{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:04:32Z","timestamp":1784012672454,"version":"3.55.0"},"posted":{"date-parts":[[2026,7,10]]},"group-title":"Genetic and Genomic Medicine","reference-count":159,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,10]]},"abstract":"<jats:title>Summary<\/jats:title>\n                <jats:p>\n                  A major challenge in Alzheimer\u2019s 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 and derive individual-specific timeline progression rates. Inferred temporal locations for each metabolomics sample along the AD timeline closely track clinical severity, while timeline progression rates capture inter-individual differences in the speed of pathophysiological progression. Genome-wide association studies of timeline progression rate identify novel loci distinct from those in AD case-control studies, notably showing no effect of the major risk locus\n                  <jats:italic>APOE<\/jats:italic>\n                  . These findings support a multidimensional paradigm of AD risk in which disease potential and progression act as partially independent factors. By explicitly modeling disease dynamics, this work reveals genetic contributions not captured by traditional approaches and provides a framework for studying AD and other progressive disorders.\n                <\/jats:p>","DOI":"10.64898\/2026.07.07.26356710","type":"posted-content","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:50:12Z","timestamp":1783723812000},"source":"Crossref","is-referenced-by-count":0,"title":["Genetic Associations with Temporal Modeling of Alzheimer\u2019s Disease Progression Supports a Novel Paradigm for Disease Risk"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5318-8225","authenticated-orcid":false,"given":"Daniel M.","family":"Jordan","sequence":"first","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4673-0781","authenticated-orcid":false,"given":"Eli","family":"Kritzer","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0450-8181","authenticated-orcid":false,"given":"Ryan C.","family":"Thompson","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anina N.","family":"Lund","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tulsi","family":"Patel","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Ronald M. 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