{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:40:25Z","timestamp":1787841625770,"version":"build-2784847793"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      Plasma biomarkers of Alzheimer's disease (AD) pathology are frequently tested in specialized research settings, which limits the generalizability of findings. Using electronic health records and banked plasma, we evaluated plasma biomarkers\u2014phosphorylated tau 217 (p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      ), \u03b2\u2010amyloid 1\u201342\/1\u201340 (A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>40<\/jats:sub>\n                      ) and p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      \u2014in a real\u2010world, diverse clinical population with multimorbidities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Participants (n\u2009=\u2009617; 44% Black\/African American; 41% female) were selected from the University of Pennsylvania Medicine BioBank with plasma assayed using Fujirebio Lumipulse. International Classification of Diseases (ICD) Ninth and Tenth Revision codes determined AD dementia (ADD) (n\u2009=\u200943), mild\u2010cognitive impairment (MCI) (n\u2009=\u2009140), unspecified\/non\u2010AD cognitive impairment (CI) (n\u2009=\u2009106), and cognitively normal cases (n\u2009=\u2009328), and other medical histories. APOE \u03b54, body mass index (BMI), metrics of kidney function (eg, estimated glomerular filtration rate [eGFR]), and liver disease were derived from electronic health records. Multivariable models identified factors related to plasma levels. Previously established cutpoints classified AD status (\u201cAD+,\u201d \u201cAD\u2212,\u201d or \u201cIntermediate\u201d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Plasma p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      had the strongest association with known AD\u2010related factors\u2014MCI, ADD, future progression to MCI\/ADD, age, and APOE \u03b54\u2014compared to p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      and A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>40<\/jats:sub>\n                      . Plasma p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      was also associated with eGFR, diabetes, and history of hearing loss. Importantly, AD\u2010related factors were most frequent\/severe for AD+ classification by p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      , whereas medical morbidities were most frequent\/severe for Intermediate classification. Exploratory analyses test p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      adjusted for eGFR to eliminate its influence on plasma levels.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>\n                      In this real\u2010world dataset, we identified effects of multimorbidities on plasma biomarkers, especially kidney function. The p\u2010tau\n                      <jats:sub>217<\/jats:sub>\n                      \/A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      ratio had low rates of Intermediate classification and may help to account for multimorbidity effects on plasma levels. ANN NEUROL 2026;99:1030\u20131045\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ana.78114","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T11:33:12Z","timestamp":1766662392000},"page":"1030-1045","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Electronic Health Records to Test Multimorbidity Influences to Plasma Biomarker Interpretation for Alzheimer's Disease"],"prefix":"10.1002","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1879-2766","authenticated-orcid":false,"given":"Katheryn A.Q.","family":"Cousins","sequence":"first","affiliation":[{"name":"Department of Neurology University of Pennsylvania  Philadelphia PA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rory","family":"Boyle","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Pennsylvania  Philadelphia PA 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