Dementia and associated brain pathology take years to develop. Effective interventions to prevent dementia have not been found, in part because interventions are targeted at individuals in a relatively late stage of dementia progression. In his doctoral thesis, Timo Pekkala, MSc (Tech), Lic Med, aimed to develop prediction models for identifying persons at risk at an earlier stage. Prediction targets included incident dementia as well as common brain pathologies underlying progressive cognitive disorders in different elderly age cohorts. An additional aim was to investigate the association of blood markers of type two diabetes (DM2) and brain amyloid deposition, a hallmark of Alzheimer’s disease (AD). The public examination of the doctoral thesis will be held online on 28 April 2020 at 12 noon.
Dementia was predicted in the Finnish population based Cardiovascular Risk Factors, Aging and Dementia (N=709 and 1,009) and Vantaa 85+ (N=245) study populations of cognitively healthy younger-old individuals (mean age 70 years) and oldest-old individuals (88 years), respectively. Multimodal predictors were used to predict incident dementia over a period of five to ten years using a Disease State Index (DSI) machine learning system. Incidences of common brain pathology were predicted in a Vantaa 85+ subpopulation (N=163, 89 years) over a four year follow up, and the prevalence of brain amyloid deposition on positron emission tomography (PET) was predicted in a Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) subpopulation (N=48) of cognitively healthy younger-old individuals (71 years) with elevated cardiovascular risks and cognition at or slightly below population norms. Both prediction models were built using the DSI. A further FINGER-PET subpopulation (N=41) was used for the analysis of blood DM2 markers using a logistic regression.
Prediction of dementia in the younger-old population succeeded well (area under the curve 0.75–0.79), and in the oldest-old population almost at the same level (0.73). Predictors of dementia for the younger old and the oldest old were different, with age and vascular health achieving less effective predictions for the older cohort. For the oldest old, dementia could be predicted more accurately than most types of brain pathology (0.61–0.72). Amyloid deposition was predicted well for the younger old (0.78) using among other modalities magnetic resonance imaging, but the prediction results were better than for the oldest old even without imaging. Cognition was a better predictor of dementia than pathology, and the apolipoprotein E genotype was a better predictor of pathology than of dementia. Out of the DM2 markers, low levels of insulin resistance markers and a low concentration of plasminogen activator inhibitor-1 were associated with a positive brain amyloid deposition status.
These results indicate that at-risk persons could be identified years before a diagnosis of dementia is given, and interventions could be targeted at those who benefit the most. Different risk factors may have to be considered when targeting dementia or specific pathologies. Prediction models for brain pathology—especially amyloid— could be used to enrich study populations with persons with a specific pathology to save costs and invasive assessments in clinical trials.
The doctoral dissertation of Timo Pekkala, Master of Science (Technology) and Licentiate of Medicine, entitled Multimodal prediction of dementia and brain pathology, will be examined at the Faculty of Health Sciences. The Opponent in the public examination will be Professor Lefkos Middleton of Imperial College London, and the Custos will be Associate Professor Alina Solomon of the University of Eastern Finland. The public examination will be held in English.
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Pekkala, Timo. Multimodal Prediction of Dementia and Brain Pathology
