{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:04:38Z","timestamp":1780391078500,"version":"3.54.1"},"reference-count":48,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) and frontotemporal dementia (FTD) have distinct pathologies but frequently overlapping clinical presentations, making early and atypical differential diagnosis challenging. Blood-based biomarkers offer a minimally invasive alternative to cerebrospinal fluid and neuroimaging measures, yet their diagnostic performance\u2014alone and in combination\u2014remains to be fully established.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To quantify the discriminative ability of plasma biomarkers for differentiating AD, FTD, and healthy controls (HC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We used a fully Bayesian classification framework, estimating Bayesian logistic regression models for all single, pairwise, and triplet combinations of six plasma biomarkers\u2014phosphorylated tau at threonine 217 (pTau217), brain-derived tau (BD-Tau), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), amyloid-\u03b2\n                      <jats:sub>40<\/jats:sub>\n                      (A\u03b2\n                      <jats:sub>40<\/jats:sub>\n                      ), and amyloid-\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      (A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      )\u2014in AD (n\u2009=\u200997), FTD (n\u2009=\u2009255), and HC (n\u2009=\u200970). Models were fitted across three contrasts (AD versus HC, FTD versus HC, AD versus FTD) and evaluated via posterior distributions of cross-validated AUC, precision, recall, F1 score, and Brier score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across 41 candidate models, NfL was the top single biomarker (mean AUC\u2009=\u20090.85), achieving strong discrimination for FTD versus HC (AUC\u2009=\u20090.94). The best two-marker panel (pTau217\u2009+\u2009NfL) improved AD versus HC (AUC\u2009=\u20090.96) and AD versus FTD (AUC\u2009=\u20090.90). Adding A\u03b2\n                      <jats:sub>42<\/jats:sub>\n                      produced the highest-ranked triplet (AUC\u2009=\u20090.95) with modest, consistent gains. Posterior coefficients were biologically coherent (AD-specific pTau217 effects; severity-linked NfL), and calibration was satisfactory, with minor overconfidence only at extreme probabilities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A parsimonious pTau217\u2009+\u2009NfL panel captures most diagnostic information in the full plasma profile, providing an accurate probabilistic classifier with interpretable uncertainty to support differential diagnosis and clinical triage in precision neurology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261439950","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:45:03Z","timestamp":1775486703000},"page":"1060-1072","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bayesian classification model for differential diagnosis of Alzheimer's disease and frontotemporal dementia using plasma biomarkers"],"prefix":"10.1177","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0822-862X","authenticated-orcid":false,"given":"Tommaso","family":"Costa","sequence":"first","affiliation":[{"name":"University of Turin"},{"name":"Computational Neuroimaging &amp; 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Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK"},{"name":"NIHR Biomedical Research Centre for Mental Health &amp; Biomedical Research Unit for Dementia at South London &amp; Maudsley NHS Foundation, London, UK"},{"name":"Banner Sun Health Research Institute, Sun City, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik","family":"Zettemberg","sequence":"additional","affiliation":[{"name":"University of Gothenburg"},{"name":"King's College London, Institute of Psychiatry, Psychology &amp; Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK"},{"name":"Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, M\u00f6lndal, Sweden"},{"name":"Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK"},{"name":"UK Dementia Research Institute at UCL, London, UK"},{"name":"Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China"},{"name":"Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1890-4193","authenticated-orcid":false,"given":"Kaj","family":"Blennow","sequence":"additional","affiliation":[{"name":"University of Gothenburg"},{"name":"NIHR Biomedical Research Centre for Mental Health &amp; 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