Associations between Cardiovascular Signal Entropy and Cognitive Performance over Eight Years

Individual differences in seated resting heart rate are associated with multisensory perceptual function in older adults

There is evidence that cardiovascular function can influence sensory processing and cognition, which are known to change with age. However, whether the precision of unisensory and multisensory temporal perception is influenced by cardiovascular activity in older adults is uncertain. We examined whether seated resting heart rate (RHR) was associated with unimodal visual and auditory temporal discrimination as well as susceptibility to the audio-visual Sound Induced Flash Illusion (SIFI) in a large sample of older adults (N = 3232; mean age = 64.17 years, SD = 7.74, range = 50-93; 56% female) drawn from The Irish Longitudinal Study on Ageing (TILDA). Faster seated RHR was associated with better discretization of two flashes (but not two beeps) and increased SIFI susceptibility when the audio-visual stimuli were presented close together in time but not at longer audio-visual temporal offsets. Our findings suggest a significant relationship between cardiovascular activity and the precision of visual and audio-visual temporal perception in older adults, thereby providing novel evidence for a link between cardiovascular function and perceptual function in aging.

Evaluation of a 3-Item Health Index in Predicting Mortality Risk: A 12-Year Follow-Up Study

This study was carried out using a large cohort (N = 4265; 416 deceased) of older, community-dwelling adults from The Irish Longitudinal Study on Ageing (TILDA). The study compared the performance of a new 3-item health index (HI) with two existing measures, the 32-item frailty index (FI) and the frailty phenotype (FP), in predicting mortality risk. The HI was based on the objective measurement of resting-state systolic blood pressure sample entropy, sustained attention reaction time performance, and usual gait speed. Mortality data from a 12-year follow up period were analyzed using Cox proportional regression. All data processing was performed using MATLAB and statistical analysis using STATA 15.1. The HI showed good discriminatory power (AUC = 0.68) for all-cause mortality, similar to FI (AUC = 0.68) and superior to FP (AUC = 0.60). The HI classified participants into Low-Risk (84%), Medium-Risk (15%), and High-Risk (1%) groups, with the High-Risk group showing a significant hazard ratio (HR) of 5.91 in the unadjusted model and 2.06 in the fully adjusted model. The HI also exhibited superior predictive performance for cardiovascular and respiratory deaths (AUC = 0.74), compared with FI (AUC = 0.70) and FP (AUC = 0.64). The HI High-Risk group had the highest HR (15.10 in the unadjusted and 5.61 in the fully adjusted models) for cardiovascular and respiratory mortality. The HI remained a significant predictor of mortality even after comprehensively adjusting for confounding variables. These findings demonstrate the effectiveness of the 3-item HI in predicting 12-year mortality risk across different causes of death. The HI performed similarly to FI and FP for all-cause mortality but outperformed them in predicting cardiovascular and respiratory deaths. Its ability to classify individuals into risk groups offers a practical approach for clinicians and researchers. Additionally, the development of a user-friendly MATLAB App facilitates its implementation in clinical settings. Subject to external validation in clinical research settings, the HI can be more useful than existing frailty measures in the prediction of cardio-respiratory risk.

Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA)

Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia).

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.

Cardiovascular Signal Entropy Predicts All-Cause Mortality: Evidence from The Irish Longitudinal Study on Ageing (TILDA)

In this study, the relationship between cardiovascular signal entropy and the risk of seven-year all-cause mortality was explored in a large sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that physiological dysregulation might be quantifiable by the level of sample entropy (SampEn) in continuously noninvasively measured resting-state systolic (sBP) and diastolic (dBP) blood pressure (BP) data, and that this SampEn measure might be independently predictive of mortality. Participants’ date of death up to 2017 was identified from official death registration data and linked to their TILDA baseline survey and health assessment data (2010). BP was continuously monitored during supine rest at baseline, and SampEn values were calculated for one-minute and five-minute sections of this data. In total, 4543 participants were included (mean (SD) age: 61.9 (8.4) years; 54.1% female), of whom 214 died. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BP SampEn and all-cause mortality. Results revealed that higher SampEn in BP signals was significantly predictive of mortality risk, with an increase of one standard deviation in sBP SampEn and dBP SampEn corresponding to HRs of 1.19 and 1.17, respectively, in models comprehensively controlled for potential confounders. The quantification of SampEn in short length BP signals could provide a novel and clinically useful predictor of mortality risk in older adults.

On Quantization Errors in Approximate and Sample Entropy

Approximate and sample entropies are acclaimed tools for quantifying the regularity and unpredictability of time series. This paper analyses the causes of their inconsistencies. It is shown that the major problem is a coarse quantization of matching probabilities, causing a large error between their estimated and true values. Error distribution is symmetric, so in sample entropy, where matching probabilities are directly summed, errors cancel each other. In approximate entropy, errors are accumulating, as sums involve logarithms of matching probabilities. Increasing the time series length increases the number of quantization levels, and errors in entropy disappear both in approximate and in sample entropies. The distribution of time series also affects the errors. If it is asymmetric, the matching probabilities are asymmetric as well, so the matching probability errors cease to be mutually canceled and cause a persistent entropy error. Despite the accepted opinion, the influence of self-matching is marginal as it just shifts the error distribution along the error axis by the matching probability quant. Artificial lengthening the time series by interpolation, on the other hand, induces large error as interpolated samples are statistically dependent and destroy the level of unpredictability that is inherent to the original signal.