An Investigation into the Relationship Between Sleep-Disordered Breathing,the Metabolic Syndrome,Cardiovascular Risk Profiles,and Inflammation Between South Asians and Caucasians Residing in the United Kingdom

Abstract

Background:

The aim of this study was to determine the prevalence of sleep-disordered breathing (SDB) in a South Asian and a Caucasian population and to compare the cardiovascular risk factors in those with SDB within these ethnic groups and determine if SDB is independently associated with the metabolic syndrome and markers of inflammation.

Methods:

A total of 1,598 participants within a U.K. multiethnic population underwent an oral glucose tolerance test, completed the Berlin Sleep Questionnaire, and provided anthropometric data and fasting bloods. Metabolic syndrome was classified according to National Cholesterol Education Program Adult Treatment Panel III criteria.

Results:

The prevalence of SDB was 28.3% and did not differ between the two ethnic groups. South Asians with SDB had a higher body fat percentage (38.4±10% vs. 35.6±9%, P=0.016), glycosylated hemoglobin (5.6±0.5% vs. 5.6±0.5%, P=0.001) and lower high-density lipoprotein cholesterol (1.21±0.23 mmol/L vs. 1.29±0.34 mmol/L, P=0.002) compared to Caucasians with SDB, who were older (59.6±8.6 years vs. 50.4±10.3 years, P<0.001) and had higher systolic blood pressure (139.8±18.5 mmHg vs. 131.7±18.6 mmHg, P<0.001). SDB was associated with metabolic syndrome after adjustment for age, gender, ethnicity, and waist circumference (odds ratio=1.54, 95% confidence interval 1.12–2.09, P=0.01). There was no independent association between SDB and markers of inflammation.

Conclusion:

The relationship between SDB and metabolic syndrome is not driven via the inflammatory pathway. The prevalence of SDB is significantly higher in those with metabolic syndrome although these South Asians had a greater cardiovascular disease (CVD) risk profile the relationship is independent of ethnicity. Routine screening for SDB within primary/secondary care may have a role in the prevention of CVD and type 2 diabetes mellitus.

Introduction

T he term “sleep-disordered breathing” (SDB) describes a group of disorders characterized by abnormal breathing patterns during sleep and affects between 20% and 30% of the general adult population.^1,2 SDB encompasses a number of obstructive sleep disorders, with simple snoring at one end of the spectrum and severe obstructive sleep apnea (OSA) at the other. OSA is characterized by the recurrent collapse of the upper airway and cessation (apnea) or reduction of airflow (hypopnea), leading to arousal from sleep to reinitiate breathing. Sufferers consequently experience fragmented sleep and consequential sleep deprivation.

OSA is associated with obesity, specifically visceral obesity³ and is an established risk factor for cardiovascular disease (CVD)^4,5 and more recently incident CVD.⁶ It is reported to affect 4% of adult males and 2% of adult females in the general population.⁷ It has been proposed that waist circumference is a better determinant of obesity than body mass index (BMI) because it addresses the distribution of excess fat mass.⁸ Visceral fat, unlike peripheral fat, is implicated in the pathophysiology of type 2 diabetes mellitus (T2DM), the metabolic syndrome, OSA, and CVD.^9
–11 Visceral fat is not simply an inert mass of cells but an endocrine organ in its own right.⁹ It actively secretes a large variety of biologically active proteins that have dual metabolic and immunomodulatory effects thought to contribute to a level of subclinical inflammation commonly associated with T2DM and OSA.^12,13 Thus, BMI is considered a cruder measure of obesity, and waist circumference has now been included in the criteria for the diagnosis of the metabolic syndrome.¹⁴

There are a number of existing definitions for the diagnosis of metabolic syndrome.^14
–16 The metabolic syndrome comprises a cluster of cardiovascular risk factors—impaired glucose metabolism, dyslipidemia, hypertension, and visceral obesity. Metabolic syndrome is a significant risk factor for the future development of T2DM and of CVD. Metabolic syndrome and SDB are both common disorders affecting between 22% and 39% of the general adult population within developed countries.^17,18 They share a common set of characteristics, including insulin resistance, visceral obesity, and subclinical inflammation.

