Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.
Background:
In women after menopause, the incidence of diabetes mellitus increases. Increased insulin resistance (IR), decreased glucose effectiveness (GE), and the first and second phases of insulin secretion (FPIS and SPIS), are the four most important factors that trigger glucose intolerance and diabetes (diabetogenic factor [DF]). In the cross-sectional study, we enrolled non-diabetic women between the ages of 45 and 60 years to observe the changes in DFs during the perimenopausal period and to elucidate the underlying mechanisms of diabetes in menopausal women.
Methods:
We randomly enrolled 4,194 women who underwent health checkups. Using demographic and biochemical data, IR, FPIS, SPIS, and GE were calculated using previously published equations. The relationship between the DFs and age was evaluated using a simple correlation.
Results:
Body mass index, blood pressure, fasting plasma glucose, low-density lipoprotein-cholesterol, triglyceride, and SPIS were higher, and GE was lower in older women (≥ 52 years old). A significant decrease in GE and increased SPIS were observed with age. However, no changes were observed in IR or FPIS.
Conclusion:
The IR and FPIS did not change during perimenopause. Increased SPIS may compensate for the decrease in GE, which is probably one of the reasons for the higher incidence of diabetes in menopausal women.
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