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Talboom JS, Håberg A, De Both MD, Naymik MA, Schrauwen I, Lewis CR, Bertinelli SF, Hammersland C, Fritz MA, Myers AJ, Hay M, Barnes CA, Glisky E, Ryan L, Huentelman MJ. Family history of Alzheimer's disease alters cognition and is modified by medical and genetic factors. eLife 2019; 8:46179. [PMID: 31210642 PMCID: PMC6615857 DOI: 10.7554/elife.46179] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 06/13/2019] [Indexed: 01/02/2023] Open
Abstract
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.
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Affiliation(s)
- Joshua S Talboom
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
| | - Asta Håberg
- Norwegian University of Science and Technology, Trondheim, Norway
| | - Matthew D De Both
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
| | - Marcus A Naymik
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
| | - Isabelle Schrauwen
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
| | - Candace R Lewis
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
| | | | | | - Mason A Fritz
- The Translational Genomics Research Institute, Phoenix, United States
| | | | - Meredith Hay
- Arizona Alzheimer's Consortium, Phoenix, United States.,University of Arizona, Tucson, United States
| | - Carol A Barnes
- Arizona Alzheimer's Consortium, Phoenix, United States.,University of Arizona, Tucson, United States
| | - Elizabeth Glisky
- Arizona Alzheimer's Consortium, Phoenix, United States.,University of Arizona, Tucson, United States
| | - Lee Ryan
- Arizona Alzheimer's Consortium, Phoenix, United States.,University of Arizona, Tucson, United States
| | - Matthew J Huentelman
- The Translational Genomics Research Institute, Phoenix, United States.,Arizona Alzheimer's Consortium, Phoenix, United States
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Zhang R, Simon G, Yu F. Advancing Alzheimer's research: A review of big data promises. Int J Med Inform 2017; 106:48-56. [PMID: 28870383 DOI: 10.1016/j.ijmedinf.2017.07.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 07/18/2017] [Accepted: 07/23/2017] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Rui Zhang
- Institute for Health Informatics and College of Pharmacy, University of Minnesota, Minneapolis, MN, United States.
| | - Gyorgy Simon
- Institute for Health Informatics and Department of Medicine, University of Minnesota, Minneapolis, MN, United States.
| | - Fang Yu
- School of Nursing, University of Minnesota, Minneapolis, MN, United States.
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Jonaitis E, La Rue A, Mueller KD, Koscik RL, Hermann B, Sager MA. Cognitive activities and cognitive performance in middle-aged adults at risk for Alzheimer's disease. Psychol Aging 2014; 28:1004-14. [PMID: 24364404 DOI: 10.1037/a0034838] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Erin Jonaitis
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Asenath La Rue
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Kimberly D Mueller
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Rebecca L Koscik
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Bruce Hermann
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Mark A Sager
- Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
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Koscik RL, Rue AL, Jonaitis EM, Okonkwo OC, Johnson SC, Bendlin BB, Hermann BP, Sager MA. Emergence of mild cognitive impairment in late middle-aged adults in the wisconsin registry for Alzheimer's prevention. Dement Geriatr Cogn Disord 2014; 38:16-30. [PMID: 24556849 PMCID: PMC4104157 DOI: 10.1159/000355682] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/13/2013] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Rebecca L. Koscik
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health
| | - Asenath La Rue
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health
| | - Erin M. Jonaitis
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health
| | - Ozioma C. Okonkwo
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health
| | - Sterling C. Johnson
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health
| | - Barbara B. Bendlin
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health
| | - Bruce P. Hermann
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health,Department of Neurology, University of Wisconsin School of Medicine and Public Health
| | - Mark A. Sager
- Department of Medicine, Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health
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