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Increased epicardial adipose tissue in young adults with congenital heart disease comorbid with major depressive disorder
Journal of Affective Disorders 257 (2019) 678–683
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Research paper
Increased epicardial adipose tissue in young adults with congenital heart disease comorbid with major depressive disorder
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Kai G. Kahla, , Daniela Fraccarollob, Lotta Wintera, Johann Bauersachsb, Mechthild Westhoff-Bleckb a b
Dep. of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Germany Dep. of Cardiology and Angiology, Hannover Medical School, Adult Congenital Heart Centre, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Adult congenital heart disease Major depressive disorder Epicardial adipose tissue
Objective: Congenital heart disease is the most common congenital malformation. In adult congenital heart disease (ACHD), the prevalence of major depressive disorder (MDD) is increased. Beyond its immanent health risks, increased epi‑ and paracardial adipose tissue has been described in MDD. Epicardial adipose tissue (EAT) is a fat depot surrounding the heart, and it is hypothesized to be associated with coronary artery disease, leftventricular dysfunction and atrial fibrillation, being frequent problems in ACHD long-term management. We here examined whether EAT is increased in depressed patients with ACHD. Methods: Two-hundred and ten ACHD outpatients (mean age 35.5y, 43% female) were included. MDD was diagnosed according to DSM-IV criteria using expert interviews. EAT was measured using echocardiography. Further assessments comprised NT-proBNP, left and right ventricular end-diastolic diameter, left-ventricular ejection fraction, smoking behavior and physical activity. Results: Of 210 patients, 53 (25.2%) were diagnosed with MDD. EAT was increased in depressed ACHD (F = 5.04; df = 1; p = 0.026). Depressed male patients were less physically active (p < 0.05) and smoked more cigarettes (p < 0.05). EAT was positively predicted by depression severity (p = 0.039), body mass index (p < 0.001), and negatively predicted by physical activity (p = 0.019). Conclusions: The presence of MDD is associated with an increased amount of EAT in ACHD, and is dependent on depression severity. Further, the amount of EAT is at least in part mediated by a more sedentary lifestyle. Given the long-term health risks associated with increased EAT, interventions aiming at increased physical activity, smoking cessation and early identification of comorbid MDD may be recommended in ACHD.
Introduction Congenital heart disease (CHD) is the most common congenital malformation diagnosed in newborns, with birth prevalence reported to be 0.9–1% of live births worldwide (Hoffman et al., 2004). Congenital heart disease comprise a spectrum of anatomical malformations ranging from specific lesions such as ventricular septal defect, to complex lesion e.g. transposition of the great arteries or double outlet right ventricle (Stout et al., 2019). Due to early diagnosis and advances in the fields of cardiac surgery and interventional cardiology survival of CHD patients who reach adulthood has significantly improved, and approximately 1.2 million adults with CHD have been reported for Europe (Moons et al., 2006). Further, higher rates of major depressive disorder (MDD) have been observed in ACHD (Westhoff-Bleck et al., 2016). Heart failure (HF) is increasingly recognized as cause of mortality in
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adults with congenital heart disease (ACHD) (Alshawabkeh and Opotowsky, 2016). Left-ventricular dysfunction plays an important role in HF and has been correlated to epicardial adipose tissue (EAT) (Lin et al., 2013; Vural et al., 2014). EAT is an ectopic fat tissue with close proximity to the myocardium and to coronary artery vessels, that exerts paracrine effects on nearby anatomic structures (Iacobellis and Bianco, 2011). EAT correlates with the presence of coronary atherosclerosis independent of other risk factors, myocardial ischemia, and major adverse cardiovascular events (Mancio et al., 2018), coronary artery calcification (Kim et al., 2015), high risk plaques (Nerlekar et al., 2017), and measures of left-ventricular structure and function, such as left ventricular mass (Iacobellis et al., 2004), left-ventricular end-diastolic volume (Park et al., 2014; Watanabe et al., 2016), impaired diastolic filling (Dabbah et al., 2014), and left-ventricular diastolic dysfunction (Nakanishi et al., 2017).
Corresponding author. E-mail address: [email protected] (K.G. Kahl).
https://doi.org/10.1016/j.jad.2019.07.070 Received 9 May 2019; Received in revised form 3 July 2019; Accepted 29 July 2019 Available online 30 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 257 (2019) 678–683
K.G. Kahl, et al.
Assessment of psychiatric disorders and behavioral factors
Pericardial adipose tissue was found increased in patients with major depressive disorder (MDD) (Kahl et al., 2014), particularly those with a chronic disease course (Kahl et al., 2017). In these studies, pericardial adipose tissue was defined as the composite of epicardial (located between the myocardium and the visceral pericardium) and paracardial adipose tissue (located outside the visceral pericardium on the external surface of the parietal pericardium). Beside alterations in neurotransmission, MDD is associated with a variety of behavioral, endocrine and immune disturbances, comprising impaired physical activity, increased sedentary behavior (Schuch et al., 2016), hyperactivity of the hypothalamus-pituitary adrenal axis with increased adrenal glands and hypercortisolism (Heuser, 1998; Kahl et al., 2015), and increased levels of interleukin-6 and tumor-necrosis factor-α (Goldsmith et al., 2016). These factors are likely to play a role in the accumulation of EAT in MDD. In ACHD, increased rates of MDD have been found in several studies when expert interviews were chosen for diagnostic process (Bromberg et al., 2003; Horner et al., 2000; Kovacs et al., 2009a; Westhoff-Bleck et al., 2016). However, EAT and its relation to MDD has not been studied in ACHD. We therefore aimed to evaluate the role of EAT in ACHD. We hypothesized increased EAT in ACHD comorbid with MDD.
Psychiatric diagnosis was made by an experienced psychologist (L.W.) or psychiatrist (K.G.K.) using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental disorders, 4th edition. Both raters were blinded for echocardiographic data obtained from ACHD patients. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS). Participants completed a demographic survey that included educational, marital, employment status, smoking habits (expressed as pack-years), and alcohol drinking behavior (expressed as drinks consumed per week). Physical activity and exercise were assessed using a 6-point Likert scale with descriptors described as “no physical activity or exercise training” (1); “occasional physical activity (such as walking) or exercise training” (2); “light physical activity or exercise training, but less then 1× weekly (3); moderate physical activity or exercise: regular physical activity (cycling or walking) or exercise training 1× weekly (4); often, more than 1× exercise training weekly, or cycling plus regular walking; and “very often, exercise training more than 3× weekly” (Cuppett and Latin, 2002). Alcohol consumption was assessed as drinks per week, and cigarette smoking by pack-years (cigarettes per day × years smoking)/20).
