Machine learning amplifies the effect of parental family history of Alzheimer's disease on list learning strategy

Family history of Alzheimer's disease alters cognition and is modified by medical and genetic factors

In humans, a first-degree family history of dementia (FH) is a well-documented risk factor for Alzheimer’s disease (AD); however, the influence of FH on cognition across the lifespan is poorly understood. To address this issue, we developed an internet-based paired-associates learning (PAL) task and tested 59,571 participants between the ages of 18–85. FH was associated with lower PAL performance in both sexes under 65 years old. Modifiers of this effect of FH on PAL performance included age, sex, education, and diabetes. The Apolipoprotein E ε4 allele was also associated with lower PAL scores in FH positive individuals. Here we show, FH is associated with reduced PAL performance four decades before the typical onset of AD; additionally, several heritable and non-heritable modifiers of this effect were identified.

Advancing Alzheimer's research: A review of big data promises

OBJECTIVE

To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD.

METHOD

Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria.

RESULTS

Thirty-eight articles were included in this review. They can be categorized into seven research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3), and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are AD Neuroimaging Initiative (ADNI) (n=16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis.

CONCLUSION

Big data are increasingly used to address AD-related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-utilized opportunity for advancing AD research.

Cognitive activities and cognitive performance in middle-aged adults at risk for Alzheimer's disease

Cognitive activity is thought to provide some protection against dementia, but the mechanism and timing of these effects are unknown. Data for this study were drawn from the Wisconsin Registry for Alzheimer's Prevention (WRAP), an at-risk middle-aged sample (mean age = 54 years) enriched for parental family history of Alzheimer's disease (AD). We had two main aims: (a) to determine the relative contribution of three facets of cognitive activity-education, occupational complexity with data, and cognitive leisure activities-to WRAP participants' cognitive performance; and (b) to assess for interactions between genetic risk factors and cognitive activity in explaining cognitive performance. Results from mixed effects models indicate that some of the variance usually attributed to education may be more closely accounted for by cognitive activities later in life. Overall, our analyses suggest cautious optimism for cognitive activities, especially game playing, as a strategy for preserving cognitive strengths in midlife.

Emergence of mild cognitive impairment in late middle-aged adults in the wisconsin registry for Alzheimer's prevention

AIM

It is difficult to reliably detect the earliest signs of Alzheimer's disease (AD)-associated cognitive impairment. Our aim was to compare 3 psychometric methods of identifying amnestic mild cognitive impairment (aMCI) in a middle-aged longitudinal cohort enriched for AD risk.

METHODS

Wisconsin Registry for Alzheimer's Prevention (WRAP) participants with 3 waves of cognitive assessment over approximately 6 years were coded as meeting each of 3 psychometric aMCI definitions: (a) 'aMCI standard-baseline' used published norms to establish cutoffs for baseline performance; (b) 'aMCI robust-baseline' applied WRAP-specific robust norms to baseline, and (c) 'aMCI robust-multiwave' applied these robust norms across 3 waves of assessment. Each group was compared to a cognitively healthy subset.

RESULTS

Half the aMCI standard-baseline and one third of the aMCI robust-baseline group reverted to normal ranges at follow-up. Only the aMCI robust-multiwave method had an aMCI × age interaction showing significantly worse age-related memory declines in the aMCI group compared to the cognitively healthy group over 6 years of follow-up.

CONCLUSION

Both cross-sectional methods showed instability over time, with many reverting to normal performance after baseline. The multiwave approach identified a group who showed progressive memory declines over 3 visits. Being able to detect progressive decline in late middle age is a critical step in improving prevention efforts.