Evaluation of Risk Factors Associated with Epicardial Fat Tissue Thickness İn Patients with Stage 4 and Stage 5 Chronic Kidney Disease

doi:10.21203/rs.3.rs-5216232/v1

Purpose: Chronic kidney disease (CKD) is a global public health issue, often associated with high mortality and morbidity, especially due to cardiovascular diseases. Epicardial adipose tissue (EAT), the visceral fat surrounding the heart, has been recognized as a significant factor in cardiovascular risk. This study aims to assess the relationship between EAT thickness and body composition in patients with stage 4 and 5 CKD usingbioelectrical impedance analysis (BIA).

Methods: The study included 80 patients with stage 4 and 5 CKD who were not on dialysis. Body composition was measured using BIA, and EAT thickness was assessed using transthoracic echocardiography. Exclusion criteria included heart failure, morbid obesity, pregnancy, and pacemaker use, among others. Various demographic, clinical, and biochemical parameters were also recorded.

Results: Patients with stage 5 CKD had significantly higher EAT thickness compared to stage 4 CKD patients. EAT thickness showed a positive correlation with age, BMI, blood pressure, C-reactive protein (CRP), and triglyceride levels, and a negative correlation with albumin and HDL levels. Multivariate analysis revealed that increased systolic blood pressure, fat tissue mass, low HDL, and low albumin were independent predictors of EAT thickness.

Conclusion: EAT thickness is higher in advanced CKD patients and is associated with several cardiovascular risk factors. Measuring EAT thickness in CKD patients using non-invasive methods like echocardiography could be valuable in predicting cardiovascular risks. Addressing the factors that contribute to increased EAT thickness may improve clinical outcomes for CKD patients.

Chronic Kidney Disease

Stage

Epicardial Fat Tissue Thickness

Bioelectrical impedance analysis

Because disorders like diabetes mellitus (DM) and hypertension are becoming more common worldwide, chronic kidney disease (CKD) is a global public health concern with a rising prevalence in the community, high costs, and poor clinical outcomes [1]. Studies carried out in the US have shown that at least 6% of adults had stage 1 and stage 2 CKD, with 4.5% having stages 3 and 4 CKD [2].

Acute kidney disease causes harm to almost all of the systems and organs. The first of these is the circulatory system. Cardiovascular diseases account for the majority of deaths and morbidities associated with CKD [3]. Diabetes mellitus, hypertension, and dyslipidemia are conventional risk factors for patients with CKD in the pathogenesis of cardiovascular disease. Inflammation, elevated oxidative stress, endothelial damage, hyperparathyroidism, anemia, and insulin resistance are examples of non-traditional risk factors [4].

The visceral adipose tissue of the heart is called epicardial adipose tissue (EAT) [5]. EAT is the region between the visceral pericardium and myocardium. Measurement of EAT thickness is made by transthoracic echocardiography (ECHO), computed tomography and magnetic resonance imaging methods. Evaluation with ECHO has come to the forefront compared to other imaging methods due to its features such as being easily accessible, fast, not exposed to radiation, and being non-invasive [6]. EAT has two contrasting spectrums of function. It has been linked to both myocardial fibrosis and impaired left ventricular diastolic dysfunction [5], despite its beneficial effects on the cardiovascular system [7]. Because of these properties, EAT is expected to play a key role in cardiovascular risk assessment. According to recent research, there is a substantial correlation between EAT and heart failure, atrial fibrillation, atherosclerosis, coronary artery disease, and death [7, 8].

Following research in patients receiving hemodialysis [9] and peritoneal dialysis [10] EAT has been found to represent a unique cardiovascular (CV) risk factor in patients with end-stage renal disease (ESRD). The idea that people with stage 4-5 CKD who do not get dialysis are more likely to experience cardiovascular events when their EAT thickness is higher is not well-researched.[11, 12].

Bioelectrical impedance analysis (BIA) is a low-cost, non-invasive, readily available, and simple to use measurement technique that may be used to assess body fluid status and analyze body composition metrics including lean mass and body fat mass [13].

In this investigation, we assessed EAT thickness in patients with stage 4-5 CKD who were at high risk of cardiovascular events. We also sought to look at the connection between EAT thickness and the BIA-determined body composition.