Material and methods
Assessment of cardiac disease
Participants
Each patient was thoroughly examined by a cardiologist. Functional status was determined according to the New-York Heart Classification (NYHA class) (Chacko, 1995). Echocardiography was performed in all patients to evaluate cardiac morphology and function. Cardiac defects were categorized as simple, of moderate or of great complexity using the Warns classification (Warnes et al., 2001). A summary of cardiovascular characteristics of the study population is given in Table 1. Echocardiographic assessment of EAT was derived from two-dimensional standard parasternal long axis/short axis views at end-diastole. EAT thickness was measured on the right free ventricular wall perpendicular to the aortic annulus. The cardiologist performing echocardiographic assessments (M.W.-B.) was blind for the status of patients concerning MDD.
The PSYConHEART study is an ongoing project aiming at examining morbidity and mortality factors in ACHD. The study protocol conforms to the ethical guidelines of the 1975 declaration of Helsinki and was approved by the local ethics committee. All patients gave their informed written consent prior to study entry. Patients were recruited from the ACHD outpatient clinic of the Dep. of Cardiology and Angiology at the Hannover Medical School (Hannover, Germany). The inclusion criteria were as follows: (1) structural congenital heart disease, (2) ability to read and complete the informed consent form and questionnaires in German, and (3) age of 18 or older. Exclusion criteria were pregnancy and instable cardiac condition. Currently, two-hundred fifteen ACHD patients were recruited, of whom five had to be excluded due to missing data. The final sample consists of 120 male, and 90 female patients. Details of the underlying heart disease and treatment are given in Table 1.
Statistical analysis Data were analyzed using IBM SPSS (Version 25, 2016). Group comparisons (ACHD versus ACHD with MDD) were determined performing Chi-square test for categorical variables, and t-Test for nominal variables. Since gender, age and BMI are known factors that influence epicardial adipose tissue, we used ANCOVA with age and BMI as potentially confounding variables, group (ACHD versus ACHD with MDD) and gender as independent variables, and epicardial adipose tissue as dependent variable. Regression analysis was performed according to the stepwise BACKWARD method, with epicardial adipose tissue as dependent variable. All factors that were different between the groups were included as potential predictors in the regression model. These factors comprised: BMI, gender MADRS sum score, school-years, sportscore, NYHA-class, systolic blood pressure, and QRS-time. Age was also included as potential predictor, since others found an effect of age on the accumulation of epicardial adipose tissue (Shi et al., 2015). All data are given as mean ± SD. A p-value <0.05 was considered statistically significant.
Table 1 Cardiovascular characteristics of the study population according to Warnes classification (19). (n) Simple shunts Atrioventricular septal defect Mitral valve disease Anomalous pulmonary venous connection Bicuspid aortic valve Subaortic stenosis Supravalvular aortic stenosis Coarctation Congenital pulmonary stenosis Double chambered right ventricle Tetralogy of fallot Ebstein anomaly Marfan syndrome D-transposition: atrial switch D-transposition: arterial switch Congenital corrected transposition Fontan type circulation Eisenmenger syndrome Common arterial trunc Total
15 12 6 1 27 6 1 24 9 2 31 6 17 25 1 3 17 6 1 210
Results Group description concerning anthropometric and psychosocial factors Fifty-three ACHD patients (25.2%) were diagnosed with MDD. More female compared to male ACHD patients were diagnosed with MDD 679
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group (ACHD: 3.4 ± 1.1 versus ACHD + MDD: 2.7 ± 1.7), while cigarette smoking expressed as pack-years and alcohol drinking behavior (expressed as drinks per week) were similar between the groups (Table 2). Further analysis concerning possible differences between male and female with ACHD +/- MDD was performed using ANOVA. We observed group differences for physical activity (F = 3.3; df = 3; p = 0.019), cigarette smoking (F = 3.6; df = 3; p = 0.014), and alcohol intake (F = 6.3; df = 3, p < 0.001). The corresponding post-hoc analyses revealed the following results: Male depressed ACHD patients were less physically active, and smoked more cigarettes than male ACHD without depression (p < 0.05, respectively). Further, male ACHD (with and without depression) were drinking more alcohol compared to their female counterparts (p < 0.05) (supplemental Table 1)
Table 2 Psychosocial, lifestyle, cardiologic and laboratory data in 210 adults with congenital heart disease (ACHD) with and without major depressive disorder. ACHD (N = 157)
Female (N/%) 60 (38.2%) Age(y) 35.1 ± 11.4 BMI 24.8 ± 4.9 Education(N/%) Secondary modern 28/153 (17.8%) school Junior high school 67/153 (42.7%) High school 62/153 (39.5%) Partnered (N/%) 89/153 (74.8%) Depression sum score 3.6 ± 4.1 (MADRS) Age at first surgical 11.1 ± 15.2 procedure (y) Number of surgical 1.4 ± 1.1 procedures Lifestyle and health behavior Sport-score 3.4 ± 1.1 Pack-years 2.5 ± 6.1 Drinks per week 2.0 ± 3.3 Cardiologic data NYHA 1.2 ± 0.55 RRsyst (mmHg) 112.3 ± 15.2 QRS (ms) 120.5 ± 28.5 QTc (ms) 427.5 ± 28.5 LVEDD 53.5 ± 7.5 RVEDD 38.4 ± 8.4 Left ejection fraction 56.3 ± 8.5 Laboratory parameter CRP (mg/dl) 3.1 ± 5.3 LDL (mg/dl) 115.8 ± 32.2 HDL (mg/dl) 53.2 ± 13.4 HbA1c (%) 5.3 ± 0.3 BNP (ng/L) 194.9 ± 294.7 Creatinin (µmol/L) 77.9 ± 14.8
ACHD with MDD (N = 53)
P
30 (56.6%) 36.4 ± 11.9 26.6 ± 4.3
0.015 n.s. 0.022 0.045
7/53 (13.2%) 33/53 (62.3%) 13/30 (24.5%) 30/53 (56.6%) 16.4 ± 6.3a,b
n.s. <0.001
9.1 ± 13.0
n.s.
