Progression along data-driven disease timelines is predictive of Alzheimer’s disease in a population-based cohort

• Published in: NeuroImage (Orlando, Fla.) Vol. 238; p. 118233
• Main Authors: Venkatraghavan, Vikram, Vinke, Elisabeth J., Bron, Esther E., Niessen, Wiro J., Ikram, M. Arfan, Klein, Stefan, Vernooij, Meike W.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2021; Elsevier Limited; Elsevier
• Subjects: Aging; Alzheimer's disease; APOE; Apolipoprotein E; Biomarkers; Clinical trials; Cognitive ability; Datasets; Dementia; Disease progression modeling; Event-based model; Geriatrics; Investigations; Machine learning; Magnetic resonance imaging; Medical imaging; Neurodegenerative diseases; Neuroimaging; Older people; Phenotypes; Population; Population studies; Population study; Population-based studies; Disease progression modeling; APOE; Alzheimer’s disease; Population study; Event-based model
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118233

•APOE ϵ4 carriers and non-carriers have significantly different disease timelines•Progression along imaging-based data-driven disease timelines is predictive of AD•Disease timelines were found to be generalizable to a population-based cohort•Incident AD and controls have significantly different disease stage trajectories Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.