Componente genético da doença coronária e interação com os fatores de risco tradicionais / Andreia Micaela de Sousa Pereira ; orient. Roberto Palma dos Reis
Main Author Pereira, Andreia Micaela de Sousa Secondary Author Reis, Roberto José Palma dos Language Português. Country Portugal. Publication Lisboa : NOVA Medical School, Faculdade de Ciências Médicas, 2017 Description [10], 244, [86] p. : il. ; 30 cm Dissertation Note or Thesis: Tese de Doutoramento, Medicina, Investigação Clínica, Universidade Nova de LisboaTopical name Coronary Artery Disease
Genes
Genetic Risk Score
Framingham Score
Risk prediction
Risk factors
Academic Dissertation
Portugal CDU 616 Classification Medicina
Investigação Clínic Online Resources Click here to access the eletronic resource http://hdl.handle.net/10362/43066 List(s) this item appears in: Teses NL
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Monografia | Biblioteca NMS|FCM | PER12 TeseD-2017 | Presencial/Restrito | 20180137NL |
Tese de Doutoramento, Medicina, Investigação Clínica, Universidade Nova de Lisboa
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 in 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;p0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories with In conclusion, GRS provided a better incremental value in intermediate and high-risk subgroups. In these subgroups, inclusion of genotyping may be considered to better stratify cardiovascular risk.
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