1.3 ± 0.9
n.s.
2.7 ± 1.7 4.2 ± 8.7 1.6 ± 3.4
0.013 n.s. n.s.
1.4 ± 0.6 107.1 ± 13.7 116.0 ± 32.6 423.6 ± 47.0 52.9 ± 5.5 39.7 ± 9.7 56.9 ± 9.0
0.09 0.033 0.001 n.s. n.s. n.s. n.s.
3.6 ± 6.6 124.0 ± 27.8 50.0 ± 11.6 5.4 ± 0.3 184.8 ± 222.1 76.5 ± 16.6
n.s. n.s. n.s. n.s. n.s. n.s.
Epicardial adipose tissue (EAT) is enlarged in ACHD with comorbid MDD ANCOVA with EAT as dependent variable, group (ACHD versus ACHD + MDD) and gender as covariates, and BMI and age as potential confounders revealed a main effect of group (F = 5.05; df = 1; P = 0.026), and a group x gender effect (F = 7.4; df = 1; P = 0.007). Furthermore, BMI emerged as significant factor contributing to EAT (F = 50.2, df = 1; P < 0.001) (Fig. 1). Post-hoc analysis revealed significantly increased EAT in depressed male patients compared to non-depressed male ACHD (p < 0.001), although this difference was not observed in female ACHD patients with/ without depression (supplemental Table 1). Predictors of epicardial adipose tissue (EAT) To explore potential factors that predict the amount of EAT a regression analysis (BACKWARD model) with EAT as dependent variable, and all factors that were different between the groups (BMI, gender, school-years, depression severity, physical activity, NYHA class, blood pressure, QRS duration) including age was performed. A significant model emerged (F = 23.1; df = 3; P < 0.001) with depression severity expressed as MADRS sum score (T_ = 2.07; ß = 0.13; P = 0.039), physical exercise (T = −2.36; ß = −0.15; P = 0.019) and BMI (T = 6.11; ß = 0.38; P < 0.001) as predictors of EAT (Table 2). We observed a trend towards age as predictor for EAT (T = 1.66; ß = 0.10; P = 0.97).
Legend: NYHA: severity of heart insufficiency according to the New York Heart Association; RRsyst: systolic blood pressure; QRS: QRS complex duration in milliseconds; QTc: QTc duration in milliseconds; LVEDD: left ventricular enddiastolic diameter; RVEDD: right ventricular end-diastolic diameter; CRP: Creactive protein; LDL: low density lipoprotein; HDL: high density lipoprotein; BNP: brain natriuretic peptide. Table 3 Regression analysis reveals depression severity, physical exercise and BMI as predictors of epicardial adipose tissue.
BMI Exercise MADRS
Regression coefficientB
Standard error
Beta
T
P
0.018 −0.018 0.004
0.003 0.008 0.002
0.38 −1.54 0.13
6.11 −2.36 2.07
<0.001 0.019 0.039
Cardiologic data and laboratory parameter NYHA class was slightly lower in ACHD (ACHD: 1.2 ± 0.55 versus
(30/90 female, and 23/120 male; P = 0.024). Among patients with MDD, 33/53 (62.2%) reported recurrent MDD, 16/53 (30.2%) had their first depressive episode before twenty years of age, and 18/53 (34%) had a positive family history for MDD. (Table 2). Groups (ACHD without MDD, referred to as ACHD; and ACHD with comorbid MDD, referred to as ACHD + MDD) were different concerning gender distribution (ACHD: 38.2% female versus ACHD + MDD: 56.6% female; P = 0.015), BMI (ACHD: 24.8 versus ACHD + MDD: 26.6; P = 0.022), school years (P = 0.045) and depression severity assessed as MADRS sum score (ACHD: 3.6 ± 4.1 versus ACHD + MDD: 16.4 ± 6.3; P < 0.001) (Table 2).
Fig. 1. Epicardial adipose tissue is increased in adults with congenital heart disease comorbid with depression (ACHD + MDD) compared to adults with congenital heart disease without depression (ACHD).
