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Cardiovascular and metabolic comorbidities in patients with obstructive sleep apnoea syndrome.
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PMID:  23326012     Owner:  NLM     Status:  Publisher    
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The purpose of this study was to evaluate the correlation between severity of obstructive sleep apnoea syndrome (OSAS), cardiovascular disease and metabolic syndrome. We recruited 1185 patients with OSAS who underwent a complete ENT examination, including nasolaryngeal fibre optic endoscopy with Müller's manoeuvre, overnight cardio-respiratory monitoring, Epworth Sleepiness Scale (ESS) to measure daytime sleepiness, body mass index (BMI), measurement of blood pressure and blood tests.SUBSEQUENTLY, SUBJECTS WERE DIVIDED INTO THREE SUBGROUPS ACCORDING TO THE APNOEA HYPOPNOEA INDEX (AHI): mild OSAS (AHI 5-15), moderate OSAS (AHI 15-30) and severe OSAS (AHI > 30). In the sample collected, 347 (262 males and 85 females) of 1185 patients suffered from mild OSAS, 363 (269 males and 94 females) from moderate OSAS and 475 (330 males and 145 females) from severe OSAS. In the group suffering from mild OSAS, we found: 127 patients affected by hypertension, 48 with diabetes, 11 with dyslipidaemia and 32 with metabolic syndrome. In the group with moderate OSAS there were 157 patients with hypertension, 63 with diabetes, 72 with dyslipidaemia and 47 with metabolic syndrome. In the group suffering from severe OSAS there were 244 patients with hypertension, 138 with diabetes, 47 with dyslipidaemia and 90 with metabolic syndrome. For data analysis, we used the Spearman correlation test adjusted according to Sidak between the dependent variable AHI and the independent variables BMI, ESS, average SO2 (SO(2med)), hypertension, diabetes mellitus, dyslipidaemia and metabolic syndrome. The results show different patterns of correlation in terms of statistical significance: BMI ρ(s) = 0.26, SO(2med) ρ(s) = -0:51, hypertension ρ(s) = -0.05, dyslipidaemia ρ(s) = 0.22 for women, and BMI ρ(s) = 0.53, ESS ρ(s) = 0.28, SO(2med) ρ(s) = -0.50, hypertension ρ(s) = 0.17, diabetes mellitus ρ(s) = 0.28 and metabolic syndrome ρ(s) = 0.26 for men. The results of the study confirm the existence of a statistically significant correlation between the severity of OSAS and BMI, ESS, average SO2, hypertension, diabetes mellitus, dyslipidaemia and metabolic syndrome.
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Authors:
M Fusetti; A B Fioretti; M Valenti; F Masedu; M Lauriello; M Pagliarella
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Title:  Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale     Volume:  32     ISSN:  1827-675X     ISO Abbreviation:  Acta Otorhinolaryngol Ital     Publication Date:  2012 Oct 
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Created Date:  2013-1-17     Completed Date:  -     Revised Date:  -    
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Nlm Unique ID:  8213019     Medline TA:  Acta Otorhinolaryngol Ital     Country:  -    
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Languages:  ENG     Pagination:  320-325     Citation Subset:  -    
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Department of Applied Clinical Sciences, University of L'Aquila, Italy.
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Journal ID (nlm-ta): Acta Otorhinolaryngol Ital
Journal ID (iso-abbrev): Acta Otorhinolaryngol Ital
Journal ID (publisher-id): Pacini
ISSN: 0392-100X
ISSN: 1827-675X
Publisher: Pacini Editore SpA
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Received Day: 21 Month: 2 Year: 2012
Accepted Day: 25 Month: 4 Year: 2012
Print publication date: Month: 10 Year: 2012
Volume: 32 Issue: 5
First Page: 320 Last Page: 325
PubMed Id: 23326012
ID: 3546405
Publisher Id: Pacini

Cardiovascular and metabolic comorbidities in patients with obstructive sleep apnoea syndrome
M. FUSETTI
A.B. FIORETTI
M. VALENTI
F. MASEDU
M. LAURIELLO
M. PAGLIARELLA
Department of Applied Clinical Sciences, University of L'Aquila, Italy
Correspondence: Address for correspondence: Marco Fusetti, viale S. Salvatore, Edificio 6, Ingresso A, Fraz. Coppito, 67100 L'Aquila, Italy. Tel. +39 348 3846032. Fax +39 06 44291878. E-mail: marco.fusetti@cc.univaq.it, marco.fusetti@libero.it

Introduction

Obstructive sleep apnoea syndrome (OSAS) is an underestimated disorder affecting 2-4% of men and 1-2% of women in the general adult population 1. The syndrome is characterized by partial or complete collapse of upper airways during sleep that is secondary to functional and/or anatomical factors 2. The most common symptoms of OSAS include chronic snoring, breathing pauses followed by loud snoring, excessive daytime sleepiness, headache at awakening and progressive deterioration of attention and memory 3.

