Dataset Information

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk.

ABSTRACT:

Background

Cognitive and biological markers have shown varying degrees of success in identifying persons who will develop dementia.

Objective

To evaluate different combinations of cognitive and biological markers and identify prediction models with the highest accuracy for identifying persons with increased dementia risk.

Methods

Neuropsychological assessment, genetic testing (apolipoprotein E -APOE), and structural magnetic resonance imaging (MRI) were performed for 418 older individuals without dementia (60-97 years) from a population-based study (SNAC-K). Participants were followed for six years.

Results

Cognitive, genetic, and MRI markers were systematically combined to create prediction models for dementia at six years. The most predictive individual markers were perceptual speed or carrying at least one APOEɛ4 allele (AUC = 0.875). The most predictive model (AUC = 0.924) included variables from all three modalities (category fluency, general knowledge, any ɛ4 allele, hippocampal volume, white matter-hyperintensity volume).

Conclusion

This study shows that combining markers within and between modalities leads to increased predictivity for future dementia. However, minor increases in predictive value should be weighed against the cost of additional tests in larger-scale screening.

SUBMITTER: Payton NM

PROVIDER: S-EPMC6027943 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk.

Payton Nicola M NM Kalpouzos Grégoria G Rizzuto Debora D Fratiglioni Laura L Kivipelto Miia M Bäckman Lars L Laukka Erika J EJ

Journal of Alzheimer's disease : JAD 20180101 2

<h4>Background</h4>Cognitive and biological markers have shown varying degrees of success in identifying persons who will develop dementia.<h4>Objective</h4>To evaluate different combinations of cognitive and biological markers and identify prediction models with the highest accuracy for identifying persons with increased dementia risk.<h4>Methods</h4>Neuropsychological assessment, genetic testing (apolipoprotein E -APOE), and structural magnetic resonance imaging (MRI) were performed for 418 ol ...[more]

PMID: 29889068

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Project description:AimsThe early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at-risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI).MethodsBased on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model.ResultsSix independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini-Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy-to-use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid-β loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations.ConclusionWe developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD-specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.

Dataset Information

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk.

Background

Objective

Methods

Results

Conclusion

Publications

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk.

Similar Datasets

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