scholarly journals Functional brain age prediction suggests accelerated aging in preclinical familial Alzheimer’s disease, irrespective of fibrillar amyloid-beta pathology

2020 ◽  
Author(s):  
Julie Gonneaud ◽  
Alex T. Baria ◽  
Alexa Pichet Binette ◽  
Brian A. Gordon ◽  
Jasmeer P. Chhatwal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Aging ◽  
Accelerated Aging ◽  
Chronological Age ◽  
Resting State Functional Connectivity ◽  
Functional Brain ◽  
Preclinical Ad ◽  
The Difference ◽  
Brain Age

AbstractWe aimed at developing a model able to predict brain aging from resting state functional connectivity (rs-fMRI) and assessing whether genetic risk/determinants of Alzheimer’s disease (AD) and amyloid (Aβ) pathology contributes to accelerated brain aging. Using data collected in 1340 cognitively unimpaired participants from 18 to 94 years old selected across multi-site cohorts, we showed that chronological age can be predicted across the whole lifespan from topological properties of graphs constructed from rs-fMRI. We subsequently used the difference between the model-predicted age and the chronological age in pre-symptomatic autosomal dominant AD (ADAD) mutation carriers and asymptomatic individuals at risk of sporadic AD and assessed the influence of genetics and Aβ pathology on brain age. Applying our predictive model in the context of preclinical AD revealed that the pre-symptomatic phase of ADAD is characterized by accelerated functional brain aging. This phenomenon is independent from, and might precede, detectable fibrillar Aβ deposition.

scholarly journals Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Julie Gonneaud ◽  
Alex T. Baria ◽  
Alexa Pichet Binette ◽  
Brian A. Gordon ◽  
Jasmeer P. Chhatwal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Brain Aging ◽  
Genetic Determinants ◽  
Resting State Functional Connectivity ◽  
Functional Brain ◽  
Clinical Phase ◽  
Amyloid Aβ ◽  
Using Data

AbstractResting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.

scholarly journals Rate of brain aging and APOE ε4 are synergistic risk factors for Alzheimer’s disease

2019 ◽  
Vol 2 (3) ◽  
pp. e201900303 ◽  
Author(s):  
Christin A Glorioso ◽  
Andreas R Pfenning ◽  
Sam S Lee ◽  
David A Bennett ◽  
Etienne L Sibille ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurological Disease ◽  
Brain Aging ◽  
Chronological Age ◽  
Apoe Ε4 ◽  
Brain Age ◽  
Molecular Brain

Advanced age and the APOE ε4 allele are the two biggest risk factors for Alzheimer’s disease (AD) and declining cognitive function. We describe a universal gauge to measure molecular brain age using transcriptome analysis of four human postmortem cohorts (n = 673, ages 25–97) free of neurological disease. In a fifth cohort of older subjects with or without neurological disease (n = 438, ages 67–108), we show that subjects with brains deviating in the older direction from what would be expected based on chronological age show an increase in AD, Parkinson’s disease, and cognitive decline. Strikingly, a younger molecular age (−5 yr than chronological age) protects against AD even in the presence of APOE ε4. An established DNA methylation gauge for age correlates well with the transcriptome gauge for determination of molecular age and assigning deviations from the expected. Our results suggest that rapid brain aging and APOE ε4 are synergistic risk factors, and interventions that slow aging may substantially reduce risk of neurological disease and decline even in the presence of APOE ε4.

scholarly journals Falls Associate with Neurodegenerative Changes in ATN Framework of Alzheimer’s Disease

2020 ◽  
Vol 77 (2) ◽  
pp. 745-752
Author(s):  
Audrey Keleman ◽  
Julie K. Wisch ◽  
Rebecca M. Bollinger ◽  
Elizabeth A. Grant ◽  
Tammie L. Benzinger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Connectivity ◽  
Hippocampal Volume ◽  
Cross Sectional Study ◽  
Amyloid Pet ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
Preclinical Ad ◽  
Amyloid Accumulation

