Banca de QUALIFICAÇÃO: RANNAPAULA LAWRYNHUK URBANO FERREIRA

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : RANNAPAULA LAWRYNHUK URBANO FERREIRA
DATE: 14/09/2026
TIME: 08:00
LOCAL: REMOTA
TITLE:

ASSOCIATION BETWEEN THE MIXTURE OF ESSENTIAL AND TOXIC ELEMENTS IN WHOLE BLOOD AND THE RISK OF TYPE 2 DIABETES AND PREDIABETES: A CASE-CONTROL STUDY

KEY WORDS:

Type 2 diabetes; prediabetes; co-exposure; machine learning; trace elements.

PAGES: 33
BIG AREA: Ciências da Saúde
AREA: Nutrição
SUMMARY:

Aims: An imbalance between essential and toxic elements can act as endocrine disruptors, by influencing metabolic pathways associated with insulin resistance. However, multicollinearity and co-exposure to multiple elements make it difficult to identify metallomic biomarkers. Objective: To investigate the association between individual and combined exposure to essential and toxic elements and the risk of developing prediabetes (PD) and type 2 diabetes (T2D). Methods: A case-control study involving three groups matched by age and sex: controls (CT, n=50), prediabetes (PD, n=50), and T2D (T2D, n=50). Whole blood concentrations of barium (Ba), cadmium (Cd), cobalt (Co), copper (Cu), zinc (Zn), selenium (Se), thallium (Tl), lead (Pb), manganese (Mn), nickel (Ni), uranium (U), mercury (Hg), and boron (B) were determined by inductively coupled plasma mass spectrometry (ICP-MS). Sociodemographic and clinical variables, as well as biochemical markers, were also collected. The discriminatory power of the elements was assessed using penalized logistic regression (Elastic Net) and multiclass Random Forest, with interpretation conducted using SHAP and partial dependence plots (PDP) for the elements with the greatest impact. The combined effect of the mixture was estimated using quantile-based g computation (QGComp) and bayesian kernel machine regression (BKMR). Results: The median age was 54 years, and 68% were women. A robust panel of five elements (Ba, Cd, Pb, Cu, and Ni) was defined by the agreement between Elastic Net and Random Forest (triangulation), with an overall accuracy of 67%. Model interpretation using SHAP and PDP confirmed Ba, Pb, and Cd as the main discriminators between the groups. The mixture analysis using QGComp revealed a positive association between combined exposure to these elements and the risk of PD (OR = 1.44; 95% CI: 1.01–2.04) and T2D (OR = 1.88; 95% CI: 1.23–2.86). The highest posterior inclusion probabilities (PIPs) in the BKMR model were observed for Ba, Cd, and Pb, indicating greater relevance of these elements for the risk of PD and T2D. The overall effect of the mixture was greater in the highest quantiles of combined exposure for T2D and PD. Conclusion: Supervised and machine learning models demonstrated that combined exposure to Ba, Cd, Pb, Cu, and Ni is associated with an increased risk of PD and T2D, with Ba, Cd, and Pb being the main contributors to this effect. 

COMMITTEE MEMBERS:
Presidente - ***.476.444-** - LUCIA DE FATIMA CAMPOS PEDROSA - UFRN
Externa ao Programa - 3218336 - BRUNA ZAVARIZE REIS - UFRNExterna à Instituição - PAULA NORMANDO DOS REIS COSTA - UFRJ