Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brainpredicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

Highlights Increased BMI, WHR, and waist size associated with lower cerebral blood flow. Waist size +1cm associated with same reduction in cerebral blood flow as +1year age. Higher levels of physical activity shown to potentially modify these associations.

The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we describe a new method to visualise individual trial (raw) information obtained from the SART test, vis-à-vis age, and groups based on mobility status in a large population-based study of ageing in Ireland. A thresholding method, based on the individual trial number of mistakes, was employed to better visualise poorer SART performances, and was statistically validated with binary logistic regression models to predict mobility and cognitive decline after 4 years. Raw SART data were available for 4864 participants aged 50 years and over at baseline. The novel visualisation-derived feature bad performance, indicating the number of SART trials with at least 4 mistakes, was the most significant predictor of mobility decline expressed by the transition from Timed Up-and-Go (TUG) < 12 to TUG ≥ 12 s (OR = 1.29; 95% CI 1.14–1.46; p < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03–1.21; p = 0.011), in models adjusted for multiple covariates. However, no SART-related variables resulted significant for the risk of cognitive decline, expressed by a decrease of ≥ 2 points in the Mini-Mental State Examination (MMSE) score. This novel multimodal visualisation could help clinicians easily develop clinical hypotheses. A threshold approach to the evaluation of SART performance in older adults may better identify subjects at higher risk of future mobility decline.

Assessment of the cerebrovascular and cardiovascular response to standing has prognostic value for a range of outcomes in the older adult population. Studies generally attempt to control for standing speed differences by asking participants to stand in a specified time but little is known about the range of transition times observed. This study aimed to characterize how standing speed associates with cardiovascular and cerebrovascular measures following transition from supine to standing. Continuous cerebral oxygenation, heart rate, systolic and diastolic blood pressure were monitored for 3 minutes after transitioning from supine to standing. An algorithm was used to calculate the time taken to transition from existing Finometer data (from the height correction unit). Linear mixed-effects models were used to assess the influence of transition time on each of the signals while adjusting for covariates. Transition time ranged from 2 to 27 s with 17% of participants taking >10 s to stand. Faster transition was associated with a more extreme decrease 10 s after standing but improved recovery at 20 s for cerebral oxygenation and blood pressure. Standing faster was associated with an elevated heart rate on initiation of stand and a quicker recovery 10 to 20 s after standing. The speed of transitioning from supine to standing position is associated with cardiovascular and cerebrovascular response in the early period after standing (<40 s). Care should be taken in the interpretation of findings which may be confounded by standing speed and statistical adjustment for standing time should be applied where appropriate.

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.