Background: Behavioral markers for Alzheimer’s disease (AD) are not included within the widely used amyloid-tau-neurodegeneration framework. Objective: To determine when falls occur among cognitively normal (CN) individuals with and without preclinical AD. Methods: This cross-sectional study recorded falls among CN participants (n = 83) over a 1-year period. Tailored calendar journals recorded falls. Biomarkers including amyloid positron emission tomography (PET) and structural and functional magnetic resonance imaging were acquired within 2 years of fall evaluations. CN participants were dichotomized by amyloid PET (using standard cutoffs). Differences in amyloid accumulation, global resting state functional connectivity (rs-fc) intra-network signature, and hippocampal volume were compared between individuals who did and did not fall using Wilcoxon rank sum tests. Among preclinical AD participants (amyloid-positive), the partial correlation between amyloid accumulation and global rs-fc intra-network signature was compared for those who did and did not fall. Results: Participants who fell had smaller hippocampal volumes (p = 0.04). Among preclinical AD participants, those who fell had a negative correlation between amyloid uptake and global rs-fc intra-network signature (R = –0.75, p = 0.012). A trend level positive correlation was observed between amyloid uptake and global rs-fc intra-network signature (R = 0.70, p = 0.081) for preclinical AD participants who did not fall. Conclusion: Falls in CN older adults correlate with neurodegeneration biomarkers. Participants without falls had lower amyloid deposition and preserved global rs-fc intra-network signature. Falls most strongly correlated with presence of amyloid and loss of brain connectivity and occurred in later stages of preclinical AD.

scholarly journals Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seth Christman ◽  
Camilo Bermudez ◽  
Lingyan Hao ◽  
Bennett A. Landman ◽  
Brian Boyd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Performance ◽  
Brain Aging ◽  
Memory Performance ◽  
Accelerated Aging ◽  
Structural Mri ◽  
Chronological Age ◽  
Adult Lifespan ◽  
Depressed Group ◽  
Brain Age

Abstract Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain–age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

scholarly journals IMPROVING BRAIN AGE PREDICTION MODELS: INCORPORATION OF AMYLOID STATUS IN ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S91-S91
Author(s):  
Maria Ly ◽  
Nishita Muppidi ◽  
Helmet Karim ◽  
Gary Yu ◽  
Akiko Mizuno ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Cumulative Exposure ◽  
Chronological Age ◽  
Aging Brain ◽  
Critical Feature ◽  
Amyloid Pathology ◽  
Brain Age ◽  
Age Prediction

Abstract Brain age prediction may serve as a promising, individualized biomarker of brain health and may help us understand the heterogeneous biological changes that occur in aging. Brain age prediction is a machine learning method that estimates an individual’s chronological age from their neuroimaging scans. If predicted brain age is greater than chronological age, that individual may have an “older” brain than expected, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer’s disease (AD). We showed that amyloid status is a critical feature for brain age prediction models by training a model on 808 individuals without significant amyloid pathology from the ADNI, OASIS-3, and IXI cohorts. Our model accurately predicted brain age in the training and independent test sets, comparable to previous published models: [r(807) = 0.94, R2 = 0.88, p=0.001, MAE = 4.9 years, p=0.001], [r(39) = 0.67, R2 = 0.45, and MAE = 4.6 years]. We demonstrated significant differences between AD diagnostic groups [F(3,431)=30.7, p<0.001], and our model was the first to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid [mean difference, 95% CI; CN-Aβ(-) (-3.4, -4.9:-1.8), CN-Aβ(+) (-0.7, -1.9:0.5)]. Ultimately, incorporation of amyloid status in brain age prediction models improves the utility of brain age as a biomarker for aging and AD.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

Identification of a Pathogenic PSEN1 Ala285Val Mutation Associated with Early-Onset Alzheimer’s Disease

