- Email: [email protected]
Correlation between body mass index and obstructive sleep apnea severity indexes — A retrospective study
Accepted Manuscript Correlation between body mass index and obstructive sleep apnea severity indexes — A retrospective study
Domenico Ciavarella, Michele Tepedino, Claudio Chimenti, Giuseppe Troiano, Manuela Mazzotta, Maria Pia Foschino Barbaro, Lorenzo Lo Muzio, Michele Cassano PII: DOI: Reference:
S0196-0709(18)30124-8 doi:10.1016/j.amjoto.2018.03.026 YAJOT 2002
To appear in: Received date:
4 February 2018
Please cite this article as: Domenico Ciavarella, Michele Tepedino, Claudio Chimenti, Giuseppe Troiano, Manuela Mazzotta, Maria Pia Foschino Barbaro, Lorenzo Lo Muzio, Michele Cassano , Correlation between body mass index and obstructive sleep apnea severity indexes — A retrospective study. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Yajot(2017), doi:10.1016/j.amjoto.2018.03.026
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Original contribution
Correlation between Body Mass Index and Obstructive Sleep Apnea severity indexes – a retrospective study Running title: BMI and Obstructive Sleep Apnea
aDepartment
RI
PT
Domenico Ciavarellaa, Michele Tepedinob*, Claudio Chimentib, Giuseppe Troianoa, Manuela Mazzottaa, Maria Pia Foschino Barbaroc, Lorenzo Lo Muzioa, Michele Cassanoa
of Clinical and Experimental Medicine, University of Foggia, Viale Pinto, 1 , 71122
NU
SC
Foggia, Italy bDepartment of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Viale S.Salvatore, 67100 L’Aquila, Italy cDepartment of Surgical Sciences, University of Foggia, Viale Pinto, 1 , 71122 Foggia, Italy
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*Corresponding author at: Department of Biotechnological and Applied Clinical Sciences University of L’Aquila Viale S. Salvatore, Edificio Delta 6 67100 L’Aquila, Italy Phone: +39 0862 434782 Email address: [email protected]
CE
Funding statement: The authors declare that no funding was given for the realization of the present study. Declaration of interest: None.
AC
Authorship declaration: DC collected the data and drafted the manuscript, MT revised the manuscript and performed the statistical analysis, CC prepared the tables, GT and MPFB recorded polysomnography data, MM recorded polysomnography data and helped drafting the manuscript, LL measured BMI values and proof-readed the manuscript, MC supervised the work. All authors read and approved the final manuscript.
ACCEPTED MANUSCRIPT Original contribution
Correlation between Body Mass Index and Obstructive Sleep Apnea severity indexes – a retrospective study
PT
Abstract
RI
Purpose: To evaluate if Body Mass Index (BMI) is correlated to Apnea-Hypopnea Index (AHI), mean arterial
stress associated with Obstructive Sleep Apnea (OSA).
SC
oxygen saturation (SaO2) and Nadir SaO2, which are all indexes defining the severity of the respiratory
NU
Materials and methods: Seventy-five adult patients (mean age 51.4) referred for polysomnography were retrospectively recruited. BMI was calculated for each patient, as well as AHI, SaO2, and Nadir SaO2
MA
recorded during polysomnography. Spearman’s Rho test was used to evaluate if OSA severity was correlated to BMI values. First type error was set as p < 0.025.
D
Results: No correlation was observed between BMI and AHI, and between BMI and SaO2. A statistically
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significant negative correlation (r2= 0.424; p < 0.001) was found between the BMI index and the Nadir SaO2. Conclusions: Higher BMI values were correlated with lower Nadir SaO2 during overnight polysomnography.
CE
Since hypoxia stress is a risk factor for cardiovascular diseases and alters the lipid metabolism, dietary
AC
consulting should be recommended in association with other treatment modalities for OSA.
Key words: BMI, Nadir SaO2, OSA, sleep apnea Abbreviations: BMI, body mass index; AHI, apnea-hypopnea index; SaO2, mean arterial oxygen saturation; OSA, obstructive sleep apnea.
