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Inferred temporal locations for each sample align closely with clinical severity, while progression rates quantify inter-individual differences in pathophysiological speed. Genome-wide association studies of progression rate identify novel loci distinct from those in AD case-control studies, including lack of APOE effect, supporting multidimensional AD risk with partially independent disease potential and progression.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/genetic-associations-with-temporal-modeling-of-alzheimers-disease-progression-supports-a-novel-paradigm-for-disease-risk-posted-preprint-july-10-2026/395280/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/genetic-associations-with-temporal-modeling-of-alzheimers-disease-progression-supports-a-novel-paradigm-for-disease-risk-posted-preprint-july-10-2026/395280.png","ImageObject",300,407,{"name":92,"@type":93},"Stanley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-26",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and method are used to model Alzheimer’s disease progression?","Question",{"text":112,"@type":113},"The work applies a data-driven timeline inference framework to sparse longitudinal blood metabolomics data to reconstruct AD timelines and estimate individual-specific progression rates.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do the inferred timeline results relate to clinical severity?",{"text":117,"@type":113},"Inferred temporal locations for each metabolomics sample closely track clinical severity, indicating alignment between modeled time positions and patient status.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the genome-wide association findings suggest about AD risk?",{"text":121,"@type":113},"Genetic associations for timeline progression rate reveal novel loci distinct from case-control studies, notably showing no effect of APOE, supporting a multidimensional paradigm where disease potential and progression act as partially independent factors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},395280,1791314093,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336477405376,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","medRxiv preprint doi: [https://doi.org/10.64898/2026.07.07.26356710](https://doi.org/10.64898/2026.07.07.26356710); this version posted July 10, 2026. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY-NC-ND 4.0 International license .  \nGenetic Associations with Temporal Modeling of Alzheimer’s Disease Progression Supports a Novel Paradigm for Disease Risk  \nAuthors:  \nDaniel M. Jordan* , 1, 2, 3, 4 , Eli Kritzer* , 1, 2, 3, 5, 6 , Ryan C. Thompson1, 2, 3, 7 , Anina N. Lund1, 2, 3, 7 , Tulsi Patel 1, 4, 6, 8 , Ariela Buxbaum Grice 1, 2, 3, 5, 6 , DivyaA. John1, 2, 3, 7 ; Alzheimer’s Disease Neuroimaging Initiative+ ; Alzheimer’s Disease Metabolomics Consortium ++ , Alzheimer’s Disease Sequencing Project, Alison M. Goate 1, 4, 6, 8 , Benjamin S. Glicksberg 1, 2, 3, 9 , Negar Golestani 1, 2, 3, 7 , Maria Koromina1, 2, 3, 4, 10 , Alan E. Renton1, 4, 6, 8 , Noam D. Beckmann 1, 2, 3, 7, \\#  \n* These authors contributed equally  \n\\# Correspondence: Noam D. Beckmann, [noam.beckmann@mssm.edu](noam.beckmann@mssm.edu)  \nAffiliations:  \n1 Icahn School of Medicine at Mount Sinai, New York, NY.  \n2 Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY.  \n3 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.  \n4 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.  \n5 Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.  \n6 Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY.  \n7 Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY.  \n8 Ronald M. Loeb Center for Alzheimer’s Disease , Icahn School of Medicine at Mount Sinai, New York, NY.  \n9 The Hasso Plattner Institute for Digital Health at Mount Sinai, Mount Sinai Health System, New York City, NY.  \n1  \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.  \nmedRxiv preprint doi: [https://doi.org/10.64898/2026.07.07.26356710](https://doi.org/10.64898/2026.07.07.26356710); this version posted July 10, 2026. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY-NC-ND 4.0 International license .  \n10 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY.  \n+ Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ([adni.loni.usc.edu](adni.loni.usc.edu)) . As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:  [http://adni.loni.usc.edu/wp](http://adni.loni.usc.edu/wp)  \ncontent/uploads/how to apply/ADNI Acknowledgement List. pdf  \n++ Data used in preparation of this article were generated by the Alzheimer’s Disease Metabolomics Consortium (ADMC) . As such, the investigators within theADMC provided data but did not participate in analysis or writing of this report. A complete listing of ADMC investigators can be found at: [https://sites.duke.edu/adnimetab/team/](https://sites.duke.edu/adnimetab/team/)  \nSummary  \nA major challenge in Alzheimer’s disease (AD) research is predicting who will develop AD , how it progresses , and how to slow, prevent, or reverse progression. Here, we apply a data-driven timeline inference framework to sparse longitudinal blood metabolomics data to reconstruct AD timelines a","cbCaidAeFrjKtcdV","https://ap.wps.com/l/cbCaidAeFrjKtcdV","pdf",15934587,79,"English","# Summary\n# Introduction","[{\"question\":\"What data and method are used to model Alzheimer’s disease progression?\",\"answer\":\"The work applies a data-driven timeline inference framework to sparse longitudinal blood metabolomics data to reconstruct AD timelines and estimate individual-specific progression rates.\"},{\"question\":\"How do the inferred timeline results relate to clinical severity?\",\"answer\":\"Inferred temporal locations for each metabolomics sample closely track clinical severity, indicating alignment between modeled time positions and patient status.\"},{\"question\":\"What do the genome-wide association findings suggest about AD risk?\",\"answer\":\"Genetic associations for timeline progression rate reveal novel loci distinct from case-control studies, notably showing no effect of APOE, supporting a multidimensional paradigm where disease potential and progression act as partially independent factors.\"}]","Genetic Associations with Temporal Modeling of Alzheimer’s Disease Progression Supports a Novel Paradigm for Disease Risk - Posted preprint July 10, 2026 | PDF",1790417519,199]