Obesity is a major health problem worldwide due to its growing prevalence and early development in life.^{1 - 3} The number of overweight people tends to progressively increase in both developed and developing countries, and the proportion of obese people is around one third of the normal adolescents population.^{4 - 6} As a result, complications of obesity, such as metabolic syndrome, type 2 diabetes mellitus (DM), cardiovascular disorders, respiratory disorders, and psychosocial problems tend to increase.^{7 , 8}

Obesity is typically associated with insulin resistance (IR) and glucose metabolism disorders. Adipose tissue stored in subcutaneous and visceral tissues plays an important role in the development of IR via the active proteins it secretes.⁹ The distribution of this adipose tissue is equally important, with intra-abdominal fat accumulation being closely linked to IR.¹⁰ Additionally, it is already known that subcutaneous fat tissue is correlated to IR whether or not DM is present.^{11 , 12} Recent studies have demonstrated that extra-abdominal visceral fat deposits like mediastinal and epicardial adipose tissue (EAT) are also related to IR.^{9 , 13 , 14} The association between obesity-dependent insulin resistance and EAT has not been fully explained.

Childhood obesity is an important risk factor for atrial fibrillation whereas structural remodeling is very important.¹⁵ In many studies, this close relationship was investigated with electromechanical delay (EMD), which is one of these echocardiographic markers defined as the temporal delay between the detected onset of electrical activity and the realization of force in the myocardium. EMD is an indicator of atrial conduction heterogeneity and can also be obtained easily by tissue Doppler imaging (TDI).¹⁶ In addition, it has been demonstrated that EMD is prolonged in diseases associated with insulin resistance.^{17 , 18} However, the relationship between electromechanical delay and insulin resistance in obese patients has not been studied.

The aim of our study was to determine the relationship between EAT and insulin resistance. In addition, the relationship between insulin resistance and electromechanical delay was investigated.

Enrolled patients were divided into two groups based on the presence of insulin resistance. Forty patients had insulin resistance and 54 patients did not. Both groups had similar age and gender distribution. (p = 0.102, p = 0.069, respectively). Among the anthropometric measurements, weight, height, and BMI were significantly greater in patients with insulin resistance. Additionally, diastolic and systolic blood pressure measurements were significantly greater in the IR group. A comparison of laboratory parameters revealed that the IR group had significantly higher serum insulin and glucose levels (p < 0.001, p = 0.002, respectively). The other laboratory parameters were similar between the groups ( Table 1 ). Among standard echocardiographic measurements, EAT thickness was significantly increased in the group with IR (p = 0.004). Other standard echocardiographic and laboratory parameters were similar between the two groups. Atrial electromechanical delays recorded from different annular segments are given in Table 2 . Lateral and septal PA was significantly higher in the IR group (62.2 ± 8.3 vs. 56.6 ± 8.4, p = 0.002; 46.1 ± 6.1 vs. 42.7 ± 5.9, p = 0.019 respectively). Tricuspid PA was similar between the groups. Inter- and intra-atrial EMD was significantly prolonged in the IR group compared to the group without insulin resistance (23 (18–30) vs. 19.5 (15–23.5), p < 0.010; 9.5 (6.2–10.0) vs. 6 (4–9.2), p = 0.032, respectively)