1. Introduction Obstructive Sleep Apnea (OSA) is a complex syndrome characterized by recurrent collapse of the upper airways during sleep resulting in an interruption of airflow, which requires arousal to recover airway’s
ACCEPTED MANUSCRIPT patency and normal breathing.[1] As a consequence, the patient suffers both from daytime sleepiness due to frequent arousals and from hypoventilation: this leads to several complications like decreased neurocognitive functions, mood changes, increased risk of hypertension arrhythmias, myocardial infarction, stroke, and ultimately death.[1] Diagnosis of OSA is usually made after an overnight polysomnography, when are observed more than five
PT
apnea/hypopnea events per hour of sleep and a complain of daytime sleepiness. The Apnea-Hypopnea Index (AHI, number of apnea/hypopnea events per hour of sleep) and arterial oxygen saturation (SaO2) are
RI
commonly used to grade the severity of OSA, defining a mild OSA when an AHI between 5 and 15 is
SC
observed, a moderate OSA when the AHI ranges between 15 and 30, and a severe OSA when an AHI greater
NU
than 30 is present.[2]
Data reported from the Sleep Heart Health Study on a population of almost 15,000 subjects, more than
MA
10% had some degree of sleep respiratory disorder with daytime somnolence, and 1.6% had OSA diagnosed from their physician, towards an estimated real prevalence of over 4% of the general population.[3] Similar
D
data have been found in an Italian population: in an adult population ranging from 30 to 69 years of age a
PT E
prevalence of 2.7% was observed,[4] while in children some authors reported a prevalence between 1% and 1.8%.[5]
Risk factors for OSA comprehend male gender, middle age, ethnicity, craniofacial soft and hard tissues
CE
morphology[6] that alters the dimension and shape of upper airways, smoke, alcohol, and obesity.[7] In
AC
particular, a complex relationship between OSA and obesity has been described, where both conditions influence one another. Prevalence of OSA among obese subjects exceeds 40%, 70% of adult OSA patients are obese,[8–10] and some authors reported a relative risk of OSA from obesity of nearly 10 or more.[11] Increased body fat and obesity worsen the reduction of upper airway lumen size, increasing the risk of collapse and obstruction.[12] Peppard and colleagues in a longitudinal study with 4-years follow-up on over 600 participants, observed that a weight gain of 10% was correlated with a 32% increase in AHI, while a weight loss of 10% resulted in a 26% reduction of AHI.[13] Abdominal and neck fat accumulation (defined as central obesity) seems to be more related to OSA than general obesity,[1] in fact neck size correlated with decreased SaO2 more than weight and Body Mass Index (BMI, weight divided per squared height) in a
ACCEPTED MANUSCRIPT sample of 1.001 men.[14] Other authors, in fact, found no statistically significant correlation between change in BMI and in AHI.[15,16] The aim of the present study was, therefore, to evaluate if BMI correlates with AHI, SaO2, and Nadir SaO2 in adult subjects affected by OSA. The null hypothesis was that no correlation exists between the selected
PT
variables.
RI
2. Material and methods
SC
The present study complies with the STROBE guidelines for observational studies.
NU
Sample size calculation for a first type error of 0.025, a power of 90%, and an expected correlation coefficient of 0.4 corresponding to a large effect size,[17] resulted in a required number of 68 subjects.
MA
The polysomnography records of 96 patients referring for sleep-endoscopy because were refusing Continuous positive airway pressure (CPAP) treatment, from January 2015 to June 2017, were
D
retrospectively screened for the following inclusion criteria:
- age below 60 years old
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- diagnosis of OSA
CE
Exclusion criteria were moderate to high cigarettes and alcohol consumption. Patient’s data were treated according to the Declaration of Helsinki’s guidelines; no ethical committee approval was required due to
AC
the retrospective nature of the study. Polysomnography was conducted following the recommendations of the American Academy of Sleep Medicine manual for OSA diagnosis.[18] For each patient, the following data were extracted and stored for further analysis: - Body Mass Index (BMI), defined as patient’s weight divided per squared height (kg/m2) as described by Khosla and Lowe;[19] - Apnea/Hypopnea Index (AHI), defined as the number of apnea and/or hypopnea events per hour of sleep; - SaO2, the mean value of arterial blood’s oxygen saturation overnight, expressed as a percentage;
ACCEPTED MANUSCRIPT - Nadir SaO2, the lowest value of SaO2 registered during polysomnography. Obstructive apnea was defined as an interruption of airflow of ≥ 90% compared with baseline for ≥ 10 s while there was evidence of persistent respiratory effort; hypopnea was defined as an amplitude reduction of ≥ 30% in airflow for ≥ 10 s that was associated with an oxygen desaturation of ≥ 3% and arousal.[20]
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2.2 Statistical analysis A Shapiro-Wilk normality test was used to assess data distribution type. Then, a Pearson or a Spearman’s
RI
rho correlation were computed, depending if data were normally or not normally distributed, to test
SC
whether the BMI index correlates with the AHI index, with the mean SaO2, and with the Nadir SaO2 index. Descriptive statistics were also calculated. After applying Bonferroni’s correction for multiple testing, the
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first type error was set as p < 0.025. Statistical analysis was performed using SPSS software (SPSS for
MA
Windows, Version 13.0. Chicago, SPSS Inc.).
