NUTOX: A Multi-Level Approach to Nutritional and Toxicological Risk Assessment in Cabo Verde

• Applicant: Dr. Sylva Mareike Schäfer
• Subject Area: Nutritional Sciences ; Toxicology, Laboratory Medicine
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 579011361

The NUTOX project examines the interface between nutritional epidemiology and toxicological risk assessment in Cabo Verde, a country shaped by nutritional transition and a high dependence on food imports. Traditional plant-based diets are increasingly replaced by highly processed, energy-dense products. At the same time, obesity, diabetes, and cardiovascular diseases are rising and already account for 70% of national mortality. Furthermore, the import-dominated food supply entails risks from pesticide residues, heavy metals, mycotoxins, and other contaminants. However, systematic data on dietary behaviour and toxicological exposure are lacking, which hampers the development of effective prevention strategies. This project aims to develop the first integrated framework for nutritional and toxicological risk assessment in Cabo Verde The project employs two main data sources: (1) the ENCAVE survey (2018–2019), which combined dietary questionnaires and biomarkers (n = 433) and provides the first detailed nutrition and health data for Cabo Verde’s most populous island, Santiago Island; and (2) the ALSEMAC database, which is currently being created and which will contain extensive measurements of contaminants in plant- and animal-based foods. The project is structured into four work packages (WPs). WP1 analyses dietary patterns, nutrient intake, and their associations with obesity and diabetes using ENCAVE data (n = 433) and blood contaminant levels. WP2 profiles contaminant burdens in locally consumed foods from the ALSEMAC project, produces a harmonised dataset, and ranks foods by toxicological contamination. WP3 combines dietary and contaminant data to model individual exposure, validates these estimates with biomonitoring data from blood samples, and identifies high-risk foods and vulnerable population groups. WP4 develops a machine learning–based Food Safety Risk Score, which employs explainable AI (SHAP, LIME) to translate complex exposure patterns into concrete dietary recommendations. By integrating nutritional and toxicological research, the project provides a scientific foundation for public health policy in Cabo Verde and offers a transferable approach for other countries undergoing similar nutrition transitions. The results will be disseminated through an interactive dashboard, open-access datasets, peer-reviewed publications, and policy briefs for health authorities and nutrition counselling.

• DFG Programme: WBP Fellowship
• International Connection: Spain
• Host: Professor Dr. Octavio Perez Luzardo