ArticleJournal of Alzheimer's disease : JAD2022
Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Discriminative performance of externally validated dementia risk prediction models: a systematic review and meta-analysis.BMC medicine · 2026Pooled it
- Correlated decline of cognitive and motor phenotypes and ADRD pathologies in old age.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAlzheimer's disease (AD) is a progressive disorder without a cure. Develop risk prediction models for detecting presymptomatic AD using non-cognitive measures is necessary to enable early interventions.
objectiveExamine if non-cognitive metrics alone can be used to construct risk models to identify adults at risk for AD dementia and cognitive impairment.
methodsClinical data from older adults without dementia from the Memory and Aging Project (MAP, n = 1,179) and Religious Orders Study (ROS, n = 1,103) were analyzed using Cox proportional hazard models to develop risk prediction models for AD dementia and cognitive impairment. Models using only non-cognitive covariates were compared to models that added cognitive covariates. All models were trained in MAP, tested in ROS, and evaluated by the AUC of ROC curve.
resultsModels based on non-cognitive covariates alone achieved AUC (0.800,0.785) for predicting AD dementia (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.916,0.881). A model with a single covariate of composite cognition score achieved AUC (0.905,0.863). Models based on non-cognitive covariates alone achieved AUC (0.717,0.714) for predicting cognitive impairment (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.783,0.770). A model with a single covariate of composite cognition score achieved AUC (0.754,0.730).
conclusionRisk models based on non-cognitive metrics predict both AD dementia and cognitive impairment. However, non-cognitive covariates do not provide incremental predictivity for models that include cognitive metrics in predicting AD dementia, but do in models predicting cognitive impairment. Further improved risk prediction models for cognitive impairment are needed.
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Registered trials
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