A nomogram based on radiomics intermuscular adipose analysis to indicate arteriosclerosis in patients with newly diagnosed type 2 diabetes

Linking Diabetic Retinopathy Severity to Coronary Artery Disease Risk Factors in Type 2 Diabetic Patients

BACKGROUND

Diabetes mellitus (DM) is a common metabolic disorder characterized by hyperglycemia, leading to chronic complications, notably cardiovascular diseases such as coronary artery disease (CAD). Diabetic retinopathy (DR), a leading cause of blindness, may serve as a non-invasive marker for CAD. This study investigates the correlation between DR and CAD to explore its diagnostic potential in diabetic populations.

METHODS

A cross-sectional study was conducted over one year in a general hospital, involving 100 type 2 DM patients with retinopathy. DR was classified as mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative retinopathy, based on fundus examinations. Data on age, duration of diabetes, cholesterol levels, glycated hemoglobin (HbA1C), and ECG (electrocardiography) findings were collected. Statistical analysis included frequency analysis, chi-square tests for association between categorical variables, and significance testing with p-values. Data were analyzed using IBM SPSS Statistics for Windows, Version 20.0 (Released 2011; IBM Corp., Armonk, New York, United States). Descriptive statistics were characterized by categorical and continuous variables. The chi-square test determined associations between qualitative variables, with significance set at p<0.05.

RESULTS

The mean age of patients was 57.13 ± 8.51 years. Age and duration of diabetes were significant predictors of retinopathy severity (p<0.001). Proliferative retinopathy was found exclusively in patients over 70 years. Lower cholesterol levels (<200 mg/dL) were significantly associated with less severe retinopathy (p=0.033), whereas higher cholesterol levels (>200 mg/dL) did not show a statistically significant association with retinopathy severity (p=0.772). Patients with HbA1C levels between 6.5% and 8.5% predominantly had milder forms of retinopathy, as indicated by the significant p-value (<0.001). In contrast, patients with HbA1C levels above 8.5% are more likely to have severe NPDR or proliferative diabetic retinopathy (PDR), but this association was not statistically significant (p=0.582). ECG abnormalities increased with retinopathy severity (p=0.002). Hypertension was significantly linked to cardiac changes in retinopathy patients (p<0.001), while smoking and family history of CAD were not significant factors. This study's cross-sectional design limits causality inference. The single-center sample of 100 patients may not be broadly generalizable. Reliance on self-reported data introduces potential recall bias, and confounding factors such as diet, physical activity, and additional comorbidities were not accounted for. The lack of a control group further limits comparative analysis. Future longitudinal studies with larger, diverse samples are needed.

CONCLUSION

Retinopathy in DM patients is significantly associated with cardiac changes and other risk factors such as hypertension, dyslipidemia, and poor glycemic control. Aggressive management of these factors is essential. Retinopathy can serve as a predictor of CAD in diabetic patients.

Correlation between the cardiometabolic index and arteriosclerosis in patients with type 2 diabetes mellitus

BACKGROUND

The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM).

METHODS

This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV).

RESULTS

Patients were categorized into three groups based on their CMI: Group C1 (CMI < 0.775; n = 750), Group C2 (CMI: 0.775-1.355; n = 743), and Group C3 (CMI > 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P < 0.05). The CMI was positively correlated with systolic blood pressure (r = 0.74, P < 0.001). Multivariate analysis revealed that an increased CMI contributed to a greater risk for arteriosclerosis (OR = 1.87, 95%CI: 1.66-2.10, P < 0.001). Compared to the C1 group, the C2 group and C3 group had a greater risk of developing arteriosclerosis, with ORs of 4.55 (95%CI: 3.57-5.81, P<0.001) and 5.56 (95%CI: 4.32-7.17, P<0.001), respectively. The association was notably stronger in patients with a BMI below 21.62 kg/m² than in those with a BMI of 21.62 kg/m² or higher (OR = 4.53 vs. OR = 1.59).

CONCLUSIONS

These findings suggest that the CMI is a relevant and independent marker of arteriosclerosis in patients with T2DM and may be useful in the risk stratification and management of these patients.

Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy

Background

Accurate determination of the types of lymphadenopathy is of great importance in disease diagnosis and treatment and is usually confirmed by pathological findings. Radiomics is a non-invasive tool that can extract quantitative information from medical images. Our study was designed to develop a non-invasive radiomic approach based on multiphase contrast-enhanced ultrasound (CEUS) images for the classification of different types of lymphadenopathy.

Methods

A total of 426 patients with superficial suspected lymph nodes (LNs) from three centres were grouped into a training cohort (n=190), an internal testing cohort (n=127), and an external testing cohort (n=109). The radiomic features were extracted from the prevascular phase, vascular phase, and postvascular phase of the CEUS images. Model 1 (the conventional feature model), model 2 (the multiphase radiomics model), and model 3 (the combined feature model) were established for lymphadenopathy classification. The area under the curve (AUC) and confusion matrix were used to evaluate the performance of the three models. The usefulness of the models was assessed in different threshold probabilities by decision curve analysis.

Results

There were 139 patients (32.6%) with benign LNs, 110 patients (25.8%) with lymphoma, and 177 patients (41.5%) with metastatic LNs in our population. Finally, twenty features were selected to construct the radiomics models for these three types of lymphadenopathy. Model 2 integrating multiphase images of the CEUS yielded the AUCs of 0.838, 0.739, and 0.733 in the training cohort, internal testing cohort, and external testing cohort, respectively. After the combination of conventional features and radiomic features, the AUCs of model 3 improved to 0.943, 0.823 and 0.785 in the training cohort, internal testing cohort, and external testing cohort. Besides, model 3 had an accuracy of 81.05%, sensitivity of 80%, and specificity of 90.43% in the training cohort. Model performance was further confirmed in the internal testing cohort and external testing cohort.

Conclusions

We constructed a combined feature model using a series of CEUS images for the classification of the lymphadenopathies. For patients with superficial suspected LNs, this model can help clinicians make a decision on the LN type noninvasively and choose appropriate treatments.