3. Results
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From the 96 records initially screened, 75 patients fulfilled the inclusion criteria and were included in the
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study sample. The mean age was 51.4 years old, with a male:female ratio of 16.7:1. 3.2 Statistical analysis
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Descriptive statistics are reported in Table 1. All data were not normally distributed, except for BMI,
AC
therefore Spearman’s rho test was used for all the variables. The null hypothesis was partially rejected: no correlation was present between the BMI index and the AHI index, and between BMI and SaO2 (Table 2). On the other hand, a highly statistically significant negative correlation (p < 0.001) was found between the BMI index and the Nadir SaO2 (Table 2).
4. Discussion
ACCEPTED MANUSCRIPT The selected sample showed a mean BMI of 35.3, which means that patients were by mean overweight; on the other hand, nine patients had a normal weight, 19 had class I obesity, 2 had class II obesity, and 2 had class III obesity. According to AHI values, 9 patients had a mild OSA, 23 patients had a moderate OSA, while 43 patients had a severe OSA. No correlation was found between BMI and AHI: this finding is in contrast with those of some
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authors,[11,13] but in accordance with the observation of others.[15,16] Similarly, other authors observed that neck and abdominal fat, and neck circumference were more effective in predicting AHI and OSA
RI
severity than general obesity defined by BMI calculation.[1,14] On the other hand, it must be underlined
SC
that AHI values for the present study showed a large standard deviation.
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Also, no correlation was found between BMI and mean SaO2 percentage, suggesting that mean arterial oxygen saturation was similar among patients with different BMI. On the other hand, a statistically
MA
significant large negative correlation (r = -0.424, p < 0.001) was observed between BMI and Nadir SaO2, which means that higher BMI values resulted in lower minimum values of SaO2 during overnight
D
polysomnography. These findings can be interpreted as general obesity as described by BMI, results in
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more severe episodes of obstruction that results in lower minimum values of measured SaO2 (Nadir SaO2) during overnight polysomnography, rather than a general worsening of OSA as described by AHI and mean SaO2.
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Hypoxia stimulates the carotid chemoreceptors and cause a secondary sympathetic activation that results
AC
in blood pressure rise.[21] The chronic recurrent sympathetic activation and subsequent vasoconstriction has an effect on the risk of cardio-vascular disease (CVD) over the years.[22] Severe episodes of acute hypoxia like the ones observed in the present study for patients with higher BMI – and demonstrated by the lower Nadir SaO2 values – are capable of activating responses that can lead to acute cardiac events.[23] In addition, the sympathetic activations elicited by hypoxia episodes in OSA patients persist during daytime wakefulness in normoxic conditions: this persistent sympathetic activation leads to systemic hypertension (HTN) and increased cardiac sympathetic tone.[24] Therefore, OSA shares some of the pathophysiologic mechanisms associated with obesity-related diseases and contributes to the worsening of conditions like HTN and CVD, which are also associated with obesity.
