Genetic determinants of zinc status: a cross-population validation of a polygenic risk score and its association with cardiometabolic biomarkers.

• Grant number: 25/26170-7
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: March 01, 2026
• End date: August 31, 2026
• Field of knowledge: Health Sciences - Nutrition - Nutrition Biochemistry
• Principal Investigator: Marcelo Macedo Rogero
• Grantee: Graziela Biude Silva Duarte
• Supervisor: Dolores Corella Piquer
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Universitat de València, Spain
• Associated to the scholarship: 23/03023-3 - Association between polygenic risk score and zinc status of adolescents, adults, and elderly individuals with insulin resistance: a gene-nutrient analysis in a population-based study, BP.PD

Abstract

Genetic variations, particularly single-nucleotide polymorphisms (SNPs), significantly influence individual nutrient metabolism and susceptibility to chronic diseases. Polygenic risk scores (PRS) offer a powerful tool to evaluate this genetic predisposition by aggregating the cumulative effects of multiple SNPs. However, the portability and predictive accuracy of PRS often vary across genetically diverse populations, which is a critical challenge for their clinical implementation. Zinc (Zn) is an essential trace element for several biological functions. While individual SNPs in Zn metabolism genes have been linked to metabolic traits, a specific PRS for Zn status has not been widely developed or validated across ancestries. The aim of this research project is to validate a novel PRS for Zn status (PRS-Zn) developed in a Brazilian population (2015 ISA-Nutrition Study) in a European population cohort (OBENUTIC) and to evaluate its association with biomarkers of cardiometabolic risk. A cross-ethnic, dual-cohort design will be used to validate the PRS-Zn (15 selected SNPs involved in Zn metabolism). Sociodemographic, lifestyle, anthropometric measures, biochemical markers, dietary intake, and genetic data will be analyzed. Statistical analyses will include multiple linear regression and binary logistic regression used to assess associations. Receiver Operating Characteristic (ROC) curve analysis will further assess the predictive capacity of the PRS-Zn for these outcomes. All models will be rigorously adjusted for key confounding variables. The findings hold significant potential for advancing precision nutrition by informing personalized dietary strategies, ultimately contributing to the prevention and management of metabolic diseases.