As the population ages, late-onset Alzheimer's disease (AD) is becoming an increasingly important public health issue. Clinical trials targeted at reducing AD progression have demonstrated that patients continue to decline despite therapeutic intervention. Thus, there is a pressing need for new treatments aimed at novel therapeutic targets. A shift in focus from risk to resilience has tremendous potential to have a major public health impact by highlighting mechanisms that naturally counteract the damaging effects of AD neuropathology. Interestingly, at autopsy, approximately 30% of cognitively normal individuals have the pathological features of AD. Research from our group has begun to uncover genetic factors that explain some of the observed disconnect between neuropathology and clinical dementia. More specifically, through integration of in vivo biomaker and autopsy data into a unified model of resilience, we are able to perform large, comprehensive analyses of genetic resilience. 


Defining Resilience 

Quantifying resilience is challenging. Past research was often limited to individuals with “asymptomatic” Alzheimer’s disease (AD), a phenomenon in which individuals show no memory loss or other symptoms of AD during life, but after death their brains show all the signs of this disease (e.g., β-amyloid plaques and aggregation of tau). However, this definition of resilience drastically compromises statistical power by focusing on only a small subset of the population. Adding to the challenge, resilience definitions often combine the construct of reserve (e.g., predisposition towards protection) with resilience (e.g., better than expected performance despite neuropathology).

Our work addresses these challenges by leveraging a continuous resilience measure. Response to pathology appears to be best represented on a continuum given that the vast majority of cognitively normal individuals have at least some neuropathology at autopsy, and there is substantial heterogeneity across people who meet pathological criteria for AD. Building on the cognitive and brain reserve literature, we integrated co-calibrated cognitive data along with AD biomarkers to construct composite resilience measures (Figure 1). Our composite resilience measures can be harmonized across datasets, strongly predict protection from cognitive impairment (Figure 2), are easy to interpret, and enable large, well-powered resilience analyses.

 

PLS path model results are presented; the goodness of fit was 0.76.

Figure 1. Partial least squares (PLS) path model results. PLS path model results are presented; the goodness of fit was 0.76. Each first-order latent variable is presented as an oval. The variables included in each latent trait because we used reflective measurement. For the resilience metrics, each rectangle represents the residuals from a single linear regression model relating the given biomarker to the given outcome. The second-order latent variable (global resilience) is presented in a dotted oval. The loadings for each first-order latent variable are presented numerically above the bold arrows pointing to global resilience. (Hohman et al., Neurology, 2016)

 

Line graph shows higher levels of global resilience are related to slower rates of cognitive decline, particularly among individuals who are positive for biomarkers of AD neuropathology.

Figure 2: Higher levels of global resilience are related to slower rates of cognitive decline, particularly among individuals who are positive for biomarkers of AD neuropathology. (Hohman et al., Neurology, 2016.)

Characterizing Drivers of Resilience  

Identifying the molecular factors that underlie the resilience observed in asymptomatic AD may provide novel therapeutic targets for clinical intervention and provide additional insight into the genetic architecture of AD.

Robust phenotypes of resilience calculated by leveraging AD biomarkers and baseline brain aging outcomes provide insight into which individuals are at greatest risk of short-term decline. Such understanding of drivers of resilience are needed to further our understanding of the mechanisms that protect individuals from the clinical manifestation of AD dementia, especially among biomarker-positive individuals. 

 

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Figure 3: (Dumitrescu et al. In Press, Brain.)

 

Validating Drivers of Resilience

Robust Vascular endothelial growth factor (VEGF) is associated with the clinical manifestation of Alzheimer's disease (AD). However, the role of the VEGF gene family in neuroprotection is complex due to the number of biological pathways they regulate. Our studies in the associations between brain expression of VEGF genes with cognitive performance and AD pathology have found that VEGF ligand and receptor genes, specifically genes relevant to FLT4 and FLT1 receptor signaling, are associated with cognition, longitudinal cognitive decline, and AD neuropathology. Future work should confirm these observations at the protein level to better understand how changes in VEGF transcription and translation relate to neurodegenerative disease. 

 

 

Scatterplot

Figure 4: FLT1 expression associates with (A) longitudinal cognition, (B) clinical diagnosis, and (C) amyloid pathology. (Hohman et. al 2019, Molecular Psychiatry)

 

Scatterplots show (A)  NRP1 expression associations with global cognitive performance at the final neuropsychological assessment, stratified by APOE-ε4 allele status and (B) VEGFA expression associations with global cognitive performance at the final neuropsychological assessment, stratified by APOE-ε4 allele statusFigure 5: (A) NRP1 expression associations with global cognitive performance at the final neuropsychological assessment, stratified by APOE-ε4 allele status. Overall interaction:NRP1 × APOE-ε4, β=−0.28, p.fdr=0.007; APOE-ε4 carriers, β=−0.17, p=0.038; APOE-ε4 non-carriers, β=0.11, p=0.004. (BVEGFA expression associations with global cognitive performance at the final neuropsychological assessment, stratified by APOE-ε4 allele status. Overall interaction: VEGFA × APOE-ε4, β=−0.03, p.fdr=0.026; APOE-ε4 carriers, β=−0.03, p=0.019; APOE-ε4 non-carriers, β=0.004, p=0.4. (Hohman et al. 2020, Neurobiology of Aging)

Collaborators

Relevant Publications

2020

2019

2018

  • Deming Y, Dumitrescu L, Barnes LL, Thambisetty M, Kunkle B, Gifford KA, Bush WS, Chibnik LB, Mukherjee S, De Jager PL, Kukull W, Huentelman M, Crane PK, Resnick SM, Keene CD, Montine TJ, Schellenberg GD, Haines JL, Zetterberg H, Blennow K, Larson EB, Johnson SC, Albert M, Moghekar A, Del Aguila JL, Fernandez MV, Budde J, Hassenstab J, Fagan AM, Riemenschneider M, Petersen RC, Minthon L, Chao MJ, Van Deerlin VM, Lee VM, Shaw LM, Trojanowski JQ, Peskind ER, Li G, Davis LK, Sealock JM, Cox NJ, Goate AM, Bennett DA, Schneider JA, Jefferson AL, Cruchaga C, Hohman TJ. Sex-specific genetic predictors of Alzheimer's disease biomarkers. Acta neuropathologica. 2018 Jul 2. PMID: 29967939 [PubMed]

2017

2016

2015

2014


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