ACCEPTED MANUSCRIPT On the other hand, OSA seems to modify the lipid metabolism: intermittent hypoxia has shown to increase the levels of angiopoietin-like 4, a potent inhibitor of lipoprotein lipase, and this decreases the body’s clearance of lipoprotein and increases fasting serum levels of triglycerides and very low density lipoprotein cholesterol.[25] Patients affected by OSA are characterized by either frequent cravings for carbohydrates or the consumption of greater amounts of total calories derived from protein and fat,[26] poor sleeping
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causes fatigue that may lead to a decrease in physical activity and compensatory increase in caloric intake resulting in weight gain, and severe OSA group are characterized by lower concentrations of High-density
RI
lipoprotein cholesterol (HDL-C).[27] Dietary habits seem to play an important role: alcohol consumption
SC
and nutrients (dietary fiber) are associated with OSA severity, and some authors showed a negative
NU
association between liking food rich in fiber and OSA severity and a positive association with high fat food, which could be explained by high levels of ghrelin and leptin resistance.[28] However, evidence from a
MA
meta-analysis outlined that OSA treatment with CPAP, which is the treatment of choice for OSA patient, does not alter triglycerides, Low-density lipoprotein cholesterol (LDL-C) or HDL-C levels, demonstrating that
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it may have no clinically important effect on lipid metabolism.[29] Therefore, dietary consulting should be
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taken under consideration by health professionals as a fundamental aid for OSA patients with an increased BMI.
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Limitations
Although confounding variables like alcohol and cigarettes consumption were used as exclusion criteria,
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other lifestyle factors that could act like source of bias, like dietary habits and physical activity were not controlled due to the retrospective nature of this investigation, thus representing the main limitation of the present study.
5. Conclusion In overweight and class I obesity patients, higher BMI values correlates with lower values of Nadir SaO2. No correlation was observed between BMI and AHI, and between BMI and SaO2. The stress caused by severe
ACCEPTED MANUSCRIPT hypoxia episodes demonstrated by the lower Nadir SaO2 values increases CVD risks: since obesity and OSA are binded by a two-way relationship, dietary consulting to reduce and control the BMI should be
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recommended.
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[1]
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References
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2008;31:S303–9. doi:10.2337/dc08-s272. International Classification of Sleep Disorders: Diagnostic and Coding Manual (ICSD-2). 2nd ed.
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Westchester, IL: American Academy of Sleep Medicine; 2005. GOTTLIEB DJ, WHITNEY CW, BONEKAT WH, IBER C, JAMES GD, LEBOWITZ M, et al. Relation of Sleepiness to Respiratory Disturbance Index. Am J Respir Crit Care Med 1999;159:502–7. doi:10.1164/ajrccm.159.2.9804051. [4]
Cirignotta F, D’Alessandro R, Partinen M, Zucconi M, Cristina E, Gerardi R, et al. Prevalence of every night snoring and obstructive sleep apnoeas among 30-69-year-old men in Bologna, Italy. Acta Neurol Scand 1989;79:366–72.
ACCEPTED MANUSCRIPT [5]
Brunetti L, Rana S, Lospalluti ML, Pietrafesa A, Francavilla R, Fanelli M, et al. Prevalence of obstructive sleep apnea syndrome in a cohort of 1,207 children of southern Italy. Chest 2001;120:1930–5.
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Luzzi V, Di Carlo G, Saccucci M, Ierardo G, Guglielmo E, Fabbrizi M, et al. Craniofacial morphology
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meta-analysis of randomised controlled trials. Sleep Breath 2013;17:925–35. doi:10.1007/s11325-
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Gami AS, Caples SM, Somers VK. Obesity and obstructive sleep apnea. Endocrinol Metab Clin North
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leptin, and cardiovascular risk factors in men with obstructive sleep apnea. Chest 2002;122:829–39. [12]
Schwab RJ. Genetic determinants of upper airway structures that predispose to obstructive sleep apnea. Respir Physiol Neurobiol 2005;147:289–98. doi:10.1016/j.resp.2005.06.006.
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Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal study of moderate weight change and sleep-disordered breathing. J Am Med Assoc 2000;284:3015–21. doi:10.1001/jama.284.23.3015.
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Stradling JR, Crosby JH. Predictors and prevalence of obstructive sleep apnoea and snoring in 1001
ACCEPTED MANUSCRIPT middle aged men. Thorax 1991;46:85–90. [15]
Sforza E, Addati G, Cirignotta F, Lugaresi E. Natural evolution of sleep apnoea syndrome: A five year longitudinal study. Eur Respir J 1994;7:1765–70. doi:10.1183/09031936.94.07101765.
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Pendlebury ST, Pépin JL, Veale D, Lévy P. Natural evolution of moderate sleep apnoea syndrome:
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Cohen J. A Power Primer. Psychol Bull 1992;112:155–9. doi:10.1038/141613a0.