It is recognized that people of South Asian origin have an increased risk of developing the metabolic syndrome, T2DM, and CVD.^19
–21 The development of these conditions, particularly within the westernized South Asian population, occurs at an earlier age and lower body mass.^20,21 The key determinant of this increased risk is thought to be increased visceral fat mass in the South Asian population. However, there are currently no studies addressing the prevalence of SDB and metabolic syndrome in a migrant South Asian population compared to a Caucasian population.

The aims of this study were to determine the relative prevalence of SDB within migrant South Asian and Caucasian populations and to determine the cardiovascular risk profile in those with and without SDB. A further aim was to determine associations between SDB and metabolic syndrome in addition to blood-borne markers of inflammation.

Methods

Subjects

Following full ethics committee approval, subjects were recruited from an ongoing systematic community T2DM screening program. The rationale and design of the ADDITION-Leicester study have been described previously.²² Briefly, South Asians between 25 and 75 years of age and Caucasians between 40 and 75 years of age without preexisting (general practice diagnosis and database recorded) T2DM, terminal illnesses with a likely prognosis of <12 months, psychiatric illness likely to hinder informed consent, pregnancy, or lactation were invited to participate from 20 general practices in urban, suburban, and rural Leicestershire, England, United Kingdom. A total of 6,041 participants (48% male, 22% South Asian) were recruited in the main trial. Each participant underwent an oral glucose tolerance test (OGTT) with 75 grams of glucose, and fasting blood samples were taken for determination of lipid profile, glycosylated hemoglobin (HbA1c), insulin, and glucose. Between November 1, 2005, and December 31, 2006, consecutive participants were additionally invited to participate in this substudy at their baseline visit. Upon providing written informed consent for this substudy, data were collected from 1598 subjects, of which 262 (20.9) provided additional fasting blood samples for the measurement of biological markers of inflammation on the same day as data collected for the main trial.

Anthropometric measurements

Anthropometric measurements were collected for each participant, including height, weight, and body fat percentage (Tanita TBE 611, Tanita, West Drayton, UK). Waist circumference was measured at the point of minimal abdominal circumference located halfway between the navel and the lower end of the sternum. Hip measurement was measured at the greatest protrusion of the gluteal muscles as viewed from the side. Three separate blood pressure readings were taken (sitting without crossed legs at 5-min intervals (Omron M5-1, HEM-757-E model). The mean of the last two readings was then calculated.

Biochemical analyses

Venous blood samples were collected following a 12-h fast. Quantitation of high-density lipoprotein cholesterol (HDL-C) was performed using the ultra-HDL assay (UHDL), and serum cholesterol was determined using the cholesterol enzymatic assay. Serum triglyceride was measured using the Triglyceride Glycerol Phosphate Oxidase assay. All of the above are products of Abbott Clinical Chemistry, and each assay was performed on the ARCHITECH c Systems^™/AEROSET systems. Quantitation of serum HbA1c was performed using high-performance liquid chromatography (HPLC) on an automated glycohemoglobin analyzer (HLC-723G, Tosoh Bioscience Ltd, UK), and plasma glucose was measured using the hexokinase method. These assays were undertaken in the pathology laboratories within University Hospitals Leicester, and repeated testing was carried out if the coefficient of variation (CV) was ≥20%. High-sensitivity C-reactive protein (hsCRP) was analyzed on an ABX Pentra Clinical Chemistry Analyzer using a latex-enhanced immunoturbidimetric assay (Horiba Group, Montpellier, France). This assay has a mean within (intra-) assay variation of 2% and between (inter-) assay reproducibility of 2.6%. Adiponectin was analyzed using the AutoDELFIA 1235 automatic immunoassay system (PerkinElmer, Turku, Finland) with R&D Systems monoclonal antibodies. hsCRP and adiponectin analyses were conducted at Unilever Discover (Colworth Science Park, Bedford UK). Repeated testing was carried out when the CV was ≥15% for replicate adiponectin results.

Classification of metabolic syndrome

Metabolic syndrome status was determined according to the 2004 modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III).¹⁴ This definition was chosen because it is commonly used within clinical practice and is a proven marker for the progression of metabolic syndrome to T2DM and CVD.²³ If participants presented with three or more of the following risk factors they were classified as having metabolic syndrome: Fasting glucose ≥5.5 mmol/L; triglycerides ≥1.7 mmol/L; blood pressure ≥130/≥85 mmHg; waist circumference ≥102 cm (males) and ≥88 cm (females); and HDL-C<1.0 mmol/L (males) and <1.3 mmol/L (females).

