Epidemiological studies frequently classify groups based on phenotypes like self-reported skin color/race, which inaccurately represent genetic ancestry and may lead to misclassification, particularly among individuals of multiracial backgrounds. This study aimed to characterize both global and local genome-wide genetic ancestries and to assess their relationship with self-reported skin color/race in an admixed population of Sao Paulo city. We analyzed 226,346 single-nucleotide polymorphisms from 841 individuals participating in the population-based ISA-Nutrition study. Our findings confirmed the admixed nature of the population, demonstrating substantial European, significant Sub-Saharan African, and minor Native American ancestries, irrespective of skin color. A correlation was observed between global genetic ancestry and self-reported color-race, which was more evident in the extreme proportions of African and European ancestries. Individuals with higher African ancestry tended to identify as Black, those with higher European ancestry tended to identify as White, and individuals with higher Native American ancestry were more likely to self-identify as Mixed, a group with diverse ancestral compositions. However, at the individual level, this correlation was notably weak, and no deviations were observed for specific regions throughout the individual’s genome. Our findings emphasize the significance of accurately defining and thoroughly analyzing race and ancestry, especially within admixed populations.

Background/Objectives: There is a lack of empirical studies of out-of-pocket health expenditures associated with dyslipidemias, which are major cardiovascular risk factors, especially in underrepresented admixed populations. The study investigates associations of health costs with lipid traits, GWAS-derived genetic risk scores (GRSs), and other cardiometabolic risk factors. Methods: Data from the observational cross-sectional 2015 ISA-Nutrition comprised lifestyle, environmental factors, socioeconomic and demographic variables, and biochemical and genetic markers related to the occurrence of cardiometabolic diseases. GWAS-derived genetic risk scores were estimated from SNPs previously associated with lipid traits. There was phenotypic and genetic information available for 490 independent individuals, which was used as inputs for random forests and logistic regression to explain private quantitative and categorical health costs. Results: There were significant correlations between GRSs and their respective lipid phenotypes. The main relevant variables across techniques and outcome variables comprised income per capita, principal components of ancestry, diet quality, global physical activity, inflammatory and lipid markers, and LDL-c GRS and non-HDL-c GRS. The area under the ROC curve (AUC) of quartile-based categorical health expenditure without GRSs was 0.76. GRSs were not significant for this categorical outcome. Conclusions: We present an original contribution to the investigation of determinants of private health expenditures in a highly admixed population, providing insights on associations between genetic and socioeconomic dimensions of health in Brazil. Ancestry information was also among the main factors contributing to health expenses, providing a novel view of the role of genetic ancestry on cardiometabolic risk factors and its potential impact on health costs.