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Iber C, Ancoli-Israel S, Chesson A, Quan S. The AASM Manual for the Scoring of Sleep and Associated
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Berry R, Brooks R, Gamaldo C, Harding S, LLoyd R, Marcus C, et al. The AASM Manual for the Scoring
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of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Version 2. Darien, IL: American Academy of Sleep Medicine; 2015. Lesske J, Fletcher EC, Bao G, Unger T. Hypertension caused by chronic intermittent hypoxia--
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influence of chemoreceptors and sympathetic nervous system. J Hypertens 1997;15:1593–603. Drager LF, Yao Q, Hernandez KL, Shin M-K, Bevans-Fonti S, Gay J, et al. Chronic intermittent hypoxia
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induces atherosclerosis via activation of adipose angiopoietin-like 4. Am J Respir Crit Care Med 2013;188:240–8. doi:10.1164/rccm.201209-1688OC. [23]
Kuniyoshi FHS, Pusalavidyasagar S, Singh P, Somers VK. Cardiovascular consequences of obstructive sleep apnoea. Indian J Med Res 2010;131:196–205.
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Sharma N, Lee J, Youssef I, Salifu MO, McFarlane SI. Obesity, Cardiovascular Disease and Sleep Disorders: Insights into the Rising Epidemic. J Sleep Disord Ther 2017;6:1–16. doi:10.4172/21670277.1000260.
ACCEPTED MANUSCRIPT [25]
Kasivisvanathan V, Shalhoub J, Lim C, Shepherd A, Thapar A, Davies A. Hypoxia-inducible factor-1 in arterial disease: a putative therapeutic target. Curr Vasc Pharmacol 2011;9:333–49.
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Vasquez M, Goodwin J, Drescher A, Smith T, Quan S. Associations of dietary intake and physical activity with sleep disordered breathing in the apnea positive pressure long-term efficacy study
[27]
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(APPLES). J Clin Sleep Med 2008;4:411–8. Stelmach-Mardas M, Mardas M, Iqbal K, Kostrzewska M, Piorunek T. Dietary and cardio-metabolic
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risk factors in patients with Obstructive Sleep Apnea: cross-sectional study. PeerJ 2017;5:e3259.
[28]
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doi:10.7717/peerj.3259.
Smith SS, Waight C, Doyle G, Rossa KR, Sullivan KA. Liking for high fat foods in patients with
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Xu H, Yi H, Guan J, Yin S. Effect of continuous positive airway pressure on lipid profile in patients with obstructive sleep apnea syndrome: A meta-analysis of randomized controlled trials.
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Atherosclerosis 2014;234:446–53. doi:10.1016/j.atherosclerosis.2014.03.034.
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[29]
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Obstructive Sleep Apnoea. Appetite 2014;78:185–92. doi:10.1016/j.appet.2014.03.019.
Domenico Ciavarella, Michele Tepedino, Claudio Chimenti, Giuseppe Troiano, Manuela Mazzotta, Maria Pia Foschino Barbaro, Lorenzo Lo Muzio, Michele Cassano PII: DOI: Reference:
S0196-0709(18)30124-8 doi:10.1016/j.amjoto.2018.03.026 YAJOT 2002
To appear in: Received date:
4 February 2018
Please cite this article as: Domenico Ciavarella, Michele Tepedino, Claudio Chimenti, Giuseppe Troiano, Manuela Mazzotta, Maria Pia Foschino Barbaro, Lorenzo Lo Muzio, Michele Cassano , Correlation between body mass index and obstructive sleep apnea severity indexes — A retrospective study. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Yajot(2017), doi:10.1016/j.amjoto.2018.03.026
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Original contribution
Correlation between Body Mass Index and Obstructive Sleep Apnea severity indexes – a retrospective study Running title: BMI and Obstructive Sleep Apnea
aDepartment
RI
PT
Domenico Ciavarellaa, Michele Tepedinob*, Claudio Chimentib, Giuseppe Troianoa, Manuela Mazzottaa, Maria Pia Foschino Barbaroc, Lorenzo Lo Muzioa, Michele Cassanoa
of Clinical and Experimental Medicine, University of Foggia, Viale Pinto, 1 , 71122
NU
SC
Foggia, Italy bDepartment of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Viale S.Salvatore, 67100 L’Aquila, Italy cDepartment of Surgical Sciences, University of Foggia, Viale Pinto, 1 , 71122 Foggia, Italy
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*Corresponding author at: Department of Biotechnological and Applied Clinical Sciences University of L’Aquila Viale S. Salvatore, Edificio Delta 6 67100 L’Aquila, Italy Phone: +39 0862 434782 Email address: [email protected]
CE
Funding statement: The authors declare that no funding was given for the realization of the present study. Declaration of interest: None.
