Poster Presentations: IC-P LONGITUDINAL OUTCOMES AND PROFILES OF DISTINCT BIOMARKER CLUSTERS BASED ON VASCULAR DISEASE, NEURODEGENERATION, AND AMYLOID DEPOSITION
Prashanthi Vemuri1, Scott A. Przybelski1, Timothy G. Lesnick1, David S. Knopman1, Mary M. Machulda1, Michelle M. Mielke1, Rosebud O. Roberts1, Val J. Lowe1, Yonas E. Geda2, Walter Rocca1, Ronald C. Petersen1, Clifford R. Jack, Jr.1, 1Mayo Clinic, Rochester, MN, USA; 2Mayo Clinic, Scottsdale, AZ, USA. Contact e-mail: Vemuri. [email protected]
Background: The goal of this study was to cluster cognitively normal (CN) individuals from a population sample based on imaging biomarkers (cerebrovascular disease [CVD], cortical thickness, amyloid load) and a) compare the demographics, modifiable and non-modifiable risk factor profiles; and b) investigate the longitudinal cognitive outcomes of each of these distinct biomarker clusters. Methods: We identified 410 CN aged 70-89 from Mayo Clinic
Study of Aging with baseline Structural MRI, FLAIR-MRI, and PIBPET; and at least one clinical follow-up. We use.d hierarchical clustering on global cortical thickness, global amyloid load, and presence or absence of CVD of all subjects; and found four distinct biomarker clusters. Even though there was overlap of pathologies in each cluster, we labeled subjects based on their predominant biomarker characteristics (illustrated in Figure): 1) Low Pathology[n¼106], 2) CVD dominant (100% subjects had CVD but little evidence of neurodegeneration and amyloid)[n¼93], 3) Neurodegeneration dominant (subjects with significant neurodegeneration but low amyloid)[n¼115], 4) Amyloid dominant (subjects with significant amyloidosis)[n¼96]. We compared the demographics and risk factor profiles of each of the groups using Kruskal-Wallis rank sum or chi-squared test and estimated the incidence rate of cognitive impairment in each group. Results: Men were more prevalent in the neurodegeneration and amyloid dominant groups, amyloid dominant group had a high proportion of APOE4 carriers, and low pathology individuals were younger (p<0.001). Education, job-score, mid-life cognitive activity, midlife physical activity, diabetes, hypertension, dyslipidemia, BMI, and smoking status did not differ by groups. There was higher cognitive (p¼0.05) and physical activity (p¼0.04) in late-life (last 12 months) in the low pathology group probably due to the fact that subjects with pathology are less likely to participate in these activities.
The annual progression rates to mild cognitive impairment (MCI) were 1.76 for low pathology, 5.57 for vascular dominant, 5.29 for neurodegeneration dominant, and 11.47 for amyloid dominant. Conclusions: We found significant differences in the profiles of subjects in each of the “biomarker clusters”. Low pathology individuals are at the least, and amyloid dominant group subjects (includes N-/N+ and V-/V+) were at the greatest risk of progression to MCI.
S. Duke Han1,2, Patricia A. Boyle1, Konstantinos Arfanakis1,3, Debra A. Fleischman1, Lei Yu1, David A. Bennett1, 1Rush University Medical Center, Chicago, IL, USA; 2VA Long Beach Healthcare System, Long Beach, CA, USA; 3Illinois Institute for Technology, Chicago, IL, USA. Contact e-mail: [email protected]
Background: Literacy has been associated with maintenance of neurocognitive function in aging, and financial literacy is important for beneficial financial decision making in old age. We previously showed that higher financial literacy is associated with greater functional connectivity between anterior and posterior brain regions even after adjusting for cognitive function. Here we tested the hypothesis that higher financial literacy would be associated with indicators of white matter integrity in older persons without dementia. Methods: Three hundred and forty-six participants without dementia (mean age¼81.36, mean education¼15.39, male/female¼79/267, mean MMSE¼28.52) from the Rush Memory and Aging Project, a clinical-pathological cohort study of aging, were scanned using diffusion tensor imaging (DTI). Financial literacy was assessed using a series of questions imbedded as part of an ongoing decision making study. Fractional anisotropy (FA) was calculated using TORTOISE. We tested the hypothesis that higher financial literacy is associated with higher FA in white matter according to voxel-wise analyses using Tract-Based Spatial Statistics (TBSS), adjusting for the effects of age, education, sex, and white matter hyperintensities. We then repeated the analysis also adjusting for cognitive function. Results: Analyses revealed multiple significant voxels of positive associations between FA and financial literacy, and many of these remained significant even after accounting for cognitive function. Significant FA results implicated white matter tracts connecting anterior and posterior brain regions. No negative associations were observed for FA and financial literacy. Conclusions: Greater financial literacy is associated with higher diffusion anisotropy in white matter of nondemented older adults, even after adjusting for cognitive function. These results suggest that greater white matter integrity may be a mechanism that supports functional connectivity associations observed with financial literacy in old age.
Figure. An illustration of the dusters identified using hierarchical clustering. This is simplistic representation because there is still some overlap of pathologies in each of the clusters. The big circles indicate the three major biomarker pathologies and numbers indicate the distinct biomarker clusters (1- Low pathology, 2-CVD dominant, 3-Neurodegeneration dominant, 4-Amyloid dominant).
FINANCIAL LITERACY IS ASSOCIATED WITH WHITE MATTER INTEGRITY IN OLD AGE
CLINICAL AND NEUROIMAGING PREDICTORS OF PSYCHOLOGICAL WELL-BEING AS MEASURED BY THE PURPOSE IN LIFE SCALE IN COGNITIVELY NORMAL OLDER INDIVIDUALS
Sehily Y. Jaimes1,2, Nancy J. Donovan2,3, David C. Hsu2, Alex S. Dagley4, Aaron P. Schultz3,5,6, Rebecca Amariglio2, Dorene M. Rentz2,3, Keith A. Johnson3,4, Reisa A. Sperling2,3,5, Gad A. Marshall2,3, 1 Massachusetts General Hospital, Harvard, Boston, MA, USA; 2Brigham and Women’s Hospital, Boston, MA, USA; 3Harvard Medical School, Boston, MA, USA; 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; 5Massachusetts General Hospital and the Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; 6Massachusetts General Hospital, Boston, MA, USA. Contact e-mail: [email protected]