Browsing by Author "Degenhardt, Elisabeth"

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    Empirically Defining Trajectories of Late-Life Cognitive and Functional Decline
    (IOS, 2015-11) Hochstetler, Helen; Trzepacz, Paula T.; Wang, Shufang; Yu, Peng; Case, Michael; Henley, David B.; Degenhardt, Elisabeth; Leoutsakos, Jeannie-Marie; Lyketsos, Constantine G.; Department of Psychiatry, IU School of Medicine
    Background: Alzheimer’s disease (AD) is associated with variable cognitive and functional decline, and it is difficult to predict who will develop the disease and how they will progress. Objective: This exploratory study aimed to define latent classes from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database who had similar growth patterns of both cognitive and functional change using Growth Mixture Modeling (GMM), identify characteristics associated with those trajectories, and develop a decision tree using clinical predictors to determine which trajectory, as determined by GMM, individuals will most likely follow. Methods: We used ADNI early mild cognitive impairment (EMCI), late MCI (LMCI), AD dementia, and healthy control (HC) participants with known amyloid-β status and follow-up assessments on the Alzheimer’s Disease Assessment Scale - Cognitive Subscale or the Functional Activities Questionnaire (FAQ) up to 24 months postbaseline. GMM defined trajectories. Classification and Regression Tree (CART) used certain baseline variables to predict likely trajectory path. Results: GMM identified three trajectory classes (C): C1 (n = 162, 13.6%) highest baseline impairment and steepest pattern of cognitive/functional decline; C3 (n = 819, 68.7%) lowest baseline impairment and minimal change on both; C2 (n = 211, 17.7%) intermediate pattern, worsening on both, but less steep than C1. C3 had fewer amyloid- or apolipoprotein-E ɛ4 (APOE4) positive and more healthy controls (HC) or EMCI cases. CART analysis identified two decision nodes using the FAQ to predict likely class with 82.3% estimated accuracy. Conclusions: Cognitive/functional change followed three trajectories with greater baseline impairment and amyloid and APOE4 positivity associated with greater progression. FAQ may predict trajectory class.
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    Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer’s Disease
    (Karger, 2016-03) Trzepacz, Paula T.; Hochstetler, Helen; Yu, Peng; Castelluccio, Peter; Witte, Michael M.; Dell'Agnello, Grazia; Degenhardt, Elisabeth; Department of Psychiatry, IU School of Medicine
    Aims: To assess how hippocampal volume (HV) from volumetric magnetic resonance imaging (vMRI) is related to the amyloid status at different stages of Alzheimer's disease (AD) and its relevance to patient care. Methods: We evaluated the ability of HV to predict the florbetapir positron emission tomography (PET) amyloid positive/negative status by group in healthy controls (HC, n = 170) and early/late mild cognitive impairment (EMCI, n = 252; LMCI, n = 136), and AD dementia (n = 75) subjects from the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity (ADNI-GO) and ADNI2. Logistic regression analyses, including elastic net classification modeling with 10-fold cross-validation, were used with age and education as covariates. Results: HV predicted amyloid status only in LMCI using either logistic regression [area under the curve (AUC) = 0.71, p < 0.001] or elastic net classification modeling [positive predictive value (PPV) = 72.7%]. In EMCI, age (AUC = 0.70, p < 0.0001) and age and/or education (PPV = 63.1%), but not HV, predicted amyloid status. Conclusion: Using clinical neuroimaging, HV predicted amyloid status only in LMCI, suggesting that HV is not a biomarker surrogate for amyloid PET in clinical applications across the full diagnostic spectrum.