Introduction

Diabetic cardiomyopathy, which was first confirmed by Rubler et al.1 generally manifests as pathological cardiac remodeling and systolic and diastolic dysfunction. However, changes in myocardial function in the early stage of diabetic cardiomyopathy are usually ignored and underestimated. Cardiovascular complications are the main cause of death in type 2 diabetes mellitus (T2DM) patients. Therefore, early identification followed by timely intervention is vital for individuals with T2DM at high risk of cardiovascular complications.

Diabetic cardiomyopathy is the result of the interaction of many factors. Studies have demonstrated that insulin resistance (IR) and/or hyperinsulinemia are also involved in the development of diabetic cardiomyopathy2,3. IR is defined as reduced efficiency of insulin in promoting the uptake and utilization of glucose in the body. It causes an imbalance in glucose and lipid metabolism, which leads to metabolic syndrome and type 2 diabetes, and is also recognized as a risk factor for cardiovascular disease (CVD). In the past, the glucose clamp technique and the insulin resistance index (HOMA-IR) were the two main methods used to evaluate IR. However, these two methods are difficult to apply on a large scale in clinical practice because of high costs and complicated calculations4. The triglyceride-glucose (TyG) index is a novel biological parameter used for identifying insulin resistance, easy to calculate, and proven more beneficial for evaluating IR than HOMA-IR5. Several studies have demonstrated that the TyG index is closely related to CVD6,7. Chen Y et al. confirmed that in T2DM patients, a higher TyG index was significantly associated with subclinical LV systolic dysfunction, which was assessed by two-dimensional speckle tracking technology (2D-STE), a new method for the early evaluation of subclinical myocardial dysfunction8.

However, it remains unclear whether the TyG index and subclinical RV systolic dysfunction are related in T2DM patients. Accordingly, the aim of this study was to determine the relationship between the TyG index and subclinical RV systolic dysfunction in T2DM patients.

Methods

Study population

This study was conducted according to the principles of the Declaration of Helsinki and was approved by the Human Subjects Committee of Changzhou No.2 People’s Hospital. Also, written informed consent was obtained from all participants.

A total of 170 consecutively hospitalized T2DM patients with preserved left ventricular ejection fraction (LVEF) were included in this research. The T2DM patients were divided into three groups according to the tertile of the TyG index: the T1 group (TyG index ≤ 9.1495, n = 52), the T2 group (9.1495 < TyG index ≤ 9.7611, n = 54), and the T3 group (TyG index > 9.7611, n= 52). T2DM was diagnosed in accordance with the World Health Organization criteria, which were as follows: fasting plasma glucose ≥ 7.0 mmol/L on 2 occasions and/or 2-hour plasma glucose value during a 75 g oral glucose tolerance test ≥ 11.1 mmol/L9. The exclusion criteria were as follows: (1) type 1 diabetes mellitus; (2) LVEF < 50%; (3) coronary artery disease (coronary artery stenosis diagnosed by CT examination ≥ 50%); (4) atrial fibrillation (including paroxysmal atrial fibrillation and persistent atrial fibrillation); (5) congenital heart disease; (6) cardiomyopathy; (7) valvular heart disease (mild and above); (8) pulmonary embolism, (9) chronic obstructive pulmonary disease, and (10) cerebrovascular disease (Fig. 1).

Fig. 1
figure 1

Flow diagram of T2DM patients selection.

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Clinical variables and biochemical measurements

Demographic data, including age, sex, height, weight and blood pressure (systolic blood pressure: SBP, diastolic blood pressure: DBP), were obtained from all T2DM patients. Body mass index (BMI) was defined as weight/height2 (kg/m2). After at least 12 h of overnight fasting, the levels of fasting plasma glucose (FPG), glycosylated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured with an automatic biochemical analyzer. The TyG index was subsequently calculated as follows: Ln (fasting TG [mg/dL] × fasting glucose [mg/dL]/2), as previously described10.

