Predicting Risk of Cognitive Decline in Very Old Adults Using Three Models: The Framingham Stroke Risk Profile; the Cardiovascular Risk Factors, Aging, and Dementia Model; and Oxi-Inflammatory Biomarkers

Stephanie L. Harrison, PhD; Anton J. M. de Craen, PhD; Ngaire Kerse, PhD; Ruth Teh, PhD; Antoneta Granic, PhD; Karen Davies, PhD; Keith A. Wesnes, PhD; Wendy P. J. den Elzen, PhD; Jacobijn Gussekloo, PhD; Thomas B. L. Kirkwood, PhD; Louise Robinson, MD; Carol Jagger, PhD; Mario Siervo, PhD; Blossom C. M. Stephan, PhD

Disclosures

J Am Geriatr Soc. 2017;65(2):381-389. 

In This Article

Discussion

Three prospective studies of 85-year-old individuals free of stroke or dementia at baseline from the United Kingdom, the Netherlands, and New Zealand found that higher FSRP and CAIDE risk scores and higher oxi-inflammatory load derived from a cumulative score of three cardiovascular biomarkers were associated with incident global impairment. Furthermore, incorporating oxi-inflammatory load scores into the FSRP or CAIDE model improved the ability of the risk models to predict incident global cognitive impairment, although this could only be determined using two of the three studies (Newcastle 85+ Study, Leiden 85-plus Study) because the LiLACS NZ Study did not have the homocysteine measures needed to determine oxi-inflammatory load.

Several longitudinal studies have found a positive association between higher scores from the Framingham and CAIDE risk models and greater risk of cognitive decline or dementia (for a review of studies see[21]), but when investigating specific cognitive domains, the results have been inconsistent with regard to which cognitive domains higher cardiovascular risk may affect.[5–8,11,33] Furthermore, the majority of studies have focused on midlife and younger old populations, and no study has previously examined the association between cardiovascular risk models and cognitive function in very old adults.

Studies in younger populations have found that homocysteine, IL-6, and CRP predict cognitive decline.[13–15] Previous findings in the Newcastle 85 + Study have found cross-sectional associations between these biomarkers and global cognitive impairment measured using the MMSE.[29] Similarly, previous findings in the Leiden 85-plus Study have found cross-sectional associations between homocysteine and cognitive impairment, but this association was not found with rate of cognitive decline.[34]

In the current study, higher levels of biomarkers for oxidative stress and inflammation were longitudinally associated with greater risk of developing global and domain-specific cognitive (speed and attention) impairment. Biomarkers of cardiovascular risk may be useful for identifying individuals at risk of future cognitive impairment, because oxidative stress and inflammation are implicated in the pathophysiology of dementia. Higher levels of oxidative stress, impaired cellular function linked to abnormal protein accumulation, and modification of molecular structures may have direct effects on neuronal structure and integrity, affecting cognitive function.[35] Furthermore, inflammation is thought to be important in neurodegeneration, contributing to the development of some of the classic hallmarks of Alzheimer's pathology such as amyloid-beta plaques.[36] These findings have been formalized in the theory of inflammaging as a critical factor in the pathogenesis of age-related chronic cardiovascular and neurodegenerative diseases.[37,38]

Current dementia risk prediction models are not sufficient to detect those at greatest risk of developing cognitive impairment or dementia.[16] Factors incorporated into current dementia risk prediction models include demographic factors (e.g., age, sex, ethnicity), subjective cognitive complaints, functioning (as measured using activity of daily living scales), neuropsychological test scores, health-related measures (e.g., history of CVD, body mass index), lifestyle measures (e.g., smoking status, alcohol intake), dietary factors (e.g., folic acid and fish intake), magnetic resonance imaging results (e.g., white matter disease), and others (e.g., family history of dementia). The best models were described as those that incorporated a diverse range of risk factors, but it is most likely that there will not be one model that is suitable for all populations, and different dementia risk models may need to be developed for different age groups.[39] No such dementia risk model has been validated in a very old population. The development of a highly accurate model for discriminating those at high risk of future dementia from those at medium and low risk would be required before screening the older population for future risk of dementia in primary care practice could become a possibility. Incorporation of the biomarkers investigated in this study into the classical cardiovascular risk factors of the FSRP or CAIDE models may be useful to investigate when developing a dementia risk prediction model for very old adults.

There are strengths and limitations to this study. The Newcastle 85+, Leiden 85-plus, and LiLACS NZ cohorts are prospective longitudinal cohort studies of very old adults, and this is the first study that has aggregated data from all three cohorts.

There are some limitations. The LiLACS NZ Study did not have homocysteine levels needed to determine oxi-inflammatory load, so findings for oxi-inflammatory load are based on the Newcastle 85+ and Leiden 85-plus studies only.

Results were not always consistent across the studies; these discrepancies may be due to differences in the assays used to determine CRP and IL-6 between the two studies, because the Leiden 85-plus Study used less-sensitive assays, which led to the attribution of values of 0 for results lower than the limit of detection (number of 0 values: CRP = 82, IL-6 = 119); differences in the cohorts themselves (e.g., education levels; the Leiden 85+ Study did not have years of education, which was needed to calculate CAIDE scores, the LiLACS Study did not have the apolipoprotein E4 measurement, which was required for the CAIDE model); differences in the cognitive tests used to assess domain-specific cognitive function; the smaller sample size of the Leiden 85-plus and LiLACS NZ studies; or a cohort effect related to differences in birth year between cohorts (1913–1915 (Leiden 85-plus Study), 1921 (Newcastle 85+ Study), 1925 (LiLACS NZ Study)).

Further research in very old adults is needed to gain a full understanding of the association between the Framingham models, the CAIDE models, or cardiovascular biomarkers and cognitive decline, in particular with respect to which cognitive domains may be most likely to be affected.

There is no recommended tool for identifying very old individuals at risk of developing cognitive impairment or dementia.[16] Combining oxi-inflammatory load with the FSRP or the CAIDE model may improve the ability of these models to predict cognitive changes. Biomarkers would be relatively easy to measure in a clinical setting and could potentially provide clinicians with an overview of an individual's cardiovascular health in addition to their future risk of cognitive impairment. Intervention strategies to reduce oxi-inflammatory load could potentially target improvements in cardiovascular health and cognitive function.

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