Frailty index transitions over eight years were frequent in The Irish Longitudinal Study on Ageing.

Background: The frailty index (FI) is based on accumulation of health deficits. FI cut-offs define non-frail, prefrail and frail states. We described transitions of FI states in The Irish Longitudinal Study on Ageing (TILDA). Methods: Participants aged ≥50 years with information for a 31-deficit FI at wave 1 (2010) were followed-up over four waves (2012, 2014, 2016, 2018). Transitions were visualized with alluvial plots and probabilities estimated with multi-state Markov models, investigating the effects of age, sex and education. Results: 8174 wave 1 participants were included (3744 men and 4430 women; mean age 63.8 years). Probabilities from non-frail to prefrail, and non-frail to frail were 18% and 2%, respectively. Prefrail had a 19% probability of reversal to non-frail, and a 15% risk of progression to frail. Frail had a 21% probability of reversal to prefrail and 14% risk of death. Being older and female increased the risk of adverse FI state transitions, but being female reduced the risk of transition from frail to death. Higher level of education was associated with improvement from prefrail to non-frail. Conclusions: FI states are characterized by dynamic longitudinal transitions and frequent improvement. Opportunities exist for reducing the probability of adverse transitions

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 dataset into 3 subgroups, based on the evolution of SART performance after 4 years. Raw SART data were available for 3468 participants aged 50 years and over at baseline. The previously reported SART visualisation-derived feature 'bad performance', indicating the number of SART trials with at least 4 mistakes, and its evolution over time, combined with the fuzzy c-mean (FCM) algorithm, individuated 3 clusters corresponding to 3 degrees of physiological dysregulation. The biggest cluster (94% of the cohort) was constituted by healthy participants, a smaller cluster (5% of the cohort) by participants who showed improvement in cognitive and psychological status, and the smallest cluster (1% of the cohort) by participants whose mobility and cognitive functions dramatically declined after 4 years. We were able to identify in a cohort of relatively high-functioning community-dwelling adults a very small group of participants who showed clinically significant decline. The selected smallest subset manifested not only mobility deterioration, but also cognitive decline, the latter being usually hard to detect in population-based studies. The employed techniques could identify at-risk participants with more specificity than current methods, and help clinicians better identify and manage the small proportion of community-dwelling older adults who are at significant risk of functional decline and loss of independence

Plasma lutein and zeaxanthin concentrations associated with musculoskeletal health and incident frailty in The Irish Longitudinal Study on Ageing (TILDA)

peer-reviewedIntroduction Lutein and zeaxanthin are diet-derived carotenoids that are proposed to help mitigate frailty risk and age-related declines in musculoskeletal health via their anti-oxidant and anti-inflammatory properties. Therefore, this study aimed to investigate the association between lutein and zeaxanthin status and indices of musculoskeletal health and incident frailty among community-dwelling adults aged ≥50 years in the Irish Longitudinal Study on Ageing (TILDA). Methods Cross-sectional analyses (n = 4513) of plasma lutein and zeaxanthin concentrations and grip strength, usual gait speed, timed up-and-go (TUG), probable sarcopenia (defined as grip strength <27 kg in men, <16 kg in women), and bone mass (assessed using calcaneal broadband ultrasound stiffness index) were performed at Wave 1 (2009–2011; baseline). In the longitudinal analyses (n = 1425–3100), changes in usual gait speed (at Wave 3, 2014–2015), grip strength (Wave 4, 2016) and TUG (at Wave 5, 2018), incident probable sarcopenia (at Wave 4) and incident frailty (Fried's phenotype, Frailty Index, FRAIL Scale, Clinical Frailty Scale-classification tree, at Wave 5) were determined. Data were analysed using linear and ordinal logistic regression, adjusted for confounders. Results Cross-sectionally, plasma lutein and zeaxanthin concentrations were positively associated with usual gait speed (B [95 % CI] per 100-nmol/L higher concentration: Lutein 0.59 [0.18, 1.00], Zeaxanthin 1.46 [0.37, 2.55] cm/s) and inversely associated with TUG time (Lutein −0.07 [−0.11, −0.03], Zeaxanthin −0.14 [−0.25, −0.04] s; all p 0.05). Plasma lutein concentration was positively associated with bone stiffness index (0.54 [0.15, 0.93], p 0.05). Conclusion Higher plasma lutein and zeaxanthin concentrations at baseline were associated with a reduced likelihood of incident frailty after ~8 years of follow up. Baseline plasma lutein and zeaxanthin concentrations were also positively associated with several indices of musculoskeletal health cross-sectionally but were not predictive of longitudinal changes in these outcomes over 4–8 years.Horizon 2020 Marie Skłodowska-Curie ActionsThis work was supported by the Teagasc Research Leaders 2025 programme co-funded by Teagasc and the European Union's Horizon 2020 - Research and Innovation Framework Programme under the H2020 Marie Skłodowska-Curie Actions grant agreement number 754380. TILDA is funded by Atlantic Philanthropies, the Irish Department of Health and Irish Life. Roman Romero-Ortuno is funded by a grant from Science Foundation Ireland under grant number 18/FRL/6188

