Risk assessment of type 2 diabetes in northern China based on the logistic regression model

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS

OBJECTIVE

The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning methods have proven effective in enhancing prediction accuracy. However, existing approaches may lack interpretability regarding underlying mechanisms. Therefore, we aim to employ an interpretable machine learning approach utilizing nationwide cross-sectional data to predict pre-diabetic risk and quantify the impact of potential risks.

METHODS

The LASSO regression algorithm was used to conduct feature selection from 30 factors, ultimately identifying nine non-zero coefficient features associated with pre-diabetes, including age, TG, TC, BMI, Apolipoprotein B, TP, leukocyte count, HDL-C, and hypertension. Various machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANNs), Decision Trees (DT), and Logistic Regression (LR), were employed to compare predictive performance. Employing an interpretable machine learning approach, we aimed to enhance the accuracy of pre-diabetes risk prediction and quantify the impact and significance of potential risks on pre-diabetes.

RESULTS

From the China Health and Nutrition Survey (CHNS) data, a cohort of 8,277 individuals was selected, exhibiting a disease prevalence of 7.13%. The XGBoost model demonstrated superior performance with an AUC value of 0.939, surpassing RF, SVM, DT, ANNs, Naive Bayes, and LR models. Additionally, Shapley Additive Explanation (SHAP) analysis indicated that age, BMI, TC, ApoB, TG, hypertension, TP, HDL-C, and WBC may serve as risk factors for pre-diabetes.

CONCLUSION

The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM.

Predictive value of circulating miR-409-3p for major adverse cardiovascular events in patients with type 2 diabetes mellitus and coronary heart disease

OBJECTIVES

To investigate the serum levels of miR-409-3p in patients with type 2 diabetes mellitus (T2DM) complicated with coronary heart disease (CHD) and its effect on high glucose (HG)-induced myocardial cell injury.

METHODS

A total of 250 patients with T2DM admitted to our hospital from April 2020 through April 2022 were enrolled as the study subjects, and then grouped into T2DM+CHD (group #1) and T2DM (group #2). Real-time quantitative PCR (RT-qPCR) was used to measure the levels of serum miR-409-3p. The clinical performance of miR-409-3p was evaluated. The human cardiomyocyte AC16 cells were cultured in vitro and treated with HG. MTT assay and flow cytometry were performed to detect cell viability and apoptosis, respectively. Bioinformatic analyses were performed to explore the potential mechanism of miR-409-3p in T2DM complicated with CHD.

RESULTS

The expression level of miR-409-3p was increased in the T2DM+CHD group and had a relative high diagnostic value for distinguishing patients with T2DM+CHD from patients with T2DM alone. Correlation analysis showed that serum miR-409-3p was positively associated with the Gensini score and adverse cardiovascular events; miR-409-3p knockdown alleviated HG-induced AC16 cell damage and reduced cell apoptosis. CREB1, BCL2, and SMAD2 were the top 3 hub genes of miR-409-3p.

CONCLUSION

Serum miR-409-3p may serve as a potential diagnostic and prognostic biomarker for predicting T2DM complicated with CHD and forecast adverse events. Targeting miR-409-3p may be a novel therapeutic strategy to intervene in the development of T2DM+CHD.

Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world.

OBJECTIVE

To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making.

METHODS

A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People's Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram.

RESULTS

Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858-0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701-0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818-0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients.

CONCLUSION

SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.

Application of national early warning score in assessing postoperative illness severity in elderly patients with gastrointestinal illnesses

BACKGROUND

Population aging is a social problem that is being faced in most countries.

OBJECTIVE

To apply the National Early Warning Score (NEWS) for an early warning on the vital signs and consciousness of elderly patients who are hospitalized in the gastrointestinal surgical department and to provide a reference for early detection of changes in illness severity in elderly patients by studying the correlation between NEWS value and changes in illness severity.

METHODS

We enrolled 528 elderly patients who were hospitalized in the gastrointestinal surgical department of a tertiary grade A hospital in Guizhou Province between June 2020 and May 2021, to analyze how NEWS max value correlates with illness severity and obtain the optimal NEWS cutoff value for both potentially critically ill and critically ill elderly patients using the receiver operating characteristic (ROC) curve.

RESULTS

There were statistically significant differences in NEWS values between elderly patients with various illness severities (P< 0.05). NEWS values correlated positively with illness severity (r= 0.605, P< 0.001). Based on the ROC curve, early warning trigger values for NEWS to identify potentially critically ill, critically ill and terminally ill elderly patients were 6, 7 and 8, respectively. The area under the curve (AUC) for potentially critically ill, critically ill and terminally ill elderly patients was 0.907, 0.921 and 0.939, respectively. NEWS performed better in detecting patient illness severity than Modified Early Warning Score (MEWS) in AUC, sensitivity, specificity, and Youden's index, with statistically significant differences (P< 0.05).

CONCLUSION

An early warning on the vital signs and consciousness of hospitalized elderly patients using NEWS can facilitate advanced detection of changes in illness severity of elderly patients by medical staff and enable timely treatment, thus significantly lowering the risks of illness deterioration.

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing−Tianjin−Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing−Tianjin−Hebei region are high-risk areas for diabetes.