Dataset Information

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

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 ?-amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, ?-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: ?4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; ?2 predicted dementia, but it was protective against amyloid and neuropathological AD; and ?3?3 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.

SUBMITTER: Hall A

PROVIDER: S-EPMC6343349 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Hall Anette A Pekkala Timo T Polvikoski Tuomo T van Gils Mark M Kivipelto Miia M Lötjönen Jyrki J Mattila Jussi J Kero Mia M Myllykangas Liisa L Mäkelä Mira M Oinas Minna M Paetau Anders A Soininen Hilkka H Tanskanen Maarit M Solomon Alina A

Alzheimer's research & therapy 20190122 1

<h4>Background</h4>We developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort.<h4>Methods</h4>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 ...[more]

PMID: 30670070

Similar Datasets

Project description:Recent studies suggest that delirium is associated with risk of dementia and also acceleration of decline in existing dementia. However, previous studies may have been confounded by incomplete ascertainment of cognitive status at baseline. Herein, we used a true population sample to determine if delirium is a risk factor for incident dementia and cognitive decline. We also examined the effect of delirium at the pathological level by determining associations between dementia and neuropathological markers of dementia in patients with and without a history of delirium. The Vantaa 85+ study examined 553 individuals (92% of those eligible) aged ?85 years at baseline, 3, 5, 8 and 10 years. Brain autopsy was performed in 52%. Fixed and random-effects regression models were used to assess associations between (i) delirium and incident dementia and (ii) decline in Mini-Mental State Examination scores in the whole group. The relationship between dementia and common neuropathological markers (Alzheimer-type, infarcts and Lewy-body) was modelled, stratified by history of delirium. Delirium increased the risk of incident dementia (odds ratio 8.7, 95% confidence interval 2.1-35). Delirium was also associated with worsening dementia severity (odds ratio 3.1, 95% confidence interval 1.5-6.3) as well as deterioration in global function score (odds ratio 2.8, 95% confidence interval 1.4-5.5). In the whole study population, delirium was associated with loss of 1.0 more Mini-Mental State Examination points per year (95% confidence interval 0.11-1.89) than those with no history of delirium. In individuals with dementia and no history of delirium (n?=?232), all pathologies were significantly associated with dementia. However, in individuals with delirium and dementia (n?=?58), no relationship between dementia and these markers was found. For example, higher Braak stage was associated with dementia when no history of delirium (odds ratio 2.0, 95% confidence interval 1.1-3.5, P?=?0.02), but in those with a history of delirium, there was no significant relationship (odds ratio 1.2, 95% confidence interval 0.2-6.7, P?=?0.85). This trend for odds ratios to be closer to unity in the delirium and dementia group was observed for neuritic amyloid, apolipoprotein ? status, presence of infarcts, ?-synucleinopathy and neuronal loss in substantia nigra. These findings are the first to demonstrate in a true population study that delirium is a strong risk factor for incident dementia and cognitive decline in the oldest-old. However, in this study, the relationship did not appear to be mediated by classical neuropathologies associated with dementia.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets