A number of vascular diseases and vascular risk factors including diabetes, hypertension, hyperlipidemia, smoking, and obesity have been implicated but not consistently established as risk factors for Alzheimer's disease (AD). In addition, studies using a combination of these risk factors to predict AD risk have reported only modest accuracy. Current predictive models for AD have typically characterized risk exposure by assessing vascular markers at a single point in time at the baseline. Such characterization fails to capture potential changes or variability over the relatively long latency period prior to he onset of AD symptoms. These static predictive models also ignore the vast heterogeneity in individuals' longitudinal vascular markers over time. We propose a secondary data analysis developing dynamic models using longitudinally collected vascular markers to predict AD risk. We will merge electronic medical records of participants enrolled in the Indianapolis cohort of the longitudinal community-based Indianapolis-Ibadan Dementia Project (IIDP) with research data collected in the IIDP. The IIDP has enrolled a total of 4,105 African Americans aged 65 or older and followed the participants for up to 19 years with cognitive evaluation, clinical diagnosi and risk factor information at regularly scheduled intervals every 2 to 3 years. Our analyses will focus on longitudinally measured vascular markers including blood pressure, lipids, hemoglobin A1C and fasting glucose levels obtained from electronic medical records for the risk of AD.
In Aim 1, we will compare longitudinal vascular risk factor profiles between participants with AD and those with normal cognition and determine whether differences in longitudinal vascular profiles are accounted for by differences in medication use.
In Aim 2, we will develop a dynamic risk assessment algorithm for AD using longitudinal vascular markers and compare the performance of this new algorithm with existing AD assessment risk scores.
In Aim 3 we will identify longitudinal vascular characteristics associated with conversion to dementia in participants with mild cognitive impairment (MCI).
In Aim 4, we will examine the association between longitudinal vascular marker trajectories and longitudinal cognitive function using functional regression models to determine how changes in the vascular markers are related to changes in cognitive function.

Public Health Relevance

The proposed project will be the first study to explore a dynamic relationship between multiple longitudinal vascular measures and AD in an African American cohort. Results from this study can provide a more accurate AD risk assessment method based on data already routinely collected in clinical practices. Our results may also lead to better strategies for potential interventions in elderly individuals.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG045350-03
Application #
9041471
Study Section
Neurological, Aging and Musculoskeletal Epidemiology (NAME)
Program Officer
Anderson, Dallas
Project Start
2014-07-01
Project End
2017-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Indiana University-Purdue University at Indianapolis
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
603007902
City
Indianapolis
State
IN
Country
United States
Zip Code
46202
Khan, Sikandar; Biju, Ashok; Wang, Sophia et al. (2018) Mobile critical care recovery program (m-CCRP) for acute respiratory failure survivors: study protocol for a randomized controlled trial. Trials 19:94
Ganguli, Mary; Albanese, Emiliano; Seshadri, Sudha et al. (2018) Population Neuroscience: Dementia Epidemiology Serving Precision Medicine and Population Health. Alzheimer Dis Assoc Disord 32:1-9
Hendrie, Hugh C; Zheng, Mengjie; Lane, Kathleen A et al. (2018) Changes of glucose levels precede dementia in African-Americans with diabetes but not in Caucasians. Alzheimers Dement 14:1572-1579
Wang, Sophia; Hammes, Jessica; Khan, Sikandar et al. (2018) Improving Recovery and Outcomes Every Day after the ICU (IMPROVE): study protocol for a randomized controlled trial. Trials 19:196
Hendrie, Hugh C; Zheng, Mengjie; Li, Wei et al. (2017) Glucose level decline precedes dementia in elderly African Americans with diabetes. Alzheimers Dement 13:111-118
Wang, Sophia; Mosher, Chris; Perkins, Anthony J et al. (2017) Post-Intensive Care Unit Psychiatric Comorbidity and Quality of Life. J Hosp Med 12:831-835
Yang, Lili; Yu, Menggang; Gao, Sujuan (2016) Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome. J Stat Comput Simul 86:3682-3700
Lai, Dongbing; Xu, Huiping; Koller, Daniel et al. (2016) A MULTIVARIATE FINITE MIXTURE LATENT TRAJECTORY MODEL WITH APPLICATION TO DEMENTIA STUDIES. J Appl Stat 43:2503-2523
Yang, Lili; Yu, Menggang; Gao, Sujuan (2016) Prediction of coronary artery disease risk based on multiple longitudinal biomarkers. Stat Med 35:1299-314