Assessment of SDB

The Berlin Sleep Questionnaire (BSQ) was used to classify participants as high or low risk for SDB. This validated sleep questionnaire has a reported sensitivity of 86% and specificity of 77% for predicting SDB (defined by a Respiratory Disturbance Index >5%²⁴) and is thought to be more sensitive than other methods used in clinical practice.²⁴ For the purpose of this paper, we refer to the high-risk group as the SDB group. All completed questionnaires were double-entered by the data entry company Abacus, Luton. The results were then scored using a standard algorithm.²⁴

Statistical analyses

All statistical analyses were conducted using SPSS for Windows Version 14 (SPSS; Chicago; IL). Data are either presented as mean±standard deviation (SD), or, in the case of categorical data, as number and percentage in each category, unless otherwise stated. Independent t-test, Mann–Whitney test, and chi-squared test were performed to compare those subjects with and without SDB. Analysis of covariance (ANCOVA) was performed for adjusted differences in cardiovascular risk factors between ethnic groups with SDB. Logistic regression was performed to determine whether SDB is independently associated with metabolic syndrome in this population. Logistic regression was additionally used for determining if SDB is independently associated with hsCRP and adiponectin in a subset of this population; hsCRP and adiponectin were categorized into quartiles, of which the upper and lower quartiles were compared in this analysis.

Results

The demographics of all study participants (n=1,598) are given in Table 1. The prevalence of SDB was significantly higher in males compared to females (33.2% vs. 25.1%, P<0.001). The prevalence of SDB was not significantly different between the two ethnic groups.

Table 1.

Characteristics of Those Participants with and without Sleep-Disordered Breathing

	Non-SDB n=1,137 (71.2%)	SDB n=461 (28.8%)
Males	493 (66.85%)	245 (33.2%)	P<0.001
(n=738)
Females	644 (74.8%)	216 (25.1%)
(n=860)
Caucasians	875 (70.4%)	368 (29.6%)	N.S.
(n=1,243)
South Asians	216 (74.7%)	73 (25.3.%)
(n=289)
Mean age (years)	60.1±9.0	48.3±11.4	P<0.0001

SDB, sleep-disordered breathing; N.S., not significant.

Logistic regression was carried out to investigate the association of body fat percentage, BMI, and waist circumference, with SDB adjusting for age and gender. A 1% increase in body fat percentage was associated with an increased odds of SDB [odds ratio (OR)=1.15, 95% confidence interval (CI) 1.12–1.16, P<0.001]; a one-unit increase in BMI was associated with an increased odds of SDB (OR=1.24, 95% CI 1.21–1.28, P<0.001); a 1-cm increase in waist circumference was associated with an increased odds of SDB (OR=1.10, 95% CI 1.08–1.09, P<0.001).

Ethnic comparison of cardiovascular risk factors within the SDB group

Independent sample t-tests were used to identify differences in the markers of cardiovascular risk between South Asian and Caucasian populations with SDB (Table 2). Percentage body fat and HbA1c were significantly greater in the South Asian population. Age, systolic blood pressure (SBP), and HDL-C were found to be significantly higher in the Caucasian group. To investigate whether age is a confounding factor here, all South Asian subjects under 40 years of age [64 (4.5%)] were removed from this analysis, and a linear regression performed adjusting for age and gender. Age and HbA1c were found to be significantly associated with ethnicity and SDB. Furthermore, body fat percentage was greater in the South Asian group and a trend toward higher total cholesterol in Caucasians was evident (Table 2).

Table 2.

