Hematological indices and their correlation with glucose control parameters in a prediabetic rat model

Counteractive role of Terminalia catappa leaf extract on hematological and coagulation disturbance in Type 2 diabetic rats

Background and Aim

Diabetes mellitus is a leading cause of mortality worldwide associated with hyperglycemia-induced hematological aberrations and thromboembolic complications. This study aimed to explore the modulatory effect of Terminalia catappa leaf aqueous crude extract (TCLE) on hematological and coagulation disturbances in a Type 2 diabetic rat model.

Materials and Methods

High-fat diet streptozotocin-induced diabetic rats were treated orally with 400 and 800 mg/kg body weight TCLE daily for 28 days. Full blood count, coagulation parameters, plasma calcium (Ca), and erythrocyte glycogen (GLYC) levels were assessed using standard procedures.

Results

Terminalia catappa leaf aqueous crude extract treatment had a significant (p < 0.05) prolonging effect on clotting and bleeding times while increasing Ca, GLYC and mean corpuscular volume in diabetic rats. On the other hand, lymphocytes (LYM), platelet (PLT) count, mean PLT volume, neutrophil-LYM ratio (NLR), and PLT-LYM ratio (PLR) of TCLE-treated diabetic animals were significantly reduced (p < 0.05) compared with untreated diabetic animals. Lymphocyte, PLT count, NLR, and PLR correlated positively (p < 0.05) with plasma glucose, while a significant positive association was observed between Ca and GLYC. On the other hand, a strong negative association (p < 0.05) was observed between clotting time and fasting plasma glucose.

Conclusion

These findings suggest that T. catappa leaf extract may be useful in reversing diabetic-mediated hematological anomalies due to its anticoagulant and anti-anemic activities.

Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning

BACKGROUND

Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care.

PATIENTS AND METHODS

Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods-namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected.

RESULTS

After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model.

CONCLUSION

The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.