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

Li, Zhiming; Han, Dan; Qi, Tianfu; Deng, Jie; Li, Lili; Gao, Chao; Gao, Wei Dong; Chen, Haiyan; Zhang, Lihua; Chen, Wei

doi:10.1186/s12872-023-03082-5

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Meta-analyses consistently indicate a significant association between higher HbA1C levels and an increased incidence of congestive heart failure ( 27 ). Recent studies have identified HbA1C as an independent predictor of left ventricular myocardial deformation and tissue abnormalities in patients with T2DM and preserved ejection fraction ( 29 ). However, Shun Yokota suggested that left ventricular diastolic function is more strongly associated with hyperglycemic variability rather than HbA1C levels in T2DM ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

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

Li,

Zhao,

Yuan

et al. 2023

Front. Endocrinol.

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BackgroundWe 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.MethodsA 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.ResultsDiabetic 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.ConclusionsIn 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.

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Section: Discussionmentioning

confidence: 99%

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

Li,

Zhao,

Yuan

et al. 2023

Front. Endocrinol.

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“…Insulin insensitivity, insulin deficiency, and compromised cellular activity are hallmarks of diabetes mellitus, a metabolic disease brought on by a combination of environmental and genetic factors [1]. As to the 2021 report of the International Diabetes Federation, diabetes is diagnosed in 536.6 million persons globally between the ages of 20 and 79.…”

Section: Introductionmentioning

confidence: 99%

Pima Indian Diabetes Database and Machine Learning Models for Diabetes Prediction

Xie

2024

HSET

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As a matter of fact, diabetes mellitus is becoming a growing epidemic worldwide in recent years which attracts great attention of researchers. In reality, as a result of the illness and its complications, the strain on healthcare systems is rising. Therefore, it is crucial to identify diabetes early in order to shield individuals from major consequences. With this in mind, in this study, four ML models are used for the imputation of dataset (K-NN) and the prediction of diabetes (LR, SVM and RF). According to the analysis, the results show that the LR model is slightly better than the RF as well as SVM models, with a prediction accuracy of 0.7913 and a precision of 0.8571. Ultimately, it can be said that there is a lot of promise when employing ML models to diagnose diabetes early on based on the evaluations. Overall, these results shed light on guiding further exploration of diabetes prediction.

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“…It is important to note that these adverse effects are directly caused by hyperglycemia, which is elevated blood sugar levels. This is because the body has trouble controlling blood sugar levels or cannot properly use the insulin it produces [1]. The hormone insulin helps glucose reach and be available to the cells.…”

Section: Introductionmentioning

confidence: 99%

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

Iparraguirre-Villanueva,

Espinola-Linares,

Flores Castañeda

et al. 2023

Diagnostics

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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.

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

Cited by 4 publications

References 39 publications

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

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

Pima Indian Diabetes Database and Machine Learning Models for Diabetes Prediction

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

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