Citations 2
Association of Plasma Biomarkers of Alzheimer's Disease with Incident Tau PET Positivity
Graff‐Radford, Jonathan, Syrjanen, Jeremy A. , Vemuri, Prashanthi, Lowe, Val J. , Wiste, Heather J. , Schwarz, Christopher G, Therneau, Terry M. , Binette, Alexa Pichet, Smith, Ruben, Mattsson‐Carlgren, Niklas, Palmqvist, Sebastian, Knopman, D., Cogswell, Petrice M, Petersen, Ronald, Hansson, Oskar H., Jack, Clifford R., Graff‐Radford, Jonathan , Vemuri, Prashanthi , Schwarz, Christopher G. , Algeciras‐Schimnich, Alicia , Binette, Alexa Pichet , Smith, Ruben , Mattsson, Niklas , Palmqvist, Sebastian , Stomrud, Erik , Cogswell, Petrice M. , Petersen, Ronald, Hansson, Oskar , Jack, Clifford R.
Alzheimer's & Dementia | December 24, 2025 | Volume: 21 | Issue: S2 | Pages: No page available
Abstract Background The objective of this study was to determine the predictive value of amyloid‐PET versus plasma %p‐tau217 (defined as the ratio of phosphorylated to non‐phosphorylated Tau217 × 100), Aβ42/40, and the amyloid probability score (APS2; which combines of Aβ42/40 and %p‐tau217) in tau‐PET transitions (T‐ to T+). Method Participants included were from the Mayo Clinic Study of Aging (MCSA) and had plasma markers measured by mass spectrometry (IP‐MS; C2N), had an amyloid‐PET scan, and had a tau‐PET negative scan (SUVR<1.29, temporal meta‐ROI) at the baseline, along with one or more follow‐up tau‐PET scans. Cox proportional hazard models were used to evaluate the relationship between C2N plasma %P‐Tau217 and APS2 and incident tau‐PET positivity (SUVR>=1.29) adjusting for age, sex, and APOE ε4. An analysis was also performed using amyloid‐PET centiloid as the predictor. Both the plasma markers and amyloid‐PET centiloid were scaled so that a one‐unit change corresponds to the respective interquartile range (IQR) for comparability of hazard ratios (HR). The analyses were repeated in the BioFINDER‐2 cohort for validation. Result 255 MCSA participants (122 female [47.8%] were included with a median age of 71.9 (IQR: 65.8, 78.8) years old, of which 37 progressed to tau‐PET positivity. At baseline, 238 (93.7%) were cognitively unimpaired. Higher baseline %p‐tau217 (HR: 1.52 [95% CI: 1.28, 1.8]), amyloid‐PET centiloid (HR: 1.47 [95% CI: 1.20, 1.79]), and APS2 (HR: 1.62 [95% CI: 1.22, 2.16]) were associated with an increased risk of progressing to tau‐PET positive (Figure 1). Aβ42/40 (HR: 1.06 [95% 0.604–1.867] was not associated with incident tau positivity Among 605 participants who were tau negative at index date in BioFINDER‐2 (316 (52.2%) cognitively unimpaired), with a median age of 70.2 (IQR: 60.5‐76.4), 33 progressed to tau positivity with higher %p‐tau217 (HR: 1.8 [95% CI: 1.5, 2.17]), amyloid‐PET centiloid (HR: 2.3 [95% CI: 1.77, 2.97]), and lower Aβ 42/40 (HR: 0.42 [95% CI: 0.21, 0.85]) at index date all associated with an increased risk of progressing to tau‐PET positive. Conclusion In two independent cohorts , %p‐tau217 provides predictive information about those who will convert from tau negative to tau positive on PET scan with the associations being similar to amyloid‐PET.
Prediction of neuropathology using the FDG‐PET based patient similarity network
Zhang, Gemeng , Barnard, Leland, Botha, Hugo, Gogineni, Venkatsampath, Graff‐Radford, Jonathan, Huls, Sean J. , Stricker, John L. , Fan, Winnie Z. , Wiste, Heather J. , Machulda, Mary M., Murray, Melissa E., Reichard, R. Ross, Gunter, Jeffrey L. , Schwarz, Christopher G, Kantarci, Kejal, Whitwell, Jennifer L., Josephs, K., Knopman, David S., Boeve, Brad F, Petersen, Ronald, Jack, Clifford R., Lowe, Val J. , Jones, David T., Barnard, Leland R , Botha, Hugo , Corriveau‐Lecavalier, Nick , Graff‐Radford, Jonathan , Burkett, Brian J. , Johnson, Derek R. , Khurana, Aditya , Min, Hoon-Ki Paul , Senjem, Matthew L. , Machulda, Mary M. , Murray, Melissa E. , Dickson, Dennis W. , Nguyen, Aivi T. , Reichard, R. Ross , Schwarz, Christopher G. , Kantarci, Kejal , Whitwell, Jennifer L. , Knopman, David S. , Petersen, Ronald, Jack, Clifford R. , Jones, David T.
Alzheimer's & Dementia | December 24, 2025 | Volume: 21 | Issue: S1 | Pages: No page available
Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.