Conventional echocardiography examination and two-dimensional speckle-tracking echocardiography analysis

Transthoracic echocardiography was performed by an experienced cardiologist using a E9 ultrasound system (GE Healthcare, USA) by an experienced cardiologist. Routine echocardiography parameters of RV were measured from the apical 4-chamber view (focusing on the right ventricle). The basal diameter and middle diameter of the RV were measured during end-diastole. The RV end-diastolic area (RVA-D) and RV end-systolic area (RVA-S) were measured by manually defining the endocardial border of the RV, and the right ventricular fractional area change (RV-FAC) was calculated as the difference between the end-diastolic area and the end-systolic area divided by the end-diastolic area and expressed as a percentage. Tricuspid annular plane systolic excursion (TAPSE) was measured through M-mode with the cursor aligned along the direction of the tricuspid lateral annulus. The early (E) and late (A) transtricuspid inflow velocities were determined using pulse-wave Doppler. The peak systolic, early diastolic and late diastolic velocities of the tricuspid annulus were measured using tissue Doppler imaging (TDI S, TDI E, and TDI A). The LVEF was calculated according to the biplane Simpson’s rule. The RV FWLS was analyzed using Echo PAC PC software, version 203 (GE Healthcare, Horten, Norway). Three consecutive cardiac cycles images of apical four chamber of RV were acquired during an end-expiratory breath-hold and transferred to EchoPAC software. The endocardial border of the RV was traced manually in the end-systolic frame at the point in the cardiac cycle in which the endocardial border was the clearest. Then the software would generate a region of interest automatically, and adjusted the region of interest to make the myocardial included well. If it was not feasible to track 1 or more segments, the case was excluded. The RV free wall was automatically divided into three segments: basal, middle, and apical. RV free wall systolic longitudinal strain (RV FWLS) was calculated as the mean of the values of the three segments of the RV free wall. The predefined cutoff of RV FWLS < 20% was adopted to evaluate subclinical RV systolic dysfunction11. Left ventricular global longitudinal strain (LV GLS) was analyzed as previously described in detail12. Because RV FWLS and LV GLS were negative values, we took their absolute values for a simpler interpretation.

Repeatability test

The measurement of RV FWLS was repeated in 20 randomly selected subjects by the same physician (intraobserver variability) and another physician (interobserver variability) to analyze its intraobserver and interobserver variability.

Statistical analysis

Continuous variables are expressed as the mean ± standard deviation (SD) if normally distributed after the Kolmogorov‒Smirnov test and were compared using an independent Student’s t test and one-way analysis of variance (ANOVA). Least significant difference was used to compare the 2 samples when variance was homogeneous, and Dunnett T3 method was used when variance was heterogenetic, according to Levene test for homogeneity of variance. A nonparametric test was performed when the data did not have a normal distribution, and the results are expressed as medians (interquartile ranges). Categorical variables are expressed as n (%) of the sample and were compared using the chi-square test or Fisher’s exact test. The correlations between clinical risk factors and RV FWLS were tested using Pearson correlation coefficients. Three forced-entry logistic regression models were used to determine the independent associations of RV FWLS < 20% with the TyG index: Model 1 (an unadjusted model), Model 2 (a multivariable model) adjusted for age and sex, and Model 3 (a multivariable model) adjusted for age, sex, BMI, SBP, LV GLS, hypertension, and dyslipidemia. The intraclass correlation coefficient (ICC) was used to evaluate intraobserver and interobserver variability, and an ICC ≥ 0.75 indicated good reliability. All statistical analyses were conducted using the SPSS version 23.0 software for Windows (SPSS, Chicago, Illinois, USA). A two-tailed p value < 0.05 indicated statistical significance.

Results

Twelve of the 170 T2DM patients were excluded from the final analysis for meeting the exclusion criteria (3 patients with congenital heart disease, 2 patients with coronary artery disease, 3 patients with atrial fibrillation, 2 patients with valvular heart disease, and 2 patients with poor image quality).