Group differences concerning lifestyle factors/health behaviors The self-estimated amount of exercise was higher in the ACHD 680
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Of note, the leading causes of death in the long-term management of ACHD patients are heart failure, ischemic heart disease and arrhythmias. In the CONCOR registry of more than 10.000 ACHD patients, progressive heart failure (24.5%) and arrhythmias (21.9%) were the most single cardiac causes of death (Zomer et al., 2011). In the study by Yu and colleagues in 3068 ACHD patients from Australia, 11% died during a 6.2y observation time. The leading single causes of death attributable to cardiac reasons were heart failure (17%), sudden cardiac death (23%) and acute or chronic ischemic heart disease (20%) (Yu et al., 2018). Interestingly, these conditions are at least in part be influenced by the amount of EAT, and this association has particularly been described for heart failure, arrhythmias and ischemic heart disease (Zomer et al., 2011). Therefore our results of increased EAT in depressed ACHD are highly relevant for long-term treatment. First, it is recommended that MDD is properly diagnosed in ACHD patients. Second, EAT may emerge as a prognostic relevant biomarker that can easily be assessed during routine echocardiographic assessments. Third, EAT has been demonstrated to be amenable to exercise interventions. Studies in non-depressed individuals showed favorable effects of aerobic exercise on epi‑ and pericardial adipose tissue in obese and non-obese individuals (Bairapareddy et al., 2018; Fernandez-Del-Valle et al., 2018; Honkala et al., 2017). In depressed patients, a supervised and individualized 6 week exercise intervention was effective in improving physical fitness and reducing epicardial adipose tissue (Kahl et al., 2016; Kerling et al., 2015). Strengths and Limitations: All individuals received an expert interview, the (Structured Clinical Interview according to DSM-IV (SKID), by a psychologist (L.W.) or a psychiatrists (K.G.K.) with at least 5 years practice. Concerning limitations, exercise was determined using a selfrating 6-point Likert scale. Objective measures of physical activity may more properly assess the intensity of physical activity. Recent data derived from the NHANES study demonstrated similar results concerning metabolic health in relation to objective (accelerometer) and subjective assessments of physical activity (Thakkar et al., 2018). However, accelerometer assessment required 4 valid days of wear time, being infeasible with the mode of operation in a nationwide health care center. Second, the cross-sectional design makes it difficult to draw causal conclusions from our results. Furthermore it can be discussed whether the chosen method of EAT measurement provides an accurate assessment. We quantified EAT at a single location perpendicular to the aortic annulus at end-diastole. Some authors prefer measurements at end-systole to avoid the impact of adipose tissue compression evoked by ventricular diastolic expansion. Currently there is still no consensus on this topic. Furthermore, EAT is variable in distribution and total volume. Currently echocardiographic thickness assessment of EAT has not been validated against magnetic resonance imaging derived volume and thickness of EAT. Nevertheless, several studies suggest, that echocardiographic single location measurements at either end-diastole or at end-systole seem suitable to provide relevant information of metabolic and cardiovascular status (Iacobellis and Bianco, 2011; Kim et al., 2015; Mancio et al., 2018; Nerlekar et al., 2017; Vural et al., 2014; Watanabe et al., 2016). Conclusion: Taken together, we demonstrate increased epicardial adipose tissue in depressed patients with ACHD. Unhealthy lifestyle factors, especially physical inactivity and unhealthy diet leading to overweight/obesity, are related to increased EAT, and are amenable to specific interventions. Given the biobehavioral changes associated with MDD, screening for MDD should be recommended as part of ACHD diagnostic routine. Further, integration of interventions aiming at improvement of physical fitness, smoking cessation and healthy diet are also recommended in the long-term treatment of ACHD.
ACHD+MDD: 1.4 ± 0.6; P = 0.09). Systolic blood pressure (ACHD: 112.3 ± 15.2 versus ACHD + MDD: 107.1 ± 13.7; P = 0.33) and QRS time (ACHD: 120.5 ± 28.5 versus ACHD + MDD: 116.0 ± 32.6; P = 0.001) were slightly higher in the ACHD group. All determined laboratory parameter including HbA1c, CRP and brain natriuretic peptide (BNP) were similar between the groups. Further results of the post-hoc analyses are reported in supplementary Table 1. Discussion The main findings of our study are that 1) depressed patients with ACHD have higher amounts of EAT, 2) EAT is positively predicted by depression severity, BMI, and negatively predicted by physical activity. Of notice, in our study EAT was not predicted by NYHA class; and 3) depressed male ACHD patients have worse lifestyle factors compared to their non-depressed counterparts. Particularly, physical activity is lower and cigarette smoking is higher. Major depressive disorder has been shown to worsen the disease course and predict mortality in several cardiovascular disorders, such as ischemic and non-ischemic heart failure (Fan et al., 2014; Freedland et al., 2011), coronary heart disease (Barth et al., 2004; Nicholson et al., 2006), myocardial infarction (Meijer et al., 2013, 2011; van Melle et al., 2004; Wu and Kling, 2016) (Hosseini et al., 2014), hypertension (Oganov et al., 2011), and ischemic heart disease (Chazov et al., 2007). MDD has also been demonstrated as a strong predictor of arrhythmic deaths in patients with atrial fibrillation and heart failure (FrasureSmith et al., 2009). These studies indicate growing evidence that MDD has an important influence on the disease course in several cardiologic diseases, and that MDD has an independent prognostic value. Although several studies in adults with congenital heart disease have found increased rates of MDD, it has to be evaluated whether MDD has also prognostic importance in this disease (Bromberg et al., 2003; Kovacs et al., 2009b; Westhoff-Bleck et al., 2016). The potential mechanisms by which MDD might alter the disease course and mortality in cardiovascular disease is still a matter of debate. Numerous studies have investigated biobehavioral mechanisms to explain the effects of MDD on cardiovascular morbidity and mortality in patients with coronary heart disease. MDD is associated with several cardiovascular risk factors such as smoking, sedentary lifestyle, less physical fitness, obesity, diabetes mellitus, the metabolic syndrome or its single factors (Kahl et al., 2012; Skala et al., 2006). Further, studies demonstrated decreased adherence to treatment recommendations of depressed patients in CHD (Carney et al., 1995; Gehi et al., 2005). There has been less research on mechanisms that may account for the negative prognostic effect of MDD in other cardiovascular diseases. However there might be substantial overlap in the candidate mechanisms discussed in coronary heart disease, since the above described biobehavioral alterations have also been demonstrated in MDD per se (Ghanei Gheshlagh et al., 2016; Kerling et al., 2016; Luppino et al., 2010; Mezuk et al., 2008; Stubbs et al., 2018a, 2018b). Further potential mechanisms that link MDD with cardiovascular and metabolic disorders include alterations of the hypothalamus-pituitary-adrenal system with subsequent relative hypercortisolism (Carroll et al., 2012), increased adrenal glands (Kahl et al., 2015), altered platelet activation (Maes et al., 1996), increased rates of the metabolic syndrome (Kahl et al., 2012), a dysbalance of cytokine networks with elevated interleuin-6 and tumor-necrosis factor-α (Kohler et al., 2017), increased intima-media thickness (Greggersen et al., 2011) and body-composition changes (Kahl et al., 2018). In particular, increased amounts of pericardial adipose tissue have been observed in depressed patients (Kahl et al., 2014), with the highest amounts in chronic MDD (Kahl et al., 2017). In these studies, the term “pericardial” adipose tissue comprised the epicardial compartment, located between myocardium and visceral pericardium, and the adipose tissue on the parietal surface of the pericardium.
Author statement This statement is to certify that all authors have seen and approved 681
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the manuscript being submitted, have contributed significantly to the work, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission to the Journal of Affective Disorders. We attest that the article is the Authors' original work, has not received prior publication and is not under consideration for publication elsewhere. On behalf of all Co-Authors, the corresponding Author shall bear full responsibility for the submission. Any changes to the list of authors, including changes in order, additions or removals will require the submission of a new author agreement form approved and signed by all the original and added submitting authors.