In recent years, OSAS has been defined as a cardio-metabolic disorder 4. It is considered an important risk factor for serious systemic diseases like hypertension, obesity and diabetes 5, 7. Indeed, intermittent hypoxia and sleep deprivation or defragmentation typical of OSAS, in the long term, can entail pathophysiological changes that induce the onset of these diseases 8, 9.

A study of more than 6000 patients showed that individuals with OSAS had a risk of hypertension that was increased two-fold, while the risks of ischaemic heart disease and cerebrovascular disease were increased by 3- and 4-fold, respectively compared to the control population 10. An increased prevalence of OSAS and sleep-disorder breathing (SDB) has been reported in type 2 diabetes mellitus, and up to 70% of obese patients with diabetes have moderate to severe OSAS. In addition, the length and quality of sleep is indicative of glycaemic control, as OSAS affects glucose tolerance due to an increase in insulin resistance 11.

From a study on 281 severe snorers with OSAS, strong reciprocal association between the OSAS and the metabolic syndrome emerged 12. Metabolic syndrome is a cluster of metabolic abnormalities including hyperglycaemia, dyslipidaemia [involving elevated triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C)], hypertension and abdominal obesity. The definition of metabolic syndrome includes 3 or more of the following parameters: waist circumference (WC) > 102 cm in men and > 88 cm in women, HDL-C < 40 mg/dl in men and < 50 mg/dl in women, TG ≥ 150 mg/dl, blood pressure (BP) ≥ 130/85 mmHg and fasting plasma glucose ≥110 mg/dl. These conditions predispose the subject to increased risk of developing diabetes mellitus (DM), pro-inflammatory and pro-thrombotic conditions and cardiovascular disease 13, 14.

Some authors have shown that the prevalence of OSAS in patients with BMI >30 varies from 40 to 60%. In addition, the high degree of obesity, especially if visceral, is considered a crucial pathogenetic factor for the onset and exacerbation of OSAS. In fact, if OSAS is not treated, the patient gradually increases in weight, while, conversely, weight loss reduces the OSAS severity 15.

However, the common association of OSAS with hypertension and obesity in general population makes it difficult to separate their respective independent role in the long-term cardiovascular and metabolic consequences associated with OSAS.

This justifies the importance of better understanding the possible relationship between severity of OSAS and metabolic syndrome.


Materials and methods

Our study was performed on 1185 patients observed from January 2000 to June 2011 (324 women and 861 men), suffering from OSAS with mean age 13.7 ± 55.1 years (range: 15-83) and mean BMI of 29.6 ± 5.8 (range: 19-52).

All patients underwent a general physical examination, complete overnight cardio-respiratory monitoring (8 channels), blood tests (glucose, cholesterol, triglycerides), monitoring of blood pressure (BP), complete ENT visit, nasolaryngeal fibre optic endoscopy with Muller manoeuvre, Epworth sleepiness scale (ESS) to measure daytime sleepiness and calculation of body mass index (BMI). Complete overnight cardio-respiratory monitoring included finger pulse oximeter, strain gauges for thoracic and abdominal efforts, one electrocardiography (ECG) lead, nasal airflow (pressure cannula), sensor for body position and a digital microphone for snoring detection. Based on the results of cardio-respiratory monitoring, patients were divided in three subgroups according to the severity of OSAS: mild OSAS (AHI 5-15), moderate OSAS (AHI 15-30) and severe OSAS (AHI > 30). Patients with BP ≥ 130/85 mmHg or already on medical therapy for hypertension were considered hypertensive. Patients with blood glucose ≥110 mg/dl or already on medication for diabetes were considered diabetic. Patients with triglycerides ≥150 mg/dl and HDL cholesterol <40 mg/dl in men and < 50 mg/dl in women were classified as dyslipidaemic. A BMI > 28 was considered as obese. Diagnosis of metabolic syndrome was made in the presence of at least 3 of the 5 significant clinical signs (obesity, hypertension, diabetes mellitus, hypertriglyceridaemia, hypercholesterolaemia). The study was conducted according to the guidelines on biomedical research involving human subjects (Declaration of Helsinki). Informed consent was obtained from each patient.

