Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study

Show simple item record Hall, Anette Pekkala, Timo Polvikoski, Tuomo van Gils, Mark Kivipelto, Miia Lötjönen, Jyrki Mattila, Jussi Kero, Mia Myllykangas, Liisa Mäkelä, Mira Oinas, Minna Paetau, Anders Soininen, Hilkka Tanskanen, Maarit Solomon, Alina 2019-01-27T04:23:17Z 2019-01-27T04:23:17Z 2019-01-22
dc.identifier.citation Alzheimer's Research & Therapy. 2019 Jan 22;11(1):11
dc.description.abstract Abstract Background We developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort. Methods We included participants without dementia at baseline and at least 2 years of follow-up (N = 245) for dementia prediction or with autopsy data (N = 163) for pathology. A supervised machine learning method was used for model development, considering sociodemographic, cognitive, clinical, vascular, and lifestyle factors, as well as APOE genotype. Neuropathological assessments included β-amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, α-synuclein pathology, hippocampal sclerosis, and TDP-43. Results Prediction model performance was evaluated using AUC for 10 × 10-fold cross-validation. Overall AUCs were 0.73 for dementia, 0.64–0.68 for Alzheimer’s disease (AD)- or amyloid-related pathologies, 0.72 for macroinfarcts, and 0.61 for microinfarcts. Predictors for dementia were different from those in previous reports of younger populations; for example, age, sex, and vascular and lifestyle factors were not predictive. Predictors for dementia versus pathology were also different, because cognition and education predicted dementia but not AD- or amyloid-related pathologies. APOE genotype was most consistently present across all models. APOE alleles had a different impact: ε4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; ε2 predicted dementia, but it was protective against amyloid and neuropathological AD; and ε3ε3 was protective against dementia, neurofibrillary tangles, and CAA. Very few other factors were predictive of pathology. Conclusions Differences between predictors for dementia in younger old versus oldest old populations, as well as for dementia versus pathology, should be considered more carefully in future studies.
dc.language.iso eng
dc.publisher BioMed Central
dc.subject Dementia
dc.subject Neuropathology
dc.subject Oldest old
dc.subject Prediction
dc.subject Supervised machine learning
dc.title Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study en 2019-01-27T04:23:17Z
dc.rights.copyrightholder The Author(s).

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