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Research paper
Increased epicardial adipose tissue in young adults with congenital heart disease comorbid with major depressive disorder
T
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Kai G. Kahla, , Daniela Fraccarollob, Lotta Wintera, Johann Bauersachsb, Mechthild Westhoff-Bleckb a b
Dep. of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Germany Dep. of Cardiology and Angiology, Hannover Medical School, Adult Congenital Heart Centre, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Adult congenital heart disease Major depressive disorder Epicardial adipose tissue
Objective: Congenital heart disease is the most common congenital malformation. In adult congenital heart disease (ACHD), the prevalence of major depressive disorder (MDD) is increased. Beyond its immanent health risks, increased epi‑ and paracardial adipose tissue has been described in MDD. Epicardial adipose tissue (EAT) is a fat depot surrounding the heart, and it is hypothesized to be associated with coronary artery disease, leftventricular dysfunction and atrial fibrillation, being frequent problems in ACHD long-term management. We here examined whether EAT is increased in depressed patients with ACHD. Methods: Two-hundred and ten ACHD outpatients (mean age 35.5y, 43% female) were included. MDD was diagnosed according to DSM-IV criteria using expert interviews. EAT was measured using echocardiography. Further assessments comprised NT-proBNP, left and right ventricular end-diastolic diameter, left-ventricular ejection fraction, smoking behavior and physical activity. Results: Of 210 patients, 53 (25.2%) were diagnosed with MDD. EAT was increased in depressed ACHD (F = 5.04; df = 1; p = 0.026). Depressed male patients were less physically active (p < 0.05) and smoked more cigarettes (p < 0.05). EAT was positively predicted by depression severity (p = 0.039), body mass index (p < 0.001), and negatively predicted by physical activity (p = 0.019). Conclusions: The presence of MDD is associated with an increased amount of EAT in ACHD, and is dependent on depression severity. Further, the amount of EAT is at least in part mediated by a more sedentary lifestyle. Given the long-term health risks associated with increased EAT, interventions aiming at increased physical activity, smoking cessation and early identification of comorbid MDD may be recommended in ACHD.
Introduction Congenital heart disease (CHD) is the most common congenital malformation diagnosed in newborns, with birth prevalence reported to be 0.9–1% of live births worldwide (Hoffman et al., 2004). Congenital heart disease comprise a spectrum of anatomical malformations ranging from specific lesions such as ventricular septal defect, to complex lesion e.g. transposition of the great arteries or double outlet right ventricle (Stout et al., 2019). Due to early diagnosis and advances in the fields of cardiac surgery and interventional cardiology survival of CHD patients who reach adulthood has significantly improved, and approximately 1.2 million adults with CHD have been reported for Europe (Moons et al., 2006). Further, higher rates of major depressive disorder (MDD) have been observed in ACHD (Westhoff-Bleck et al., 2016). Heart failure (HF) is increasingly recognized as cause of mortality in
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adults with congenital heart disease (ACHD) (Alshawabkeh and Opotowsky, 2016). Left-ventricular dysfunction plays an important role in HF and has been correlated to epicardial adipose tissue (EAT) (Lin et al., 2013; Vural et al., 2014). EAT is an ectopic fat tissue with close proximity to the myocardium and to coronary artery vessels, that exerts paracrine effects on nearby anatomic structures (Iacobellis and Bianco, 2011). EAT correlates with the presence of coronary atherosclerosis independent of other risk factors, myocardial ischemia, and major adverse cardiovascular events (Mancio et al., 2018), coronary artery calcification (Kim et al., 2015), high risk plaques (Nerlekar et al., 2017), and measures of left-ventricular structure and function, such as left ventricular mass (Iacobellis et al., 2004), left-ventricular end-diastolic volume (Park et al., 2014; Watanabe et al., 2016), impaired diastolic filling (Dabbah et al., 2014), and left-ventricular diastolic dysfunction (Nakanishi et al., 2017).
Corresponding author. E-mail address: [email protected] (K.G. Kahl).
https://doi.org/10.1016/j.jad.2019.07.070 Received 9 May 2019; Received in revised form 3 July 2019; Accepted 29 July 2019 Available online 30 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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Assessment of psychiatric disorders and behavioral factors
Pericardial adipose tissue was found increased in patients with major depressive disorder (MDD) (Kahl et al., 2014), particularly those with a chronic disease course (Kahl et al., 2017). In these studies, pericardial adipose tissue was defined as the composite of epicardial (located between the myocardium and the visceral pericardium) and paracardial adipose tissue (located outside the visceral pericardium on the external surface of the parietal pericardium). Beside alterations in neurotransmission, MDD is associated with a variety of behavioral, endocrine and immune disturbances, comprising impaired physical activity, increased sedentary behavior (Schuch et al., 2016), hyperactivity of the hypothalamus-pituitary adrenal axis with increased adrenal glands and hypercortisolism (Heuser, 1998; Kahl et al., 2015), and increased levels of interleukin-6 and tumor-necrosis factor-α (Goldsmith et al., 2016). These factors are likely to play a role in the accumulation of EAT in MDD. In ACHD, increased rates of MDD have been found in several studies when expert interviews were chosen for diagnostic process (Bromberg et al., 2003; Horner et al., 2000; Kovacs et al., 2009a; Westhoff-Bleck et al., 2016). However, EAT and its relation to MDD has not been studied in ACHD. We therefore aimed to evaluate the role of EAT in ACHD. We hypothesized increased EAT in ACHD comorbid with MDD.
Psychiatric diagnosis was made by an experienced psychologist (L.W.) or psychiatrist (K.G.K.) using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental disorders, 4th edition. Both raters were blinded for echocardiographic data obtained from ACHD patients. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS). Participants completed a demographic survey that included educational, marital, employment status, smoking habits (expressed as pack-years), and alcohol drinking behavior (expressed as drinks consumed per week). Physical activity and exercise were assessed using a 6-point Likert scale with descriptors described as “no physical activity or exercise training” (1); “occasional physical activity (such as walking) or exercise training” (2); “light physical activity or exercise training, but less then 1× weekly (3); moderate physical activity or exercise: regular physical activity (cycling or walking) or exercise training 1× weekly (4); often, more than 1× exercise training weekly, or cycling plus regular walking; and “very often, exercise training more than 3× weekly” (Cuppett and Latin, 2002). Alcohol consumption was assessed as drinks per week, and cigarette smoking by pack-years (cigarettes per day × years smoking)/20).