Descriptive statistics stratified by the variables OSAS and SEX are in Tables I and II. Given the ordinal nature of the variable OSAS, a Sidak adjusted (α = 0.05) Spearman correlation with BMI, ESS, average SO2 (SO2med), hypertension, diabetes mellitus (DM), dyslipidaemia and metabolic syndrome was calculated for both sexes. The correlation structure gave us the rationale to set a linear regression model in which AHI occurs as a dependent variable with BMI, SO2med, hypertension, dyslipidaemia, ESS, DM and metabolic syndrome as explicative variables that were statistically significant at P < 0.05. The coefficients of the regression model (Table III) estimated the impact of each predictor on AHI response. Moreover, we analyzed variance inflation factors to determine possible collinearities among the explanatory variables used. We achieved a mean VIF of 1.82 less than 10 so that the choice of explanatory variables was not a cause of concern 16. Statistical analysis was carried out using the statistical software STATA ver. 11.


Results

In the sample cohort, 347 of 1185 patients were suffering from mild OSAS (262 males and 85 females), 363 from moderate OSAS (269 males and 94 females) and 475 from severe OSAS (330 males and 145 females). The average age of patients with mild OSAS was 52.77 ± 13.49 years (range: 20-76), in moderate OSAS was 56.10 ± 14.32 years (range: 20-83) and in severe OSAS was 55.95 ± 13.26 years (range: 15-75). In the group of patients with mild OSAS, 127 were hypertensive (81 males and 46 females, mean 0.36 ± 0.48), while there were 48 diabetics (16 males and 32 females, mean 0.14 ± 0.34), 11 with dyslipidaemia (11 males and 0 females, mean 0.32 ± 0.17) and 32 with metabolic syndrome (4 males and 28 females, mean 0.09 ± 0.29). In the moderate OSAS group, 157 were hypertensive (81 males, 76 females, mean 0.43 ± 0.50), while there were 63 diabetics (33 males and 30 females, mean 0.17 ± 0.38), 72 with dyslipidaemia (34 males, 38 females, mean 0.20 ± 0.40) and 47 with metabolic syndrome (17 males, 30 females, mean 0.13 ± 0.34). Finally, in the group of severe OSAS patients, 244 were hypertensive (165 males, 79 females, mean 0.51 ± 0.50), and there were 138 diabetic patients (102 males, 36 females, mean 0.29 ± 0.45), 47 with dyslipidaemia (4 males, 43 females, mean 0.99 ± 0.30) and 90 with metabolic syndrome (67 males and 23 females, mean 0.19 ± 0.39). The distribution of the sample of subjects with OSAS stratified by gender is shown in Table I.

The average values and the relative standard deviation of AHI , BMI, ESS, average O2 saturation, diabetes mellitus, dyslipidaemia and metabolic syndrome are shown in Table II.

The results show different Spearman correlation patterns, Sidak adjusted (α = 0.05), in terms of statistical significance between men and women with respectively: BMI (ρs = 0.26), SO2MEDs = -0.51), hypertension (ρs = -0.05), dyslipidaemia (ρs = 0.22) for women, and BMI (ρs = 0.53), ESS (ρs = 0.28), SO2 MEDs = -0.50), hypertension (ρs = 0.17), DM (ρs = 0.28), metabolic syndrome (ρs = 0.26) for men (Fig. 1). The linear regression model showed strong statistical dependence between AHI and all the independent variables considered, as shown in Table III.


Discussion

Our study shows evidence for a correlation between OSAS and BMI, diabetes mellitus, hypertension, dyslipidaemia, metabolic syndrome, and consequently between OSAS and cardiovascular disease. Furthermore, the results demonstrated a stronger association between the predictors considered and the higher degrees of OSAS (moderate and severe), a plausible hypothesis that still needed to be statistically confirmed. A more careful interpretation of these results suggested that hypertension and dyslipidaemia are more associated with OSAS in females than in males. Conversely, ESS, diabetes mellitus and metabolic syndrome seemed to be more associated with OSAS in males than in females.

Our study supports recent evidence for a major public health impact of OSAS. However, the pathophysiologic mechanism by which OSAS contributes independently to cardiovascular and metabolic pathology remains unclear.

