Considering Cumulative Risk for Cardiovascular Disease in Relation to Cognitive Function: a Comment on DeRight et al.

Cardiovascular risk factors, such as hypertension, diabetes, obesity, and dyslipidemia, aggregate within individuals. In the practice of clinical medicine there is tremendous interest in risk stratification and development of multivariate models that predict incident disease or progression to clinical endpoints. These equation models can be used to identify high risk persons who can then be targeted for disease prevention or intervention with lifestyle or pharmacological therapy with the goal of decreasing future clinical events. Some of the more prominent and widely used risk factor equations emanate from the Framingham Study and include the Framingham risk models for development of coronary heart disease or cardiovascular disease (CVD), and the Framingham Stroke Risk Profile (1). Also available are various non-Framingham risk factor models, and prediction models for the development of dementia and cognitive impairment such as the Cardiovascular Risk Factors Aging and Dementia (CAIDE) model (2). Adding measures of neuroimaging, cerebrospinal fluid, and biomarkers (e.g., beta-amyloid 42) to those obtained in routine clinical evaluation may significantly improve the prediction of dementia. However, these tests are expensive and are not routinely performed as part of clinical practice, thus leading to efforts to further improve prediction models using data obtained as part of standard clinical care. Evidence is accumulating that, irrespective of specific risk factor model, combinations of modifiable vascular risk factors are associated with cognitive decline and incident dementia (3), although it has been suggested that the Framingham scales may offer the best predictive utility (4). The article by DeRight et al. (5) in this issue provides an important meta-analytic review of the relation of Framingham composite risk scores to cognitive function in 54,564 participants across 19 studies published since 1994. Higher risk scores were associated with lower levels of cognitive performance. Effect sizes were uniformly small, but relatively greater for tests of attention, executive functions, memory, and global performance as compared to visuospatial function. These findings highlight the importance of considering cumulative risk for CVD in predicting brain health outcomes. The work by DeRight et al. strongly suggests future directions for research in this area, one of which is more detailed consideration of age. In that regard, the age ranges of the samples available for the meta-analyses tended to be fairly young, having an average 9% risk for a clinical event. Underrepresentation of the oldest old (due to selective attrition and exclusion of those with known dementia and mild cognitive impairment) may underestimate the adverse effects of CVD risk factors on cognition. To complicate matters, risk composites are less accurate in the elderly, and indeed some studies suggest that higher levels of risk factors may predict better outcomes in the oldest old (e.g., 6). More generally, the validity of risk equations has been shown to vary across sociodemographic groups, patient populations, and countries. Furthermore, multiple studies suggest the presence of nonlinear relations of CVD risk factors (e.g., blood pressure, total cholesterol, body mass index) to cognitive performance that may differ as a function of age (7). Lastly, as noted by the authors, risk factor composites such as the Framingham scales typically include age despite that it may be better viewed as a moderator variable. Heterogeneity in effect estimates derived via the present meta-analysis suggests that additional vulnerability and resilience factors may be operative. Prior literature has indicated that sex, race/ethnicity, education, and other socioeconomic status indicators should be evaluated (6). Yet, as per above, it remains an empirical question as to whether sociodemographic variables provide better predictive power as part of risk factor equations or as moderator variables. Understanding synergistic associations of risk factors to cognitive endpoints may provide additional translational information. Several other dimensions of heterogeneity are also important to consider. First, heterogeneity was noted with respect to the severity of CVD risk factors, with several outliers evident. Equations may underestimate risk in those with extreme values. Variability in duration of risk factors is also considered critical, but is notoriously difficult to capture. Individuals with younger age of onset of CVD risk factors may be at particularly high risk for the subsequent development of cognitive impairment and dementia. Second, with use of heterogeneous risk factor composites, it remains unclear whether select variables (or clusters of variables) are driving associations. Third, there is substantial heterogeneity within and between cognitive tests in terms of associated domains of function. In that regard, neuropsychological tests rarely tap into a single dimension of functioning. Subsequent classification of tests into larger domains or composites further increases heterogeneity. Lastly, study designs vary, and associations may differ in cross-sectional versus longitudinal investigations. Results of this meta-analysis provide compelling evidence for the role of commonly measured cardiovascular risk factors in promoting cognitive decline. Further identification of common and disparate mechanistic pathways for individual risk factors and their combinations is needed. Aggressive and sustained cardiovascular risk factor intervention may reduce cognitive decline and preserve brain health with aging.