Hemoglobin A1c in type 2 diabetes mellitus patients with preserved ejection fraction is an independent predictor of left ventricular myocardial deformation and tissue abnormalities

Association between glycosylated hemoglobin levels, diabetes duration, and left ventricular diastolic dysfunction in patients with type 2 diabetes and preserved ejection fraction: a cross-sectional study

Background

We aimed to explore the intricate interplay between glycated hemoglobin (HbA1C) levels, disease duration, and left ventricular diastolic dysfunction in patients with type 2 diabetes mellitus (T2DM) characterized by preserved ejection fraction.

Methods

A cross-sectional study was conducted at the Second Affiliated Hospital of Hebei Medical University from January 2022 to December 2022. A total of 114 inpatients from the Department of Endocrinology were randomly selected based on the inclusion and exclusion criteria. Patients with T2DM were stratified into three subgroups, each comprising 38 patients, based on disease duration and HbA1C levels. A sub-analysis was conducted to explore variations among these three distinct groups. A control group comprised 38 age, gender, body mass index (BMI), and smoking habit-matched healthy volunteers form the Physical Examination Center of the same hospital. General demographic information, biochemical results, and echocardiographic data were collected, and correlation and linear regression analyses were performed.

Results

Diabetic patients exhibited lower E/A values (0.85 (0.72, 1.17) vs. 1.20 (0.97, 1.30)) and elevated E/e' values (9.50 (8.75, 11.00) vs. 9.00 (7.67, 9.85)) compared to their normal controls. In the subgroup analysis, patients with a disease duration exceeding 2 years displayed reduced E/A values (0.85 (0.75, 1.10) vs. 1.10 (0.80, 1.30)) and elevated E/e' values (9.80 (9.20, 10.80) vs. 8.95 (7.77, 9.50)) in comparison to those with a disease duration of ≤2 years, p<0.05. Among patients with a disease duration surpassing 2 years, those with higher HbA1C levels exhibited lower E/A values (0.80 (0.70, 0.90) vs. (0.85 (0.75, 1.10)) and higher E/e' values (11.00 (9.87, 12.15) vs. 9.80 (9.20, 10.80)) in contrast to patients with low HbA1C levels, p<0.05. Multiple linear regression analysis identified HbA1C (β=0.294, p<0.001) and disease duration (β=0.319, p<0.001) as independent risk factors for the E/A value in diabetes patients. Furthermore, HbA1C (β=0.178, p=0.015) and disease duration (β=0.529, p<0.001) emerged as independent risk factors for the E/e' value in diabetic patients.

Conclusions

In individuals with T2DM exhibiting preserved ejection fraction, the presence of left ventricular diastolic dysfunction is significantly associated with HbA1C levels and the duration of diabetes.

Left ventricular longitudinal strain in patients with type 2 diabetes mellitus is independently associated with glycated hemoglobin level

OBJECTIVE

Left ventricular and left atrial strain are sensitive and reliable markers for evaluating cardiac function in patients with type 2 diabetes mellitus (T2DM), with interactions between the two parameters. The present study aimed to analyze the correlation between global longitudinal strain (GLS) of the left ventricle and glycated hemoglobin (HbA1c) levels in patients with T2DM.

METHODS

A total of 292 patients clinically diagnosed with T2DM were selected and divided into three groups according to HbA1c level. The strains of the left atrium and left ventricle in the three groups of T2DM patients with different HbA1c levels were compared. Univariate and multivariate (including left atrial functional indicators) linear regression analyses were performed to assess the relationship between strain indicators and HbA1c levels. Generalized additive models were used to examine the relationship between strain indicators and HbA1c levels.

RESULTS

There were significant differences among the three groups in terms of age, microalbuminuria, total cholesterol, fasting blood glucose, postprandial blood glucose, and HbA1c level, and left atrial conduit longitudinal strain (LAScd) and GLS (p < .05). Univariate and multivariate linear regression analyses revealed that, as HbA1c levels increased, the absolute value of GLS gradually decreased (p < .001). Curve fitting revealed a positive correlation between HbA1c level and GLS, which was not affected by left atrial function.

CONCLUSION

Left ventricular GLS was independently correlated with HbA1c level in patients with T2DM and was not affected by left atrial function.

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.