Knopman, David S.

Abstract A workgroup assembled by the Alzheimer’s Association recently described a conceptual framework for Alzheimer’s disease biological staging based on amyloid and tau PET imaging. However, specific tau PET cut points were left to be determined, a step necessary prior to clinical application. We sought to operationalize and evaluate Alzheimer’s disease biological staging by identifying meaningful tau PET cut points to define the four biological stages in a well-characterized participant cohort and describe the features of individuals placed into the different biological stages. The primary analysis included 896 participants in the Mayo Clinic Study of Aging or the Mayo Clinic Alzheimer’s Disease Research Center longitudinal cohorts. A validation cohort consisted of 328 participants in the Alzheimer’s Disease Neuroimaging Initiative. Both cognitively normal and impaired individuals with positive amyloid PET and evaluable tau PET imaging were included. Tau PET cut points were identified with Gaussian mixture models to characterize Alzheimer’s disease biological stage in study participants with different clinical diagnoses and objective degrees of cognitive impairment as measured by Mini-Mental State Examination. A tau PET cut point in the medial temporal region and two cut points in the temporoparietal region were identified to produce, collectively, the four Alzheimer’s disease biological stages described in the revised criteria. Increasing stage was associated with greater likelihood of mild cognitive impairment and dementia diagnosis and worsening cognitive performance on Mini-Mental State Examination and Clinical Dementia Rating Sum of Boxes, a result that was reproduced in the independent Alzheimer’s Disease Neuroimaging Initiative cohort. This study provided empirical validation for the concept of using amyloid PET and tau PET to separate subjects with biomarker-proven Alzheimer’s disease into four biological stages with distinct characteristics.

Objective The objective of this study was to determine the predictive value of amyloid‐positron emission tomography (PET) versus the plasma ratio of phosphorylated tau at threonine 217 (p‐tau217) to non‐phosphorylated tau217 (%p‐tau217) for tau‐PET transitions (T− to T+). The added value of combining plasma amyloid‐β 42 and amyloid‐β 40 (Aβ42/40) and %p‐tau217 into an amyloid probability score (APS2) was also assessed. Methods Mayo Clinic Study of Aging (MCSA) participants had plasma markers measured at via mass spectrometry (MS), an amyloid‐PET scan, and a tau‐PET (meta‐temporal region of interest [ROI]) negative scan (standardized uptake value ratio [SUVR] <1.29) at the index (baseline) date, along with one or more follow‐up tau‐PET scans. The BioFINDER‐2 cohort was used for validation. Cox proportional hazards models adjusted for age, sex, and apolipoprotein ( APOE ) ε4 were used to assess predictors, with scaling to the interquartile range (IQR) for comparability of hazard ratios (HR). Results Among 255 tau‐PET negative MCSA participants (median age: 71.9 years), 37 converted to tau‐PET positive (median follow‐up time: 3.81 years). Higher %p‐tau217 (HR: 1.52 [95% CI: 1.28–1.80]), amyloid‐PET centiloid (HR: 1.47 [95% CI: 1.20–1.79]), and APS2 (HR: 1.62 [95% CI: 1.22–2.16]) predicted tau‐PET conversion. However, Aβ42/40 (HR: 0.94 [95% CI: 0.54–1.66]) was not associated with tau‐PET conversion. In the BioFINDER‐2 cohort (605 tau‐negative, median age: 70.2), 33 converted to tau‐positive (median follow‐up time: 2 years), with higher %p‐tau217 (HR: 1.80 [95% CI: 1.50–2.17]), amyloid‐PET centiloid (HR: 2.29 [95% CI: 1.77–2.97]), and lower Aβ42/40 (HR: 2.38 [95% CI: 1.17–4.83]) predicting conversion. Interpretation In two cohorts, %p‐tau217 was associated with tau‐PET conversion, comparable to amyloid‐PET. APS2 also predicted conversion in the MCSA cohort, whereas Aβ42/40 predicted conversion in the BioFINDER‐2 cohort, which had more individuals with cognitive impairment. ANN NEUROL 2025;98:1249–1260

Abstract INTRODUCTION Primary age‐related tauopathy (PART) is defined by neurofibrillary tangles (NFTs) with absent‐minimal amyloid beta (Aβ) plaques. Currently, definitive diagnosis of PART occurs with autopsy. This study investigated whether [ 18 F]fluorodeoxyglucose positron emission tomography (FDG‐PET) could detect PART‐related metabolic changes and assessed the impact of common co‐pathologies. METHODS We performed a retrospective cross‐sectional study of 88 individuals (mean age 85.6) with autopsy‐confirmed PART (Braak I to IV; Thal phases 0 to 2) who underwent ante mortem FDG‐PET. Visual ratings and standardized uptake value ratios (SUVRs) were analyzed in medial and lateral temporal lobes, inferior temporal pole, precuneus, and posterior cingulate regions. RESULTS Medial temporal hypometabolism was observed in PART, in the presence of co‐pathologies. Argyrophilic grain disease and TAR DNA‐binding protein 43 were associated with greater hypometabolism ( p < 0.01). Lewy body disease affected parietal regions. DISCUSSION FDG‐PET reveals that PART‐related hypometabolism occurs when co‐pathologies are present, but PART alone appears to have minimal effect on medial temporal lobe hypometabolism. Highlights FDG‐PET hypometabolism worsens with Braak NFT stage. FDG‐PET detects mild lateral temporal lobe hypometabolism in PART alone. PART alone has a minimal effect on medial temporal lobe hypometabolism. Medial temporal hypometabolism is worse in PART with TDP‐43 and especially AGD. The presence of LBD contributes to parietal hypometabolism on FGD‐PET in PART.

Abstract There is an urgent need to improve dementia ascertainment robustness in real-world studies assessing drug effects on dementia risk. We developed algorithms to dementia identification algorithms using Medicare claims (inpatient/outpatient/prescription) from 3318 Visit 5 (2011-2013) and 1828 Visit 6 (2016-2017) participants of the Atherosclerosis Risk in Communities (ARIC) Study, validated against ARIC's rigorous syndromic dementia classification. Algorithm performance was compared to existing algorithms (Jain, Bynum, Lee). We further evaluated algorithms effectiveness in a 20% random Medicare sample aged ≥70 years who initiating liraglutide or dipeptidyl peptidase 4 inhibitors (DPP4i) to assess 3-year adjusted risk difference (aRD) for dementia. Our incident dementia algorithm required two dementia diagnostic codes within 1-year, or one dementia code plus a new dementia prescription within 90 days. It achieved a positive predictive value (PPV) of 69.2%, specificity of 99.0%, and sensitivity of 34.6% (population prevalence: 8.8%), comparable to extant algorithms (PPV, 58.7–68.6%; sensitivity 25.5–40.4%). Prevalent dementia algorithm (without requiring incident diagnoses/prescriptions) demonstrated similar performance. In the Medicare sample, dementia risk ranged from 3.0% to 12.5%, aRD comparing liraglutide to DPP4i varied −1.2% to −3.6%, with our algorithm closely matching the Bynum algorithm. Algorithm selection significantly impacts treatment effect estimates, highlighting its importance in in pharmacoepidemiologic research.