Hematological profile associated with type 2 diabetes mellitus

Prediction Pressure Ulcers in High Care Unit Patients: Evaluating Risk Factors and Predictive Scale Using a Prospective Cross-Sectional Study

Background  The incidence of ulcer pressure in the high care unit (HCU) was relatively high and could be reliably predicted using tools such as the Norton and Jackson/Cubbin scales. However, other risk factors, such as age, gender, consciousness, systemic condition, duration of treatment, and use of restraint, may contribute to the occurrence of ulcer pressure. This study was conducted to analyze the relationship of various risk factors for pressure ulcers and prediction of ulcer pressure, using Norton and Jackson/Cubbin scale, to incident pressure ulcers in HCU patient. Methods  This study utilized a prospective cross-sectional study design to analyze various risk factors for ulcer pressure development in a patient admitted to the HCU, including age, gender, blood profile, consciousness, duration of treatment, and use of restraint. The Norton and Jackson/Cubbin scale was employed to predict pressure ulcers. The relationship between the risk factors and the prediction of pressure ulcer incidents was evaluated using multiple logistic binary regression analysis. Result  Both the Norton and Jackson/Cubbin scales predicted a lower risk of pressure ulcer development (60.98 and 99.02%, respectively). This prediction is consistent with the low incidence of pressure injuries found, which is only 4.39%. Furthermore, the relationship between the identified risk factor (gender, duration of treatment in HCU and use of restraint) and the prediction and incident of pressure ulcer was not significant ( p  > 0.05). Thus, it is suggested that these risk factors may not strong predictors of pressure ulcer development. Conclusion  This study's result indicated no significant relationship exists between possible identified risk factors and the development of pressure ulcers in HCU patients. However, the Norton and Jackson/Cubbin scales were reliable predictors of pressure ulcer occurrence, with both scales predicting a lower risk of pressure ulcer development.

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.

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

Background

Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

Aims

This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

Methods

A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

Results

The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

Conclusion

This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

Assessment of antidiabetic effect of 4-HIL in type 2 diabetic and healthy Sprague Dawley rats

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia and insulin resistance. 4-hydroxyisoleucine (4-HIL) is a non-proteinogenic amino acid isolated from the fenugreek seeds and has enormous pharmacological activities. The present study was undertaken to investigate the antihyperglycemic effect of 4-HIL in streptozotocin (STZ)-induced diabetic rats. Moreover, its toxicity was evaluated in vitro and in vivo employing human embryonic kidney cells (HEK-293) and healthy rats, respectively. In experiment 1, STZ-induced diabetic male rats were subjected to an oral treatment of 4-HIL (100 mg/kg), while experiment 2 deals with the effects of 4-HIL on healthy male and female rats following oral administration. The treatment (experiment 1) declined the elevated blood glucose level, feed intake, and increased body weight(s). Additionally, blood glucose impairment was improved as observed by OGTT and IPGT tests. Pancreatic histopathology revealed mild changes in the 4-HIL group. Moreover, experiment 2 showed increased body weight, normal blood glucose levels (male—106.06 ± 7.49 mg/dl and female—100.06 ± 14.69 mg/dL), hematological parameters, and histopathological profiles in the treatment group. 4-HIL did not affect the viability of HEK-293 cells, and no signs of toxicity were observed in healthy rats. Therefore, the study concludes that 4-HIL has potential antihyperglycemic activity without any toxic effects.

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

White blood cells and platelet profiles of diabetic patients at University of Gondar specialized referral hospital: A comparative cross-sectional study

BACKGROUND

Altered level of many hematological parameters such as white blood cells (WBC) and platelet function has been observed in diabetes mellitus (DM) patients. Therefore, this study aimed to determine the WBC and platelet profiles and their association with anthropometric measurement and blood pressure in DM patients and healthy controls.

METHOD

A comparative cross-sectional study was conducted on a total of 246 participants at the University of Gondar Specialized Referral Hospital. Venous blood with K₂ EDTA anticoagulant was drawn and analyzed by using Sysmex KX21N hematology analyzers for WBC and platelet parameters. Data were analyzed using Statistical Package for Social Sciences (SPSS) version 20. Results were presented as frequency and mean ± standard deviation (SD). The independent sample t test was used to compare quantitative variables between DM and control groups. The bivariate (spearman's rank) correlation was used to analyze continuous variables. A p-value ˂ 0.05 was considered as statistically significant.

RESULTS

The mean platelet count was significantly higher among diabetics (252.77 ± 77.7) compared to non-diabetic controls (208.22 ± 68), p < 0.001. Similarly, the total WBC count was higher among DM patients (6.95 ± 2.23) than in the controls (6.15 ± 1.95), p = 0.04. A significant negative correlation was also found between neutrophil and duration of illness in DM patients. Besides, there is a significant positive correlation between WBC and lymphocyte number with systolic blood pressure (SBP) in DM patients.

CONCLUSION

Platelet and WBC count were significantly higher in DM patients than in the controls. Therefore, routine screening and profile checking of those abnormal indices is recommended to minimize DM-related complications.

Non-HDL cholesterol predictive factor of type 2 diabetes in the city of Tlemcen

BACKGROUND

type 2 diabetes mellitus (T2DM) is associated with disorders of lipoprotein metabolism mixed dyslipidemia. The purpose of this study is to verify whether non high density lipoprotein cholesterol (non-HDL-C) can contribute to the development of T2DM in a population in the extreme western Algeria.

METHODS

The study was conducted in Tlemcen region on a sample of 1852 subjects,1059 with T2DM and 793 controls, these were evaluated for biochemical parameters, measured using standard enzyme procedures (SFBC or IFCC recommendations) on the Beckman CX7^® PLC (Beckman-Coulter^®, NY, USA). All the information related to the disease were collected from the patients and recorded using predesigned questionnaire.

RESULTS

The logistic model retained, the two levels of non-HDL-C: 130 mg/dl < non-HDL-C≤160 mg/dl (OR = 0,69; 95% CI = 0,49-0,97, P = 0,033) and non-HDL-C>190 mg/dl (OR = 2,22; 95% CI = 1,31-3,76, P = 0,003), inbreeding (OR = 1,71; 95% CI = 1,44-2,04, P = 0,000) and the two age groups 60-70 years (OR = 2,14; 95% CI = 1,47-3,1, P ≪ 0,001) and 70 years and over (OR = 2,26; 95% CI = 1,51-3,38, P ≪ 0,001).

CONCLUSIONS

The logistic model shows that non-HDL-C contributes to the development of type 2 diabetes in our population.