Patients with OSAS are always exposed to intermittent hypoxia and reoxygenation deriving from the cycles of apnoea/arousals. Recent studies suggest that sustained hypoxia would lead to oxidative stress and activation of a systemic inflammatory response 17, 18, with increases in general blood antioxidant activity and in production of proinflammatory cytokines, including tumour necrosis factor α and interleukin 6, 19-21. Such alterations would profoundly affect endothelial function 22, and may contribute to the development of increased platelet activity, elevated plasma fibrinogen levels and reduced fibrinolytic capacity, leading to blood hypercoagulability, atherosclerosis and cardiovascular imbalances associated with OSAS 23-25.

Sustained hypoxia, high sympathetic output, dysregulation of the hypothalamo-pituitary axis and alterations in inflammatory pathways seem to be involved in the pathogenesis of disorders of glucose metabolism in patients suffering from OSAS 26. Keckeis et al. demonstrated that a strong link exists between OSAS and insulin resistance, impaired glucose tolerance and type 2 diabetes mellitus. Nevertheless, it is difficult to separate the role of obesity and the role of OSAS in the development of glucose metabolism disorders 27.

OSAS and obesity seem to be part of a vicious circle supported by adipocytokines. Leptin has received the most attention 28, 29 of all adipokines, and is a tissue-derived cytokine involved in control of body weight and a variety of biological functions. Serum levels of leptin serum are correlated with BMI and AHI 30, probably due to the intermittent low cellular oxygen tension typical of OSAS, and seem to improve after treatment with CPAP 31. Moreover, some authors found a significant correlation between a polymorphism in the leptin receptor (LEPR) and OSAS, supporting the hypothesis of a strong genetic basis in OSAS. Other studies have demonstrated that serum levels of ghrelin, a 28-amino acid polypeptide hormone with an appetite-stimulating effect, tend to be higher in patients with OSAS and are correlated with both AHI and ESS 32.

The strength of our study was examination of patients with OSAS and association with comorbidities. In many cases, patients with OSAS who did not report the investigated comorbidities might have other systemic disease (not yet diagnosed), and this misclassification could underestimate the severity of the disease.

The present study had some limitations, however. The major limitation was the absence of a control group without OSAS to evaluate the presence and prevalence of comorbidities in a normal population. We did not have data about other confounders such as smoking status, alcohol intake and exercise frequency, which is another limitation.


Conclusions

The results of the present study corroborate our initial hypothesis as a statistically significant correlation between OSAS and hypertension, diabetes, dyslipidaemia and metabolic syndrome was found. The controversial results in the literature and the lack of knowledge underlying the pathogenesis of OSAS and its comorbidities suggest the need for further studies. It will be useful to further evaluate whether and how the treatment and resolution of OSAS may influence the course of these diseases related to cardiovascular and metabolic risk factors.


