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Background: Premature coronary artery disease (PCAD) has a poor prognosis and a high mortality and disability rate. Accurate prediction of the risk of PCAD is very important for the prevention and early diagnosis of this disease. Machine learning (ML) has been proven a reliable method used for disease diagnosis and for building risk prediction models based on complex factors. The aim of the present study was to develop an accurate prediction model of PCAD risk that allows early intervention. Methods: We performed retrospective analysis of single nucleotide polymorphisms (SNPs) and traditional cardiovascular risk factors (TCRFs) for 131 PCAD patients and 187 controls. The data was used to construct classifiers for the prediction of PCAD risk with the machine learning (ML) algorithms LogisticRegression (LRC), RandomForestClassifier (RFC) and GradientBoostingClassifier (GBC) in scikit-learn. Three quarters of the participants were randomly grouped into a training dataset and the rest into a test dataset. The performance of classifiers was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and concordance index. R packages were used to construct nomograms. Results: Three optimized feature combinations (FCs) were identified: RS-DT-FC1 (rs2259816, rs1378577, rs10757274, rs4961, smoking, hyperlipidemia, glucose, triglycerides), RS-DT-FC2 (rs1378577, rs10757274, smoking, diabetes, hyperlipidemia, glucose, triglycerides) and RS-DT-FC3 (rs1169313, rs5082, rs9340799, rs10757274, rs1152002, smoking, hyperlipidemia, high-density lipoprotein cholesterol). These were able to build the classifiers with an AUC >0.90 and sensitivity >0.90. The nomograms built with RS-DT-FC1, RS-DT-FC2 and RS-DT-FC3 had a concordance index of 0.94, 0.94 and 0.90, respectively, when validated with the test dataset, and 0.79, 0.82 and 0.79 when validated with the training dataset. Manual prediction of the test data with the three nomograms resulted in an AUC of 0.89, 0.92 and 0.83, respectively, and a sensitivity of 0.92, 0.96 and 0.86, respectively. Conclusions: The selection of suitable features determines the performance of ML models. RS-DT-FC2 may be a suitable FC for building a high-performance prediction model of PCAD with good sensitivity and accuracy. The nomograms allow practical scoring and interpretation of each predictor and may be useful for clinicians in determining the risk of PCAD.

Objective: The present study aimed to investigate the association between the rs10757274 SNP (present on locus 9p21 in the gene for CDKN2BAS1) and coronary artery disease (CAD) in a local population of Pakistan. Methods: It was a case-control study. An allele-specific PCR-based strategy was used for the identification of genotypes. A total of 350 samples were used for the investigation, out of which 220 samples were CAD patients and 130 samples were normal healthy individuals. Effects of parameters, like family history of CAD, smoking, presence of diabetes, and hypertension, in changing the chances of CAD were studied. Odds ratio was estimated with 95% confidence interval. Results: A strong association was observed between CAD and factors, like smoking (OR: 1.666; 95% CI: 1.042-2.664), presence of hypertension (OR: 26.55; 95% CI: 15.95-44.20), diabetes (OR: 3.009; 95% CI: 1.841-4.920), and family history of CAD (OR: 4.9; 95% CI: 2.965-8.099). Results for the association between the genotype on the basis of rs10757274 showed a strong association between the GG genotype and the occurrence of CAD (OR: 9.603; 95% CI: 5.746-16.05). Conclusion: The present results suggest the importance of the 9p21 locus in modulating the chances of CAD. [ABSTRACT FROM AUTHOR]

Background: Premature coronary artery disease (PCAD) has a poor prognosis and a high mortality and disability rate. Accurate prediction of the risk of PCAD is very important for the prevention and early diagnosis of this disease. Machine learning (ML) has been proven a reliable method used for disease diagnosis and for building risk prediction models based on complex factors. The aim of the present study was to develop an accurate prediction model of PCAD risk that allows early intervention., Methods: We performed retrospective analysis of single nucleotide polymorphisms (SNPs) and traditional cardiovascular risk factors (TCRFs) for 131 PCAD patients and 187 controls. The data was used to construct classifiers for the prediction of PCAD risk with the machine learning (ML) algorithms LogisticRegression (LRC), RandomForestClassifier (RFC) and GradientBoostingClassifier (GBC) in scikit-learn. Three quarters of the participants were randomly grouped into a training dataset and the rest into a test dataset. The performance of classifiers was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and concordance index. R packages were used to construct nomograms., Results: Three optimized feature combinations (FCs) were identified: RS-DT-FC1 (rs2259816, rs1378577, rs10757274, rs4961, smoking, hyperlipidemia, glucose, triglycerides), RS-DT-FC2 (rs1378577, rs10757274, smoking, diabetes, hyperlipidemia, glucose, triglycerides) and RS-DT-FC3 (rs1169313, rs5082, rs9340799, rs10757274, rs1152002, smoking, hyperlipidemia, high-density lipoprotein cholesterol). These were able to build the classifiers with an AUC >0.90 and sensitivity >0.90. The nomograms built with RS-DT-FC1, RS-DT-FC2 and RS-DT-FC3 had a concordance index of 0.94, 0.94 and 0.90, respectively, when validated with the test dataset, and 0.79, 0.82 and 0.79 when validated with the training dataset. Manual prediction of the test data with the three nomograms resulted in an AUC of 0.89, 0.92 and 0.83, respectively, and a sensitivity of 0.92, 0.96 and 0.86, respectively., Conclusions: The selection of suitable features determines the performance of ML models. RS-DT-FC2 may be a suitable FC for building a high-performance prediction model of PCAD with good sensitivity and accuracy. The nomograms allow practical scoring and interpretation of each predictor and may be useful for clinicians in determining the risk of PCAD., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s). Published by IMR Press.)

Ischemic stroke (IS) is the leading cause of disability. Researchers have demonstrated that IS is more a multifactorial disorder than a single-factor disease. At present, no consistent conclusions have been reached on susceptibility loci for IS on chromosome 9p21. We conducted this meta-analysis to verify whether genetic loci on chromosome 9p21 reported domestically and abroad could be responsible for IS in Chinese populations. We analyzed data from eight studies that covered a total of 9756 individuals with Chinese ancestry comprising 4254 cases and 5502 controls. Each of the four reported susceptibility loci (rs2383206, rs2383207, rs10757274, and rs10757278) was analyzed separately. The odds ratios (ORs) of rs2383206 and rs10757274 were 1.09 (95% confidence interval (CI): 1.02-1.06, P = 0.01) and 1.09 (95% CI: 1.01-1.17, P = 0.03), respectively. For rs2383207, OR value was 0.91 (95% CI: 0.84-0.98, P = 0.01). No statistical association was identified for rs10757278. We have verified previous associations for IS in Chinese populations on chromosome 9p21. Loci rs2383206 and rs10757274 may increase susceptibility to IS. Mutation at locus rs2383207 may be beneficial. However, we are unable to identify any association between rs10757278 and IS.