Table 1

	Obese patients with insulin resistance (n = 40)	Obese patients without insulin resistance (n = 54)	p
Age, median (IQR), years	13 (11–16)	12 (9–15)	0.102
Height, median (IQR), m	1.65 (1.55–1.70)	1.60 (1.43–1.65)	0.009
Weight, mean ± SD, kg	83.47 ± 21.22	64.13 ± 18.24	< 0.001
BMI, median (IQR)	31.8 (28.1–36.9)	29.7 (25–32.3)	0.019
Female/male (n)	20/20	37/17	0.069
SBP, median (IQR), mmHg	110 (100–130)	100 (80–130)	0.003
DBP, median (IQR), mmHg	80 (70–90)	70 (60–80)	0.016
Heart rate, mean ± SD, beats/min	72 ± 6	72 7	0.945
Laboratory findings
Blood glucose, mean ± SD, mg/dL	92 ± 8	87 ± 6	0.002
Insulin, median (IQR), µIU/L	32.5 (24.6–42.7)	13.6 (10.4–16.8)	< 0.001
HbA1C, median (IQR), %	5.5 (5.2–5.6)	5.4 (5.1–5.6)	0.590
Urea, mean ± SD, mg/dL	9.9 ± 3.2	9.5 ± 1.9	0.606
ALT median (IQR), U/L	25 (16–34)	22 (17–28)	0.167
AST mean ± SD, U/L	24.9 ± 8.7	25.3 ± 7.5	0.807
Total Protein, median (IQR), g/dL	7.6 (7.3–7.9)	7.6 (7.2–8.0)	0.672
Albumin, mean ± SD,	4.7 ± 0.2	4.8 ± 0.4	0.577
Triglycerides, median (IQR), mg/dL	131 (112–155)	118 (90–154)	0.289
Total cholesterol, mean ± SD, mg/dL	171.2 ± 32.3	160.6 ± 21.5	0.079
HDL cholesterol, mean ± SD, mg/dL	41.8 ± 9.0	41.0 ± 8.7	0.686
LDL cholesterol, median (IQR), mg/ dL	89.5 (73.2–113.7)	86.5 (73–104.5)	0.491
TSH, median (IQR), mIU/L	2.55 (1.15–3.16)	2.79 (2.06–3.87)	0.061
T4, median (IQR), ng/dL	1.21 (1.13–1.33)	1.17 (1.06–1.30)	0.172
Cortisol, mean ± SD, µg/Dl	10.4 ± 6.4	10.0 ± 4.8	0.760
WBC, median (IQR), x103/mm3	8330 (7232–10150)	8385 (7365–9900)	0.921
Hemoglobin, mean ± SD, g/dL	13.8 ± 0.9	13.5 ± 1.0	0.171
Platelet count, mean ± SD, 103/mm3,	333 ± 77	340 ± 72	0.595
RDW, median (IQR), %	13.6 (13.1–14.2)	13.4 (12.8–14.0)	0.147
Echocardiographic parameters
EF, median (IQR), %	70 (70–72)	72 (70–72)	0.667
LVDD, mean ± SD, mm	4.3 ± 0.5	4.1 ± 0.4	0.059
LVSD, mean ± SD, mm	3.3 ± 0.6	3.2 ± 0.5	0.329
LA diameter, median (IQR), cm	3.0 (3.0–3.4)	3.1 (3.0–3.3)	0.792
RA diameter, mean ± SD, mm	3.3 ± 0.5	3.2 ± 0.5	0.165
RV thickness, median (IQR), cm	0.50 (0.42–0.60)	0.50 (0.40–0.55)	0.121
RV diameter, median (IQR), mm	2.7 (2.4–3.0)	2.5 (2.2–2.8)	0.176
Posterior wall thickness, median (IQR), mm	0.80 (0.70–0.90)	0.70 (0.70–0.80)	0.111
Septal thickness, median (IQR), mm	0.80 (0.70–0.90)	0.70 (0.70–0.90)	0.664
Epicardial adipose tissue, median (IQR), mm	7.15 (5.5–8.8)	5.5 (3.3–7.7)	0.004

BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HbA1c: hemoglobin A1c; ALT: alanine aminotransferase; AST: aspartate aminotransferase; HDL: high-density lipoprotein; LDL: low-density lipoprotein; TSH: thyroid stimulating hormone; RDW: red cell distribution width; EF: ejection fraction; LVDD: left ventricle end diastolic dimension; LVSD: left ventricle end systolic dimension; LA: LEFT atrium; RA: right atrium; RV: right ventricle; WBC: white blood cell. Data are presented as mean±standard deviation (SD), number and percentage, or median and interquartile ranges (IQR). p < 0.05 was considered statistically significant.

Table 2

	Obese patients with insulin resistance (n = 40)	Obese patients without insulin resistance (n = 54)	p
Lateral PA, mean±SD, ms	62.2 ± 8.3	56.6 ± 8.4	0.002
Septal PA, mean ± SD, ms	46.1 ± 6.1	42.7 ± 5.9	0.019
Tricuspid PA, mean ± SD, ms	38.0± 5.8	36.1 ± 4.7	0.088
Inter-atrial EMD, median (IQR), ms	23 (18–30)	19.5 (15–23.5)	0.010
Intra-atrial EMD, median (IQR), ms	9.5 (6.2–10.0)	6 (4–9.2)	0.032

PA: Time interval from the onset of P-wave on surface ECG to the beginning of Am wave interval with tissue Doppler echocardiography; EMD: Electromechanical delay, IQR: Interquartile ranges.

