Epicardial adipose tissue thickness as a predictor of gestational diabetes mellitus: a prospective cohort study

Gestational diabetes mellitus (GDM), the most common metabolic disorder of pregnancy, is a long term risk of comorbidities for maternal and neonatal. The study aimed to assess the association between the echocardiographic epicardial adipose tissue (EAT) and the risk for gestational diabetes mellitus (GDM) in the early second trimester. Singleton pregnancies were enrolled in this study between September 2017 and January 2019 during their 16–20 gestational week. Odds ratio (OR) and 95% confidence intervals (CIs) of individual maternal factors as potential predictors for GDM were calculated using generalized linear models. The receiver-operating-characteristic (ROC) analysis was conducted to assess the discriminative capacity of any individual maternal factor in predicting GDM.


Conclusions
Echocardiographic EAT thickness is positively and significantly associated with GDM risk and adverse outcomes related to GDM. Echocardiographic EAT is a simple method to predict the development of GDM prior to actual clinical diagnosis. This method provides a new early window of opportunity to implement primary prevention strategies to prevent GDM and reduce the related adverse maternal and perinatal outcomes.

Background
Gestational diabetes mellitus (GDM), the most common metabolic disorder of pregnancy, is defined as "the type of glucose intolerance that develops in the second and third trimester of pregnancy, resulting in hyperglycemia of variable severity" [1]. The offspring of women with GDM are at an increased risk of macrosomia, neonatal hypoglycemia, and hyperbilirubinemia. The prevalence of GDM varies from 1-20%, and is rising worldwide, parallel to the increment in the prevalence of obesity and type 2 diabetes mellitus (T2DM) [2]. As reported before, the prevalence of GDM in a population of pregnant women usually reflects the prevalence of T2DM in that population [3].
It is important to predict GDM early in pregnancy to enable early interventions to prevent GDM and reduce the adverse maternal and perinatal outcomes. There are currently no established guidelines for the prediction of GDM and no effective modalities for the prevention of its future development prior to actual diagnosis.
Overweight/obesity is a main risk factor for GDM [4]. Body mass index (BMI) is a broad measurement of body fat, and clinicians usually use BMI to assess maternal obesity during pregnancy. However, BMI can't reflect the accumulation and mass of fat very sensitively, especially visceral adipose tissue (VAT) [5]. The previous study found BMI may not be a good predictor for GDM, particularly around the first trimester [6,7]. Recently, a promising ultrasound parameter, epicardial adipose tissue (EAT), has emerged interest to clinical doctors. EAT is the visceral heart adipose and has the lipogenic capacity [8]. EAT thickness is an independent predictor of visceral adiposity [9], showed a close relationship with metabolic syndrome and diabetes [10,11]. Echocardiography is a simple method to measure EAT thickness, which can nicely reflect VAT. Also, lipid profiles were enrolled in this study, which were popular predictors for GDM [12,13]. This study was aimed to investigate the association between the EAT thickness and GDM, and assess the effectiveness of EAT thickness as a predictor for GDM at 16-20 gestational week (GW).

Study design, setting and population
This prospective cohort study was conducted at the First Affiliated Hospital of China Medical University. All participants were admitted to our obstetric clinic between September 2017 and January 2019. All participants provided written informed consent and the study protocol was approved by the Medical Ethics Review Board of China Medical University (Shenyang, Liaoning, China).

Inclusion and exclusion criteria
Participants were eligible: (1) a singleton pregnancy; (2) had their first pregnancy visit during 16-20 GW. Gestational age was determined by ultrasound within 3 months of pregnancy confirmation; (3) signed the informed consent and provided complete medical history. Participants were not eligible if they had a history of (1) diabetes; (2) hypertension; (3) cardiovascular diseases.

Data collection during 16-20 GW
Anthropometric parameters such as weight, height, heart rate, systolic blood pressure, and diastolic blood pressure were measured, and BMI was calculated [14].
A peripheral blood sample of the participants was collected in a vacutainer collection tube before 20 GW. The lipid profile including triglyceride, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an auto-analyzer (AU1000; Olympus, Tokyo, Japan).
Echocardiographic evaluation was performed in the left lateral position using a Philips iE33 system (Philips Medical Systems, Bothell, WA, USA) and a 1.5/5 MHz phased array probe with a frame rate of 60-90 fps. All images and measurements were obtained from standard views according to the recommendations of the American Society of Echocardiography for chamber quantification [15,16]. All images were digitally stored and analyzed offline using customized software (Qlab;

Philips Medical Systems)
The left ventricle end-diastolic dimension was obtained in the parasternal long-axis view. Left ventricle end-diastolic volume and end-systolic volume were obtained using the biplane modified Simpson's method. Stroke volume, cardiac output, and left ventricle ejection fraction were used as standard indexes of left ventricle systolic function. The peak of early diastolic velocity (E wave) and the peak of late diastolic velocity (A wave) across the mitral valve were obtained. The ratio between them (E/A) was used as a standard index of left ventricular diastolic function.

Data collection during 24-28 GW
GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups [17]. A diagnosis of GDM was made when one or more of the test parameters equaled or exceeded the following cut points: fasting 5.1 mmol/L, 1-h 10.0 mmol/L, or 2-h 8.5 mmol/L.

Postpartum Follow up
Adverse GDM-associated outcomes were recorded, including large for gestational age, neonatal hypoglycemia, admission to neonatal intensive care unit (NICU), preterm delivery, and hyperbilirubinemia [4,18].

