Scholarship 23/13133-0 - Inteligência artificial, Endocrinologia - BV FAPESP
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DEVELOPMENT OF A PREDICTIVE MODEL FOR CLINICAL OUTCOMES USING IMMUNOLOGICAL, METABOLIC, AND THYROID FUNCTION BIOMARKERS: APPLICATION OF ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE.

Grant number: 23/13133-0
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Start date until: January 01, 2024
End date until: December 31, 2024
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal Investigator:Lucas Leite Cunha
Grantee:Henrique Luca Lucchesi
Host Institution: Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil

Abstract

Aging is defined as a continuous process of the body adapting to internal and external stressors, accompanied by remodeling of biological systems such as the immune, endocrine, and metabolic systems. Some authors suggest that senescence is associated with increased pro-inflammatory activity, reflected by higher circulating levels of immune biomarkers such as TNF-alpha, IL-6, and acute-phase proteins. Thus, our hypothesis is that immune, metabolic, and thyroid function biomarkers may be related to common clinical outcomes during aging. The aim of this project is to use artificial intelligence technology to identify predictors of clinical outcomes among immune, metabolic, and thyroid function biomarkers in adult individuals participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brazil). This project is part of a prospective cohort study developed using baseline and clinical follow-up data from the ELSA-Brazil study. It involves proposing an analytical data cut of this cohort, integrating demographic, clinical, and molecular information already produced by ELSA-Brazil. ELSA currently has 15,105 participants, aged 35 to 74 years at enrollment. Of these, 998 patients had immune biomarkers measured at the time of enrollment. All participants undergo annual telephone interviews to assess clinical outcomes such as cardiovascular events, new chronic diseases, and death. We will consider demographic, clinical, immune biomarkers, metabolic markers, and thyroid function as independent variables. The primary outcome will be death, and secondary outcomes will include cardiovascular events and new chronic diseases. Event-free survival will be calculated using Kaplan-Meier survival curves with log-rank analysis. A multivariate logistic regression model will be applied using clinical, immune, metabolic, and thyroid function variables as dependent variables and clinical events as outcome variables. All tests will be performed at a significance level of p <0.05. Furthermore, with the assistance of artificial intelligence technologies, we will integrate immune, metabolic, and thyroid function variables that may potentially predict clinical events to establish a predictive mathematical model for clinical outcomes. It is possible that our project may develop a new risk prediction tool that can assist physicians in tailoring clinical monitoring, as well as uncovering potential new biomarkers and therapeutic targets in clinical practice.

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