Clinical characteristics

Table 1 shows the comparison of clinical characteristics by TyG index tertiles [T1 (TyG index ≤ 9.1495), T2 (9.1495 < TyG index ≤ 9.7611), and T3 (TyG index > 9.7611)]. Patients in the T3 group were older than those in the T1 and T2 groups and had higher incidences of hypertension and dyslipidemia, accompanied by higher levels of BMI, SBP, DBP, FPG, HbA1c TC, TG, LDL-C and lower level of HDL-C (all P < 0.05). Compared with the T1 group, the values of BMI, SBP, FPG, HbA1c TC, TG, and LDL-C in the T2 group were significantly higher (all P < 0.05). There were no differences in diabetes duration or medication treatment among the three groups (all P > 0.05).

Table 1 Clinical characteristics of participants by tertiles of the TyG index. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein; LDL-C, low-density lipoprotein; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin A1c; TyG index, triglyceride–glucose index; ACEi/ARB, angiotensin converting enzyme inhibitor/angiotensin II receptor blockers; CCB, calcium channel blockers; SGLT-2I, sodium–glucose co-transporter-2 inhibitors; DPP-4I, dipeptidyl peptidase-4 inhibitor; GLP-1RA, glucagon like peptide-1receptor agonist.
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Echocardiographic parameters

Table 2shows the comparison of echocardiographic parameters by TyG index tertiles. From the lowest to the highest tertiles of the TyG index, the RV FWLS decreased significantly (20.28, 19.29 and 18.56; P < 0.01). There were no differences in the RVd-base, RVd-mid, RVA-D, RVA-S, RV-FAC, TDI S, TDI E, TDI A, TAPSE, or LVEF (all P > 0.05) (Fig. 2).

Table 2 Echocardiographic parameters of participants by tertiles of the TyG index. RVd-base, right ventricle basal diameter; RVd-mid, right ventricle middle diameter; RVA-D, right ventricular area at end-diastolic; RVA-S, right ventricular area at end-systolic; RV-FAC, right ventricular fractional area change; TAPSE, tricuspid annular plane systolic excursion; E, peak early diastolic flow velocity of tricuspid valve; A, peak late diastolic flow velocity of tricuspid valve; TDI S, tricuspid annular peak systolic velocity; TDI E, tricuspid annular early diastolic velocity; TDI A, tricuspid annular late diastolic velocity; RV FWLS, right ventricular free-wall longitudinal strain; LVEF, left ventricular ejection fraction; LV GLS, left ventricular global longitudinal strain.
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Fig. 2
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The representative images of RV FWLS and the related value of the TyG index in three groups.

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Association of clinical risk factors with RV FWLS

Bivariate correlation analysis with Pearson’s correlation indicated that age, BMI, SBP, TC, TG, LDL-C, FPG, HbA1c, and TyG index were negatively correlated with RV FWLS (all P < 0.05), whereas LV GLS was positively correlated with RV FWLS (P < 0.05) (Table 3). According to the univariate analysis, the TyG index was significantly correlated with RV FWLS < 20% (OR 1.988, 95% CI 1.195–3.308; P = 0.008). After adjusting for age and sex, the TyG index was still related to RV FWLS < 20% (OR 1.809; 95% CI 1.072–3.053; P = 0.026), which was maintained after further adjustment for BMI, SBP, LV GLS, hypertension, and dyslipidemia (OR 1.888, 95% CI 1.005–3.545; p = 0.048) (Table 4).

Table 3 Correlation tests of potential risk factors for RV FWLS. BMI, body mass index; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin A1c; RV FWLS, right ventricular free-wall longitudinal strain; LV GLS, left ventricular global longitudinal strain; TyG index, triglyceride–glucose index.
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Table 4 Multivariate logistic regression analysis of the TyG index and subclinical right ventricular systolic dysfunction.
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Intraobserver and interobserver variability

The ICCs of the intraobserver and interobserver variabilities of the RV FWLS were 0.978 (95% CI 0.890–0.993) and 0.911 (95% CI 0.788–0.964), respectively, indicating good reliability.

Discussion

To our knowledge, our study is the first to reveal that, in T2DM patients, an increased TyG index is independently associated with subclinical RV systolic dysfunction, which is evaluated by 2D-STE.