Cardiovascular Risk Factors in Caucasians and South Asians with Sleep Disordered Breathing

	Independent t-test			ANCOVA ^a
Variable	C (mean±SD)	SA (mean±SD)	P	C (mean±SD)	SA (mean±SD)	P
Age (years)	59.0±8.6	50.4±10.3	<0.0001	59.0±8.6^b	52.8±8.6^b	<0.0001
SBP (mmHg)	139.8±18.5	131.7±18.6	<0.0001	139.8±18.5	133.1±18.6	0.29
DBP (mmHg)	87.8±10.5	86.4±10.6	0.4	87.8±10.5	86.9±10.5	0.39
WC (cm)	102.2±12.2	103.1±11.6	0.5	102.2±12.2	102.8±10.9	0.56
% BF (%)	35.6±9	38.4±10	0.016	36.1±7	38.2±10	0.022
BMI (kg/m²)	30.9±4.8	31.9±5.01	0.12	30.9±4.8	32.2±4.9	0.24
Triglycerides (mmol/L)	1.69±1.05	1.56±0.88	0.57	1.69±1.1	1.55±0.88	0.24
Total cholesterol (mmol/L)	5.6±1.2	5.2±1.0	0.06	5.6±1.2	5.3±1.1	0.064
HDL-C (mmol/L)	1.29±0.34	1.21±0.23	0.002	1.29±0.34	1.23±0.23	0.34
LDL (mmol/L)	3.5±1.0	3.3±0.9	0.19	3.53±1.00	3.32±0.95	0.08
HbA1c (%)	5.6±0.5	5.9±0.6	0.001	5.7±0.5	6.0±0.63	<0.0001

ANCOVA performed only on those persons of ≥40 years of age.

Reports age- and gender-adjusted results, with exception of age adjusted for gender only.

ANCOVA, analysis of covariance; C, Caucasian; SA, South Asian; SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; BMI, body mass index; BF, body fat; HDL-C, high-density lipoprotein cholesterol; LDL, low-density lipoprotein; HbA1c, glycosylated hemoglobin.

SDB and the metabolic syndrome

A total of 353 (22.0%) subjects were excluded due to incomplete data that are required for the determination of metabolic syndrome classification. There was no significant difference in age, gender, and ethnicity between those included and excluded from the analyses; however, those not included in analyses had higher a waist circumference (99 cm±12.0 cm vs. 93.8 cm±13.4 cm, P<0.001 respectively) and prevalence of SDB (42.4% vs. 28.1%, P<0.001). There was no significant difference in the prevalence of SDB between the South Asian and Caucasian participants who were excluded from the analyses (49% and 42% respectively, P=0.31).

In all, 1,249 (77.9%) participants were included in the final analyses, of whom 738 (46.2%) were male. The mean age was 57.3±10.7 years, and the mean BMI was 28±4.9 kg/m². The mean waist circumference was 99.4±11.1 cm and 89.8±13.1 cm for males and females, respectively. A total of 223 (17.9%) were of South Asian origin and the remaining 988 (79.1%) were Caucasian. The overall prevalence of SDB as determined by the BSQ was 28.3% and the overall prevalence of metabolic syndrome as classified by the NCEP ATP III¹⁴ criteria was 25.5%. The prevalence of SDB was significantly higher in those with metabolic syndrome compared to those without (41.5% vs. 18.9%, P<0.0001).

Logistic regression was performed to determine whether SDB was independently associated with the metabolic syndrome. The presence of hypertension was determined using the NCEP ATP III criteria.¹⁴ Age, gender, ethnicity, hypertension, and waist circumference were included as covariates as they represent a group of risk factors for both SDB and the metabolic syndrome.

In the unadjusted model, SDB was associated with increased odds of metabolic syndrome (OR=3.04, 95% CI 2.32–3.98, P<0.0001). This remained significant after adjusting for age, gender, and ethnicity (OR=3.09, 95% CI 2.35–4.06, P<0.0001). Further adjustment for waist circumference attenuated the relationship but the association remained significant (OR=1.54, 95% CI 1.12– 2.09, P=0.01).

Association of SDB with hsCRP and adiponectin

A subset of 262 (20.9%) participants consented to have additional blood samples taken for the measurement of biological markers of inflammation. The overall prevalence of SDB was 36.6% in this subset. The levels of adiponectin were significantly lower in those with SDB compared to those without (18.8±10.6 vs. 23.4±13.7 μg/mL, P=0.003, respectively). The levels of CRP did not differ significantly between the two groups (3.9 vs. 3.7 mg/L, P=0.69, respectively). The data for CRP and adiponectin were not normally distributed with a Kolmogorov–Smirnov statistic of 0.255 (P<0.001) for CRP and 0.112 (P<0.001); therefore, these variables were log transformed. For these analyses, hsCRP and adiponectin were categorized into their upper and lower quartile ranges. Logistic regression was used to determine the relationship between SDB and hsCRP and SDB and adiponectin, respectively (Table 3). Neither hsCRP nor adiponectin (upper vs. lower quartile) were independently associated with SDB after adjusting for age, gender, ethnicity, waist circumference, and presence of the metabolic syndrome.

