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Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
Journal article   Open access   Peer reviewed

Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

Tiago Azevedo, Richard A. I Bethlehem, David J Whiteside, Nol Swaddiwudhipong, James B Rowe, Pietro Lió, Timothy Rittman, Lisa C Silbert, Betty Lind, Rachel Crissey, …
Communications medicine, Vol.3(1), 100
12/01/2023
DOI: 10.1038/s43856-023-00313-w
PMCID: PMC10359360
PMID: 37474615
url
https://doi.org/10.1038/s43856-023-00313-wView
Published (Version of record) Open Access

Abstract

Background Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer’s disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. Methods We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer’s Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer’s disease. Results We show that the cohort with a neuroimaging Alzheimer’s phenotype has a cognitive profile in keeping with Alzheimer’s disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. Conclusions This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer’s disease. Plain language summary Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer’s disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer’s disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer’s disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.
 Alzheimers Disease Biomarkers Traumatic Brain Injury Datasets

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