AC
Authorship declaration: DC collected the data and drafted the manuscript, MT revised the manuscript and performed the statistical analysis, CC prepared the tables, GT and MPFB recorded polysomnography data, MM recorded polysomnography data and helped drafting the manuscript, LL measured BMI values and proof-readed the manuscript, MC supervised the work. All authors read and approved the final manuscript.
ACCEPTED MANUSCRIPT Original contribution
Correlation between Body Mass Index and Obstructive Sleep Apnea severity indexes – a retrospective study
PT
Abstract
RI
Purpose: To evaluate if Body Mass Index (BMI) is correlated to Apnea-Hypopnea Index (AHI), mean arterial
stress associated with Obstructive Sleep Apnea (OSA).
SC
oxygen saturation (SaO2) and Nadir SaO2, which are all indexes defining the severity of the respiratory
NU
Materials and methods: Seventy-five adult patients (mean age 51.4) referred for polysomnography were retrospectively recruited. BMI was calculated for each patient, as well as AHI, SaO2, and Nadir SaO2
MA
recorded during polysomnography. Spearman’s Rho test was used to evaluate if OSA severity was correlated to BMI values. First type error was set as p < 0.025.
D
Results: No correlation was observed between BMI and AHI, and between BMI and SaO2. A statistically
PT E
significant negative correlation (r2= 0.424; p < 0.001) was found between the BMI index and the Nadir SaO2. Conclusions: Higher BMI values were correlated with lower Nadir SaO2 during overnight polysomnography.
CE
Since hypoxia stress is a risk factor for cardiovascular diseases and alters the lipid metabolism, dietary
AC
consulting should be recommended in association with other treatment modalities for OSA.
Key words: BMI, Nadir SaO2, OSA, sleep apnea Abbreviations: BMI, body mass index; AHI, apnea-hypopnea index; SaO2, mean arterial oxygen saturation; OSA, obstructive sleep apnea.
1. Introduction Obstructive Sleep Apnea (OSA) is a complex syndrome characterized by recurrent collapse of the upper airways during sleep resulting in an interruption of airflow, which requires arousal to recover airway’s
ACCEPTED MANUSCRIPT patency and normal breathing.[1] As a consequence, the patient suffers both from daytime sleepiness due to frequent arousals and from hypoventilation: this leads to several complications like decreased neurocognitive functions, mood changes, increased risk of hypertension arrhythmias, myocardial infarction, stroke, and ultimately death.[1] Diagnosis of OSA is usually made after an overnight polysomnography, when are observed more than five
PT
apnea/hypopnea events per hour of sleep and a complain of daytime sleepiness. The Apnea-Hypopnea Index (AHI, number of apnea/hypopnea events per hour of sleep) and arterial oxygen saturation (SaO2) are
RI
commonly used to grade the severity of OSA, defining a mild OSA when an AHI between 5 and 15 is
SC
observed, a moderate OSA when the AHI ranges between 15 and 30, and a severe OSA when an AHI greater
NU
than 30 is present.[2]
Data reported from the Sleep Heart Health Study on a population of almost 15,000 subjects, more than
MA
10% had some degree of sleep respiratory disorder with daytime somnolence, and 1.6% had OSA diagnosed from their physician, towards an estimated real prevalence of over 4% of the general population.[3] Similar
D
data have been found in an Italian population: in an adult population ranging from 30 to 69 years of age a
PT E
prevalence of 2.7% was observed,[4] while in children some authors reported a prevalence between 1% and 1.8%.[5]
Risk factors for OSA comprehend male gender, middle age, ethnicity, craniofacial soft and hard tissues
CE
morphology[6] that alters the dimension and shape of upper airways, smoke, alcohol, and obesity.[7] In
AC
particular, a complex relationship between OSA and obesity has been described, where both conditions influence one another. Prevalence of OSA among obese subjects exceeds 40%, 70% of adult OSA patients are obese,[8–10] and some authors reported a relative risk of OSA from obesity of nearly 10 or more.[11] Increased body fat and obesity worsen the reduction of upper airway lumen size, increasing the risk of collapse and obstruction.[12] Peppard and colleagues in a longitudinal study with 4-years follow-up on over 600 participants, observed that a weight gain of 10% was correlated with a 32% increase in AHI, while a weight loss of 10% resulted in a 26% reduction of AHI.[13] Abdominal and neck fat accumulation (defined as central obesity) seems to be more related to OSA than general obesity,[1] in fact neck size correlated with decreased SaO2 more than weight and Body Mass Index (BMI, weight divided per squared height) in a
ACCEPTED MANUSCRIPT sample of 1.001 men.[14] Other authors, in fact, found no statistically significant correlation between change in BMI and in AHI.[15,16] The aim of the present study was, therefore, to evaluate if BMI correlates with AHI, SaO2, and Nadir SaO2 in adult subjects affected by OSA. The null hypothesis was that no correlation exists between the selected
PT
variables.