Table 3.

Association of Sleep-Disordered Breathing with hsCRP and Adiponectin (Lower vs. Upper Quartile)

Biomarker	Adjustment variables	Odds ratio	95% CI	P
hsCRP^a	Age, gender, ethnicity, and metabolic syndrome	2.66	1.27–5.54	0.009
	Age, gender, ethnicity, metabolic syndrome, and adiponectin^b	2.24	0.91–5.49	0.079
Adiponectin^a	Age, gender, ethnicity, and metabolic syndrome	−1.37	0.51–1.05	0.09
	Age, gender, ethnicity, metabolic syndrome, and hsCRP^b	−1.29	0.53–1.13	0.17

Categorized into upper and lower quartiles.

Continuous variable.

hsCRP, high-sensitivity C-reactive protein; CI, confidence interval.

Discussion

This study shows that the prevalence of SDB is slightly lower in the migrant South Asian population when compared with the Caucasian population, although this did not reach statistical significance. Our study also demonstrates that within the SDB group the cardiovascular risk profiles between South Asians and Caucasians differ significantly. The South Asian group was younger and had lower levels of HDL-C, higher HbA1c levels, and higher percentages of body fat. This is consistent with current data reporting the premature development of T2DM and CVD within the South Asian population.^19
–21 However, the Caucasian group had significantly higher SBP, which has been recently reported in a cross-sectional study comparing the parameters of body composition amongst other measures in two groups of young nondiabetic Caucasian and Asian Indian volunteers.²⁵ This result may be a reflection of the Caucasian group being on average 10 years older than the South Asian group. This is supported further by the fact that when adjusted for age and gender, HbA1c and body fat percentage remained associated with ethnicity and SDB, but SBP did not. Interestingly, when the sample was cut to include only those subjects of ≥40 years (n=10), the South Asian group remained significantly younger (6.2 years, P<0.0001). This could additionally help explain the nonsignificant trend toward higher total cholesterol in the Caucasian group. These differences are clinically significant given that the BSQ only identifies those at high risk of SDB and does not confirm diagnosis. Hence, even at this level there are distinct differences present in South Asians, who are clearly at a greater cardiovascular risk.

SDB was associated with body fat percentage, BMI, and waist circumference after adjusting for age and gender. This further supports the fact that fat mass and its distribution are key risk factors for SDB. Furthermore, when these markers of cardiovascular risk are combined to represent a group with or without the metabolic syndrome, as defined by the NCEP ATP III criteria, a clear association between the metabolic syndrome and SDB is observed, with the presence of metabolic syndrome representing a key risk factor for SDB. The likelihood of metabolic syndrome was 3.04 times higher in those with SDB after adjusting for age and gender. This remained significant even after additionally adjusting for ethnicity, presence of hypertension, and waist circumference (OR=1.54, 95% CI 1.12–2.09, P=0.01). These results support similar findings by Neito et al.²⁶ who reported that patients with moderate-to-severe SDB [defined as apnea–hypopnea index (AHI) ≥15 or those receiving therapy for their SDB] were five times more likely to have metabolic syndrome compared to those without. These results are expectedly higher than what we report here because full polysomnography was used for the clinical diagnosis and assessment of severity of SDB in their patients. We additionally report the prevalence of SDB to be significantly greater in those with the metabolic syndrome. Together these results add support to the pathological effects of visceral obesity.

These findings support those of Coughlin et al., who demonstrated that OSA was independently associated with an increase in the cardiovascular risk factors that comprise the metabolic syndrome and its overall prevalence.²⁷ However, we have additionally demonstrated in a multiethnic population that this association is independent of age, gender, ethnicity, presence of hypertension, and waist circumference. This is important because these are the risk factors common to these two disorders and a strong relationship between SDB and metabolic syndrome remains significant even after adjusting for these factors suggesting there is an additional factor at play. The prevalence of SDB is high within the adult population; however, it frequently remains undiagnosed. This is of concern given that the prevalence of SDB will increase with the predicted increase in levels of obesity. Additionally, those with OSA are at increased risk of cardiac arrhythmias during sleep²⁸ and stroke.²⁹ There is, therefore, a considerable need for the identification of this “at-risk” population. Our data show that the prevalence of metabolic syndrome is greater in those with SDB, and previous studies have demonstrated that treatment of OSA can improve the cardiovascular risk profile.^30,31 Identification and treatment of this population could facilitate prevention of T2DM and CVD.

