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Nomogram developed with selenoprotein S (SelS) genetic variation and clinical characteristics predicting risk of coronary artery disease in a Chinese population.

ABSTRACT: Background:Selenoprotein S (SelS) is a novel selenoprotein encoded by the SelS gene on chromosome 15q26.3. SelS is associated with the development of diabetes, dyslipidemia and macrovascular complications. However, the relationship between genetic polymorphisms of SelS and coronary artery disease (CAD) remains unclear. Methods:In the present study, we genotyped four single nucleotide polymorphisms (rs117613208, rs117512970, rs986500879, rs542989868) of SelS gene using direct sequencing method in a case-control study (576 CAD cases and 452 control subjects). Furthermore, we developed a predictive model using SelS genetic variation and clinical variables to predict risk of CAD. Results:We found that rs117613208 T allele was more frequent in the CAD cases than that in the controls. Logistic regression analysis suggested after adjustment of other confounders, the difference remained significant between the two groups [odds ratio (OR) =2.107, 95% confidence interval (CI): 1.239-3.583, P<0.006]. Using SelS rs117613208 T allele, age, smoking, diabetes, hypertension, apolipoprotein A1 (apoA1), and lipoprotein A [Lp(a)] (GASDLY score), we developed a diagnostic model of CAD (AUC: 0.806, 95% CI: 0.776-0.836, P<0.001, sensitivity: 74.7%, specificity:75.5%). Conclusions:The present study suggested that genetic polymorphism of SelS was independent associated with CAD and GASDLY score may be a novel diagnostic model for CAD in a Chinese population.

SUBMITTER: Wang DY

PROVIDER: S-EPMC7487398 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Nomogram developed with selenoprotein S (SelS) genetic variation and clinical characteristics predicting risk of coronary artery disease in a Chinese population.

Wang Ding-Yu DY Wu Ting-Ting TT Zheng Ying-Ying YY Ma Yi-Tong YT Xie Xiang X

Cardiovascular diagnosis and therapy 20200801 4

<h4>Background</h4>Selenoprotein S (SelS) is a novel selenoprotein encoded by the <i>SelS</i> gene on chromosome 15q26.3. SelS is associated with the development of diabetes, dyslipidemia and macrovascular complications. However, the relationship between genetic polymorphisms of SelS and coronary artery disease (CAD) remains unclear.<h4>Methods</h4>In the present study, we genotyped four single nucleotide polymorphisms (rs117613208, rs117512970, rs986500879, rs542989868) of <i>SelS</i> gene usin ...[more]

PMID: 32968632

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Project description:ObjectiveTo establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40 years) based on data mining technology.Materials and methodsThis was a 3-year retrospective cohort study, which belonged to the sub-cohort of the Chinese Reaction Study. The research period was from March 2011 to December 2014. A total of 1834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model.ResultsThe predictive factors included in the prediction model were age, neck circumference, waist-to-height ratio, BMI, triglyceride, impaired fasting glucose, dyslipidemia, osteopenia, smoking history, and strenuous exercise. The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95% CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1 and 100%, the nomogram had a good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870.ConclusionsThis novel nomogram can effectively predict the 3-year incidence risk of OP in the male population. It also helps clinicians to identify groups at high risk of OP early and formulate personalized intervention measures.

Dataset Information

Nomogram developed with selenoprotein S (SelS) genetic variation and clinical characteristics predicting risk of coronary artery disease in a Chinese population.

Publications

Nomogram developed with selenoprotein S (SelS) genetic variation and clinical characteristics predicting risk of coronary artery disease in a Chinese population.

Similar Datasets

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