Showing papers by "Mark A. Mintun published in 2023"
TL;DR: Southekal et al. as discussed by the authors evaluated a temporal lobe composite (Eτ ) volume of interest (VOI) compared to a previously described global neocortical VOI.
Abstract: Abstract Background There is an increasing interest in utilizing tau PET to identify patients early in Alzheimer’s disease (AD). In this work, a temporal lobe composite ( Eτ ) volume of interest (VOI) was evaluated in a longitudinal flortaucipir cohort and compared to a previously described global neocortical VOI. In a separate autopsy-confirmed study, the sensitivity of the Eτ VOI for identifying intermediate (B2) neurofibrillary tangle (NFT) pathology was evaluated. Methods A total of 427 subjects received flortaucipir, florbetapir, MRI, and cognitive evaluation at baseline and 18 months. In a separate autopsy study, 67 subjects received ante-mortem flortaucipir scans, and neuropathological findings were recorded according to NIA-AA recommendations by two experts. Two VOIs: Eτ comprising FreeSurfer volumes (bilateral entorhinal cortex, fusiform, parahippocampal, and inferior temporal gyri) transformed to MNI space and a previously published global AD signature-weighted neocortical VOI (AD signature ) (Devous et al., J Nucl Med 59:937–43, 2018), were used to calculate SUVr relative to a white matter reference region (PERSI) (Southekal et al., J Nucl Med Off Publ Soc Nucl Med 59:944–51, 2018). SUVr cutoffs for positivity were determined based on a cohort of young, cognitively normal subjects. Subjects were grouped based on positivity on both VOIs ( Eτ+ /AD signature +; Eτ+ /AD signature –; Eτ −/AD signature −). Groupwise comparisons were performed for baseline SUVr, 18-month changes in SUVr, neurodegeneration, and cognition. For the autopsy study, the sensitivity of Eτ in identifying intermediate Braak pathology (B2) subjects was compared to that of AD signature-weighted neocortical VOI. The average surface maps of subjects in the Eτ+ /AD signature − group and B2 NFT scores were created for visual evaluation of uptake. Results Sixty-four out of 390 analyzable subjects were identified as Eτ+ /AD signature –: 84% were Aβ+, 100% were diagnosed as MCI or AD, and 59% were APOE ε4 carriers. Consistent with the hypothesis that Eτ+ /AD signature – status reflects an early stage of AD, Eτ+ /AD signature – subjects deteriorated significantly faster than Eτ– /AD signature – subjects, but significantly slower than Eτ+ /AD signature + subjects, on most measures (i.e., change in AD signature SUVr, Eτ ROI cortical thickness, and MMSE). The AD signature VOI was selective for subjects who came to autopsy with a B3 NFT score. In the autopsy study, 12/15 B2 subjects (including 10/11 Braak IV) were Eτ+ /AD signature –. Surface maps showed that flortaucipir uptake was largely captured by the Eτ VOI regions in B2 subjects. Conclusion The Eτ VOI identified subjects with elevated temporal but not global tau ( Eτ+ /AD signature –) that were primarily Aβ+, APOE ε4 carriers, and diagnosed as MCI or AD. Eτ+ /AD signature – subjects had greater accumulation of tau, greater atrophy, and higher decline on MMSE in 18 months compared to Eτ −/AD signature − subjects. Finally, the Eτ VOI identified the majority of the intermediate NFT score subjects in an autopsy-confirmed study. As far as we know, this is the first study that presents a visualization of ante-mortem FTP retention patterns that at a group level agree with the neurofibrillary tangle staging scheme proposed by Braak. These findings suggest that the Eτ VOI may be sensitive for detecting impaired subjects early in the course of Alzheimer’s disease.
2 citations
TL;DR: A pooled analysis of potentially efficacious antibodies lecanemab, aducanumab, solanezumab and donanemab showed slightly better efficacy in APOE ε4 carriers than in non-carriers as mentioned in this paper .
Abstract: INTRODUCTION
Apolipoprotein E (APOE) ε4 may interact with response to amyloid-targeting therapies.
METHODS
Aggregate data from trials enrolling participants with amyloid-positive, early symptomatic Alzheimer's disease (AD) were analyzed for disease progression.
RESULTS
Pooled analysis of potentially efficacious antibodies lecanemab, aducanumab, solanezumab, and donanemab shows slightly better efficacy in APOE ε4 carriers than in non-carriers. Carrier and non-carrier mean (95% confidence interval) differences from placebo using Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were -0.30 (-0.478, -0.106) and -0.20 (-0.435, 0.042) and AD Assessment Scale-Cognitive subscale (ADAS-Cog) values were -1.01 (-1.577, -0.456) and -0.80 (-1.627, 0.018), respectively. Decline in the APOE ε4 non-carrier placebo group was equal to or greater than that in carriers across multiple scales. Probability of study success increases as the representation of the carrier population increases.
DISCUSSION
We hypothesize that APOE ε4 carriers have same or better response than non-carriers to amyloid-targeting therapies and similar or less disease progression with placebo in amyloid-positive trials.
HIGHLIGHTS
Amyloid-targeting therapies had slightly greater efficacy in apolipoprotein E (APOE) ε4 carriers. Clinical decline is the same/slightly faster in amyloid-positive APOE ε4 non-carriers. Prevalence of non-carriers in trial populations could impact outcomes.
TL;DR: In this article , a deep learning neural network model was proposed to predict polygenic risk of Alzheimer's disease. But, the model was not able to capture the complexity of genomic data, and current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction.
Abstract: Abstract Background The polygenic nature of Alzheimer’s disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual’s genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. Methods We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. Results The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. Conclusion Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.
TL;DR: In randomized clinical trials (RCTs) for Alzheimer's disease (AD), cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are currently used for the detection and monitoring of AD pathological features as discussed by the authors .
Abstract: In randomized clinical trials (RCTs) for Alzheimer’s Disease (AD), cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are currently used for the detection and monitoring of AD pathological features. The use of less resource-intensive plasma biomarkers could decrease the burden to study volunteers and limit costs and time for study enrollment. Blood-based markers (BBMs) could thus play an important role in improving the design and the conduct of RCTs on AD. It remains to be determined if the data available on BBMs are strong enough to replace CSF and PET biomarkers as entry criteria and monitoring tools in RCTs.