Age-related normative changes in cerebral perfusion: data from The Irish Longitudinal Study on Ageing (TILDA)

Objective To establish normative reference values for total grey matter cerebral blood flow (CBFGM) measured using pseudo-continuous arterial spin labelling (pCASL) MRI in a large cohort of community-dwelling adults aged 54 years and older. Background Quantitative assessment of CBFGM may provide an imaging biomarker for the early detection of those at risk of neurodegenerative diseases, such as Alzheimer's and dementia. However, the use of this method to differentiate normal age-related decline in CBFGM from pathological reduction has been hampered by the lack of reference values for cerebral perfusion. Methods The study cohort comprised a subset of wave 3 (2014-2015) participants from The Irish Longitudinal Study on Ageing (TILDA), a large-scale prospective cohort study of individuals aged 50 and over. Of 4,309 participants attending for health centre assessment, 578 individuals returned for 3T multi-parametric MRI brain examinations. In total, CBFGM data acquired from 468 subjects using pCASL-MRI were included in this analysis. Normative values were estimated using Generalised Additive Models for Location Shape and Scale (GAMLSS) and are presented as percentiles, means and standard deviations. Results The mean age of the cohort was 68.2 ± 6.9 years and 51.7% were female. Mean CBFGM for the cohort was 36.5 ± 8.2 ml/100g/min. CBFGM decreased by 0.2 ml/100g/min for each year increase in age (95% CI =-0.3,-0.1; p ≤ 0.001) and was 3.1 ml/100g/min higher in females (95% CI = 1.6, 4.5; p ≤ 0.001)

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

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 1359 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 brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing

Associations between Neurocardiovascular Signal Entropy and Physical Frailty

In this cross-sectional study, the relationship between noninvasively measured neurocardiovascular signal entropy and physical frailty was explored in a sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that dysfunction in the neurovascular and cardiovascular systems, as quantified by short-length signal complexity during a lying-to-stand test (active stand), could provide a marker for frailty. Frailty status (i.e., &ldquo;non-frail&rdquo;, &ldquo;pre-frail&rdquo;, and &ldquo;frail&rdquo;) was based on Fried&rsquo;s criteria (i.e., exhaustion, unexplained weight loss, weakness, slowness, and low physical activity). Approximate entropy (ApEn) and sample entropy (SampEn) were calculated during resting (lying down), active standing, and recovery phases. There was continuously measured blood pressure/heart rate data from 2645 individuals (53.0% female) and frontal lobe tissue oxygenation data from 2225 participants (52.3% female); both samples had a mean (SD) age of 64.3 (7.7) years. Results revealed statistically significant associations between neurocardiovascular signal entropy and frailty status. Entropy differences between non-frail and pre-frail/frail were greater during resting state compared with standing and recovery phases. Compared with ApEn, SampEn seemed to have better discriminating power between non-frail and pre-frail/frail individuals. The quantification of entropy in short length neurocardiovascular signals could provide a clinically useful marker of the multiple physiological dysregulations that underlie physical frailty