2020 ◽  
Vol 17 (5) ◽  
pp. 438-445
Author(s):  
Van Giau Vo ◽  
Jung-Min Pyun ◽  
Eva Bagyinszky ◽  
Seong S.A. An ◽  
Sang Y. Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Temporal Lobe ◽  
Early Onset ◽  
Parietal Lobe ◽  
Disease Diagnosis ◽  
Random Coil ◽  
Magnetic Resonance Imaging Scan ◽  
Preclinical Ad ◽  
Early Onset Alzheimer’S Disease

Background: Presenilin 1 (PSEN1) was suggested as the most common causative gene of early onset Alzheimer’s Disease (AD). Methods: Patient who presented progressive memory decline in her 40s was enrolled in this study. A broad battery of neuropsychological tests and neuroimaging was applied to make the diagnosis. Genetic tests were performed in the patient to evaluate possible mutations using whole exome sequencing. The pathogenic nature of missense mutation and its 3D protein structure prediction were performed by in silico prediction programs. Results: A pathogenic mutation in PSEN1 (NM_000021.3: c.1027T>C p.Ala285Val), which was found in a Korean EOAD patient. Magnetic resonance imaging scan showed mild left temporal lobe atrophy. Hypometabolism appeared through 18F-fludeoxyglucose Positron Emission Tomography (FDG-PET) scanning in bilateral temporal and parietal lobe, and 18F-Florbetaben-PET (FBB-PET) showed increased amyloid deposition in bilateral frontal, parietal, temporal lobe and hence presumed preclinical AD. Protein modeling showed that the p.Ala285Val is located in the random coil region and could result in extra stress in this region, resulting in the replacement of an alanine residue with a valine. This prediction was confirmed previous in vitro studies that the p.Trp165Cys resulted in an elevated Aβ42/Aβ40 ratio in both COS-1 and HEK293 cell lines compared that of wild-type control. Conclusion: Together, the clinical characteristics and the effect of the mutation would facilitate our understanding of PSEN1 in AD pathogenesis for the disease diagnosis and treatment. Future in vivo study is needed to evaluate the role of PSEN1 p.Ala285Val mutation in AD progression.

scholarly journals Association between methylation of BIN1 promoter in peripheral blood and preclinical Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Hu ◽  
Lan Tan ◽  
Yan-Lin Bi ◽  
Wei Xu ◽  
Lin Tan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Late Onset ◽  
Early Stage ◽  
Susceptibility Gene ◽  
Subjective Cognitive Decline ◽  
Preclinical Ad ◽  
T Tau ◽  
New Evidence

AbstractThe bridging integrator 1 (BIN1) gene is the second most important susceptibility gene for late-onset Alzheimer’s disease (LOAD) after apolipoprotein E (APOE) gene. To explore whether the BIN1 methylation in peripheral blood changed in the early stage of LOAD, we included 814 participants (484 cognitively normal participants [CN] and 330 participants with subjective cognitive decline [SCD]) from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database. Then we tested associations of methylation of BIN1 promoter in peripheral blood with the susceptibility for preclinical AD or early changes of cerebrospinal fluid (CSF) AD-related biomarkers. Results showed that SCD participants with significant AD biological characteristics had lower methylation levels of BIN1 promoter, even after correcting for covariates. Hypomethylation of BIN1 promoter were associated with decreased CSF Aβ42 (p = 0.0008), as well as increased p-tau/Aβ42 (p = 0.0001) and t-tau/Aβ42 (p < 0.0001) in total participants. Subgroup analysis showed that the above associations only remained in the SCD subgroup. In addition, hypomethylation of BIN1 promoter was also accompanied by increased CSF p-tau (p = 0.0028) and t-tau (p = 0.0130) in the SCD subgroup, which was independent of CSF Aβ42. Finally, above associations were still significant after correcting single nucleotide polymorphic sites (SNPs) and interaction of APOE ɛ4 status. Our study is the first to find a robust association between hypomethylation of BIN1 promoter in peripheral blood and preclinical AD. This provides new evidence for the involvement of BIN1 in AD, and may contribute to the discovery of new therapeutic targets for AD.

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