Cardiovascular disease risk models and longitudinal changes in cognition: A systematic review

Stephanie Harrison, Jie Ding, Eugene Tang, Mario Siervo, Louise Robinson, Carol Jagger, Blossom Stephan

    Research output: Contribution to journalReview articlepeer-review

    33 Citations (Scopus)

    Abstract

    Background: Cardiovascular disease and its risk factors have consistently been associated with poor cognitive function and incident dementia. Whether cardiovascular disease prediction models, developed to predict an individual's risk of future cardiovascular disease or stroke, are also informative for predicting risk of cognitive decline and dementia is not known. Objective: The objective of this systematic review was to compare cohort studies examining the association between cardiovascular disease risk models and longitudinal changes in cognitive function or risk of incident cognitive impairment or dementia. Materials and Methods: Medline, PsychINFO, and Embase were searched from inception to March 28, 2014. From 3,413 records initially screened, 21 were included. Results: The association between numerous different cardiovascular disease risk models and cognitive outcomes has been tested, including Framingham and non- Framingham risk models. Five studies examined dementia as an outcome; fourteen studies examined cognitive decline or incident cognitive impairment as an outcome; and two studies examined both dementia and cognitive changes as outcomes. In all studies, higher cardiovascular disease risk scores were associated with cognitive changes or risk of dementia. Only four studies reported model prognostic performance indices, such as Area Under the Curve (AUC), for predicting incident dementia or cognitive impairment and these studies all examined non-Framingham Risk models (AUC range: 0.74 to 0.78). Conclusions: Cardiovascular risk prediction models are associated with cognitive changes over time and risk of dementia. Such models are easily obtainable in clinical and research settings and may be useful for identifying individuals at high risk of future cognitive decline and dementia.

    Original languageEnglish
    Article numbere114431
    Pages (from-to)Art: e114431
    Number of pages14
    JournalPLoS One
    Volume9
    Issue number12
    DOIs
    Publication statusPublished - 5 Dec 2014

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