The echocardiographic parameters that showed correlations with HOMA-IR are summarized in Table 3 . EAT thickness, inter- and intra-atrial EMD, lateral and septal PA were positively correlated to HOMA-IR.

Table 3

Variables Correlating with HOMA-IR
	R	P
Epicardial adipose tissue	0.422	< 0.001
Inter-atrial EMD	0.360	< 0.001
Intra-atrial EMD	0.345	0.001
Lateral PA	0.451	< 0.001
Septal PA	0.305	0.001

HOMA-IR: homeostatic model assessment for insulin resistance; PA: time interval from the onset of P-wave on surface ECG to the beginning of Am wave interval with tissue Doppler echocardiography; EMD: electromechanical delay.

The best cut-off value for EAT for the prediction of insulin resistance was >3.85 mm, with 92.5% specificity and 68.5% sensitivity (AUC = 0.672; 95% CI, 0.563–0.781; p = 0.002 ( Figure 1 ).

Figure 1

In the multivariate logistic regression model using the backward stepwise method, EAT thickness (OR = 1.256, 95% CI: 1.016–1.53, p=0.035) and SBP (OR = 1.039, 95% CI: 1.007–1.072, p = 0.015) still remained significant predictors of IR after adjusting for the confounding variables, which were both found to be statistically significant in the univariate analysis ( Table 4 ).

Table 4

	Univariate Analysis						Multivariate Analysis
	B	S.E.	Wald	P	OR	%95 CI	B	S.E.	Wald	P	OR	%95 CI
Epicardial adipose tissue	0,275	0,098	7,886	0,005	1,317	1,087–1,596	0,228	0,108	4,423	0,035	1,256	1,016–1.553
Systolic blood pressure	0,044	0,015	8,384	0,004	1,045	1,014–1,077	0,038	0,016	5,896	0,015	1,039	1,007–1.072
Diastolic blood pressure	0,049	0,019	6,959	0,008	1,050	1,013–1,089
PA Lateral	0,078	0,027	8,661	0,003	1,081	1,026–1,139
PA Septum	0,095	0,038	6,386	0,012	1,100	1,022–1,184
Inter-atrial EMD	0,066	0,029	5,123	0,024	1,068	1,009–1,130
Intra-atrial EMD	0,149	0,065	5,352	0,021	1,161	1,023–1,318
Height	3,845	1,671	5,292	0,021	46,737	1,767–1236,369
Weight	0,032	0,011	8,804	0,003	1,033	1,011–1,055

All the variables from Table 1 and Table 2 were examined and only those significant at p < 0.05 level are shown in univariate analysis. Multivariate logistic regression analyses including all the variables in univariate analysis with the enter method. p < 0.05 was considered statistically significant. Non-significant variables in the multivariate logistic regression analysis were not included in table. B: Beta coefficients; CI: Confidence interval; EMD: Electromechanical delay. OR: odds ratio; PA: time interval from the onset of P-wave on surface ECG to the beginning of Am wave interval with tissue Doppler echocardiography; S.E.: standard error; Wald: Wald test.

This study investigated the relationship between epicardial adipose tissue and insulin resistance among obese children. It has been shown that epicardial adipose tissue is positively correlated to IR and an independent predictor of IR.

IR denotes a condition of relative insensitivity of peripheral tissues (e.g. muscle, liver, and adipose tissue) to the effects of the hormone. IR plays a pivotal role in the development and progression of cardio-metabolic risk factors that, in association with obesity, due to lipolytic effects of adipocytes, leading to large amounts of free fatty acids and impaired secretion of adipokines, both involved in the modulation of insulin sensitivity.^{20 - 22} Although the prevalence of IR is variable among obese patients, Gabato et al. reported it to be 29.1% in their study.²³ In many other studies, this rate has been shown to be over 50%.^{24 - 27} In our study, the rate of IR was found to be 43%. The reason for this difference can be explained by the use of constant HOMA-IR value in other studies, but, in our study, we used HOMA-IR percentile values according to age and gender.

