The purpose of this study was to compare the performance of logistic regression, artificial neural networks (ANNs) and decision tree models for predicting diabetes or prediabetes using common risk factors. Participants came from two communities in Guangzhou, China; 735 patients confirmed to have diabetes or prediabetes and 752 normal controls were recruited. A standard questionnaire was administered to obtain information on demographic characteristics, family diabetes history, anthropometric measurements and lifestyle risk factors. Then we developed three predictive models using 12 input variables and one output variable from the questionnaire information; we evaluated the three models in terms of their accuracy, sensitivity and specificity. The logistic regression model achieved a classification accuracy of 76.13% with a sensitivity of 79.59% and a specificity of 72.74%. The ANN model reached a classification accuracy of 73.23% with a sensitivity of 82.18% and a specificity of 64.49%; and the decision tree (C5.0) achieved a classification accuracy of 77.87% with a sensitivity of 80.68% and specificity of 75.13%. The decision tree model (C5.0) had the best classification accuracy, followed by the logistic regression model, and the ANN gave the lowest accuracy.
Background Carotid artery stenosis, mainly caused by carotid atherosclerosis, is related to ischemic stroke. This study was to investigate whether monocyte/lymphocyte ratio (MLR) was associated with increased severity of carotid stenosis in patients with ischemic stroke. Methods A total of 395 participants with ischemic stroke were retrospectively analyzed. The severity of carotid stenosis was evaluated by ultrasound examination. Patients were divided into two groups: nonsevere stenosis group and severe stenosis group. Multivariate logistic analysis was used to evaluate risk factors. Results A significant correlation was found between MLR and the severity of carotid stenosis in patients with ischemic stroke. MLR was the independent risk factor of carotid stenosis (OR: 9.74, 95% CI: 1.16–81.54). In the ROC curves analysis, a cutoff value of 0.20 for MLR predicted the severity of carotid stenosis with a sensitivity of 80.40% and specificity of 26.40% (ROC area under the curve: 0.598, 95% CI: 0.53–0.67, p = .004). Conclusion Monocyte/lymphocyte ratio plays important roles in carotid stenosis in patients with ischemic stroke and is an independent risk factor of the severity of carotid stenosis. Therefore, MLR might be considered a potential index in the diagnosis of carotid stenosis in patients with ischemic stroke.
To study the pathogenesis of diabetes mellitus (DM) and identify new biomarkers, high‐throughput RNA sequencing provides a technical means to explore the regulatory network of MD gene expression. To better elucidate the genetic basis of DM, we analysed the circRNA and mRNA expression profiles in serum samples from diabetic patients. The circRNAs and mRNAs with abnormal expression in the DM group and non‐diabetic group (NDM) were classified by RNA sequencing and differential expression analysis. The circRNA‐miRNA‐mRNA regulatory network reveals the mechanism by which competitive endogenous RNAs (ceRNAs) regulate gene expression. The biological functions and interactions of circRNA and mRNA were analysed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential expression analysis showed that 441 circRNAs (366 up‐regulated, 75 down‐regulated) and 683 mRNAs (354 up‐regulated, 329 down‐regulated) were significantly differentially expressed in the DM group compared with the NDM group. Screening of the differential genes at the nodes of the interaction network showed that a single circRNA could interact with multiple miRNAs and then jointly regulate more mRNAs. In addition, the expressions of circRNA CNOT6 and AXIN1 as well as mRNA STAT3, MYD88, and B2M were associated with the progression of diabetes. Enrichment pathway analysis indicated that differentially expressed circRNA and mRNA may participate in Nod‐like receptor signalling pathway, insulin signalling pathway, sphinolipid metabolism pathway, and ribosome pathway, and play a role in the pathogenesis of diabetes. This study provides a theoretical basis for elucidating the molecular mechanism of DM occurrence and development at circRNA and mRNA levels.
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