Klunk, William E.

AbstractINTRODUCTIONAs the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others.METHODSWe used hierarchical clustering to group 19 cross‐sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation.RESULTSThree primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors.DISCUSSIONThese results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

AbstractBackgroundThough pathogenic variations in PSEN1 leading to Autosomal Dominant Alzheimer's disease (ADAD) are all highly penetrant, there is substantial inter‐individual variability in the rates of cognitive decline and biomarker change observed in ADAD. We hypothesized that this inter‐individual variability may be associated with the location of the pathogenic variant within PSEN1. We tested this hypothesis using biomarker and cognitive data from the Dominantly Inherited Alzheimer Network (DIAN) observational study.MethodPSEN1 pathogenic variant carriers [grouped based on whether their ADAD genotype affected transmembrane (PSEN1 TM; N = 125) or cytoplasmic (PSEN1 CY; N = 62) domains] and non‐carriers [NC; N = 176] enrolled in DIAN underwent clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF). Linear mixed effects models with a restricted cubic spline were used to determine cross‐sectional differences in clinical and biomarker measures between NC, PSEN1 TM, and PSEN1 CY groups, across the disease course (estimated years to symptom onset; EYO).ResultBackground characteristics are reported in Table 1. PSEN1 CY and PSEN1 TM groups had elevated β‐amyloid (PiB‐PET) and phosphorylated‐tau levels (CSF pTauT181) compared to NC, but these biomarker measures were not different between PSEN1 TM and CY groups (Figure 1a‐b). Additionally, the PSEN1 TM group had decreased glucose metabolism compared to NC but was similar to the PSEN1 CY group (Figure 1c). However, we observed that PSEN1 TM carriers had significantly smaller hippocampal volume, greater lateral ventricle volume, and greater cognitive impairment (Figure 1d‐f) across EYO as compared to PSEN1 CY carriers.ConclusionDespite similar β‐amyloid PET and CSF pTauT181 levels across EYO, individuals with pathogenic variants affecting transmembrane domains in PSEN1 may have more rapid progression of disease as measured by cognitive function and neurodegeneration as compared to those with variants affecting cytoplasmic domains. These results have implications both for understanding the heterogeneity in ongoing ADAD clinical trials and for better understanding the pathobiology of ADAD.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

Abstract Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir‐based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter‐individual variability of the two tracers were compared using multivariate linear models both cross‐sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

Abstract Introduction We sought to establish the relationships between standard postmortem measures of AD neuropathology and antemortem [ 11 C]PIB–positron emission tomography ([ 11 C]PIB‐PET) analyzed with the Centiloid (CL) method, a standardized scale for Aβ‐PET quantification. Methods Four centers contributed 179 participants encompassing a broad range of clinical diagnoses, PET data, and autopsy findings. Results CL values increased with each CERAD neuritic plaque score increment (median −3 CL for no plaques and 92 CL for frequent plaques) and nonlinearly with Thal Aβ phases (increases were detected starting at phase 2) with overlap between scores/phases. PET‐pathology associations were comparable across sites and unchanged when restricting the analyses to the 56 patients who died within 2 years of PET. A threshold of 12.2 CL detected CERAD moderate‐to‐frequent neuritic plaques (area under the curve = 0.910, sensitivity = 89.2%, specificity = 86.4%), whereas 24.4 CL identified intermediate‐to‐high AD neuropathological changes (area under the curve = 0.894, sensitivity = 84.1%, specificity = 87.9%). Discussion Our study demonstrated the robustness of a multisite Centiloid [ 11 C]PIB‐PET study and established a range of pathology‐based CL thresholds.

AbstractIntroduction18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer's disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11C‐Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET.MethodsFDG, early frames of PiB images, and relative PiB delivery rate constants (PiB‐R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional cross‐sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history.ResultsMetabolism and perfusion images were spatially correlated. Regional PiB‐R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity.DiscussionHypometabolism estimated by PiB‐R1 may provide a measure of brain perfusion without increasing radiation exposure.

AbstractWith increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long‐term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.

Significance Beta-amyloid plaque accumulation, glucose hypometabolism, and neuronal atrophy are hallmarks of Alzheimer’s disease. However, the regional ordering of these biomarkers prior to dementia remains untested. In a cohort with Alzheimer’s disease mutations, we performed an integrated whole-brain analysis of three major imaging techniques: amyloid PET, [ 18 F]fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism followed by atrophy. Critically, however, not all regions lose metabolic function, and not all regions atrophy, even when there is significant amyloid deposition. These regional disparities have important implications for clinical trials of disease-modifying therapies.