A total of 80 patients with a diagnosis of stage 4 (n=26) and stage 5 (n=54) CKD over the age of 18, regardless of gender, who applied to the nephrology clinic and polyclinic between April 2017 and June 2017, were included in the study. Patients with heart failure (EF<50%), ischemic heart disease, morbid obesity (body mass index (BMI)>35), pregnancy, pacemaker use, presence of amputated extremities, diagnosis of malignancy, history of cerebrovascular accident, patients aged <18 years, >80 years, patients with hemodynamic instability and those receiving dialysis were excluded from the study. The Modification of Diet in Renal Disease (MDRD) formula, which has six variables, was used to compute the glomerular filtration rate (GFR). In accordance with the KDIGO clinical practice recommendations, CKD was classified [14]. The patients' age, gender, height, body weight, and BMI were recorded, as were the systolic and diastolic blood pressures, the etiological causes of CKD, and the biochemical parameters measured after a 12-hour fast, including total cholesterol, low- and high-density lipoproteins (LDL and HDL), triglycerides, C reactive protein (CRP), and parathormon (PTH).

A multifrequency bioelectrical impedance analyzer (BIA, Body37 Composition Monitor, BCM, Fresenius Medical Care D GmbH) was used to determine the body composition. Lean tissue mass (LTM, kg), fat tissue mass (FTM, kg), adipose tissue mass (ATM, kg), lean tissue index (LTI, LTM/height2; kg/m2), and fat tissue index (FTI=FTM/height2, kg/m2) values were recorded.

Using a 2.5 MHz probe and two-dimensional M-mode echocardiography (HP HD 211, Philips, The Netherlands), the EAT thickness of each patient was assessed. The same cardiologist conducted each evaluation.

In the echocardiographic examination, the area between the right ventricular free wall and the pericardium was evaluated as EAT. EAT was measured using parasternal short-long axis images. Groups were compared using these parameters. The study was approved by the local ethics committee of Dicle University Faculty of Medicine (13.03.2017/ 64).

Statistical Analysis

Version 24.0 of the Statistical Package for Social Sciences (SPSS Inc. Chicago, IL) was used to statistically analyze the study data. The normality of the distribution of the study variables was evaluated using both visual (histogram and likelihood graphics) and analytical (Kolmogorov-Smirnov/Shapiro-Wilk tests) methods. The mean and standard deviation of the descriptive analyses were shown for variables with a normal distribution. For intergroup comparisons of variables with normally distributed variables, the student's t-test was employed. The chi-square test was used to determine how frequently the groups differed from one another. Pearson correlation analysis tests were used to look at the relationship between continuous variables based on the normality of the distribution. Stepwise multiple linear regression analysis was utilized to illustrate the independent variables of EAT in individuals with stage 4-5 chronic kidney disease. P-values below 0.05 were regarded as statistically significant.

A total of 80 patients, 26 of whom were diagnosed with stage 4 and 54 with Stage 5 CKD, were included in our study. 52.5% (n:42) of these patients were male. The mean age of the patients was 52.91±16.37 years. There were no disparities in age or gender across the groups. BMI and SBP were significantly different in stage 5 (p:0.008 and p:0.015) (Table 1).

Table 2-3 displays the groups' laboratory parameters echocardiographic and bioelectrical impedance analyzer parameters data. The mean GFR of the patients was 12.30±6.50, ejection fraction (EF) was 58.70±6.0%, and EAT thickness was 6.4±1.21 mm. When the causes of CKD of the patients were examined, 41% had DM, 24% had hypertension, 11% had glomerulonephritis and 24% had other causes. GFR, and HDL levels were significantly different in stage 5 CKD (p<0.001, p:0.042), whereas urea, creatinine, and Ca x P levels were significantly different (p<0.001, p<0.001, p:0.04; accordingly). EF values did not differ statistically significantly between patients with stage 4 and stage 5 CKD (p:0.993). Patients with stage 5 CKD were found to have EAT thicknesses that were statistically significantly greater than those of those with stage 4 CKD (p:0.003). FTM and FTI levels were observed to be statistically substantially lower in Stage 5 CKD patients compared to Stage 4 CKD patients when the groups were evaluated using BIA parameters (p:0.004, p:0.005, respectively). In terms of ATM, LTM, and LTI levels, there was no discernible difference between patients with stage 4 and stage 5 CKD.

