RESUMO
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.
Assuntos
Doenças Cardiovasculares , Diabetes Mellitus Tipo 2 , Adulto , Humanos , Glicemia/metabolismo , Doenças Cardiovasculares/complicações , Estudos de Casos e Controles , Estudos Transversais , Fibronectinas , Hemoglobinas Glicadas , Fatores de Risco de Doenças Cardíacas , Obesidade/complicações , Fatores de RiscoRESUMO
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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The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassiï¬cation were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.
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The utility of genetic risk scores (GRS) as independent risk predictors remains inconclusive. Here, we evaluate the additive value of a multi-locus GRS to the Framingham risk score (FRS) in coronary artery disease (CAD) risk prediction. A total of 2888 individuals (1566 coronary patients and 1322 controls) were divided into three subgroups according to FRS. Multiplicative GRS was determined for 32 genetic variants associated to CAD. Logistic Regression and Area Under the Curve (AUC) were determined first, using the TRF for each FRS subgroup, and secondly, adding GRS. Different models (TRF, TRF+GRS) were used to classify the subjects into risk categories for the FRS 10-year predicted risk. The improvement offered by GRS was expressed as Net Reclassification Index and Integrated Discrimination Improvement. Multivariate analysis showed that GRS was an independent predictor for CAD (OR = 1.87; p<0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p<0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories. In conclusion, GRS provided a better incremental value in intermediate subgroup. In this subgroup, inclusion of genotyping may be considered to better stratify cardiovascular risk.
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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.
Assuntos
Doença da Artéria Coronariana/genética , Predisposição Genética para Doença/genética , Estudos de Casos e Controles , Feminino , Testes Genéticos , Genótipo , Humanos , Masculino , Pessoa de Meia-Idade , Portugal , Prognóstico , Curva ROC , Medição de Risco , Fatores de RiscoRESUMO
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.