| Grant number: | 24/01918-6 |
| Support Opportunities: | Research Projects - Thematic Grants |
| Start date: | May 01, 2025 |
| End date: | April 30, 2030 |
| Field of knowledge: | Health Sciences - Physiotherapy and Occupational Therapy |
| Principal Investigator: | Tiago da Silva Alexandre |
| Grantee: | Tiago da Silva Alexandre |
| Host Institution: | Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil |
| Associated researchers: | Andrew Steptoe ; Cesar Messias de Oliveira ; Eduardo Ferriolli ; Gustavo Duque ; Luciana Correia Alves ; Mika Kivimaki |
Abstract
One of the great scientific challenges in aging research is trying to explain changes in the mechanisms of crosstalk muscle, bone, adipose tissue, and brain capable of harming skeletal muscle, increasing the risk of dementia, dependence, frailty and negative outcomes in people as they age. However, answers to many current questions depend on expensive and complex methodologies, technology availability, multidisciplinary teams, and international collaborations to be tested on a large scale and in real situations, not controlled in clinical trials. Therefore, thanks to the proponent's ongoing partnership with the Department of Epidemiology and Public Health at University College London, it will be possible to use, in this proposal, data from the UK Biobank, which has MRI measurements of the brain and the whole body of more than 40,000 people "linked" to information from the UK National Health Service (NHS) through the Hospital Episodes Statistics (HES) database as well as data from the English Longitudinal Study of Ageing (ELSA) and the Brazilian Longitudinal Study of Aging (ELSI-Brazil). Thus, using these data sets, the objectives of the present proposal are:1) to analyse, using machine learning and deep learning, how much the reduction in the volume of cortical and subcortical anatomical structures and brain activation are associated with the reduction of muscle mass and with increased intramuscular fat infiltration, all measured by magnetic resonance imaging (MRI), as well as reduced muscle strength;2) to analyse how much the increase in visceral fat and subcutaneous fat influence the increase in intramuscular fat infiltration and the reduction in thigh muscle mass, measured by MRI;3) to verify the impact of this combination of reduced muscle mass with a high intramuscular fat infiltration on the decline in muscle strength and mobility, on the risk of falls and fractures, on carbohydrate and lipid metabolism disorders, on the incidence of cardiovascular diseases, dementia and the risk of frailty and cardiovascular mortality;4) to analyse how much the combination of reduced muscle mass with high intramuscular fat infiltration influences bone mineral density and trabecular bone score, measured by MRI, as well as the shape with which these conditions, associated or not, increase the risk of falls, fractures and hospitalizations, and 5) to analyse whether the principal results obtained from UK Biobank e o HES data set (much more complexes measurements) could feeding machine and deep learning models with data from ELSA and ELSI-Brazil to find a predictive model for frailty and healthy aging, considering socioeconomic differences between England and Brazil. Therefore, as hypotheses, it is believed that: 1) there is a pattern of alterations in anatomical structures, cortical and subcortical volumes, added to patterns of alterations in brain activations capable of to reduce muscle mass, increase intramuscular fat infiltration and reduce muscle strength; 2) the increase in visceral and subcutaneous fat influence the reduction in muscle mass and the increase in intramuscular fat infiltration of the thigh; 3) the combination of low muscle mass and high infiltration of intramuscular fat in the thigh is associated with a decline in muscle strength, mobility, risk of falls, disturbances in the metabolism of carbohydrates and lipids, the incidence of cardiovascular diseases, dementia, frailty and cardiovascular mortality; 4) the combination of reduced muscle mass with high intramuscular fat infiltration associated with reduced bone mineral density and trabecular bone score increase the risk of falls, fractures and hospitalizations, and 5) the complexes measurements and results obtained from Biobank and HES can feeding machine and deep learning systems to use simple measurements obtained from longitudinal studies creating predictive models for frailty and healthy aging that can be applied in clinical practice. (AU)
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