Left ventricular structural alterations are accompanied by subclinical systolic dysfunction in type 2 diabetes mellitus patients with concomitant hyperlipidemia: An analysis based on 3D speckle tracking echocardiography

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.

The Systolic and Diastolic Cardiac Function of Patients With Type 2 Diabetes Mellitus: An Evaluation of Left Ventricular Strain and Torsion Using Conventional and Speckle Tracking Echocardiography

Objectives: This study aimed to quantify left ventricular (LV) myocardial strain and torsion in patients with type 2 diabetes mellitus (T2DM) and evaluate their systolic and diastolic function using conventional and speckle tracking echocardiography. Methods: Forty-seven patients with T2DM were divided into a group without microvascular complications (the DM A group) and a group with microvascular complications (the DM B group), while another 27 healthy participants acted as the control group. All the participants had had an echocardiography examination. All the original data were imported into EchoPAC workstation for the analysis and quantification of LV strain and torsion. Results: Compared with the control group, the LV end-diastolic volume, end-systolic volume, and ejection fraction of the DM A and DM B groups showed no significant differences, but the global longitudinal strain and the global circular strain were reduced in the DM B group. There were significant differences in the left ventricular relative wall thickness (RWT), left ventricular mass index (LVMI), the early mitral valvular blood flow velocity peak/left ventricular sidewall mitral annulus late peak velocity, left ventricular sidewall mitral annulus early peak velocity/left ventricular sidewall mitral annulus late peak velocity, isovolumic relaxation time, peak twisting, peak untwisting velocity (PUV), untwisting rate (UntwR), time peak twisting velocity (TPTV), and time peak untwisting velocity (TPUV) between the DM A, DM B, and control groups. While the peak twisting velocity (PTV) was slower in the DM B group compared with the control group, the RWT, PTV, PUV, UntwR, TPTV, and TPUV in the DM B group were significantly different from the DM A group. Conclusion: The cardiac function of patients with T2DM in its early stages, when there are no microvascular complications, could be monitored with the analysis of two-dimensional strain and torsion.

Three-Dimensional Global Left Ventricular Myocardial Strain Reduced in All Directions in Subclinical Diabetic Cardiomyopathy: A Systematic Review and Meta-Analysis

Background

Three‐dimensional (3D) speckle tracking echocardiography can identify subclinical diabetic cardiomyopathy without geometric assumption and loss of speckle from out‐of‐plane motions. There is, however, significant heterogeneity among the previous reports. We performed a systematic review and meta‐analysis to compare 3D strain values between adults with asymptomatic, subclinical diabetes mellitus (ie, patients with diabetes mellitus without known clinical manifestations of cardiac disease) and healthy controls.

Methods and Results

After systematic review of 5 databases, 12 valid studies (544 patients with diabetes mellitus and 489 controls) were eligible for meta‐analysis. Pooled means and mean difference (MD) using a random‐effects model for 3D global longitudinal, circumferential, radial, and area strain were calculated. Patients with diabetes mellitus had an overall 2.31 percentage points lower 3D global longitudinal strain than healthy subjects (16.6%, 95% CI, 15.7–17.6 versus 19.0; 95% CI, 18.2–19.7; MD, −2.31, 95% CI, −2.72 to −2.03). Similarly, 3D global circumferential strain (18.9%; 95% CI, 17.5–20.3 versus 20.5; 95% CI, 18.9–22.1; MD, −1.50; 95% CI, −2.09 to −0.91); 3D global radial strain (44.6%; 95% CI, 40.2–49.1 versus 48.2; 95% CI, 44.7–51.8; MD, −3.47; 95% CI, −4.98 to −1.97), and 3D global area strain (30.5%; 95% CI, 29.2–31.8 versus 32.4; 95% CI, 30.5–34.3; MD, −1.76; 95% CI, −2.74 to −0.78) were also lower in patients with diabetes mellitus. Significant heterogeneity was noted between studies for all strain directions (inconsistency factor [I²], 37%–78%). Meta‐regression in subgroup analysis of studies using the most popular vendor found higher prevalence of hypertension as a significant contributor to worse 3D global longitudinal strain. Higher hemoglobulin A_1c was the most significant contributor to worse 3D global circumferential strain in patients with diabetes mellitus.

Conclusions

Three‐dimensional myocardial strain was reduced in all directions in asymptomatic diabetic patients. Hypertension and hemoglobin A_1c were associated with worse 3D global longitudinal strain and 3D global circumferential strain, respectively.

Registration

URL: https://www.crd.york.ac.uk/prospero; unique identifier: CRD42020197825.

Peak systolic longitudinal rotation: a new tool for detecting left ventricular systolic function in patients with type 2 diabetes mellitus by two-dimensional speckle tracking echocardiography

BACKGROUND

Type 2 diabetes mellitus (T2DM) is one of the most prevalent cardiac and cerebrovascular risk factors. The study aimed to find a new way to investigate left ventricle (LV) systolic dysfunction in T2DM patients using two-dimensional speckle tracking echocardiography (2D-STE).

METHODS

Fifty-one untreated T2DM patients and 52 normal control subjects were enrolled for the research. Apical four-chamber view was acquired by two-dimensional echocardiography. Segmental and global peak systolic longitudinal rotation (PSLR) degrees were measured by the software of EchoPAC.

RESULTS

In T2DM patients, global PSLR prominently rotated clockwise, while in normal subjects, global PSLR degrees were so small and almost had no PSLR. HBA1c negatively correlated with apex and global PSLR, that is, T2DM patients with higher HBA1c had a larger clockwise apex and global PSLR. ROC analysis showed that PSLR could detect the accuracy of LV systolic dysfunction.

CONCLUSION

Cardiac clockwise global PSLR was found in T2DM patients. The cardiac contractile function in T2DM patients was impaired. The new tool of PSLR can conveniently detect cardiac systolic dysfunction in T2DM patients. HBA1c could predict systolic dysfunction in T2DM patients.