Our study additionally sought to identify an association between SDB, metabolic syndrome, and the inflammatory and the adipokine pathways. Inflammation is the local protective response to tissue injury accompanied by a systemic reaction—the acute-phase response. This systemic response involves changes in the circulating levels of acute-phase reactants, including hsCRP,¹² which are activated by proinflammatory cytokines. There is increasing evidence to support the hypothesis that low-grade systemic inflammation is involved in the development of insulin resistance and subsequently T2DM.¹²

Adiponectin, an antiinflammatory adipokine, is exclusively secreted by adipose tissue. Plasma adiponectin levels are negatively correlated with BMI and body fat.³² It is also negatively correlated with fasting proinsulin and insulin response, a relationship that remains after adjusting for adiposity.³² Our results indicated that, whereas adiponectin levels were found to be lower in those with SDB, this relationship is not independent of age, gender, ethnicity, or the presence of metabolic syndrome. Although it has been suggested that inflammation be included in the definition of metabolic syndrome, these data do not support this. However, it is important to acknowledge that the quantitation of inflammatory biomarkers was a secondary outcome measure of this study and it therefore may not carry enough power to identify this putative relationship. Further research in a larger sample is required to characterize this relationship.

A limitation of this study is the use of a subjective measure of SDB. This type of data collection relies heavily on a clear interpretation of the questions and an honest and concise answer from the participant. In a multiethnic population such as this, a large variation in the interpretation of such a questionnaire is likely to exist. This may explain the lack of difference in the prevalence of SDB between our two ethnic groups, even though the South Asian group had a worse cardiovascular risk profile and would therefore expect a higher prevalence of SDB. In addition, BSQ only describes “high-risk” and “low-risk” groups. To further establish the strength of these results, an overnight sleep study on each participant, or at least a representative subset, would need to be carried out. This would also provide a means to test what effect the severity of SDB has on all of the parameters explored here. However, this questionnaire is a validated sleep questionnaire and is widely used in the clinical setting to determine whether a sleep assessment is required. Finally, our data may not be truly representative of our total data set, because 353 (22.0%) were not included in the metabolic syndrome analyses, and this group had significantly greater waist circumferences and a greater prevalence of SDB.

In summary, our data indicate a link between SDB, metabolic syndrome, and cardiovascular risk. We have found a high prevalence of SDB in both ethnic groups, but the South Asian group displayed a more adverse cardiovascular risk profile than the Caucasian group. We have shown that SDB is associated with the metabolic syndrome and that this is independent of the common risk factors that these two conditions share. These data support the International Diabetes Federation (IDF) consensus statement released in 2008³³ on sleep apnea and T2DM—“people with OSA should be routinely screened for possible metabolic disorders and cardiovascular risk” and indeed those with metabolic disorders should be screened for sleep apnea. Unfortunately, it is not possible to identify the direction of causality with these data. The manifestations of SDB could be driving the development of metabolic dysfunction; for example, intermittent hypoxic events, due to cessation of breath, prevent the parasympathetic nervous system from dominating during sleep, resulting in raised blood pressure in addition to a state of hypersympathetic activity, which in turn could drive a state of hyperglycemia that could then lead to a level of insulin resistance and a proinflammatory state if persistent. On the other hand, excess adiposity, the major characteristic of the metabolic syndrome, may be causing the occlusion of the upper airway during sleep when muscle tone is reduced, thus leading to SDB. This is an area that warrants future research and further recognition within the medical field, given that treatment of one disorder will indeed provide positive clinical outcomes for the other.

Footnotes

Acknowledgments

We would like to thank the Biotechnology and Biological Sciences Research Council (BBSRC, Swindon, UK) and Unilever (Corporate Research, Colworth Science Park, UK) for financial support. We would also like to thank University Hospitals of Leicester who supported this research.

Author Disclosure Statement

No competing financial interests exist

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