Análise dos fatores de risco para o Diabetes Mellitus e a síndrome metabólica utilizando modelagem de equações estruturais

Abstract

Metabolic syndrome is characterized by complex metabolic changes and has been widely studied around the world due to its negative health outcomes for individuals and its strong association with type 2 diabetes mellitus and cardiovascular diseases. Although the gold standard for population monitoring of diabetes is through laboratory tests, self-reported measures are equally relevant as they contribute and facilitate surveillance actions. In this sense, the general objective of the study was to evaluate the risk factors for diabetes mellitus and metabolic syndrome based on the analysis of individual e-SUS registration forms in the city of Montes Claros - MG. This is a quantitative study, with diabetic users enrolled in the primary care network in the municipality of Montes Claros between July 2018 and July 2023. This thesis proposes two products. The study referring to product 1 (one) was conducted by descriptive analysis of the documentary base. The completion of the individual e-SUS registration form and the completion of factors that correlate with the emergence of diabetes mellitus among 10,073 users were analyzed. It was identified that data such as family history, anthropometric measurements, dietary components and frequency of physical exercise, factors considered as predisposing to the disease, are not filled in on the registration forms. There is an underreporting of risk factors for diabetes mellitus, as there is no adequate description of them. Product 2 (two) was conducted through a cross-sectional, analytical study with a quantitative approach, with the objective of evaluating the interrelationships of self-reported factors associated with diabetes mellitus (DM) and the risk for MetS through structural equation modeling . The population of this study consisted of 22,841 diabetic individuals using the Family Health Strategy (ESF). It was observed that the hypertension variable had a positive influence on MS (β =0.472; p < 0.05) and weight had a direct negative influence on MS (β = -0.079; p < 0.05). The indirect influence of age on metabolic syndrome was assessed through the influence of lifestyle. The variables used for the latent variable “Lifestyle” did not significantly influence the model. In this sense, the tested model identified interrelationships that influenced SM. The designed structural equation model proved to be adequate for its objective, identifying the interrelationships that influence the occurrence of MS. Therefore, it can be validated in populations of PHC users with unique characteristics, and can be conducted in an innovative way as a support strategy for the study of MS, in order to assist in proposing strategies to promote and protect users' health, allocation of resources, planning and construction of public health policies. The results of the studies can guide the construction of action strategies in the area of primary care and primary health care, being important for the different decision-making levels.