References
1.. Bixler EO,Vgontzas AN,Lin H-M,et al. Prevalence of sleep-disordered breathing in womenAm J Respir Crit Care MedYear: 20011633Pt 160861311254512
2.. Bonsignore MR,Zito A. Metabolic effects of the obstructive sleep apnea syndrome and cardiovascular riskArch Physiol BiochemYear: 200811425526018726787
3.. Patil SP,Schneider H,Schwartz AR,et al. Adult obstructive sleep apnea. Pathophysiology and diagnosisChestYear: 200713232533717625094
4.. Gruber A,Horwood F,Sithole J,et al. Obstructive sleep apnoea is independently associated with the metabolic syndrome but not insulin resistance stateCardiovasc DiabetolYear: 20065222217078884
5.. Shahar E,Whitney CW,Redline S,et al. Sleep-disordered breathing and cardiovascular disease: lung cross-sectional results of the Sleep Heart Health StudyAm J Respir Crit Care MedYear: 2001163192511208620
6.. He J,Kryger MH,Zorick FJ,et al. Mortality and apnea index in obstructive sleep apnea. Experience in 385 male patientsChestYear: 1988949143289839
7.. Fletcher EC. Obstuctive sleep apnea and cardiovascular morbidityMonaldi Arch Chest DisYear: 19965177808901328
8.. Golbin JM,Somers VK,Caples SM. Obstructive sleep apnea, cardiovascular disease, and pulmonary hypertensionProc Am Thorac SocYear: 2008Month: 2 20020618250213
9.. Tasali E,Ip MS. Obstructive sleep apnea and metabolic syndrome, alterations in glucose metabolism and inflammationProc Am Thorac SocYear: 2008520721718250214
10.. Nieto FJ,Young TB,Lind BK,et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health StudyJAMAYear: 20002831829183610770144
11.. Tassone F,Lanfranco F,Gianotti L,et al. Obstructive sleep apnoea syndrome impairs insulin sensitivity independently of anthropometric variablesClin Endocrinol (Oxf)Year: 20035937437912919162
12.. Angelico F,Ben M,Augelletti T,et al. Obstructive sleep apnoea syndrome and the metabolic syndrome in an internal medicine settingEur J Intern MedYear: 20102119119520493421
13.. Maggio M,Lauretani F,Ceda GP,et al. Association between hormones and metabolic syndrome in older italian menJ Am Geriatr SocYear: 2006541832183817198487
14.. Endeshaw Y. Clinical characteristics of obstructive sleep apnea in community-dwelling older adultsJ Am Geriatr SocYear: 2006541740174417087702
15.. Teramoto S,Yamamoto H,Yamaguchi Y. Obstructive sleep apnea causes systemic inflammation and metabolic syndromeChestYear: 20051271074107515764798
16.. Rabe-Hesketh S,Everitt BS. A handbook of statistical analyses using StataYear: 20033rd ed.Boca Raton, FLChapman & Hall/ CRC7272
17.. Lurie A. Inflammation, oxidative stress, and procoagulant and thrombotic activity in adults with obstructive sleep apneaAdv CardiolYear: 201146436622005189
18.. Büchner NJ,Quack I,Woznowski M,et al. Microvascular endothelial dysfunction in obstructive sleep apnea is caused by oxidative stress and improved by continuous positive airway pressure therapyRespirationYear: 20118240941721311167
19.. Greenberg H,Ye X,Wilson D,et al. Chronic intermittent hypoxia activates nuclear factor-kappaB in cardiovascular tissues in vivoBiochem Biophys Res CommunYear: 200634359159616554025
20.. Basoglu OK,Sarac F,Sarac S,et al. Metabolic syndrome, insulin resistance, fibrinogen, homocysteine, leptin, and Creactive protein in obese patients with obstructive sleep apnea syndromeAnn Thorac MedYear: 2011612012521760842
21.. Amra B,Karbasi E,Hashemi M,et al. Endothelial dysfunction in patients with obstructive sleep apnoea independent of metabolic syndromeAnn Acad Med SingaporeYear: 20093846146419521652
22.. Budhiraja R,Parthasarathy S,Quan SF. Endothelial dysfunction in obstructive sleep apneaJ Clin Sleep MedYear: 2007340941517694731
23.. Quercioli A,Mach F,Montecucco F. Inflammation accelerates atherosclerotic processes in obstructive sleep apnea syndrome (OSAS)Sleep BreathYear: 20101426126920198512
24.. Kosseifi S,Bailey B,Price R,et al. The association between obstructive sleep apnea syndrome and microvascular complications in well-controlled diabetic patientsMil MedYear: 201017591391621121505
25.. McNicholas WT,Bonsignore MR,Management Committee of the COST Action B26Sleep apnoea as an independent risk factor for cardiovascular disease: current evidence, basic mechanisms and research prioritiesEur Respir JYear: 20072915617817197482
26.. Tasali E,Leproult R,Spiegel K. Reduced sleep duration or quality: relationships with insulin resistance and type 2 diabetesProg Cardiovasc DisYear: 20095138139119249444
27.. Keckeis M,Lattova Z,Maurovich-Horvat E. Impaired glucose tolerance in sleep disordersPLoS OneYear: 20105e9444e944420209158
28.. Hanaoka M,Yu X,Urushihata K,et al. Leptin and leptin receptor gene polymorphisms in obstructive sleep apnea syndromeChestYear: 2008133798517989154
29.. Zirlik S,Hauck T,Fuchs FS,et al. Leptin, obestatin and apelin levels in patients with obstructive sleep apnoea syndromeMed Sci MonitYear: 201117CR159CR16421358603
30.. Shimizu K,Chin K,Nakamura T,et al. Plasma leptin levels and cardiac sympathetic function in patients with obstructive sleep apnoea-hypopnoea syndromeThoraxYear: 20025742943411978920
31.. Lima AM,Franco CM,Castro CM,et al. Effects of nasal continuous positive airway pressure treatment on oxidative stress and adiponectin levels in obese patients with obstructive sleep apneaRespirationYear: 20107937037619590157
32.. Ursavas A,Ilcol YO,Nalci N,et al. Ghrelin, leptin, adiponectin, and resistin levels in sleep apnea syndrome: role of obesityAnn Thorac MedYear: 2010516116520835311

Figures

[Figure ID: F1]
Fig. 1. 

Spearman correlation patterns.