The HOMA-IR is a proxy estimate of IR based upon the relationship between fasting glucose and insulin levels, with higher values of HOMA-IR representing more severe IR.¹⁰ Increased IR and HOMA-IR values increase cardio-metabolic risk. There is no evidence of an association between IR measures and incident AF.²⁸ Many studies have shown that IR is closely related to atrial functions.^{29 , 30} In our study, it was observed that HOMA-IR values were positively correlated with atrial tissue Doppler parameters which are indicative of atrial function in obese children and tissue atrial conduction was increased in the IR group.

Obesity causes prolongation of electromechanical conduction time by many mechanisms such as fat inflammation on the atrial wall, increase in sympathetic nervous system activity, increased inflammatory process, adipokinin dysregulation and activation of pro-fibrotic signaling pathways. Electromechanical conduction prolongation has been shown to be prone to atrial fibrillation.³¹ IR, which is frequently associated with obesity, has an effect on atrial functions due to existing subclinical inflammation. In our study, both intra- and inter-atrial conduction time was found to be higher in obese children with insulin resistance, according to the literature, compared to the non-IR group. This may be explained by the inflammatory process associated with insulin resistance and by the delayed transmission of this inflammatory process on the atrial tissue. In the light of this information, it can be said that obese children who have insulin resistance may be more prone to atrial fibrillation.

Epicardial fat is a visceral fat accumulation that has most of the pathophysiological properties of other visceral adipose tissues, like lipid deposition and release of hormones, cytokines, and chemokines; and it also causes local inflammation.^{32 - 35} It has been shown that body fat distribution, particularly abdominal fat distribution, is correlated with epicardial adipose tissue.³³ Hence, the relationship between epicardial adipose tissue thickness obtained from echocardiography and a number of pathological conditions such as metabolic syndrome, coronary artery disease, hyperlipidemia, blood pressure elevation, and IR has been studied in obese adult and pediatric patients. Epicardial adipose tissue causes the development and/or worsening of IR by increasing free fatty acids, TNF, IL1, IL6, and resistin release and decreasing adiponectin levels.³⁶ Several studies that examined the relationship between epicardial adipose tissue and IR demonstrated a correlation between epicardial adipose tissue and BMI in obese adults.^{37 - 39} Abacı et al. showed a significant correlation with epicardial fatty tissue among obese children.⁴⁰ In line with the literature available, our study demonstrated a correlation between BMI and epicardial fatty tissue. Ishorbagy et al. reported that epicardial fatty tissue was larger in amount in obese patients than in healthy controls, although it did not predict metabolic syndrome.⁴¹ Similarly, Abacı et al. suggested that epicardial fatty tissue failed to predict IR among obese children.⁴⁰ On the other hand, we found that epicardial fatty tissue was an independent predictor of IR. The cause of this discrepancy is that our study was a nested case-control study that only included patients instead of healthy controls. Another important reason was that in our study, IR was taken using determined percentiles according to age and gender.

The relationship between arterial blood pressure and IR has been shown in many studies^{42 , 43} In our study, we found that systolic blood pressure is an independent predictor of IR. This may be due to increased fat tissue in the body that plays an important role in IR and subclinical inflammation caused by inflammatory cytokines, such as IL-6, IL-1 and TFN-alpha secreted from this adipose tissue. Subclinical inflammation can both impair endothelial function and increase blood pressure by decreasing NO release. Another possible mechanism is: there may be sympathetic activation of both the obesity and the obesity-related inflammatory process.^{36 , 44 -47}

Our study had some limitations: its main limitation was the relatively small sample size. Echocardiographic EAT is a linear measurement. Thus, it may not assess the total epicardial fat volume that varies at several myocardial locations. As a result of EAT, metabolically active tissue, inflammatory cytokines and inflammatory markers could be investigated in future studies. The absence of waist circumference measurement was another limitation, precluding the determination of a relationship between waist circumference and epicardial fat.

In conclusion, epicardial adipose tissue is a cheap, easily accessible parameter that can be easily measured with echocardiography and used to identify insulin resistance among children. Since atrial electromechanical delay increased in obese children with insulin resistance, it should be followed closely for atrial fibrillation.