Project Summary/Abstract
The cause of dementia is unknown, but it is considered to be a multifactorial disease, resulting from the
interaction of both genetic and environmental factors, which contribute to its occurrence and expression.
Genomewide association studies and populationbased studies have confirmed that the e4 allele of APOE is
the strongest genetic risk factor for dementia. However, APOE e4 is thought to be responsible for less than
50% of dementia risk, suggesting that environmental factors contribute to development of dementia in the
genetically predisposed. Given the absence of sufficient treatment options for dementia, strategies to prevent
or delay the disease onset are urgently needed. Among potentially modifiable determinants of dementia,
appropriate control of cardiometabolic risk factors [Body Mass Index (BMI), systolic and diastolic blood
pressure, diabetes and hypercholesterolemia] could be a primary strategy to reduce the incidence of dementia.
However, cardiometabolic risk factors in later life have been inconsistently associated with dementia with both
increased and decreased risk for dementia. The reasons for this discordance in findings are unclear and
perhaps partially due to not only the differing lengths of followup between the assessments of cardiometabolic
risk factors and dementia onset (reverse causation bias), but also population heterogeneity. Prior studies have
used either onetime baseline cardiometabolic risk factor measurements or longitudinal measurements
targeting only on meanlevel changes (i.e., population average trajectory) with only one trajectory while
ignoring population heterogeneity. We propose to use a nested casecontrol design with groupbased
trajectory analysis approach to analyze a large, multisite, longitudinal aging and dementia dataset—Uniform
Data Set (UDS) provided by the National Alzheimer’s Coordinating Center (NACC). Aim 1): To apply a nested
casecontrol approach to identify distinct trajectories of cardiometabolic risk factors preceding the diagnosis of
dementia (up to 15 years) and matched control group using groupbased trajectory model, and to examine the
effects of distinct trajectories of cardiometabolic risk factors, APOE genotype, and their interaction on the risk
of dementia using matched casecontrol subsamples nested in UDS cohort. Aim 2): To investigate the reverse
causation bias for the associations between cardiometabolic risk factors and dementia in aim 1. Aim 3). To
explore whether the trajectories of cardiometabolic risk factors and their interaction with APOE genotype on the
risk of dementia differ by race, sex, baseline age, and Alzheimer’s disease (AD) subtype.