Evaluation of left ventricular systolic and diastolic function in subjects with prediabetes and diabetes using cardiovascular magnetic resonance-feature tracking View Full Text


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Article Info

DATE

2021-11-15

AUTHORS

Shanshan Zhou, Zheng Zhang, Zhen Zhang, Yiyuan Gao, Gengxiao Li, Mingwu Lou, Zhiwei Zhao, Jun Zhao, Kuncheng Li, Gerald M. Pohost

ABSTRACT

AimsThe aim of this study was to evaluate alterations in left ventricular (LV) systolic and diastolic function in subjects with prediabetes and diabetes using cardiovascular magnetic resonance-feature tracking (CMR- FT).MethodsWe included 35 subjects with prediabetes, 30 subjects with diabetes, and 33 healthy controls of similar age and sex distributions who underwent CMR examination. LV global radial, circumferential, and longitudinal strain (GRS, GCS, and GLS), peak systolic strain rate (PSSR), and peak diastolic strain rate (PDSR) were measured and compared among the three groups. Pearson’s correlation and linear regression analyses were applied for statistical analyses.ResultsSubjects with prediabetes and diabetes had a significantly lower GLS than healthy controls, but there were no significant differences in ejection fraction (EF), GRS, GCS, or global radial, circumferential and longitudinal PSSR among the three groups. Global radial, circumferential, and longitudinal PDSR in patients with diabetes were all significantly lower than those in the healthy controls. Compared to subjects with prediabetes, patients with diabetes had a significantly lower global circumferential PDSR. Global longitudinal PDSR in subjects with prediabetes was significantly lower than that in healthy controls. Multivariable linear regression analyses demonstrated that elevated HbA1c levels were independently associated with decreased global circumferential and longitudinal PDSR (β = −0.203, p = 0.023; β = −0.207, p = 0.040, respectively).ConclusionsCMR-FT has potential value to evaluate early alterations in LV systolic and diastolic function in subjects with prediabetes and diabetes. Elevated HbA1c levels were independently associated with impaired LV diastolic function in the general population free of overt cardiovascular diseases. More... »

PAGES

491-499

References to SciGraph publications

  • 2016-08-26. Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use in JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE
  • 2021-05-19. MicroRNAs modulation and clinical outcomes at 1 year of follow-up in obese patients with pre-diabetes treated with metformin vs. placebo in ACTA DIABETOLOGICA
  • 2020-09-28. Detection of subclinical myocardial dysfunction in cocaine addicts with feature tracking cardiovascular magnetic resonance in JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE
  • 2019-08-12. Correlation between left ventricular myocardial strain and left ventricular geometry in healthy adults: a cardiovascular magnetic resonance-feature tracking study in THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
  • 2001-09. Mortality and causes of death in the WHO multinational study of vascular disease in diabetes in DIABETOLOGIA
  • 2020-04-23. Early detection of left atrial and bi-ventricular myocardial strain abnormalities by MRI feature tracking in normotensive or hypertensive T2DM patients with preserved LV function in BMC CARDIOVASCULAR DISORDERS
  • 2019-09-30. Pericoronary fat inflammation and Major Adverse Cardiac Events (MACE) in prediabetic patients with acute myocardial infarction: effects of metformin in CARDIOVASCULAR DIABETOLOGY
  • 2018-10-30. Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study in CARDIOVASCULAR DIABETOLOGY
  • 2020-09-04. Functional capacity and left ventricular diastolic function in patients with type 2 diabetes in ACTA DIABETOLOGICA
  • 2020-03-10. Global diastolic strain rate for the assessment of left ventricular diastolic dysfunction in young peritoneal dialysis patients: a case control study in BMC NEPHROLOGY
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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34779950


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