| Grant number: | 23/01626-2 |
| Support Opportunities: | Regular Research Grants |
| Start date: | July 01, 2023 |
| End date: | June 30, 2025 |
| Field of knowledge: | Health Sciences - Physiotherapy and Occupational Therapy |
| Agreement: | CNPq |
| Principal Investigator: | Aparecida Maria Catai |
| Grantee: | Aparecida Maria Catai |
| Host Institution: | Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil |
| City of the host institution: | São Carlos |
| Associated research grant: | 16/22215-7 - Impact of inspiratory muscle training and aging on metabolic mapping, autonomic modulation, and cardiovascular, respiratory and metabolic responses, and prediction of cardiorespiratory health through wearables, AP.TEM |
| Associated scholarship(s): | 23/09582-4 - Study of the integrated profile of cardiovascular autonomic modulation and cardiorespiratory fitness on metabolome and blood count in individuals without diagnosed diseases., BP.JD |
Abstract
Cardiovascular autonomic modulation (CAM) and cardiorespiratory fitness (CRF) are important health markers. The definition of potential metabolites which are candidates for biomarkers from the serum metabolome and that are related to a score based on the CAM and CRF profile and that may have an association with a simple blood test, would be interesting for a better understanding of the general health status of individuals in a practical way. The aim of this study is to investigate the existence of potential biomarker candidates in the serum metabolome that relate to different "health scores" based on the CAM and CRF profile and that are associated with the cellular profile of the blood. For this, a weighted score will be created relative to the CAM profile (at rest considering the variability of heart rate and blood pressure, and baroreflex sensitivity) and CRF (obtained through a cardiopulmonary exercise test) for 167 individuals, from 20 to 70 years, through principal component analysis (PCA), using the first three principal components. These individuals' weighted scores will be grouped into different groups (based on PCA octants) and compared with each individual's serum metabolome to identify potential candidate metabolites for biomarkers using PLS-DA (Partial least squares-discriminant analysis). These candidates will then be correlated with the data observed in the blood count using multiple regression. The present study has, if successful, the potential to favor conditions for people to have access to their own general health status from a simple blood test. (AU)
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