Verbal intelligence is a more robust cross-sectional measure of cognitive reserve than level of education in healthy older adults

Abstract

Background: Cognitive reserve is most commonly measured using socio-behavioural proxy variables. These variables are easy to collect, have a straightforward interpretation, and are widely associated with reduced risk of dementia and cognitive decline in epidemiological studies. However, the specific proxies vary across studies and have rarely been assessed in complete models of cognitive reserve (i.e. alongside both a measure of cognitive outcome and a measure of brain structure). Complete models can test independent associations between proxies and cognitive function in addition to the moderation effect of proxies on the brain-cognition relationship. Consequently, there is insufficient empirical evidence guiding the choice of proxy measures of cognitive reserve and poor comparability across studies.

Method: In a cross-sectional study, we assessed the validity of 5 common proxies (education, occupational complexity, verbal intelligence, leisure activities, and exercise) and all possible combinations of these proxies in 2 separate community-dwelling older adult cohorts: The Irish Longitudinal Study on Ageing (TILDA; N = 313, mean age = 68.9 years, range = 54-88) and the Cognitive Reserve/Reference Ability Neural Network Study (CR/RANN; N = 234, mean age = 64.49 years, range = 50-80). Fifteen models were created with 3 brain structure variables (grey matter volume, hippocampal volume, and mean cortical thickness) and 5 cognitive variables (verbal fluency, processing speed, executive function, episodic memory, and global cognition).

Results: No moderation effects were observed. There were robust positive associations with cognitive function, independent of brain structure, for 2 individual proxies (verbal intelligence and education) and 16 composites (i.e. combinations of proxies). Verbal intelligence was statistically significant in all models. Education was significant only in models with executive function as the cognitive outcome variable. Three robust composites were observed in more than two-thirds of brain-cognition models: the composites of (1) occupational complexity and verbal intelligence, (2) education and verbal intelligence, and (3) education, occupational complexity, and verbal intelligence. However, no composite had larger average effects nor was more robust than verbal intelligence alone.

Conclusion: These results support the use of verbal intelligence as a proxy measure of CR in cross-sectional studies of cognitively healthy older adults.

Keywords: Cognitive ageing; Cognitive decline; Cognitive reserve; Neuroimaging; Structural MRI.

Fig. 1

Schematic of basic brain structure-cognitive function models created for analysis

Fig. 2

Heatmaps showing Pearson’s correlations between individual proxies in each dataset. *p < .05, **p < .01, ***p < .001

Fig. 3

Mean R² change across datasets in all models for proxies with significant effects. + indicates composite proxies (e.g. Education + Verbal IQ = composite of educational attainment and verbal intelligence). Black vertical bars represent the mean of significant R² change values across all models for that proxy. All models were adjusted for brain structure, age, and sex

Fig. 4

Mean R² change of significant effects in all TILDA models for individual proxies. Black vertical bars represent the mean of significant R² change values across all models for that proxy. All models were adjusted for brain structure, age, and sex

Fig. 5

Mean R² change of significant effects in all TILDA models for composite proxies. Each row refers to all composites including that proxy (e.g. Verbal IQ+ refers to all composites including verbal intelligence). Black vertical bars represent the mean of significant R² change values across all models for all composites containing that proxy. All models were adjusted for brain structure, age, and sex