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SUMMARY OBJECTIVE: The aim of this study was to investigate the role of irisin in type 2 diabetes mellitus and its association with metabolic alterations and obesity. METHODS: A cross-sectional case-control study was conducted on participants treated at Centro Universitário FMABC between August 2018 and July 2019, by comparing a control group (n=14) with type 2 diabetes mellitus patients (n=16). The control group consisted of participants aged above 21 years with no chronic diseases, diabetes, smoking, or illicit drug use. The type 2 diabetes mellitus group included patients aged above 21 years, who were diagnosed with type 2 diabetes for at least 5 years (glycated hemoglobin>7%). Exclusion criteria were not willing to continue, recent hospitalization, and failure to meet inclusion criteria. Biochemical parameters included blood glucose, glycated hemoglobin, plasma irisin levels, and irisin gene expression in peripheral blood. RESULTS: Type 2 diabetes mellitus patients exhibited significantly higher plasma glucose levels [143 (40) vs. 92 (13) mg/dL, *p<0.05] and glycated hemoglobin levels [7.1% (1.6) vs. 5.6% (0.5), *p<0.05] compared to the control group. Irisin gene expression in type 2 diabetes mellitus patients was lower 0.02288 (0.08050) than the control group 8.506e-006 (1.412e-005) (p=0.06). Correlation analysis revealed a positive association between irisin expression and body mass index in type 2 diabetes mellitus (Rho=0.5221, 95%CI -0.058 to 0.838, p=0.06), while plasma irisin showed a negative correlation with body mass index (Rho=-0.656, 95%CI -0.836 to 0.215, p=0.03). No significant correlations were found between plasma glucose or glycated hemoglobin levels and irisin expression. CONCLUSION: The data suggests that body mass index directly influences plasma irisin levels and the regulation of irisin gene expression, possibly linking irisin to adiposity changes observed in obesity-related type 2 diabetes mellitus.
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Abstract Background: Genetic risk score can quantify individual's predisposition to coronary artery disease; however, its usefulness as an independent risk predictor remains inconclusive. Objective: To evaluate the incremental predictive value of a genetic risk score to traditional risk factors associated with coronary disease. Methods: Thirty-three genetic variants previously associated with coronary disease were analyzed in a case-control population with 2,888 individuals. A multiplicative genetic risk score was calculated and then divided into quartiles, with the 1st quartile as the reference class. Coronary risk was determined by logistic regression analysis. Then, a second logistic regression was performed with traditional risk factors and the last quartile of the genetic risk score. Based on this model, two ROC curves were constructed with and without the genetic score and compared by the Delong test. Statistical significance was considered when p values were less than 0.05. Results: The last quartile of the multiplicative genetic risk score revealed a significant increase in coronary artery disease risk (OR = 2.588; 95% CI: 2.090-3.204; p < 0.0001). The ROC curve based on traditional risk factors estimated an AUC of 0.72, which increased to 0.74 when the genetic risk score was added, revealing a better fit of the model (p < 0.0001). Conclusions: In conclusion, a multilocus genetic risk score was associated with an increased risk for coronary disease in our population. The usual model of traditional risk factors can be improved by incorporating genetic data.
Resumo Fundamento: O escore de risco genético pode quantificar a predisposição do indivíduo em desenvolver doença arterial coronariana; no entanto, sua utilidade como preditor de risco independente permanece inconclusiva. Objetivo: Avaliar o incremento no valor preditivo de um escore de risco genético aos fatores de risco tradicionais associados à doença arterial coronariana. Métodos: Trinta e três variantes genéticas previamente associadas à doença arterial coronariana foram analisadas em uma população caso-controle com 2888 indivíduos. Um escore de risco genético multiplicativo foi calculado e dividido em quartis, com o 1º quartil como a classe de referência. O risco coronário foi determinado por análise de regressão logística. Uma segunda regressão logística foi realizada com fatores de risco tradicionais e o último quartil do escore de risco genético. Com base nesse modelo, duas curvas ROC foram construídas com e sem o escore de risco e comparadas pelo teste de DeLong. A significância estatística foi considerada quando os valores de p eram inferiores a 0,05. Resultados: O último quartil do score de risco genético multiplicativo revelou um aumento significativo no risco de doença arterial coronariana (OR = 2,588; IC 95%: 2,090-3,204; p < 0,0001). A curva ROC baseada nos fatores de risco tradicionais estimou uma AUC de 0,72, que aumentou para 0,74 quando o score de risco genético foi adicionado, revelando um ajuste melhor do modelo (p < 0,0001). Conclusões: Em conclusão, um escore de risco genético com múltiplos loci foi associado a um risco aumentado de doença coronariana na nossa população. O modelo usual de fatores de risco tradicionais pode ser melhorado pela incorporação de dados genéticos.