RI
2. Material and methods
SC
The present study complies with the STROBE guidelines for observational studies.
NU
Sample size calculation for a first type error of 0.025, a power of 90%, and an expected correlation coefficient of 0.4 corresponding to a large effect size,[17] resulted in a required number of 68 subjects.
MA
The polysomnography records of 96 patients referring for sleep-endoscopy because were refusing Continuous positive airway pressure (CPAP) treatment, from January 2015 to June 2017, were
D
retrospectively screened for the following inclusion criteria:
- age below 60 years old
PT E
- diagnosis of OSA
CE
Exclusion criteria were moderate to high cigarettes and alcohol consumption. Patient’s data were treated according to the Declaration of Helsinki’s guidelines; no ethical committee approval was required due to
AC
the retrospective nature of the study. Polysomnography was conducted following the recommendations of the American Academy of Sleep Medicine manual for OSA diagnosis.[18] For each patient, the following data were extracted and stored for further analysis: - Body Mass Index (BMI), defined as patient’s weight divided per squared height (kg/m2) as described by Khosla and Lowe;[19] - Apnea/Hypopnea Index (AHI), defined as the number of apnea and/or hypopnea events per hour of sleep; - SaO2, the mean value of arterial blood’s oxygen saturation overnight, expressed as a percentage;
ACCEPTED MANUSCRIPT - Nadir SaO2, the lowest value of SaO2 registered during polysomnography. Obstructive apnea was defined as an interruption of airflow of ≥ 90% compared with baseline for ≥ 10 s while there was evidence of persistent respiratory effort; hypopnea was defined as an amplitude reduction of ≥ 30% in airflow for ≥ 10 s that was associated with an oxygen desaturation of ≥ 3% and arousal.[20]
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2.2 Statistical analysis A Shapiro-Wilk normality test was used to assess data distribution type. Then, a Pearson or a Spearman’s
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rho correlation were computed, depending if data were normally or not normally distributed, to test
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whether the BMI index correlates with the AHI index, with the mean SaO2, and with the Nadir SaO2 index. Descriptive statistics were also calculated. After applying Bonferroni’s correction for multiple testing, the
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first type error was set as p < 0.025. Statistical analysis was performed using SPSS software (SPSS for
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Windows, Version 13.0. Chicago, SPSS Inc.).
3. Results
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From the 96 records initially screened, 75 patients fulfilled the inclusion criteria and were included in the
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study sample. The mean age was 51.4 years old, with a male:female ratio of 16.7:1. 3.2 Statistical analysis
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Descriptive statistics are reported in Table 1. All data were not normally distributed, except for BMI,
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therefore Spearman’s rho test was used for all the variables. The null hypothesis was partially rejected: no correlation was present between the BMI index and the AHI index, and between BMI and SaO2 (Table 2). On the other hand, a highly statistically significant negative correlation (p < 0.001) was found between the BMI index and the Nadir SaO2 (Table 2).