Spearman correlation patterns OSAS vs BMI, ESS, SO2 MED, IPA, DM, Dyslip, S. Metab.

BMI: body mass index, ESS: Epworth sleepiness scale, SO2MED: average O2 saturation, DM: diabetes mellitus, Dyslip: dyslipidemia, S. Metab: metabolic syndrome.



Tables
[TableWrap ID: T1] Table I. 

Sample distribution.


OSAS
Mild Moderate Severe Total
Sex Female 85 94 145 324
Male 262 269 330 861
Total 347 363 475 1185
Hypertension Female 46 (54.12%) 76 (80.85%) 79 (54.48%) 201
Male 81 (30.92%) 81 (30.11%) 165 (50.00%) 327
Total 127 157 244 528
Diabetes Mellitus Female 32 (38.65%) 30 (31.91%) 36 (24.83%) 98
Male 16 (6.11%) 33 (12.27%) 102 (30.91%) 151
Total 48 63 138 249
Dyslipidaemia Female 0 (0.00%) 38 (40.43%) 43 (22.66%) 81
Male 11 (4.20%) 34 (12.64%) 4 (1.21%) 49
Total 11 72 47 130
Metabolic Syndrome Female 28 (32.94%) 30 (31.91%) 23 (15.86%) 81
Male 4 (1.53%) 17 (6.32%) 67 (20.30%) 88
Total 32 47 90 169

T1FN1Sample distribution of patients stratified by gender and degree of OSAS in relation to hypertension, diabetes mellitus, dyslipidaemia and metabolic syndrome.


[TableWrap ID: T2] Table II. 

Average values and relative standard deviation.


AHI BMI ESS SO2 MED DM DYSLIP MetS
OSAS Mild Women (N=85) MEAN 9.02 29.74 9.71 93.61 0.38 0.00 0.33
SD 2.61 7.54 7.30 2.66 0.49 0.00 0.47
Men (N=262) MEAN 10.10 26.38 9.11 92.18 0.06 0.04 0.02
SD 2.22 2.46 4.74 4.86 0.24 0.20 0.12
Moderate Women (N=94) MEAN 22.88 30.11 10.86 92.52 0.32 0.40 0.32
SD 3.94 4.07 4.17 2.18 0.47 0.49 0.47
Men (N=269) MEAN 22.68 27.60 8.38 91.42 0.12 0.13 0.06
SD 4.66 2.68 5.06 3.11 0.33 0.33 0.24
Severe Women (N=145) MEAN 51.22 33.19 12.22 86.86 0.25 0.30 0.16
SD 27.26 7.36 7.07 7.43 0.43 0.46 0.37
Men (N=330) MEAN 49.33 32.70 13.62 85.61 0.31 0.01 0.20
SD 17.81 5.99 6.85 8.59 0.46 0.11 0.40

T2FN1The average values and standard deviation of AHI, BMI, ESS, average O2 saturation, diabetes mellitus, dyslipidaemia and metabolic syndrome in patients with mild, moderate and severe OSAS. AHI: apnoea-hypopnoea index, BMI: body mass index, ESS: Epworth sleepiness scale; SO2MED: average O2 saturation, DM: diabetes mellitus, DYSLIP: dyslipidaemia, MetS: metabolic syndrome.


[TableWrap ID: T3] Table III. 

Linear regression model.


AHI Coef. SEM t P > |t| [95% CI]
SEX -3.14 1.23 -2.56 0.01 [-5.54; -0.73]
BMI 0.77 0.13 6.07 0.00 [0.52; 1.02]
ESS 0.86 0.09 9.78 0.00 [0.68; 1.03]
SO2 MED -1.34 0.09 -14.56 0.00 [-1.52; -1.16]
DM 10.36 2.14 4.85 0.00 [6.17; 14.56]
DYSLIP 6.89 1.85 3.72 0.00 [3.26; 10.53]
MetS -26.75 2.51 -10.64 0.00 [-31.6; -21.82]

T3FN1Linear regression results reporting the covariates coefficients estimation of AHI with SEM (standard error of mean), 95% confidence interval and their respective statistical test outcomes. BMI: body mass index, ESS: Epworth sleepiness scale, SO2MED: average O2 saturation, DM: diabetes mellitus, DYSLIP: dyslipidaemia, MetS: metabolic syndrome (P > |t| statistically significant).



Article Categories:
  • Sleep Medicine

Keywords: KEY WORDS Obstructive sleep apnoea syndrome, Metabolic syndrome, Arterial hypertension, Diabetes mellitus, Insulin resistance.

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