Alzheimer’s disease (AD) is a chronic neurodegenerative disorder affecting an estimated 5.2 million Americans in 2008. According to the Alzheimer’s Association, the number of persons with AD could reach 16 million by 2050 unless means are found to either delay or prevent the onset of the disease (Alzheimer’s Association, 2009
). Traditionally, AD has been considered a disease of older adults over age 65. However, there is increasing evidence that neurobiological changes consistent with AD can be found decades before a diagnosis is made (Bookheimer & Burggren, 2009
). The early identification of disease pathology in younger asymptomatic persons provides the opportunity to intervene at early stages and potentially modify the disease course.
A major challenge in the early recognition of AD has been to characterize the pre-clinical cognitive changes occurring in persons at increased risk, including those who may have an APOE ε4 allele or a family history of AD. There is increasing evidence that studies of pre-clinical AD may be particularly valuable for those persons with a family history (Jarvik et al., 2008
). Recent studies have found evidence for hippocampal dysfunction in neuroimaging (Bassett et al., 2006
; Johnson, Schmitz, Trivedi, et al., 2006
) and neurocognitive performance that are suggestive of pre-clinical AD in asymptomatic persons with a parental family history of AD (La Rue et al., 2008
). In addition, elevated levels of plasma amyloid β have been observed in asymptomatic first-degree relatives of AD patients (Ertekin-Taner et al., 2008
) suggesting that family history may be especially valuable in the study of preclinical AD.
Studies of preclinical AD focus on younger, relatively healthy, asymptomatic persons for whom brief cognitive screening batteries are not appropriate. As a consequence, these studies require more extensive and sensitive batteries of neuropsychological tests assessing a range of cognitive domains. These tests are often used under the assumption that underlying cognitive constructs are uniform or equivalent across groups and time. However, the tests may have a latent structure that may vary across demographic and AD risk factors (for example, gender, age, APOE genotype, or family history) seriously compromising the generalizability of findings across groups. The ability to define this latent structure, i.e., underlying cognitive domains, using a cluster of tests for each domain instead of individual tests, has the advantage of reducing redundancy and increasing the reliability of the summary measures. Assuming test measures with high quality psychometric characteristics, composite scores derived from a stable latent variable solution can be used as outcomes in further analyses reducing the risk of Type I error due to multiple tests. This may be particularly important in the early detection of AD and other dementias as well as in the monitoring of changes in cognitive functioning occurring over time in asymptomatic persons.
The examination of differences and similarities across subpopulations of interest on a given set of constructs also becomes more informative when the underlying latent structure of the test battery is invariant across patient groups. Invariance refers to the extent to which “under different conditions of observing and studying phenomena, measurement operations yield measures of the same attribute” (Horn & McArdle, 1992
, p. 117). Lack of evidence supporting model invariance compromises comparison between groups because the meaning of the underlying constructs becomes group specific (Meredith, 1993
Investigating construct comparability is a necessary approach to demonstrate that observed differences in scale scores represent true differences between groups and not differences due to systematic biases caused by non-equivalence of constructs. For example, a significant number of studies have found gender and age-related differences in verbal learning and memory (Bleecker, Bolla-Wilson, Agnew, & Meyers, 1988
; Kramer, Delis, & Daniel, 1988
; Geffen, Moar, O’Hanlon, Clark, & Geffen, 1990
). Other studies (e.g., Revell & Schaie, 2004
; Schaie, 2005
; and Hofer et al., 2002
) have found an association between cognitive performance in memory domains and the presence of an APOE ε4 allele in preclinical samples, particularly for individuals with the ε4/ε2 and ε4/ε4 pairings. Given the importance of verbal learning and memory-related constructs as possible preclinical indicators of AD, supporting evidence of measurement invariance (or equivalence between observed and latent variables) across groups (e.g., age and gender, genotype) is critical for inferential purposes. Additionally, since the interplay of AD family history, APOE genotype, and middle-age populations have not been extensively studied, there is little understanding of the latent structure of comprehensive neuropsychological test batteries used to evaluate asymptomatic populations at various levels of risk and its validity across patient groups.
The present study was undertaken with a two-fold purpose. The first aim was to define the latent structure of a psychometric test battery currently being used in a study of pre-clinical AD in the Wisconsin Registry for Alzheimer’s Prevention (WRAP; Sager, Hermann, & La Rue, 2005
). WRAP is a longitudinal cohort study of an important, but seldom studied group, i.e., asymptomatic middle aged persons with a parental family history of AD, and a control group of persons whose parents lived to late life without AD. The primary goal of the WRAP study is to define the neurobiological course of preclinical AD which is a necessary first step in developing interventions to modify the disease course. Second, once the underlying latent model was defined and the structural validity established through model comparisons, we tested for factorial invariance across groups defined by selected demographic variables (age and gender) and known genetic risk factors for AD (parental family history and APOE genotype) using a multi-group confirmatory factor analysis model (Jöreskog, 1971
We hypothesized that multiple, partially independent dimensions of cognitive performance would be identified through these analyses, including a secondary memory factor that may be sensitive to preclinical AD. In addition, given the relatively young mean age and clinically intact cognitive status of the sample, we predicted that the factor structure would be invariant for age, gender, and genetic risk subgroups.