American Journal of Respiratory and Critical Care Medicine

Rationale: Obstructive sleep apnea and systemic hypertension (SH) are highly prevalent. Although their association has been suggested in cross-sectional studies, conflicting evidence has emerged from longitudinal studies.

Objectives: To assess the association between obstructive sleep apnea and SH in the middle-aged general population.

Methods: A total of 2,148 subjects were included in a longitudinal study of the Vitoria Sleep Cohort, a general population sample aged 30–70 years. We analyzed data on office blood pressure, anthropometric measures, health history, and home polygraphy. Out of 1,557 subjects who completed the 7.5-year follow-up, 377 were excluded for having SH at baseline. The odds ratios for the incidence of SH, according to the respiratory disturbance index (RDI) at baseline, were estimated in 1,180 subjects (526 men and 654 women) after adjustment for age; sex; body mass index; neck circumference; fitness level; and alcohol, tobacco, and coffee consumption. The RDI was divided into quartiles (0–2.9, 3–6.9, 7–13.9, and ≥ 14), using the first quartile as reference.

Measurements and Main Results: The crude odds ratio for incident hypertension increased with higher RDI category with a dose–response effect (P < 0.001), but was not statistically significant after adjustment for age (P = 0.051). Adjustments for sex (P = 0.342), body mass index (P = 0.803), neck circumference (P = 0.885), and fitness level and alcohol, tobacco, and coffee consumption (P = 0.708) further reduced the strength of the association between RDI and SH. No differences were observed between men and women.

Conclusions: Our findings do not suggest an association between obstructive sleep apnea and the incidence of SH in the middle-aged general population. Long-term follow-up longitudinal studies are needed to better ascertain this association.

Scientific Knowledge on the Subject

The association between obstructive sleep apnea and systemic hypertension has been suggested in several cross-sectional studies, and in continuous positive airway pressure intervention trials. To date, however, only two observational longitudinal studies have examined this association, with conflicting results.

What This Study Adds to the Field

This prospective study does not support a causal relationship between obstructive sleep apnea and systemic hypertension in the middle-aged general population.

Obstructive sleep apnea (OSA) is a prevalent clinical disorder that affects 15–24% of middle-aged men and 9–26% of middle-aged women (1, 2). Treatment with continuous positive airway pressure (CPAP) is the most cost-effective therapy (3). Several studies suggest that OSA is associated with cardiovascular morbidity and, particularly, with systemic hypertension (SH) (49). SH is also a very common disease. It is estimated that 26.4% of adults have hypertension (10), that over 30% of patients with hypertension have OSA (11, 12), and that 45–68% of subjects with OSA have SH (13, 14).

In recent years, several cross-sectional studies have shown an association between OSA and SH, independently of age, weight, and other confounding factors (58). All these positive results have led OSA to be considered the most common cause of secondary SH (15). The existence of an association does not, however, necessarily imply causality. Data from longitudinal studies are required to assess whether subjects with OSA have an increased incidence of SH. To date, there are only two observational longitudinal studies that have evaluated OSA as an independent risk factor for SH: the Wisconsin Sleep Cohort Study (WSCS) (4) and the Sleep Heart Health Study (SHHS) (16), recently published. Although both reported elevated risks of developing SH in subjects with severe categories of OSA, the SHHS results were not statistically significant after adjusting for the effect of body mass index (BMI), whereas the WSCS found a moderate and statistically significant association. A systematic review and three meta-analyses also suggested an association between OSA and SH by demonstrating that OSA treatment with CPAP produces a significant reduction in blood pressure (BP) (1720). Given that OSA is a very common and treatable disease and that SH involves significant costs and related morbidity, assessing the potential causality of OSA as an etiology of SH should be considered a health priority (21).

The objectives of this study were to assess the association between OSA and SH in the middle-aged general population and to evaluate the differences between this association according to sex. Some of the results of this study have been previously reported in the form of abstracts (2228).

Overview and Patients

To assess the prevalence and natural history of OSA, a cohort of the general population aged between 30 and 70 years (Vitoria Sleep Cohort) was studied. The subjects were residents of Vitoria-Gasteiz, Spain, and were selected at random using population census data. After conducting a cross-sectional baseline study (1), a follow-up study was performed for 7.5 ± 0.8 years (8 ± 0.8 yr for men and 7.1 ± 0.4 yr for women). A flow chart of the study population is shown in Figure 1.

Procedure and Study Variables

The cross-sectional baseline study, the methodology of which is described in detail elsewhere (1), was divided into two phases. The first phase included a structured interview; a physical examination (weight, height, neck circumference, and BP measures); and a validated respiratory polygraphy (MESAM IV; Medizintechnik für Artz und Patient, Munich, Germany) (29). The second phase included a polysomnography. The sleep-study results from the first and second phase were used to obtain an objective validation of the MESAM IV, classifying the population according to the respiratory disturbance index (RDI). BP was measured three times with a mercury sphygmomanometer, following the recommendations of the American Heart Association (30). The mean of these values was used for the analysis. Hypertension was defined as systolic BP greater than or equal to 140 mm Hg, diastolic BP greater than or equal to 90 mm Hg, or current treatment with antihypertensive medications (15).