Associations between Cardiovascular Signal Entropy and Cognitive Performance over Eight Years

In this study, the relationship between non-invasively measured cardiovascular signal entropy and global cognitive performance was explored in a sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA), both cross-sectionally at baseline (n = 4525; mean (SD) age: 61.9 (8.4) years; 54.1% female) and longitudinally. We hypothesised that signal disorder in the cardiovascular system, as quantified by short-length signal entropy during rest, could provide a marker for cognitive function. Global cognitive function was assessed via Mini Mental State Examination (MMSE) across five longitudinal waves (8 year period; n = 4316; mean (SD) age: 61.9 (8.4) years; 54.4% female) and the Montreal Cognitive Assessment (MOCA) across two longitudinal waves (4 year period; n = 3600; mean (SD) age: 61.7 (8.2) years; 54.1% female). Blood pressure (BP) was continuously monitored during supine rest at baseline, and sample entropy values were calculated for one-minute and five-minute sections of this data, both for time-series data interpolated at 5 Hz and beat-to-beat data. Results revealed significant associations between BP signal entropy and cognitive performance, both cross-sectionally and longitudinally. Results also suggested that as regards associations with cognitive performance, the entropy analysis approach used herein potentially outperformed more traditional cardiovascular measures such as resting heart rate and heart rate variability. The quantification of entropy in short-length BP signals could provide a clinically useful marker of the cardiovascular dysregulations that potentially underlie cognitive decline

Blood Pressure Signal Entropy as a Novel Marker of Physical Frailty: Results from the FRAILMatics Clinical Cohort

In this study we investigated the association between information entropy in short length blood pressure signals and physical frailty status, in a group of patients aged 50+ recruited from the Falls and Syncope Unit at the Mercer’s Institute for Successful Ageing in St James’s Hospital, Dublin, Ireland. This work is an external clinical validation of findings previously derived in a population-based cohort from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that dysregulation (as quantified by entropy) in continuous non-invasive blood pressure signals could provide a clinically useful marker of physical frailty status. We found that in the 100 patients investigated, higher entropy in continuously measured resting state diastolic blood pressure was associated with worse physical frailty score, as measured by the Frailty Instrument for primary care of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI). Since physical frailty is defined as a pre-disability state and hence it can be difficult for clinicians to identify at an early stage, the quantification of entropy in short length cardiovascular signals could provide a clinically useful marker of the physiological dysregulations that underlie physical frailty, potentially aiding in identifying individuals at higher risk of adverse health outcomes

The Importance of Age in the Prediction of Mortality by a Frailty Index: A Machine Learning Approach in the Irish Longitudinal Study on Ageing

The quantification of biological age in humans is an important scientific endeavor in the face of ageing populations. The frailty index (FI) methodology is based on the accumulation of health deficits and captures variations in health status within individuals of the same age. The aims of this study were to assess whether the addition of age to an FI improves its mortality prediction and whether the associations of the individual FI items differ in strength. We utilized data from The Irish Longitudinal Study on Ageing to conduct, by sex, machine learning analyses of the ability of a 32-item FI to predict 8-year mortality in 8174 wave 1 participants aged 50 or more years. By wave 5, 559 men and 492 women had died. In the absence of age, the FI was an acceptable predictor of mortality with AUCs of 0.7. When age was included, AUCs improved to 0.8 in men and 0.9 in women. After age, deficits related to physical function and self-rated health tended to have higher importance scores. Not all FI variables seemed equally relevant to predict mortality, and age was by far the most relevant feature. Chronological age should remain an important consideration when interpreting the prognostic significance of an FI