The results of the Pearson correlation analysis did not indicate a significant association between the levels of LDL, PTH, urea, creatinine, EF, and Ca x P and EAT thickness. On the other hand, there was a substantial positive association between EAT thickness and age, BMI, SBP, DBP, CRP and triglyceride values, whereas there was a significant negative correlation between albumin and HDL values. A strong positive association between EAT thickness and FTM and FTI was discovered in the correlation study of EAT thickness with BIA parameters (Table 4).

Multivariate linear regression analysis was performed to determine EAT thickness and independent variables. Age, BMI, SBP, HDL, CRP, FTM, FTI, triglyceride, albumin participated in the model. It was found to be positively correlated with FTM and SBP, and negatively with HDL and albumin, with independent variables (Table 5).

In the literature, the relationship between EAT thickness and BMI values and waist circumference, which is one of the abdominal obesity measurement parameters, has been defined in studies conducted on different patient groups [15]. In study of Jasmine et al. with 94 patients with stage 3-5 CKD, they found a highly significant relationship between EAT thickness and BMI [16]. Similar to the studies in the literature, a significant positive correlation was found between EAT thickness and BMI in our study (p<0.05). It is being investigated whether lifestyle changes are sufficient for regression of EAT and/or medical treatment is required. In a study followed for 4 years by Nakazato et al. they found that EAT decreased by 2% in 54 patients with 5% weight loss, and increased by 23% in 71 patients with >5% weight gain [17].

Age and EAT thickness were observed to positively correlate in Schejbal et al.'s postmortem examinations on 200 patients [18]. Consistent with Schejbal et al.'s findings, our analysis also revealed a positive significant connection (p<0.05) between EAT and age. The replacement of muscular tissue by adipose tissue and the shift in adipose tissue's body distribution with aging have been proposed as explanations for the association between EAT thickness and age [19].

It was discovered in the Turak et al. study that EAT thickness was higher in hypertension patients than in normotensive people [20]. In our investigation, EAT thickness and both diastolic and systolic blood pressure (DBP) showed a strong positive connection (p<0.05). Furthermore, SBP was discovered to be one of the independent variables of EAT thickness in multivariate linear regression analysis (p<0.05).

In a research on 94 people with stage 3-5 CKD, Jasmine et al. discovered a strong correlation between low HDL levels and EAT thickness [21]. In our investigation, there was a negative correlation (p<0.05) between low HDL levels and EAT thickness, and a positive correlation with high triglyceride levels. Furthermore, a significant negative connection (p<0.05) was observed between HDL and the correlation between EAT thickness and independent variables in the multivariate linear regression analysis.

277 patients with Stage 3-5 CKD who were not receiving dialysis showed a favorable connection between EAT thickness and CRP level, according to Cordeiro et al. [22]. Previous research has shown that EAT increases the release of other bioactive molecules, inflammatory cytokines, and IL-6 [12]. In our study, we also found a significant positive correlation (p<0.05) between EAT and CRP, a key inflammatory parameter.

Malnutrition is a condition that can be seen in predialysis and dialysis patients. The parameter frequently used in the detection of malnutrition is the serum albumin level. Malnutrition often results in hypoalbuminemia in ESRD patients. Türkmen et al found a significant relationship between low albumin level and EAT thickness in their study of 80 predialysis patients [23]. We discovered a strong inverse relationship between low albumin levels and EAT thickness. Furthermore, low albumin level was discovered to be an independent predictor for increased EAT thickness (p<0.05) in multivariate linear regression analysis.

The increase in EAT thickness was assessed using CT in a research by Cordeiro et al. involving 277 stage 3-5 CKD patients who were not receiving dialysis [22]. The patients were divided into 3 groups (low, medium and high) according to the amount of EAT, and as a result of their study, they found higher GFR values in patients with high EAT volume. In our study, we found higher EAT thickness in Stage 5 CKD patients than in Stage 4 patients, in contrast to their study by Corderio et al. (p<0.05). We think that one of the reasons for increased EAT thickness in patients with chronic kidney disease with low GFR may be related to the increased prevalence of hypertension in patients with advanced CKD.

