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

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http://hdl.handle.net/10138/298925

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Hall , A , Pekkala , T , Polvikoski , T , van Gils , M , Kivipelto , M , Lötjönen , J , Mattila , J , Kero , M , Myllykangas , L , Mäkelä , M , Oinas , M , Paetau , A , Soininen , H , Tanskanen , M & Solomon , A 2019 , ' Prediction models for dementia and neuropathology in the oldest old : the Vantaa 85+cohort study ' , Alzheimer's research & therapy , vol. 11 , 11 . https://doi.org/10.1186/s13195-018-0450-3

Title: Prediction models for dementia and neuropathology in the oldest old : the Vantaa 85+cohort study
Author: 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
Contributor: University of Helsinki, HUSLAB
University of Helsinki, HUSLAB
University of Helsinki, HUSLAB
University of Helsinki, Neurokirurgian yksikkö
University of Helsinki, HUSLAB
University of Helsinki, HUSLAB
Date: 2019-01-22
Language: eng
Number of pages: 12
Belongs to series: Alzheimer's research & therapy
ISSN: 1758-9193
URI: http://hdl.handle.net/10138/298925
Abstract: BackgroundWe developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort.MethodsWe 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 -amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, -synuclein pathology, hippocampal sclerosis, and TDP-43.ResultsPrediction model performance was evaluated using AUC for 10x10-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: epsilon 4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; epsilon 2 predicted dementia, but it was protective against amyloid and neuropathological AD; and epsilon 3 epsilon 3 was protective against dementia, neurofibrillary tangles, and CAA. Very few other factors were predictive of pathology.ConclusionsDifferences 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.
Subject: Dementia
Neuropathology
Oldest old
Prediction
Supervised machine learning
ALZHEIMERS-DISEASE
VASCULAR DEMENTIA
APOLIPOPROTEIN-E
APOE GENOTYPE
CEREBROVASCULAR-DISEASE
COGNITIVE IMPAIRMENT
POPULATION
PREVALENCE
STATE
ASSOCIATION
3112 Neurosciences
3124 Neurology and psychiatry
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