Association between gait speed and the SHARE Frailty Instrument in a Falls and Syncope Clinic

Haemodynamic Parameters Underlying the Relationship between Sarcopenia and Blood Pressure Recovery on Standing

BACKGROUND

Sarcopenia, delayed blood pressure (BP) recovery following standing, and orthostatic hypotension (OH) pose significant clinical challenges associated with ageing. While prior studies have established a link between sarcopenia and impaired BP recovery and OH, the underlying haemodynamic mechanisms remain unclear.

METHODS

We enrolled 107 participants aged 50 and above from a falls and syncope clinic, conducting an active stand test with continuous non-invasive haemodynamic measurements. Hand grip strength and five-chair stand time were evaluated, and muscle mass was estimated using bioelectrical impedance analysis. Participants were categorised as non-sarcopenic or sarcopenic. Employing mixed-effects linear regression, we modelled the effect of sarcopenia on mean arterial pressure and heart rate after standing, as well as Modelflow®-derived parameters such as cardiac output, total peripheral resistance, and stroke volume, while adjusting for potential confounders.

RESULTS

Sarcopenia was associated with diminished recovery of mean arterial pressure during the 10-20 s period post-standing (β -0.67, p < 0.001). It also resulted in a reduced ascent to peak (0-10 s) and recovery from peak (10-20 s) of cardiac output (β -0.05, p < 0.001; β 0.06, p < 0.001). Furthermore, sarcopenia was associated with attenuated recovery (10-20 s) of total peripheral resistance from nadir (β -0.02, p < 0.001) and diminished recovery from peak (10-20 s) of stroke volume (β 0.54, p < 0.001). Notably, heart rate did not exhibit a significant association with sarcopenia status at any time interval post-standing.

CONCLUSION

The compromised BP recovery observed in sarcopenia appears to be driven by an initial reduction in the peak of cardiac output, followed by attenuated recovery of cardiac output from its peak and total peripheral resistance from its nadir. This cardiac output finding seems to be influenced by stroke volume rather than heart rate. Possible mechanisms for these findings include cardio-sarcopenia, the impact of sarcopenia on the autonomic nervous system, and/or the skeletal muscle pump.

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.

Relationship between sarcopenia and orthostatic blood pressure recovery in older falls clinic attendees

PURPOSE

Sarcopenia and delayed orthostatic blood pressure (BP) recovery are two disorders increasingly associated with adverse clinical outcomes in older adults. There may exist a pathophysiological link between the two via the skeletal muscle pump of the lower limbs. Previously in a large population-based study, we found an association between probable sarcopenia and orthostatic BP recovery. Here, we sought to determine the association between confirmed sarcopenia and orthostatic BP recovery in falls clinic attendees aged 50 years or over.

METHODS

One hundred and nine recruited patients (mean age 70 years, 58% women) underwent an active stand with non-invasive beat-to-beat haemodynamic monitoring. Hand grip strength and five-chair stands time were measured, and bioelectrical impedance analysis was performed. They were then classified as robust, probable sarcopenic or sarcopenic as per the European Working Group on Sarcopenia in Older People guidelines. Mixed effects models with linear splines were used to model the effect of sarcopenia status on orthostatic BP recovery, whilst controlling for potential confounders.

RESULTS

Probable sarcopenia was identified in 32% of the sample and sarcopenia in 15%. Both probable and confirmed sarcopenia were independently associated with an attenuated rate of recovery of both systolic and diastolic BP in the 10-20 s period after standing. Attenuation was larger for confirmed than probable sarcopenia (systolic BP β - 0.85 and - 0.59, respectively, P < 0.01; diastolic BP β - 0.65, - 0.45, P < 0.001).

CONCLUSION

Sarcopenia was independently associated with slower BP recovery during the early post-stand period. The potentially modifiable effect of the skeletal muscle pump in orthostatic haemodynamics requires further study.

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.

Comparison of Gait Speed Reserve, Usual Gait Speed, and Maximum Gait Speed of Adults Aged 50+ in Ireland Using Explainable Machine Learning

Gait speed is a measure of general fitness. Changing from usual (UGS) to maximum (MGS) gait speed requires coordinated action of many body systems. Gait speed reserve (GSR) is defined as MGS-UGS. From a shortlist of 88 features across five categories including sociodemographic, cognitive, and physiological, we aimed to find and compare the sets of predictors that best describe UGS, MGS, and GSR. For this, we leveraged data from 3,925 adults aged 50+ from Wave 3 of The Irish Longitudinal Study on Ageing (TILDA). Features were selected by a histogram gradient boosting regression-based stepwise feature selection pipeline. Each model's feature importance and input-output relationships were explored using TreeExplainer from the Shapely Additive Explanations explainable machine learning package. The meanR a d j 2 (SD) from fivefold cross-validation on training data and theR a d j 2   score on test data were 0.38 (0.04) and 0.41 for UGS, 0.45 (0.04) and 0.46 for MGS, and 0.19 (0.02) and 0.21 for GSR. Each model selected features across all categories. Features common to all models were age, grip strength, chair stands time, mean motor reaction time, and height. Exclusive to UGS and MGS were educational attainment, fear of falling, Montreal cognitive assessment errors, and orthostatic intolerance. Exclusive to MGS and GSR were body mass index (BMI), and number of medications. No features were selected exclusively for UGS and GSR. Features unique to UGS were resting-state pulse interval, Center for Epidemiologic Studies Depression Scale (CESD) depression, sit-to-stand difference in diastolic blood pressure, and left visual acuity. Unique to MGS were standard deviation in sustained attention to response task times, resting-state heart rate, smoking status, total heartbeat power during paced breathing, and visual acuity. Unique to GSR were accuracy proportion in a sound-induced flash illusion test, Mini-mental State Examination errors, and number of cardiovascular conditions. No interactions were present in the GSR model. The four features that overall gave the most impactful interactions in the UGS and MGS models were age, chair stands time, grip strength, and BMI. These findings may help provide new insights into the multisystem predictors of gait speed and gait speed reserve in older adults and support a network physiology approach to their study.