Prediabetes, heart mechanics, and echocardiography: A narrative review

Early impairment of right ventricular systolic function in patients with prediabetes and type 2 diabetes mellitus: An analysis of two-dimensional speckle tracking echocardiography

BACKGROUND

Type 2 diabetes mellitus is a metabolic disease that affects multiple target organs. Current data on right ventricular damage in type 2 diabetes, especially in prediabetes, are limited. Due to the anatomical characteristics of the right ventricle, the assessment of the right ventricle by conventional echocardiography is difficult, whereas the ultrasound two-dimensional speckle tracking echocardiography can provide information on myocardial systolic function by tracking the motion information of myocardial speckles, which can sensitively reflect myocardial mechanical changes.

AIMS

To assess the effect of prediabetes and diabetes with preserved left ventricular ejection fraction on right ventricular myocardial systolic function and to identify independent risk factors affecting right ventricular systolic function.

METHODS

A total of 49 normoglycaemic (NG) healthy individuals, 43 prediabetics (PDM), and 52 type 2 diabetics (T2DM) were recruited. All study subjects underwent conventional echocardiography and two-dimensional speckle tracking echocardiography (2D-STE).

RESULTS

The right ventricular global longitudinal strain (RVGLS) (20.80 ± 1.96% vs. 18.99 ± 3.20% vs. 16.85 ± 4.01%), left ventricular global longitudinal strain (LVGLS), and interventricular septal longitudinal strain (IVS-LS) (17.28 ± 2. 35% vs. 16.14 ± 3.22% vs. 15.53 ± 3.33%) gradually decreased from the controls, through patients with prediabetes, to those with diabetes (p < .001). Right ventricular free wall strain (RVFW-LS) was higher in the control group (25.63 ± 4.58% vs. 22.83 ± 4.83% vs. 20.79 ± 4.92%) than in the other two groups with a statistically significant difference (p < .001), while RVFW-LS was not statistically different between the prediabetic and diabetic groups. Multivariate regression analysis showed that HbA1c (β = -.626, p < .001), IVS-LS (β = .417, p < .001), and left ventricular end-diastolic diameter (LVEDd) (β = .191, p = .011) were independently correlated with RVGLS.

CONCLUSIONS

Two-dimensional speckle tracking echocardiography can sensitively detect subtle changes in the early impairment of right ventricular systolic function in patients with abnormal glucose metabolism. Type 2 diabetes is the common mechanism causing impaired myocardial mechanics in the right and left ventricles. The reduced global systolic longitudinal strain of the right ventricle was associated with reduced global septal longitudinal strain and left ventricular remodeling. HbA1c is an independent predictor of the global longitudinal strain of the right ventricle, and controlling blood glucose levels may be expected to improve the extent of myocardial damage.

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.

Phenotyping the Prediabetic Population-A Closer Look at Intermediate Glucose Status and Cardiovascular Disease

Even though the new thresholds for defining prediabetes have been around for more than ten years, there is still controversy surrounding the precise characterization of this intermediate glucose metabolism status. The risk of developing diabetes and macro and microvascular disease linked to prediabetes is well known. Still, the prediabetic population is far from being homogenous, and phenotyping it into less heterogeneous groups might prove useful for long-term risk assessment, follow-up, and primary prevention. Unfortunately, the current definition of prediabetes is quite rigid and disregards the underlying pathophysiologic mechanisms and their potential metabolic progression towards overt disease. In addition, prediabetes is commonly associated with a cluster of risk factors that worsen the prognosis. These risk factors all revolve around a common denominator: inflammation. This review focuses on identifying the population that needs to be screened for prediabetes and the already declared prediabetic patients who are at a higher risk of cardiovascular disease and require closer monitoring.