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Modeling the heterogeneity in risk of progression to Alzheimer's disease across cognitive profiles in mild cognitive impairment

Curtis Tatsuoka123*, Huiyun Tseng4, Judith Jaeger56, Ferenc Varadi7, Mark A Smith8, Tomoko Yamada1, Kathleen A Smyth13, Alan J Lerner12 and The Alzheimer's Disease Neuroimaging Initiative

Author Affiliations

1 Department of Neurology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH 44106 USA

2 Neurological Institute, University Hospitals Case Medical Center, 3619 Park East Drive, Beachwood, OH 44122 USA

3 Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 USA

4 Department of Human Development, Teachers College, Columbia University, 525 West 120th Street, New York, NY 10027 USA

5 AstraZeneca Pharmaceuticals, Clinical Development, Neuroscience, 1800 Concord Pike, Wilmington, DE 19807 USA

6 Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461 USA

7 Tanar Software, Hunting Valley, OH 44022 USA

8 Department of Pathology, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH 44106 USA

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Alzheimer's Research & Therapy 2013, 5:14  doi:10.1186/alzrt168

Published: 6 March 2013



Heterogeneity in risk of conversion to Alzheimer's disease (AD) among individuals with mild cognitive impairment (MCI) is well known. Novel statistical methods that are based on partially ordered set (poset) models can be used to create models that provide detailed and accurate information about performance with specific cognitive functions. This approach allows for the study of direct links between specific cognitive functions and risk of conversion to AD from MCI. It also allows for further delineation of multi-domain amnestic MCI, in relation to specific non-amnestic cognitive deficits, and the modeling of a range of episodic memory functioning levels.


From the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, conversion at 24 months of 268 MCI subjects was analyzed. It was found that 101 of those subjects (37.7%) converted to AD within that time frame. Poset models were then used to classify cognitive performance for MCI subjects. Respective observed conversion rates to AD were calculated for various cognitive subgroups, and by APOE e4 allele status. These rates were then compared across subgroups.


The observed conversion rate for MCI subjects with a relatively lower functioning with a high level of episodic memory at baseline was 61.2%. In MCI subjects who additionally also had relatively lower perceptual motor speed functioning and at least one APOE e4 allele, the conversion rate was 84.2%. In contrast, the observed conversion rate was 9.8% for MCI subjects with a relatively higher episodic memory functioning level and no APOE e4 allele. Relatively lower functioning with cognitive flexibility and perceptual motor speed by itself also appears to be associated with higher conversion rates.


Among MCI subjects, specific baseline cognitive profiles that were derived through poset modeling methods, are clearly associated with differential rates of conversion to AD. More precise delineation of MCI by such cognitive functioning profiles, including notions such as multidomain amnestic MCI, can help in gaining further insight into how heterogeneity arises in outcomes. Poset-based modeling methods may be useful for providing more precise classification of cognitive subgroups among MCI for imaging and genetics studies, and for developing more efficient and focused cognitive test batteries.