, UK.
The Pioneer Centre for SMARTbiomed, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. meh@sanger.ac.uk.
Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands. a.abdellaoui@amsterdamumc.nl.
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. hcm@sanger.ac.uk.
一项真实世界混合癌症队列研究（Mayo Clinic Cancer Microbiome）纳入1,364例癌症患者和287名健康对照，采用可校正癌症、合并症及人口学和临床变量相关非特异性微生物组关联的分析框架，在五类癌症中鉴定出341种最可能参与癌症发病的癌症相关菌种。在癌种内部，早期乳腺癌患者粪便胆汁酸及C. scindens水平较低，早期结直肠癌患者乳酸和Veillonella parvula升高；携带二氢嘧啶脱氢酶的Anaerostipes hadrus对5-氟尿嘧啶所致腹泻具有保护作用。研究提示该队列可用于发现癌症特异性微生物组特征和预测性生物标志物。
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Lu Yang、Vaidhvi Singh、Brent J Gawey、Jason P Sinnwell、Stephen Johnson、Elle C Billings、Trena M Van Gorp、Jonathan J Harrington、Michael Q Slama、Lisa M Till、Manavjot Singh、Thoshik R Samineni、Mojun Zhu、Krishna R Kalari、Gianrico Farrugia、Jun Chen、Ruben A T Mars、Purna C Kashyap
作者机构
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; The Benioff Center for Microbiome Medicine, University of California San Francisco, San Francisco, CA, USA.
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Gastroenterology, Mount Sinai, New York, NY, USA.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Division of Gastroenterology, Mount Sinai, New York, NY, USA.
Department of Oncology, Mayo Clinic, Rochester, MN, USA.
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: chen.jun2@mayo.edu.
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: mars.ruben@mayo.edu.
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. Electronic address: kashyap.purna@mayo.edu.
多基因风险评分广泛用于疾病风险分层，但其准确性在不同血统间存在差异。现有方法多依赖多血统数据提升代表性不足人群的预测精度，但需要对个体进行血统标注，这在临床实践中面临挑战。为此，研究者提出SPLENDID——一种针对大规模个体水平数据的惩罚回归框架，将遗传血统建模为连续变量，无需任何血统标签即可生成单一预测模型。在All of Us研究计划（n=224,364）和英国生物银行（n=340,140）的广泛模拟与分析中，SPLENDID较现有方法显著提高了预测准确性，尤其对非欧洲血统和混合血统人群。SPLENDID可作为跨多样人群稳健风险预测的工具，有助于减少遗传研究中的健康差异并促进更公平的临床实施。
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。
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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。
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