LV function has long been a subject of clinical and ultrasound studies. Most previous studies on myocardial dysfunction in patients with diabetes have focused on LV function13,14. With the deepening research on heart function, RV function has gradually entered people’s field of vision and attracts increasing attention. RV function is a critical link between systemic and pulmonary circulation. It plays an important role in the cardiac cycle and is crucial for maintaining overall cardiac function. RV dysfunction can ultimately lead to the failure of the entire heart function. Numerous studies have shown that RV function is valuable in diagnosing and predicting the prognosis of various cardiovascular and pulmonary diseases15,16. In recent years, the RV function of patients with left heart disease has also received increasing attention17,18,19. The main function of the RV is to maintain effective cardiac output. The contraction of RV mainly occurs through the shortening of longitudinal myocardial fibers. Thus, measuring RV longitudinal systolic strain by echocardiography can reflect RV systolic performance early and sensitively20. It can evaluate RV systolic dysfunction earlier than traditional parameters (e.g., TAPSE and RV-FAC), that is subclinical RV systolic dysfunction. It directly measures intrinsic RV myocardial deformation without angle dependence and is less affected by left ventricular apex motion, thus enabling more accurate early assessment of RV systolic function. Therefore, in this study, we chose to evaluate RV systolic function by analyzing RV FWLS. Insulin resistance (IR) is characterized by the reduced efficiency of insulin in promoting the uptake and utilization of glucose in the body. It can cause an imbalance in glucose and lipid metabolism, which leads to metabolic syndrome and T2DM. The TyG index is a biological parameter calculated by multiplying TG and FPG values. It is easily calculated, providing reliable measurement results, and has been proven to be an effective alternative indicator for evaluating IR21. Recent studies have shown that the TyG index is not only a reliable indicator for evaluating IR but also closely related to the development and prognosis of cardiovascular disease (CVD)22,23,24.

Several studies have suggested that a higher TyG index may be associated with subclinical LV dysfunction in T2DM patients8,25. Although previous studies have reported that RV systolic dysfunction is also present in T2DM patients with preserved LVEF26,27. Moreover, RV dysfunction and fibrosis are associated with left ventricular arrhythmias, sudden death, exercise limitation, and impaired RV cardiac output28. However, there is no study on the TyG index and subclinical RV systolic dysfunction in T2DM patients. In this study, we used 2D-STE to evaluate RV systolic function and reported that a higher TyG index is closely related to impaired RV systolic function in T2DM patients. Further multivariate logistic regression analysis revealed that after adjusting for age, sex, BMI, SBP, LV GLS, hypertension, and dyslipidemia, the TyG index was still related to RV FWLS < 20%. Thus, the TyG index was significantly associated with RV subclinical systolic dysfunction in T2DM patients, independent of generally accepted cardiovascular risk factors.

Currently, the pathogenesis of diabetic cardiomyopathy has not been fully elucidated. IR causes an imbalance in glucose metabolism in the human body, leading to inflammation and oxidative stress and causing myocardial impairment. Moreover, IR induces the production of glycation products and free radicals, as well as changes in lipid metabolism, leading to dyslipidemia, as evidenced by elevated TG levels, elevated LDL-C levels, and decreased HDL-C levels. These changes in glucose and lipid metabolism may ultimately lead to myocardial injury and fibrosis.

Our research suggests that a higher TyG index can indicate subclinical RV myocardial dysfunction in T2DM patients. This information can help guide clinicians in the early identification of T2DM patients at high risk of cardiovascular complications as well as in the implementation of early interventions to prevent cardiovascular events and reduce the risks of morbidity and mortality of patients. Moreover, these findings can also guide the early evaluation of the efficacy of hypoglycemic drugs in improving insulin resistance.

Limitations

First, the sample size was relatively small, so further large-scale studies are needed to validate our findings. Second, this was a cross-sectional retrospective study, and the specific causal relationship of the TyG index with reduced RV FWLS remains unclear. Third, owing to the absence of a cutoff value for the TyG index, we could not perform receiver operating characteristic curve analysis to determine a specific TyG index value that indicates subclinical RV myocardial dysfunction.

Conclusion

Our study demonstrated that an increase in the TyG index is independently associated with a decrease in RV FWLS. It is a good indicator for the early detection of subclinical RV myocardial dysfunction in T2DM patients.