Material and methods
Assessment of cardiac disease
Participants
Each patient was thoroughly examined by a cardiologist. Functional status was determined according to the New-York Heart Classification (NYHA class) (Chacko, 1995). Echocardiography was performed in all patients to evaluate cardiac morphology and function. Cardiac defects were categorized as simple, of moderate or of great complexity using the Warns classification (Warnes et al., 2001). A summary of cardiovascular characteristics of the study population is given in Table 1. Echocardiographic assessment of EAT was derived from two-dimensional standard parasternal long axis/short axis views at end-diastole. EAT thickness was measured on the right free ventricular wall perpendicular to the aortic annulus. The cardiologist performing echocardiographic assessments (M.W.-B.) was blind for the status of patients concerning MDD.
The PSYConHEART study is an ongoing project aiming at examining morbidity and mortality factors in ACHD. The study protocol conforms to the ethical guidelines of the 1975 declaration of Helsinki and was approved by the local ethics committee. All patients gave their informed written consent prior to study entry. Patients were recruited from the ACHD outpatient clinic of the Dep. of Cardiology and Angiology at the Hannover Medical School (Hannover, Germany). The inclusion criteria were as follows: (1) structural congenital heart disease, (2) ability to read and complete the informed consent form and questionnaires in German, and (3) age of 18 or older. Exclusion criteria were pregnancy and instable cardiac condition. Currently, two-hundred fifteen ACHD patients were recruited, of whom five had to be excluded due to missing data. The final sample consists of 120 male, and 90 female patients. Details of the underlying heart disease and treatment are given in Table 1.
Statistical analysis Data were analyzed using IBM SPSS (Version 25, 2016). Group comparisons (ACHD versus ACHD with MDD) were determined performing Chi-square test for categorical variables, and t-Test for nominal variables. Since gender, age and BMI are known factors that influence epicardial adipose tissue, we used ANCOVA with age and BMI as potentially confounding variables, group (ACHD versus ACHD with MDD) and gender as independent variables, and epicardial adipose tissue as dependent variable. Regression analysis was performed according to the stepwise BACKWARD method, with epicardial adipose tissue as dependent variable. All factors that were different between the groups were included as potential predictors in the regression model. These factors comprised: BMI, gender MADRS sum score, school-years, sportscore, NYHA-class, systolic blood pressure, and QRS-time. Age was also included as potential predictor, since others found an effect of age on the accumulation of epicardial adipose tissue (Shi et al., 2015). All data are given as mean ± SD. A p-value <0.05 was considered statistically significant.
Table 1 Cardiovascular characteristics of the study population according to Warnes classification (19). (n) Simple shunts Atrioventricular septal defect Mitral valve disease Anomalous pulmonary venous connection Bicuspid aortic valve Subaortic stenosis Supravalvular aortic stenosis Coarctation Congenital pulmonary stenosis Double chambered right ventricle Tetralogy of fallot Ebstein anomaly Marfan syndrome D-transposition: atrial switch D-transposition: arterial switch Congenital corrected transposition Fontan type circulation Eisenmenger syndrome Common arterial trunc Total
15 12 6 1 27 6 1 24 9 2 31 6 17 25 1 3 17 6 1 210
Results Group description concerning anthropometric and psychosocial factors Fifty-three ACHD patients (25.2%) were diagnosed with MDD. More female compared to male ACHD patients were diagnosed with MDD 679
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group (ACHD: 3.4 ± 1.1 versus ACHD + MDD: 2.7 ± 1.7), while cigarette smoking expressed as pack-years and alcohol drinking behavior (expressed as drinks per week) were similar between the groups (Table 2). Further analysis concerning possible differences between male and female with ACHD +/- MDD was performed using ANOVA. We observed group differences for physical activity (F = 3.3; df = 3; p = 0.019), cigarette smoking (F = 3.6; df = 3; p = 0.014), and alcohol intake (F = 6.3; df = 3, p < 0.001). The corresponding post-hoc analyses revealed the following results: Male depressed ACHD patients were less physically active, and smoked more cigarettes than male ACHD without depression (p < 0.05, respectively). Further, male ACHD (with and without depression) were drinking more alcohol compared to their female counterparts (p < 0.05) (supplemental Table 1)
Table 2 Psychosocial, lifestyle, cardiologic and laboratory data in 210 adults with congenital heart disease (ACHD) with and without major depressive disorder. ACHD (N = 157)
Female (N/%) 60 (38.2%) Age(y) 35.1 ± 11.4 BMI 24.8 ± 4.9 Education(N/%) Secondary modern 28/153 (17.8%) school Junior high school 67/153 (42.7%) High school 62/153 (39.5%) Partnered (N/%) 89/153 (74.8%) Depression sum score 3.6 ± 4.1 (MADRS) Age at first surgical 11.1 ± 15.2 procedure (y) Number of surgical 1.4 ± 1.1 procedures Lifestyle and health behavior Sport-score 3.4 ± 1.1 Pack-years 2.5 ± 6.1 Drinks per week 2.0 ± 3.3 Cardiologic data NYHA 1.2 ± 0.55 RRsyst (mmHg) 112.3 ± 15.2 QRS (ms) 120.5 ± 28.5 QTc (ms) 427.5 ± 28.5 LVEDD 53.5 ± 7.5 RVEDD 38.4 ± 8.4 Left ejection fraction 56.3 ± 8.5 Laboratory parameter CRP (mg/dl) 3.1 ± 5.3 LDL (mg/dl) 115.8 ± 32.2 HDL (mg/dl) 53.2 ± 13.4 HbA1c (%) 5.3 ± 0.3 BNP (ng/L) 194.9 ± 294.7 Creatinin (µmol/L) 77.9 ± 14.8
ACHD with MDD (N = 53)
P
30 (56.6%) 36.4 ± 11.9 26.6 ± 4.3
0.015 n.s. 0.022 0.045
7/53 (13.2%) 33/53 (62.3%) 13/30 (24.5%) 30/53 (56.6%) 16.4 ± 6.3a,b
n.s. <0.001
9.1 ± 13.0
n.s.
