Temporal Order of Alzheimer’s Disease-Related Cognitive Marker Changes in BLSA and WRAP Longitudinal Studies

Abstract

Investigation of the temporal trajectories of currently used neuropsychological tests is critical to identifying earliest changing measures on the path to dementia due to Alzheimer’s disease (AD). We used the Progression Score (PS) method to characterize the temporal trajectories of measures of verbal memory, executive function, attention, processing speed, language, and mental status using data spanning normal cognition, mild cognitive impairment (MCI), and AD from 1661 participants with a total of 7839 visits (age at last visit 77.6 SD 9.2) in the Baltimore Longitudinal Study of Aging and 1510 participants with a total of 3473 visits (age at last visit 59.5 SD 7.4) in the Wisconsin Registry for Alzheimer’s Prevention. This method aligns individuals in time based on the similarity of their longitudinal measurements to reveal temporal trajectories. As a validation of our methodology, we explored the associations between the individualized cognitive progression scores (Cog-PS) computed by our method and clinical diagnosis. Digit span tests were the first to show declines in both data sets, and were detected mainly among cognitively normal individuals. These were followed by tests of verbal memory, which were in turn followed by Trail Making Tests, Boston Naming Test, and Mini-Mental State Examination. Differences in Cog-PS across the clinical diagnosis and APOE ε4 groups were statistically significant, highlighting the potential use of Cog-PS as individualized indicators of disease progression. Identifying cognitive measures that are changing in preclinical AD can lead to the development of novel cognitive tests that are finely tuned to detecting earliest changes.

The Wisconsin Registry for Alzheimer's Prevention: A review of findings and current directions

“…Data from the core WRAP protocol and from a subset of WRAP's linked studies are accessible to qualified researchers via an online request form and data use agreement which can be linked from the Global Alzheimer's Association Interactive Network web site ( www.gaain.org ). Recent examples of data-sharing collaborations include a study that found consistency in patterns of cognitive aging progression scores across the Baltimore Longitudinal Study on Aging and WRAP [98] , a meta-analysis of APOE and sex on dementia incidence [99] , and a study involving predictive algorithms for MCI in a consortium of five preclinical AD or adult children cohorts [100] .…”

Association of Genetic Variants Linked to Late-Onset Alzheimer Disease With Cognitive Test Performance by Midlife

“…Sensitivity analyses excluding APOE showed slightly older ages of divergence, suggesting that, as expected, APOE ε4 allele carriers experience earlier cognitive decline. Our findings are consistent with prior work 1 , 15 , 16 , 17 , 18 , 21 , 40 , 41 , 42 , 43 in early- and late-onset AD, suggesting that cognitive and physiologic changes associated with AD begin at least 15 years before diagnosis—the mean age of dementia diagnosis for White UK residents is 82 years. 44 Our study advances understanding of the natural history of late-onset AD by showing that, in a generally healthy, community-dwelling sample, those at high genetic risk of developing AD in late life performed worse on several cognitive measures in midlife.…”

“…Although episodic memory changes are generally considered leading indicators of AD, 15 , 16 , 17 , 18 , 19 , 20 subtle changes in other domains, such as semantic memory, processing speed, and executive functioning, may occur at the same age or earlier. 20 , 21 , 22 , 23 The potential for symptoms to manifest in any of multiple cognitive domains can be evaluated in parallel using a hypothesis-free approach to rapidly screen numerous possible indicators of disease based on phenotypes associated with a GRS.…”

Predicting time to dementia using a quantitative template of disease progression

“…However, this is a challenging goal given that there is no scientific consensus regarding the definition of healthy aging. Several studies have approached this problem by including age and other demographic variables as covariates in addition to a disease stage variable [11,31,32] or by regressing out their effects before model fitting [5,33,34], whereas others have not included covariates to characterize trajectories that explain the natural history of AD dementia, including biological and cognitive changes that occur due to aging in addition to AD [4,[6][7][8][9]13,15]. We formulated our goal with this study as the characterization of the natural history of AD dementia, including biological and cognitive changes that occur due to aging in addition to AD pathology, and therefore did not include an adjustment for age or any other demographic variables.…”