Dataset Information

Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid ?1-42 in Alzheimer's Disease Neuroimaging Initiative participants.

ABSTRACT: Introduction:Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer's disease. Cerebrospinal fluid (CSF) amyloid ? (A?) 1-42 is an important Alzheimer's disease biomarker. However, it is inconclusive on how T2DM is related to CSF A?1-42. Methods:Participants with T2DM were selected from the Alzheimer's Disease Neuroimaging Initiative by searching keywords from the medical history database. A two-way analysis of covariance model was used to analyze how T2DM associates with CSF A?1-42 or cerebral cortical A?. Results:CSF A?1-42 was higher in the T2DM group than the nondiabetic group. The inverse relation between CSF A?1-42 and cerebral cortical A? was independent of T2DM status. Participants with T2DM had a lower cerebral cortical A? in anterior cingulate, precuneus, and temporal lobe than controls. Discussion:T2DM is positively associated with CSF A?1-42 but negatively with cerebral cortical A?. The decreased cerebral cortical A? associated with T2DM is preferentially located in certain brain regions.

SUBMITTER: Li W

PROVIDER: S-EPMC5725206 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid β1-42 in Alzheimer's Disease Neuroimaging Initiative participants.

Li Wei W Risacher Shannon L SL Gao Sujuan S Boehm Stephen L SL Elmendorf Jeffrey S JS Saykin Andrew J AJ

Alzheimer's & dementia (Amsterdam, Netherlands) 20171123

<h4>Introduction</h4>Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer's disease. Cerebrospinal fluid (CSF) amyloid β (Aβ) 1-42 is an important Alzheimer's disease biomarker. However, it is inconclusive on how T2DM is related to CSF Aβ1-42.<h4>Methods</h4>Participants with T2DM were selected from the Alzheimer's Disease Neuroimaging Initiative by searching keywords from the medical history database. A two-way analysis of covariance model was used to analyze how T2DM associates with ...[more]

PMID: 29255789

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Project description:Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.

Dataset Information

Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid ?1-42 in Alzheimer's Disease Neuroimaging Initiative participants.

Publications

Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid β1-42 in Alzheimer's Disease Neuroimaging Initiative participants.

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