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Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.

Mingyu Hao1,2, Xiaohong Huang1,3, Xueting Liu1

  • 1Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, China.

Annals of Medicine
|March 13, 2023
PubMed
Summary

This study developed a nomogram using clinical factors like age, BMI, and triglycerides to predict diastolic cardiac dysfunction in Type 2 diabetes mellitus (T2DM) patients, offering a tool for early screening and research.

Keywords:
Diabetic cardiomyopathyclinical predictive modeldiastolic cardiac dysfunctiontype 2 diabetes

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Area of Science:

  • Cardiology
  • Endocrinology
  • Medical Diagnostics

Background:

  • Diabetes mellitus, particularly Type 2 Diabetes Mellitus (T2DM), is frequently complicated by heart failure, leading to high mortality and morbidity.
  • Current diagnostic and treatment strategies for T2DM-related heart complications remain limited.
  • Diastolic cardiac dysfunction is a significant concern in T2DM patients, often preceding overt heart failure.

Purpose of the Study:

  • To develop and validate a predictive nomogram model for diastolic cardiac dysfunction in patients with T2DM.
  • To utilize readily available clinical parameters for predicting cardiac complications in T2DM.
  • To establish a reliable tool for early screening and large-scale epidemiological studies of cardiac dysfunction in T2DM.

Main Methods:

  • A cohort of 3030 T2DM patients underwent Doppler echocardiography.
  • Patients were divided into training (n=1701) and verification (n=1329) datasets.
  • Multivariable logistic regression and LASSO regression were employed to develop the predictive nomogram, with performance assessed by AUC-ROC, calibration curves, and decision curve analysis.

Main Results:

  • A nomogram was developed incorporating age, BMI, triglyceride (TG), CK-MB, serum sodium (Na), and urinary albumin/creatinine ratio (UACR).
  • The model demonstrated robust predictive performance with AUC-ROC values of 0.8307 (training) and 0.8083 (verification).
  • Calibration plots showed excellent concordance, and decision curve analysis confirmed the nomogram's clinical utility.

Conclusions:

  • Clinical parameters can effectively predict diastolic cardiac dysfunction in T2DM patients.
  • The developed nomogram serves as a valuable tool for early screening of cardiac complications in T2DM.
  • This model facilitates large-scale epidemiological research on diastolic cardiac dysfunction within the T2DM population.