skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease
Abstract INTRODUCTIONAlzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODSGiven the genetic susceptibility of AD, a multi‐factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk‐stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTSOur risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI,APOEε4– MCI, and amyloid+ MCI. DISCUSSIONOur risk score holds great potential to improve the precision of early risk assessment. HighlightsAccurate early risk assessment is critical for the success of clinical trials.A new risk score was built from integrating amyloid imaging and genetic data.Our risk score demonstrated improved capability in early risk stratification.  more » « less
Award ID(s):
1942394
PAR ID:
10542424
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Alzheimer's & Dementia
Volume:
20
Issue:
11
ISSN:
1552-5260
Format(s):
Medium: X Size: p. 7819-7830
Size(s):
p. 7819-7830
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, Alzheimer's & Dementia)
    Abstract BACKGROUNDLimited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. METHODSVascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. RESULTSBaseline analyses revealed that amyloid‐negative (Aβ–) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ– CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. CONCLUSIONOur findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ– individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid‐positive individuals had worse cognitive performance than Aβ– individuals. HighlightsVascular risk significantly affects cognition in amyloid‐negative older Koreans.Amyloid‐negative CN older adults with high vascular risk had lower baseline cognition.Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk.The study underscores the impact of vascular health on the AD disease spectrum. 
    more » « less
  2. ; ; ; ; ; ; (, Alzheimer's & Dementia)
    Abstract INTRODUCTIONIdentification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODSWe applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews ofn = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTSOur best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI‐to‐AD progression within 6 years. DISCUSSIONThe proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy‐to‐administer screening tool for MCI‐to‐AD progression prediction, facilitating development of remote assessment. HighlightsVoice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment.The study leveraged AI methods for speech recognition and processed the resulting text using language models.The developed AI‐powered pipeline can lead to fully automated assessment that could enable remote and cost‐effective screening and prognosis for Alzehimer's disease. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al (, Frontiers in Neurology)
    BackgroundSemantic intrusion errors (SIEs) are associated with mild cognitive impairment (MCI) due to Alzheimer's disease (AD). It is unknown whether accounting for maximum learning capacity still leads to an increase in SIEs when elevated plasma p-tau217, a biological indicator of underlying AD, is present. MethodsOne hundred fifty-eight older adult participants completed the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), a sensitive cognitive challenge test designed to elicit SIEs. Of these, 108 were clinically diagnosed with amnestic MCI (aMCI). Fifty-eight individuals met or exceeded a plasma p-tau217positivity of >0.55 pg/ml, while 50 individuals scored below this threshold. ResultsAfter adjusting for demographic covariates and maximum learning capacity, the aMCI p-tau217+ group evidenced more SIEs compared to aMCI p-tau217- on the first (list B1;p= 0.035) and second trials of the competing list (list B2;p= 0.006). Biological predictors such asApoEε4 status, higher p-tau217, and older age were predictors of an elevated number of SIEs [list B2:F(3,104) = 10.92;p= 0.001;R= 0.489)]. ConclusionsUnlike previous studies that used amyloid PET or other plasma biomarkers, individuals with aMCI p-tau217+ evidenced more SIEs, even after adjusting for their initial learning capacity, a covariate that has not been studied previously. These findings support that SIEs are more prevalent in the presence of underlying AD pathology and occur independent of learning deficits. 
    more » « less
  4. ; ; ; ; ; ; ; (, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring)
    Abstract INTRODUCTIONIdentifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all‐cause dementia (ACD) conversion at 5 years. METHODSCox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held‐out data subset. RESULTSOf 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors. DISCUSSIONEHR‐based prediction model had good performance in identifying 5‐year MCI to ACD conversion and has potential to assist triaging of at‐risk patients. HighlightsOf 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all‐cause dementia within 5 years.Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).Age and vascular‐related morbidities were predictors of dementia conversion.Synthetic data was comparable to real data in modeling MCI to dementia conversion. Key PointsAn electronic health record–based model using demographic and co‐morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all‐cause dementia (ACD) within 5 years.Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5‐year conversion from MCI to ACD.High body mass index, alcohol abuse, and sleep apnea were protective factors for 5‐year conversion from MCI to ACD.Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health‐care data with minimized patient privacy concern that could accelerate scientific discoveries. 
    more » « less
  5. ; ; ; ; ; ; (, BMC Bioinformatics)
    Abstract BackgroundIn Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. ResultsDGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. ConclusionOverall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email