Harnessing multiparametric biobank data to develop novel predictive models of frailty
Lead Research Organisation:
University of Strathclyde
Department Name: Inst of Pharmacy and Biomedical Sci
Abstract
Frailty is a key contributor to overall global ageing and has huge public health significance. It is a syndrome defined by an increased vulnerability to stressors that is driven by a break down of the functional (social/clinical/biochemical) processes over our lifetime. This breakdown leads to an elevated risk of developing a range of adverse events, including a fall, hospitalization or rapid changes in healthcare needs that result in increased healthcare costs. Currently over £5.6Bn per annum is spent across the UK on frail individuals in the NHS in comparison to age matched non-frail patients. In the context of an ageing population and finite health and social care resources, the early identification of individuals at a high-risk of frailty is crucial to maintaining their wellbeing, dignity and fulfilment in later life. This is also vital with respect to patient care, as understanding an individual's ability to respond and recover from clinical treatments is critical to inform the selection of appropriate healthcare interventions and maximise post-operative recovery and healthspan.
Technical Summary
Frailty is a critical concept and a key contributor to overall global ageing and impacts a large global market with high public health significance. It is a multifactorial syndrome defined by an increased vulnerability to stressors that is driven by poor resolution of homeostasis.
Risk factors promoting frailty development span sociodemographic, lifestyle, clinical and biological factors; however, the mechanisms through which these factors converge to result in frailty remain poorly understood. Existing models of frailty generally revolve around a single discipline, focusing on social risk factors, clinical presentation or biomolecular phenotype and at present, none allow for the early-stage prediction of frailty. Thus, the development of approaches that integrate multiple determinants of frailty will underpin a comprehensive assessment and understanding of the frailty syndrome. Critically, integrative approaches provide insights into how the interaction between different discipline classes of determinants can be used to predict how an individual may (not) develop frailty. Developing such prognostic tools will improve early detection models and underpin a more holistic understanding of the biological pathways implicated in frailty that hold the key to development of more effective clinical and lifestyle interventions.
Risk factors promoting frailty development span sociodemographic, lifestyle, clinical and biological factors; however, the mechanisms through which these factors converge to result in frailty remain poorly understood. Existing models of frailty generally revolve around a single discipline, focusing on social risk factors, clinical presentation or biomolecular phenotype and at present, none allow for the early-stage prediction of frailty. Thus, the development of approaches that integrate multiple determinants of frailty will underpin a comprehensive assessment and understanding of the frailty syndrome. Critically, integrative approaches provide insights into how the interaction between different discipline classes of determinants can be used to predict how an individual may (not) develop frailty. Developing such prognostic tools will improve early detection models and underpin a more holistic understanding of the biological pathways implicated in frailty that hold the key to development of more effective clinical and lifestyle interventions.
