Genomic ancestry and metabolic syndrome in individuals with type 1 diabetes from an admixed population: a multicentre, cross-sectional study in Brazil.

2020 
AIMS To evaluate the relationship between self-reported colour-race, genomic ancestry, and metabolic syndrome in an admixed Brazilian population with type 1 diabetes. METHODS We included 1640 participants with type 1 diabetes. The proportions of European, African and Amerindian genomic ancestries were determined by 46 ancestry informative markers of insertion deletion. Two different sets of analyses were performed to determine whether self-reported colour-race and genomic ancestry were predictors of metabolic syndrome. RESULTS Metabolic syndrome was identified in 29.8% of participants. In the first model, the factors associated with metabolic syndrome were: female gender (odds ratio 1.95, P < 0.001); diabetes duration (odds ratio 1.04, P < 0.001); family history of type 2 diabetes (odds ratio 1.36, P = 0.019); and acanthosis nigricans (odds ratio 5.93, P < 0.001). Colour-race was not a predictive factor for metabolic syndrome. In the second model, colour-race was replaced by European genomic ancestry. The associated factors were: female gender (odds ratio 1.95, P < 0.001); diabetes duration (odds ratio 1.04, P < 0.001); family history of type 2 diabetes (odds ratio 1.39, P = 0.011); and acanthosis nigricans (odds ratio 6.12, P < 0.001). Physical exercise (≥3 times a week) was a protective factor (odds ratio 0.77, P = 0.041), and European genomic ancestry was not associated with metabolic syndrome but showed an odds ratio of 1.77 (P = 0.05). CONCLUSIONS Although a higher level of European genomic ancestry was observed among participants with metabolic syndrome in the univariate analysis, this association did not persist after multivariable adjustments. Further prospective studies in other highly admixed populations remain necessary to better evaluate whether the European ancestral component modulates the development of metabolic syndrome in type 1 diabetes.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    30
    References
    1
    Citations
    NaN
    KQI
    []