EAT measurements can provide information about the amount of visceral adipose tissue in patients. In our study, EAT thickness was significantly correlated with BIA parameters, FTM and FTI (p<0.05). In addition, FTM was determined as an independent variable for EAT in multivariate linear regression analysis.

In 132 patients with ESRD, Okyay et al. discovered a negative significant link with LTM and a positive significant relationship between EAT and FTM [24]. There was no discernible connection between EAT and LTM or LTI in our investigation.

In summary, our study revealed that individuals with Stage 5 CKD have larger EAT thicknesses than those with Stage 4 CKD. There was a positive correlation between EAT thickness and age, BMI, CRP, SBP, DBP, triglyceride, FTM and FTI levels, and a negative correlation with albumin and HDL levels. In addition, as a result of multivariate linear regression analysis, it was determined that high systolic blood pressure, increased FTM level, low albumin and low HDL levels were independent variables for increased EAT thickness.

It is often known that cardiovascular diseases are the primary cause of death for people with chronic renal disease. We expect that measuring EAT thickness, a cardiovascular risk indicator, in this patient population utilizing useful methods like as ECHO may be helpful, and that treating the factors that influence EAT thickness may also be beneficial.

Author contributions HI, ZY: protocol/project development, manuscript writing; HI; AK, YY: data collection and management, HI, EA, AVK data analysis; HI, FYA, AKK: manuscript editing.

Funding This research received no specifc grant from any funding agency in the public, commercial or not-for-proft sectors.

Competing interests The authors declare no competing interests.

