Dietary Patterns and Common Mental Disorders Trajectories: an Analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-BRASIL).

• Grant number: 24/05600-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2025
• End date: November 30, 2026
• Field of knowledge: Health Sciences - Nutrition - Nutritional Analysis of Population
• Principal Investigator: Dirce Maria Lobo Marchioni
• Grantee: Elaine Valdna Oliveira dos Santos
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Common Mental Disorders (CMD) encompass symptoms that characterize anxiety and depression in their various forms and represent a growing public health problem, which can be influenced by diet. Current evidence is inconsistent, based on cross-sectional studies, focused on nutrients, foods or isolated bioactive compounds, and, in a smaller number of studies, dietary patterns. Furthermore, there are no reports of studies that analyze dietary patterns and changes in BMD over time. In this context, this objective research project analyzes the relationship between a posteriori eating patterns and the formation of CMD trajectories in the ELSA-Brazil cohort study. The research will be carried out using data from ELSA-Brasil, a multicenter cohort study, limited to 15,105 public servants from five Brazilian public universities and one research institute, distributed in three regions of the country, with participants aged between 35 and 74 years, of both sexes. Data regarding CMD were obtained at 3 time points: baseline (2008-2010), wave 2 (2012-2014) and wave 3 (2017-2019), using the Clinical Interview Schedule - Revised (CIS-R); To analyze food consumption, baseline data will be used, collected through the Food Frequency Questionnaire (FFQ). Dietary patterns will be obtained by exploratory factor analysis. TMC trajectories will be derived from the group-based trajectory modeling (GBTM) medium. The association between established patterns and CMD trajectories will be assessed using multinomial logistic regression.