Objective: Frailty, marked by diminished physiological reserves, heightens the risk of institutionalization, hospitalization, and morbidity. A functional microcirculatory system is crucial for maintaining healthy tissue parenchyma. This study aimed to identify predictive factors for frailty, considering altered phenotypes (e.g., gait and strength), sublingual microcirculation, and blood biomarkers in renal transplant candidates (KTAC) and non-renal failure (NRF) individuals.
Methods: This study included two groups, one with kidney transplant recipients (n=44) from the Queensland Kidney Transplant Service and the other without renal failure from the UQ St Lucia campus (n=44). Data collection included gait and strength using LEGSYS+ and a handgrip dynamometer (with a 300KG load cell), assessed microcirculation with the AVA MicroScan system, and conducted blood plasma analysis using ELISA. A short questionnaire was used to assess the frailty index (FI). Machine learning, specifically random forest plots, was then used to select the most important measurements for a predictive model of the FI score.
Results: A significant link was found between microcirculation dysregulation and reduced physiological reserves in frailty. In the KTAC group, predictive factors included blood biomarkers and microcirculatory indicators, while the NRF group emphasized age, gait, and strength. Combining these measures and introducing a dummy variable for cohorts, the model identified blood biomarkers (thrombomodulin) and microcirculatory measurement (perfused vessel density) as the primary significant factors.
Conclusions: In summary, predictors of the frailty index differed between the KTAC and the NRF groups. However, when combining both populations, blood biomarkers and microcirculatory parameters consistently emerged as the top predictors of frailty index scores. This insight deepens our understanding of the physiological reserve changes in frailty.
Keywords: -