Levey A. S, Eckardt K, Tsukomoto Y, Levin A, Coresh J, Rossert J et. All (2005) Definition and classification of chronic kidney disease: Aposition statement from Kidney Disease Improving Global Outcomes (KDIGO). Kidney International 67: 2089–2100
Harrison Principles of Internal Medicine, Textbook of Internal Medicine, 2015
Stevens, P. E. and A. Levin (2013) Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med: 158: 825-830
Pifer TB, McCullough KP, Port FK, Goodkin DA, Maroni BJ, Held PJ, et al (2002) Mortality risk in hemodialysis patients and changes in nutritional indicators: DOPPS. Kidney Int: 62:2238–2245
Ansaldo AM, Montecucco F, Sahebkar A, Dallegri F, Carbone F. (2019) Epicardial adipose tissue and cardiovascular diseases. IntJ Cardiol:278:254-260. doi: 10.1016/j.ijcard.2018.09.089.
Eroğlu S. (2015) How do we measure epicardial adipose tissue thickness by transthoracic echocardiography? Anatol J Cardiol:15:416-419. doi: 10.5152/akd.2015.5991.
Kwon SS, Choi K, Da Nam B, Lee H, Cho NJ, Park BW, Kim H, Noh H, Jeon JS, Han DC, Oh S, Kwon SH. (2021) Epicardial adipose tissue radiodensity is associated with all-cause mortality in patients undergoing hemodialysis. Sci Rep:11:23090. doi: 10.1038/s41598-021-02427-4.
Le Jemtel TH, Samson R, Ayinapudi K, Singh T (2019)Oparil S. Epicardial Adipose Tissue and Cardiovascular Disease. Curr Hypertens Rep:21:36. doi: 10.1007/s11906-019-0939-6.
Turan MN, Gungor O, Asci G, Kircelli F, Acar T, Yaprak M, Ceylan N, Demirci MS, Bayraktaroglu S, Toz H, Ozkahya M, Ok E.(2013) Epicardial adipose tissue volume and cardiovascular disease in hemodialysis patients. atherosclerosis.226:129-133. doi: 10.1016/j.atherosclerosis.2012.10.061.
Turkmen K, Ozbek O, Kayrak M, Samur C, Guler I, Tonbul HZ. (2013) Peri-aortic fat tissue thickness in peritoneal dialysis patients. Perit Dial Int.33:316-324. doi: 10.3747/pdi.2011.00165.
Karatas A, Canakci E, Bektas O, Bayrak T, Bayrak A, Altınbas A, Turkmen E. (2018) Relationship of epicardial fat tissue thickness with oxidant biomarkers in chronic kidney disease. Bratisl Lek Listy. 119:566-571. doi: 10.4149/BLL_2018_102.
Kerr JD, Holden RM, Morton AR, Nolan RL, Hopman WM, Pruss CM, Garland JS. (2013) Associations of epicardial fat with coronary calcification, insulin resistance, inflammation, and fibroblast growth factor-23 in stage 3-5 chronic kidney disease. BMC Nephrol.14:26. doi: 10.1186/1471-2369-14-26.
Donadio C, Consani C, Ardini M, Bernabini G, Caprio F, Grassi G, Lucchesi A, Nerucci B. (2005) Estimate of body water compartments and of body composition in maintenance hemodialysis patients: comparison of single and multifrequency bioimpedance analysis. J Ren Nutr.332-344. doi: 10.1016/j.jrn.2005.04.001.
Kidney Disease: Improving Global Outcomes (KDIGO] CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney inter. Suppl. 2013; 3: 1–150
Okyay K, Balcioglu AS, Tavil Y, Tacoy G, Turkoglu S, Abaci A. (2008) A relationship between echocardiographic subepicardial adipose tissue and metabolic syndrome. Int J Cardiovasc Imaging 24(6): 577-583.
Iozzo P.(2011) Myocardial, perivascular, and epicardial fat. Diabetes Care 2:371-379.
Nakazato R, Rajani R, Cheng VY et all.(2012) Weight change modulates epicardial fat burden: a 4-year serial study with non-contrast computed tomography. Atherosclerosis 220: 139–144
Schejbal V.(1989) Epicardial fatty tissue of the rightventricle morphology, morphometry and functional significance. Pneumology 43: 490–499.
Guglielmi V, Maresca L, D'Adamo M et all (2014) Age-related different relationships between ectopic adipose tissues and measures of central obesity in sedentary subjects. : e103381.
Turak O, Özcan F, Canpolat U, Mendi MA, Öksüz F, Özeke Ö et all(2014) Relation between epicardial adipose tissue thickness and blood pressure levels in prehypertension. Article in Turkish Turk Kardiyol Dern Ars 42:358-364
Jasmine D Kerr Rachel M Holden, Alexander R Morton, Robert L Nolan, Wilma M Hopman, Cynthia M Pruss and Jocelyn S Garland(2013) Associations of epicardial fat with coronary calcification, insulin resistance, inflammation, and fibroblast growth factor-23 in stage 3-5 chronic kidney disease 14:26. Doi:10.1186/1471-2369-14-26
Cordeiro AC, Amparo FC, Oliveira MA, Amodeo C, Smanio P, Pinto IM, Lindholm B, Stenvinkel P, Carrero JJ. (2015) Epicardial fat accumulation, cardiometabolic profile and cardiovascular events in patients with stages 3-5 chronic kidney disease 77:87 doi:10.111
Turkmen K, Kayikcioglu H, Ozbek O, Solak Y, Kayrak M, Samur C, Anil M, Tonbul HZ (2011) The relationship between epicardial adipose tissue and malnutrition, inflammation, atherosclerosis / calcification syndrome in ESRD patients. Clin J Am Soc Nephrol. 6:1920-1925. 10.2215/CJN.00890111.
Okyay GU, Okyay K, Solak EP et all(2015) Echocardiographic epicardial adipose tissue measurements provide information about cardiovascular risk in hemodialysis patients: Hemodialysis International 19:452–462

Table 1: Demographic and Clinic Parameters

Parameters	All patients (n=80)	Stage 4 CKD (n=26)	Stage 5 CKD (n=54)	p
Age (years)	52.91±16.37	55.85±13.29	51.50±17.60	0.224
Sex (Male/ Female)	42/38	15/11	27/27	0.634
BMI (kg/m²)	24.13±14.44	26.11±4.55	23.18±4.10	0.008
SBP (mmHg)	132.94±17.31	126.15±16.81	136.20±16.73	0.015
DBP (mmHg)	81.01±11.83	77.88±10.78	82.52±12.12	0.090

Data was presented as mean±SD, or n (%)

BMI: Body mass index, SBP: systolic blood pressure, DBP: Diastolic blood pressure

