Nol Swaddiwudhipong’s research while affiliated with University of Cambridge and other places

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.

Introduction: The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease. Methods: UK Biobank data were extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer's disease (AD), Parkinson disease, frontotemporal dementia, progressive supranuclear palsy, dementia with Lewy bodies, or multiple system atrophy were compared against individuals without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age. Results: There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in AD. Pre-diagnostic functional impairment and decline were observed in multiple diseases. Discussion: The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment trials.

INTRODUCTION The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease. METHODS UK Biobank data was extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer’s Disease, Parkinson’s Disease, Frontotemporal Dementia, Progressive Supranuclear Palsy, Dementia with Lewy Bodies, or Multiple System Atrophy, were compared against those without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age. RESULTS There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in Alzheimer’s. Pre-diagnostic functional impairment and decline was observed in multiple diseases. DISCUSSION The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment trials. Research in Context Systematic review Studies of genetic dementia cohorts provide evidence for pre-diagnostic changes in disease biomarkers and cognitive function in several genetic neurode-generative diseases. The pre-diagnostic phase of sporadic neurodegenerative disease has been less well-studied. It is unclear whether early functional or cognitive changes are detectable in sporadic neurodegenerative disease. Interpretation We have established an approach to identify cognitive and functional pre-diagnostic markers of neurodegenerative disease years before diagnosis. We found disease-relevant patterns of pre-diagnostic cognitive and functional impairment, and observed a pre-diagnostic linear decline in a number of cognitive and functional measures. Future Directions Our approach can form the basis for pre-diagnostic cognitive and functional screening to recruit into trials of disease prevention and disease modifying therapies for neurodegenerative diseases. A screening panel based on cognition and function could be followed by disease-specific biomarkers to further improve risk stratification.

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 studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train 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 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 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. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of 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. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.