Oral and Poster Papers Submitted for Presentation at the 5th Congress of the EUGMS “Geriatric Medicine in a Time of Generational Shift September 3–6, 2008 Copenhagen, Denmark

A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiological systems

Frailty in older adults is characterized by reduced physiological reserve. Gait speed reserve (GSR: maximum minus usual gait speed) could help identify frailty and act as a proxy for physiological reserve. Utilizing data from 2397 participants aged 50+ from wave 3 of The Irish Longitudinal Study on Ageing, we developed a stepwise linear regression-based machine learning pipeline to select the most important GSR predictors from 34 manually selected features across multiple domains. Variables were selected one at a time such that they maximized the mean adjusted r-squared score from a 5-fold cross-validation. A peak score of (0.16 +/- 0.03) was achieved with 14 variables (giving adjusted-r-sq…

Longitudinal Study on Sustained Attention to Response Task (SART): Clustering Approach for Mobility and Cognitive Decline

The Sustained Attention to Response Task (SART) is a computer-based go/no-go task to measure neurocognitive function in older adults. However, simplified average features of this complex dataset lead to loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we combine a novel method to visualise individual trial (raw) information obtained from the SART test in a large population-based study of ageing in Ireland and an automatic clustering technique. We employed a thresholding method, based on the individual trial number of mistakes, to identify poorer SART performances and a fuzzy clusters algorithm to partition the da…

SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline

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 bin…

Somatometric and clinical cardiovascular risk factors in midlife and older women. A tale of four European countries

Transitions in frailty phenotype states and components over 8 years: Evidence from The Irish Longitudinal Study on Ageing

Abstract Aim Fried's frailty phenotype (FP) is defined by exhaustion (EX), unexplained weight loss (WL), weakness (WK), slowness (SL) and low physical activity (LA). Three or more components define the frail state, and one or two the prefrail. We described longitudinal transitions of FP states and components in The Irish Longitudinal Study on Ageing (TILDA). Methods We included participants aged ≥50 years with FP information at TILDA wave 1 (2010), who were followed-up over four longitudinal waves (2012, 2014, 2016, 2018). Next-wave transition probabilities were estimated with multi-state Markov models. Results 5683 wave 1 participants were included (2612 men and 3071 women; mean age 63.1 y…

Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

Abstract Brain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age. 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. 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: Dokuz Eylul University (n=175), the Cogni…