1.3 ± 0.9
n.s.
2.7 ± 1.7 4.2 ± 8.7 1.6 ± 3.4
0.013 n.s. n.s.
1.4 ± 0.6 107.1 ± 13.7 116.0 ± 32.6 423.6 ± 47.0 52.9 ± 5.5 39.7 ± 9.7 56.9 ± 9.0
0.09 0.033 0.001 n.s. n.s. n.s. n.s.
3.6 ± 6.6 124.0 ± 27.8 50.0 ± 11.6 5.4 ± 0.3 184.8 ± 222.1 76.5 ± 16.6
n.s. n.s. n.s. n.s. n.s. n.s.
Epicardial adipose tissue (EAT) is enlarged in ACHD with comorbid MDD ANCOVA with EAT as dependent variable, group (ACHD versus ACHD + MDD) and gender as covariates, and BMI and age as potential confounders revealed a main effect of group (F = 5.05; df = 1; P = 0.026), and a group x gender effect (F = 7.4; df = 1; P = 0.007). Furthermore, BMI emerged as significant factor contributing to EAT (F = 50.2, df = 1; P < 0.001) (Fig. 1). Post-hoc analysis revealed significantly increased EAT in depressed male patients compared to non-depressed male ACHD (p < 0.001), although this difference was not observed in female ACHD patients with/ without depression (supplemental Table 1). Predictors of epicardial adipose tissue (EAT) To explore potential factors that predict the amount of EAT a regression analysis (BACKWARD model) with EAT as dependent variable, and all factors that were different between the groups (BMI, gender, school-years, depression severity, physical activity, NYHA class, blood pressure, QRS duration) including age was performed. A significant model emerged (F = 23.1; df = 3; P < 0.001) with depression severity expressed as MADRS sum score (T_ = 2.07; ß = 0.13; P = 0.039), physical exercise (T = −2.36; ß = −0.15; P = 0.019) and BMI (T = 6.11; ß = 0.38; P < 0.001) as predictors of EAT (Table 2). We observed a trend towards age as predictor for EAT (T = 1.66; ß = 0.10; P = 0.97).
Legend: NYHA: severity of heart insufficiency according to the New York Heart Association; RRsyst: systolic blood pressure; QRS: QRS complex duration in milliseconds; QTc: QTc duration in milliseconds; LVEDD: left ventricular enddiastolic diameter; RVEDD: right ventricular end-diastolic diameter; CRP: Creactive protein; LDL: low density lipoprotein; HDL: high density lipoprotein; BNP: brain natriuretic peptide. Table 3 Regression analysis reveals depression severity, physical exercise and BMI as predictors of epicardial adipose tissue.
BMI Exercise MADRS
Regression coefficientB
Standard error
Beta
T
P
0.018 −0.018 0.004
0.003 0.008 0.002
0.38 −1.54 0.13
6.11 −2.36 2.07
<0.001 0.019 0.039
Cardiologic data and laboratory parameter NYHA class was slightly lower in ACHD (ACHD: 1.2 ± 0.55 versus
(30/90 female, and 23/120 male; P = 0.024). Among patients with MDD, 33/53 (62.2%) reported recurrent MDD, 16/53 (30.2%) had their first depressive episode before twenty years of age, and 18/53 (34%) had a positive family history for MDD. (Table 2). Groups (ACHD without MDD, referred to as ACHD; and ACHD with comorbid MDD, referred to as ACHD + MDD) were different concerning gender distribution (ACHD: 38.2% female versus ACHD + MDD: 56.6% female; P = 0.015), BMI (ACHD: 24.8 versus ACHD + MDD: 26.6; P = 0.022), school years (P = 0.045) and depression severity assessed as MADRS sum score (ACHD: 3.6 ± 4.1 versus ACHD + MDD: 16.4 ± 6.3; P < 0.001) (Table 2).
Fig. 1. Epicardial adipose tissue is increased in adults with congenital heart disease comorbid with depression (ACHD + MDD) compared to adults with congenital heart disease without depression (ACHD).
Group differences concerning lifestyle factors/health behaviors The self-estimated amount of exercise was higher in the ACHD 680
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Of note, the leading causes of death in the long-term management of ACHD patients are heart failure, ischemic heart disease and arrhythmias. In the CONCOR registry of more than 10.000 ACHD patients, progressive heart failure (24.5%) and arrhythmias (21.9%) were the most single cardiac causes of death (Zomer et al., 2011). In the study by Yu and colleagues in 3068 ACHD patients from Australia, 11% died during a 6.2y observation time. The leading single causes of death attributable to cardiac reasons were heart failure (17%), sudden cardiac death (23%) and acute or chronic ischemic heart disease (20%) (Yu et al., 2018). Interestingly, these conditions are at least in part be influenced by the amount of EAT, and this association has particularly been described for heart failure, arrhythmias and ischemic heart disease (Zomer et al., 2011). Therefore our results of increased EAT in depressed ACHD are highly relevant for long-term treatment. First, it is recommended that MDD is properly diagnosed in ACHD patients. Second, EAT may emerge as a prognostic relevant biomarker that can easily be assessed during routine echocardiographic assessments. Third, EAT has been demonstrated to be amenable to exercise interventions. Studies in non-depressed individuals showed favorable effects of aerobic exercise on epi‑ and pericardial adipose tissue in obese and non-obese individuals (Bairapareddy et al., 2018; Fernandez-Del-Valle et al., 2018; Honkala et al., 2017). In depressed patients, a supervised and individualized 6 week exercise intervention was effective in improving physical fitness and reducing epicardial adipose tissue (Kahl et al., 2016; Kerling et al., 2015). Strengths and Limitations: All individuals received an expert interview, the (Structured Clinical Interview according to DSM-IV (SKID), by a psychologist (L.W.) or a psychiatrists (K.G.K.) with at least 5 years practice. Concerning limitations, exercise was determined using a selfrating 6-point Likert scale. Objective measures of physical activity may more properly assess the intensity of physical activity. Recent data derived from the NHANES study demonstrated similar results concerning metabolic health in relation to objective (accelerometer) and subjective assessments of physical activity (Thakkar et al., 2018). However, accelerometer assessment required 4 valid days of wear time, being infeasible with the mode of operation in a nationwide health care center. Second, the cross-sectional design makes it difficult to draw causal conclusions from our results. Furthermore it can be discussed whether the chosen method of EAT measurement provides an accurate assessment. We quantified EAT at a single location perpendicular to the aortic annulus at end-diastole. Some authors prefer measurements at end-systole to avoid the impact of adipose tissue compression evoked by ventricular diastolic expansion. Currently there is still no consensus on this topic. Furthermore, EAT is variable in distribution and total volume. Currently echocardiographic thickness assessment of EAT has not been validated against magnetic resonance imaging derived volume and thickness of EAT. Nevertheless, several studies suggest, that echocardiographic single location measurements at either end-diastole or at end-systole seem suitable to provide relevant information of metabolic and cardiovascular status (Iacobellis and Bianco, 2011; Kim et al., 2015; Mancio et al., 2018; Nerlekar et al., 2017; Vural et al., 2014; Watanabe et al., 2016). Conclusion: Taken together, we demonstrate increased epicardial adipose tissue in depressed patients with ACHD. Unhealthy lifestyle factors, especially physical inactivity and unhealthy diet leading to overweight/obesity, are related to increased EAT, and are amenable to specific interventions. Given the biobehavioral changes associated with MDD, screening for MDD should be recommended as part of ACHD diagnostic routine. Further, integration of interventions aiming at improvement of physical fitness, smoking cessation and healthy diet are also recommended in the long-term treatment of ACHD.