Statistical analysis
Statistical analysis was performed using STATA version 14.0 software. Continuous parameters were expressed as the mean ± standard deviation. Non-normally distributed parameters were expressed as the median (interquartile range).
Differences of normally distributed continuous parameters between groups were analyzed using the independent-samples t-test. Differences of non-normally distributed parameters between groups were analyzed using the Mann-Whitney U test. Differences of categorial parameters between groups were analyzed using Pearson's chi-squared test. Odds ratio (OR) and 95% confidence intervals (CIs) of individual maternal factors as potential predictors for GDM were calculated using generalized linear models. The receiver-operating-characteristic (ROC) analysis was conducted to assess the discriminative capacity of any individual maternal factor in predicting GDM. A two-tailed P < 0.05 was used to define statistical significance.

Comparison of Clinical data
A total of 489 mothers met eligibility criteria were included in the main analysis, including 69 GDM and 420 normal women (Figure 1). Table 1 revealed that the baseline of the clinical characters of participants between the GDM and control groups. The difference between the two groups in the maternal age, BMI, and lipid profiles (triglyceride, total cholesterol, and HDL-C) were statistically significant (P < 0.05). Table 2 revealed that EAT thickness significantly increased in the GDM group compared with the control group (P < 0.05). The other clinical and echocardiographic parameters were not significantly different between the two groups (P > 0.05).

Regression analysis for the presence of GDM
The results of all regression analyses were summarized in Table 3. Univariate regression analysis revealed that maternal age (OR = 1.08, 95%CI: 1.01-1. 17 Table 4 showed that the risk of adverse outcomes, including large for gestational age, neonatal hypoglycemia, admission to NICU, preterm delivery, and hyperbilirubinemia, increased in the GDM group compared with the control group (P < 0.05).

ROC analysis
We performed an ROC analysis to verify whether EAT thickness could predict GDM.
The ROC curve was shown in VAT plays a leading part in the development of obesity which is an important risk factor of GDM. As a special type of VAT, EAT has its multifaced functions. More researchers focused on the adverse effects of EAT which has been confirmed as a diabetic risk marker [11]. Two cross-sectional studies with limited samples found the difference of EAT thickness between the GDM and the control group at the late second trimester (24-28 GW) [19,20]. However, whether EAT has a significant difference in the early second trimester and then EAT becomes a predictor for GDM are unclear.
In this study, the result revealed that EAT thickness significantly increased in the GDM group compared with the control group during 16-20 GW. Meanwhile, higher EAT thickness was associated with the adverse outcomes in GDM patients. We proposed some mechanisms for the increasing EAT thickness in the GDM patients base on the existing research:

Insulin resistance (IR)
EAT as a lipid-storing depot and endocrine organ can secreting adipokines. IR is the main mechanism of GDM [21], which may be affected by the amount of adipose tissue before pregnancy and/or its increase during pregnancy. Higher retinol binding protein 4 (RBP4, as insulin resistance) and lower adiponectin (as insulin-sensitizer), which were secreted by adipose including EAT, cause insulin resistance in the GDM before 16 GW [22, 23].

Inflammatory
EAT expresses and secretes pro-inflammatory and anti-inflammatory adipokines and cytokines [24]. The first-second trimester released higher pro-inflammatory adipokines (RBP4, hs-CRP, fatty acid-binding protein-4, leptin, and visfatin) and lower anti-inflammatory adipokines (omentin-1 and adiponectin) may participate in the chronic low-grade inflammation state which has been confirmed to be associated with GDM [23]. Additionally, tumor necrosis factor (TNF), as a cytokine secreted by EFT was increasing in GDM patients [25]. In summary, EAT-induced proinflammatory milieu trigger physiopathological mechanisms such as obesity, insulin resistance, and disturbed insulin signaling cascade [26,27].
In this study, the result also showed that maternal HDL-C is negatively associated with the risk for GDM and can increase the predicted performance of EAT thickness.
Echocardiography is inexpensive and noninvasive. Using echocardiography to measure EAT thickness is not only easy to visualized and measured, but also accurate and reproducible [34]. Echocardiographic EAT has a close relationship with IR and inflammatory, can reflect the metabolic effects of IR [35]. Moreover, in women who are normoglycaemic before pregnancy but go on to develop GDM in the late gestation, there is an evidence of decreased peripheral insulin sensitivity before conception [36]. Thus, the EAT thickness increased before hyperglycemia shows up, which due to the insulin response is inadequate as the IR increases in late pregnancy. In the early second trimester, measure EAT thickness and HDL-C can evaluate the risk of GDM which may help clinicians to develop appropriate treatment strategies.
Pregnancy can be viewed as a cardiovascular stress test in that the development of certain complications has the potential to reveal a woman's susceptibility for future vascular or metabolic disease [37]. The measurement of EAT provides us a new way to screen the high risk not only for GDM but also for other cardiovascular complications related to metabolic disease. The present data support the relationship between EAT and GDM. New studies with longer follow-up duration are needed to determine whether the high-risk people for GDM identified by EAT levels are related to T2DM and cardiovascular diseases, which allows us to implement preventive measures for this population.
In our study, participants are all Asian women. However, the race may be an important consideration when analyzing the relationship between EAT and disease risk [38]. Also, there are no data of inflammatory biomarkers which may be beneficial to the effectiveness of the EAT thickness model in the early second trimester.

Conclusion
Our findings indicate that echocardiographic EAT thickness is positively and significantly associated with GDM risk and adverse outcomes related to GDM.

Availability of data and material
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate
This study obtained ethical approval from the Medical Ethics Review Board of China Medical University (Shenyang, Liaoning, China). All participants provided written informed consent.

Consent for publication
Not applicable.     Receiver operating characteristic (ROC) curves for EAT thickness and HDL-C for the predictio