4. Discussion
ACCEPTED MANUSCRIPT The selected sample showed a mean BMI of 35.3, which means that patients were by mean overweight; on the other hand, nine patients had a normal weight, 19 had class I obesity, 2 had class II obesity, and 2 had class III obesity. According to AHI values, 9 patients had a mild OSA, 23 patients had a moderate OSA, while 43 patients had a severe OSA. No correlation was found between BMI and AHI: this finding is in contrast with those of some
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authors,[11,13] but in accordance with the observation of others.[15,16] Similarly, other authors observed that neck and abdominal fat, and neck circumference were more effective in predicting AHI and OSA
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severity than general obesity defined by BMI calculation.[1,14] On the other hand, it must be underlined
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that AHI values for the present study showed a large standard deviation.
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Also, no correlation was found between BMI and mean SaO2 percentage, suggesting that mean arterial oxygen saturation was similar among patients with different BMI. On the other hand, a statistically
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significant large negative correlation (r = -0.424, p < 0.001) was observed between BMI and Nadir SaO2, which means that higher BMI values resulted in lower minimum values of SaO2 during overnight
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polysomnography. These findings can be interpreted as general obesity as described by BMI, results in
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more severe episodes of obstruction that results in lower minimum values of measured SaO2 (Nadir SaO2) during overnight polysomnography, rather than a general worsening of OSA as described by AHI and mean SaO2.
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Hypoxia stimulates the carotid chemoreceptors and cause a secondary sympathetic activation that results
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in blood pressure rise.[21] The chronic recurrent sympathetic activation and subsequent vasoconstriction has an effect on the risk of cardio-vascular disease (CVD) over the years.[22] Severe episodes of acute hypoxia like the ones observed in the present study for patients with higher BMI – and demonstrated by the lower Nadir SaO2 values – are capable of activating responses that can lead to acute cardiac events.[23] In addition, the sympathetic activations elicited by hypoxia episodes in OSA patients persist during daytime wakefulness in normoxic conditions: this persistent sympathetic activation leads to systemic hypertension (HTN) and increased cardiac sympathetic tone.[24] Therefore, OSA shares some of the pathophysiologic mechanisms associated with obesity-related diseases and contributes to the worsening of conditions like HTN and CVD, which are also associated with obesity.
ACCEPTED MANUSCRIPT On the other hand, OSA seems to modify the lipid metabolism: intermittent hypoxia has shown to increase the levels of angiopoietin-like 4, a potent inhibitor of lipoprotein lipase, and this decreases the body’s clearance of lipoprotein and increases fasting serum levels of triglycerides and very low density lipoprotein cholesterol.[25] Patients affected by OSA are characterized by either frequent cravings for carbohydrates or the consumption of greater amounts of total calories derived from protein and fat,[26] poor sleeping
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causes fatigue that may lead to a decrease in physical activity and compensatory increase in caloric intake resulting in weight gain, and severe OSA group are characterized by lower concentrations of High-density
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lipoprotein cholesterol (HDL-C).[27] Dietary habits seem to play an important role: alcohol consumption
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and nutrients (dietary fiber) are associated with OSA severity, and some authors showed a negative
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association between liking food rich in fiber and OSA severity and a positive association with high fat food, which could be explained by high levels of ghrelin and leptin resistance.[28] However, evidence from a
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meta-analysis outlined that OSA treatment with CPAP, which is the treatment of choice for OSA patient, does not alter triglycerides, Low-density lipoprotein cholesterol (LDL-C) or HDL-C levels, demonstrating that
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it may have no clinically important effect on lipid metabolism.[29] Therefore, dietary consulting should be
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taken under consideration by health professionals as a fundamental aid for OSA patients with an increased BMI.
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Limitations
Although confounding variables like alcohol and cigarettes consumption were used as exclusion criteria,
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other lifestyle factors that could act like source of bias, like dietary habits and physical activity were not controlled due to the retrospective nature of this investigation, thus representing the main limitation of the present study.
5. Conclusion In overweight and class I obesity patients, higher BMI values correlates with lower values of Nadir SaO2. No correlation was observed between BMI and AHI, and between BMI and SaO2. The stress caused by severe
ACCEPTED MANUSCRIPT hypoxia episodes demonstrated by the lower Nadir SaO2 values increases CVD risks: since obesity and OSA are binded by a two-way relationship, dietary consulting to reduce and control the BMI should be
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recommended.
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[1]
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