ACHD+MDD: 1.4 ± 0.6; P = 0.09). Systolic blood pressure (ACHD: 112.3 ± 15.2 versus ACHD + MDD: 107.1 ± 13.7; P = 0.33) and QRS time (ACHD: 120.5 ± 28.5 versus ACHD + MDD: 116.0 ± 32.6; P = 0.001) were slightly higher in the ACHD group. All determined laboratory parameter including HbA1c, CRP and brain natriuretic peptide (BNP) were similar between the groups. Further results of the post-hoc analyses are reported in supplementary Table 1. Discussion The main findings of our study are that 1) depressed patients with ACHD have higher amounts of EAT, 2) EAT is positively predicted by depression severity, BMI, and negatively predicted by physical activity. Of notice, in our study EAT was not predicted by NYHA class; and 3) depressed male ACHD patients have worse lifestyle factors compared to their non-depressed counterparts. Particularly, physical activity is lower and cigarette smoking is higher. Major depressive disorder has been shown to worsen the disease course and predict mortality in several cardiovascular disorders, such as ischemic and non-ischemic heart failure (Fan et al., 2014; Freedland et al., 2011), coronary heart disease (Barth et al., 2004; Nicholson et al., 2006), myocardial infarction (Meijer et al., 2013, 2011; van Melle et al., 2004; Wu and Kling, 2016) (Hosseini et al., 2014), hypertension (Oganov et al., 2011), and ischemic heart disease (Chazov et al., 2007). MDD has also been demonstrated as a strong predictor of arrhythmic deaths in patients with atrial fibrillation and heart failure (FrasureSmith et al., 2009). These studies indicate growing evidence that MDD has an important influence on the disease course in several cardiologic diseases, and that MDD has an independent prognostic value. Although several studies in adults with congenital heart disease have found increased rates of MDD, it has to be evaluated whether MDD has also prognostic importance in this disease (Bromberg et al., 2003; Kovacs et al., 2009b; Westhoff-Bleck et al., 2016). The potential mechanisms by which MDD might alter the disease course and mortality in cardiovascular disease is still a matter of debate. Numerous studies have investigated biobehavioral mechanisms to explain the effects of MDD on cardiovascular morbidity and mortality in patients with coronary heart disease. MDD is associated with several cardiovascular risk factors such as smoking, sedentary lifestyle, less physical fitness, obesity, diabetes mellitus, the metabolic syndrome or its single factors (Kahl et al., 2012; Skala et al., 2006). Further, studies demonstrated decreased adherence to treatment recommendations of depressed patients in CHD (Carney et al., 1995; Gehi et al., 2005). There has been less research on mechanisms that may account for the negative prognostic effect of MDD in other cardiovascular diseases. However there might be substantial overlap in the candidate mechanisms discussed in coronary heart disease, since the above described biobehavioral alterations have also been demonstrated in MDD per se (Ghanei Gheshlagh et al., 2016; Kerling et al., 2016; Luppino et al., 2010; Mezuk et al., 2008; Stubbs et al., 2018a, 2018b). Further potential mechanisms that link MDD with cardiovascular and metabolic disorders include alterations of the hypothalamus-pituitary-adrenal system with subsequent relative hypercortisolism (Carroll et al., 2012), increased adrenal glands (Kahl et al., 2015), altered platelet activation (Maes et al., 1996), increased rates of the metabolic syndrome (Kahl et al., 2012), a dysbalance of cytokine networks with elevated interleuin-6 and tumor-necrosis factor-α (Kohler et al., 2017), increased intima-media thickness (Greggersen et al., 2011) and body-composition changes (Kahl et al., 2018). In particular, increased amounts of pericardial adipose tissue have been observed in depressed patients (Kahl et al., 2014), with the highest amounts in chronic MDD (Kahl et al., 2017). In these studies, the term “pericardial” adipose tissue comprised the epicardial compartment, located between myocardium and visceral pericardium, and the adipose tissue on the parietal surface of the pericardium.
Author statement This statement is to certify that all authors have seen and approved 681
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the manuscript being submitted, have contributed significantly to the work, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission to the Journal of Affective Disorders. We attest that the article is the Authors' original work, has not received prior publication and is not under consideration for publication elsewhere. On behalf of all Co-Authors, the corresponding Author shall bear full responsibility for the submission. Any changes to the list of authors, including changes in order, additions or removals will require the submission of a new author agreement form approved and signed by all the original and added submitting authors.
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