Table 2: Laboratory Parameters

Parameters	All patients (n=80)	Stage 4 CKD (n=26)	Stage 5 CKD (n=54)	p
Urea (mg/dL)	146.16±56.49	115.46±44.37	160.94±56.06	<0.001
Creatinine (mg/dL)	5.40±3.15	2.71±0.50	6.69±3.07	<0.001
GFR (ml/dk.)	12.30±6.50	20.27±4.01	8.46±3.05	<0.001
Albumin (gr/dL)	2.95±0.74	3.13±0.64	2.87±0.77	0.118
Ca x P	37.62±8.63	34.96±7.18	38.89±9.04	0.040
PTH (pg/ml)	231.39±233.57	177.00±95.50	257.57±273.61	0.057
CRP (mg/dL)	1.56±1.29	0.93±0.64	1.86±1.41	<0.001
Triglyceride (mg/dL)	156.59±69.78	148.65±54.58	160.41±7.20	0.433
HDL (mg/dL)	35.79±9.88	39.23±10.71	34.13±9.1	0.042
Hemoglobin (gr/dL)	9.71±2.19	10.61±2.30	9.27±2.01	0.014
Hematocrit (%)	29.96±6.18	32.93±6.21	28.53±5.69	0.004
Urine volume (L)	2.03±1.42	2.19±1.68	1.95±1.28	0.513

Data was presented as mean±SD, or n (%)

GFR: glomerular filtration rate, PTH: Parathormon, CRP: C reactive protein, HDL: High density lipoprotein

Table 3: Echocardiographic and Bioelectrical İmpedance Analyzer Parameters

Parameters	All patients (n=80)	Stage 4 CKD (n=26)	Stage 5 CKD (n=54)	p
EF (%)	58.70±6.00	58.69±5.50	58.70±6.28	0.993
EAT thickness (mm)	6.4±1.21	5.9±0.09	6.7±0.12	0.003
ATM (kg)	25.40±17.47	29.28±17.99	23.53±17.06	0.180
LTM (kg)	42.27±11.83	41.22±11.74	42.77±11.94	0.585
LTI (kg/m²)	15.10±3.82	14.56±3.88	15.36±3.80	0.385
FTM (kg)	18.92±7.73	22.53±7.39	17.18±7.35	0.004
FTI (kg/m²)	9.58±4.41	11.66±4.51	8.58±4.03	0.005

Data was presented as mean±SD, or n (%)

EF: Ejection fraction, EAT: epicardial adipose tissue, ATM: Adipose tissue mass, LTM: Lean tissue mass, LTI: Lean tissue index, FTM: Fat tissue mass, FTI: Fat tissue index

Table 4. Correlation analysis of EAT thickness with BIA parameters and other parameters

Parameters	r	p
Age	0.421	<0.001
BMI	0.294	0.008
SBP	0.395	<0.001
DBP	0.450	<0.001
Urea	0.167	0.138
Creatinine	0.169	0.134
Ca x P	0.054	0.632
PTH	0.205	0.068
CRP	0.338	0.002
LDL	-0.134	0.236
HDL	-0.414	<0.001
EF	-0.074	0.517
Hematocrit	-0.099	0.383
Albumin	-0.509	<0.001
Triglyceride	0.406	<0.001
ATM	0.009	0.938
LTM	0.098	0.386
LTI	0.103	0.362
FTM	0.430	<0.001
FTI	0.399	<0.001

BMI: Body mass index, SBP: systolic blood pressure, DBP: Diastolic blood pressure, PTH: Parathormon, CRP: C reactive protein, LDL: Low density lipoprotein, HDL: High density lipoprotein, EF: Ejection fraction, EAT: epicardial adipose tissue, ATM: Adipose tissue mass, LTM: Lean tissue mass, LTI: Lean tissue index, FTM: Fat tissue mass, FTI: Fat tissue index

Table 5. Multivariate linear regression analysis between EAT and independent variables

Independent variables	Beta coefficients	% 95 Confidence Interval	Standartize beta coefficients	p value
Albumin	-0.057	-0.086/-0.027	-0.346	<0.001
FTM	0.005	0.002/0.008	0.325	<0.001
SBP	0.001	0.000/0.003	0.214	0.019
HDL	-0.002	-0.005/0.000	-0.202	0.027

No competing interests reported.

Evaluation of Risk Factors Associated with Epicardial Fat Tissue Thickness İn Patients with Stage 4 and Stage 5 Chronic Kidney Disease

Status:

Version 1

Abstract

Introduction

Materials And Methods

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1