Amyloid negativity in patients with clinically diagnosed Alzheimer disease and MCI.

OBJECTIVE: To examine the clinical and biomarker characteristics of patients with amyloid-negative Alzheimer disease (AD) and mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a prospective cohort study. METHODS: We first investigated the reliability of florbetapir- PET in patients with AD and patients with MCI using CSF-Aß1-42 as a comparison amyloid measurement. We then compared florbetapir- vs florbetapir+ patients with respect to several AD-specific biomarkers, baseline and longitudinal cognitive measurements, and demographic and clinician report data. RESULTS: Florbetapir and CSF-Aß1-42 +/- status agreed for 98% of ADs (89% of MCIs), indicating that most florbetapir- scans were a reliable representation of amyloid status. Florbetapir- AD (n = 27/177; 15%) and MCI (n = 74/217, 34%) were more likely to be APOE4-negative (MCI 83%, AD 96%) than their florbetapir+ counterparts (MCI 30%, AD 24%). Florbetapir- patients also had less AD-specific hypometabolism, lower CSF p-tau and t-tau, and better longitudinal cognitive performance, and were more likely to be taking medication for depression. In MCI only, florbetapir- participants had less hippocampal atrophy and hypometabolism and lower functional activity questionnaire scores compared to florbetapir+ participants. CONCLUSIONS: Overall, image analysis problems do not appear to be a primary explanation of amyloid negativity. Florbetapir- ADNI patients have a variety of clinical and biomarker features that differ from their florbetapir+ counterparts, suggesting that one or more non-AD etiologies (which may include vascular disease and depression) account for their AD-like phenotype.

Comparison of Visual and Quantitative Florbetapir F 18 Positron Emission Tomography Analysis in Predicting Mild Cognitive Impairment Outcomes.

IMPORTANCE: The applicability of ß-amyloid peptide (Aß) positron emission tomography (PET) as a biomarker in clinical settings to aid in selection of individuals at preclinical and prodromal Alzheimer disease (AD) will depend on the practicality of PET image analysis. In this context, visual-based Aß PET assessment seems to be the most feasible approach. OBJECTIVES: To determine the agreement between visual and quantitative Aß PET analysis and to assess the ability of both techniques to predict conversion from mild cognitive impairment (MCI) to AD. DESIGN, SETTING, AND PARTICIPANTS: A longitudinal study was conducted among the Alzheimer's Disease Neuroimaging Initiative (ADNI) sites in the United States and Canada during a 1.6-year mean follow-up period. The study was performed from September 21, 2010, to August 11, 2014; data analysis was conducted from September 21, 2014, to May 26, 2015. Participants included 401 individuals with MCI receiving care at a specialty clinic (219 [54.6%] men; mean [SD] age, 71.6 [7.5] years; 16.2 [2.7] years of education). All participants were studied with florbetapir F 18 [18F] PET. The standardized uptake value ratio (SUVR) positivity threshold was 1.11, and one reader rated all images, with a subset of 125 scans rated by a second reader. MAIN OUTCOMES AND MEASURES: Sensitivity and specificity of positive and negative [18F] florbetapir PET categorization, which was estimated with cerebrospinal fluid Aß1-42 as the reference standard. Risk for conversion to AD was assessed using Cox proportional hazards regression models. RESULTS: The frequency of Aß positivity was 48.9% (196 patients; visual analysis), 55.1% (221 patients; SUVR), and 64.8% (166 patients; cerebrospinal fluid), yielding substantial agreement between visual and SUVR data (κ = 0.74) and between all methods (Fleiss κ = 0.71). For approximately 10% of the 401 participants in whom visual and SUVR data disagreed, interrater reliability was moderate (κ = 0.44), but it was very high if visual and quantitative results agreed (κ = 0.92). Visual analysis had a lower sensitivity (79% vs 85%) but higher specificity (96% vs 90%), respectively, compared with SUVR. The conversion rate was 15.2% within a mean of 1.6 years, and a positive [18F] florbetapir baseline scan was associated with a 6.91-fold (SUVR) or 11.38-fold (visual) greater hazard for AD conversion, which changed only modestly after covariate adjustment for apolipoprotein ε4, concurrent fludeoxyglucose F 18 PET scan, and baseline cognitive status. CONCLUSIONS AND RELEVANCE: Visual and SUVR Aß PET analysis may be equivalently used to determine Aß status for individuals with MCI participating in clinical trials, and both approaches add significant value for clinical course prognostication.

Measurement of longitudinal ß-amyloid change with 18F-florbetapir PET and standardized uptake value ratios.

UNLABELLED: The accurate measurement of ß-amyloid (Aß) change using amyloid PET imaging is important for Alzheimer disease research and clinical trials but poses several unique challenges. In particular, reference region measurement instability may lead to spurious changes in cortical regions of interest. To optimize our ability to measure (18)F-florbetapir longitudinal change, we evaluated several candidate regions of interest and their influence on cortical florbetapir change over a 2-y period in participants from the Alzheimer Disease Neuroimaging Initiative (ADNI). METHODS: We examined the agreement in cortical florbetapir change detected using 6 candidate reference regions (cerebellar gray matter, whole cerebellum, brain stem/pons, eroded subcortical white matter [WM], and 2 additional combinations of these regions) in 520 ADNI subjects. We used concurrent cerebrospinal fluid Aß1-42 measurements to identify subgroups of ADNI subjects expected to remain stable over follow-up (stable Aß group; n = 14) and subjects expected to increase (increasing Aß group; n = 91). We then evaluated reference regions according to whether cortical change was minimal in the stable Aß group and cortical retention increased in the increasing Aß group. RESULTS: There was poor agreement across reference regions in the amount of cortical change observed across all 520 ADNI subjects. Within the stable Aß group, however, cortical florbetapir change was 1%-2% across all reference regions, indicating high consistency. In the increasing Aß group, cortical increases were significant with all reference regions. Reference regions containing WM (as opposed to cerebellum or pons) enabled detection of cortical change that was more physiologically plausible and more likely to increase over time. CONCLUSION: Reference region selection has an important influence on the detection of florbetapir change. Compared with cerebellum or pons alone, reference regions that included subcortical WM resulted in change measurements that are more accurate. In addition, because use of WM-containing reference regions involves dividing out cortical signal contained in the reference region (via partial-volume effects), use of these WM-containing regions may result in more conservative estimates of actual change. Future analyses using different tracers, tracer-kinetic models, pipelines, and comparisons with other biomarkers will further optimize our ability to accurately measure Aß changes over time.