A longitudinal study was subsequently performed. The start of the follow-up period for each subject coincided with the date on which the cross-sectional study was conducted. At the end of the monitoring period, a structured interview and a physical examination including weight, height, and three BP measures were undertaken. Subjects treated with CPAP or uvulopalatopharyngoplasty were excluded, as were those who had SH at baseline. Written informed consent was obtained from all participants. The study was approved by the Ethics and Clinical Trials Committee and the Research Commission of the Txagorritxu Hospital, Vitoria, Spain.

Statistical Analysis

Data were analyzed with the SPSS version 16.0 program (IBM, Chicago, IL). As a descriptive summary of data, the results for continuous variables were expressed by mean and standard deviation and for categorical variables by frequency and percentage. Confidence intervals were estimated at 95% for hypothesis testing. Quantitative variables between the two study periods were compared with Student t test for paired data, whereas categorical variables were compared with McNemar test. Differences in SH incidence, according to the RDI, were compared by chi-square test for linear trend. Logistic regression models (31) were used to determine the association between RDI and SH, with the presence of SH diagnosed during the follow-up as the dependent variable. The RDI was divided into quartiles (0–2.9, 3–6.9, 7–13.9, and ≥ 14), using the first quartile (RDI 0–2.9) as reference. Age, sex, BMI, neck circumference, smoking habit, fitness level, and alcohol and coffee consumption were included as possible confounders.

Out of 2,148 eligible subjects, 752 men (71.6%) and 805 women (73.3%) completed the follow-up study (Figure 1). Of the 1,557 subjects included, 377 (24.2%) had SH at baseline. The characteristics of the subjects who completed the longitudinal study, compared with those who were excluded, are described in Table 1. There were no significant differences in excessive daytime sleepiness, use of hypertensive medications, fitness level, alcohol consumption, previous cardiovascular disease, and self-reported history of snoring. However, the subjects excluded were significantly older, more obese, had a higher severity of OSA, a higher prevalence of SH, a lower prevalence of smoking, and a higher prevalence of postmenopausal status. In the excluded men, no significant differences were observed in age, BMI, excessive daytime sleepiness, cigarette and alcohol consumption, fitness level, previous cardiovascular disease, or self-reported history of snoring. However, the prevalence of SH was significantly higher in the excluded men, as was the severity of OSA. The women excluded from the monitoring were significantly older, had a higher BMI, a higher severity of OSA, a higher prevalence of SH, a higher prevalence of postmenopausal status, and a lower prevalence of smoking.

TABLE 1. DESCRIPTIVE CHARACTERISTICS AT BASELINE OF THE SUBJECTS WHO COMPLETED THE LONGITUDINAL STUDY COMPARED WITH THOSE WHO WERE EXCLUDED

All (n = 2,148)
Men (n = 1,050)
Women (n = 1,098)
CompletedExcludedCompletedExcludedCompletedExcluded
Variables(n = 1557)(n = 591)P Value(n = 752)(n = 298)P Value(n = 805)(n = 293)P Value
Age, yr*47.4 ± 10.451.1 ± 11.6<0.00148.6 ± 10.348.7 ± 11.90.84046.2 ± 10.553.5 ± 10.9<0.001
BMI, kg/m2*25.5 ± 3.726 ± 4.40.00726.2 ± 326.2 ± 3.70.96724.8 ± 4.225.9 ± 5.1<0.001
RDI*7.5 ± 9.110.4 ± 11.89.1 ± 10.711.2 ± 12.76.1 ± 6.99.5 ± 10.6
Median (IQR)5 (2–10)6 (3–13)<0.0016 (3–13)7 (3–15)0.0204 (2–7)6 (3–12)<0.001
RDI ≥ 14234 (15%)145 (24.5%)<0.001153 (20.3%)83 (27.9%)0.00981 (10.1%)62 (21.2%)<0.001
RDI ≥ 3048 (3.1%)34 (5.8%)0.00433 (4.4%)23 (7.7%)0.03015 (1.9%)11 (3.8%)0.068
EDS292 (18.8%)122 (20.6%)0.322157 (20.9%)65 (21.8%)0.738135 (16.8%)57 (19.5%)0.300
SBP, mm Hg*124 ± 17130 ± 19<0.001129 ± 16132 ± 180.006120 ± 16127 ± 19<0.001
DBP, mm Hg*79 ± 1080 ± 110.00381 ± 982 ± 100.16176 ± 1078 ± 110.008
SH377 (24.2%)202 (34.2%)<0.001226 (30.1%)118 (39.6%)0.003151 (18.8%)84 (28.7%)<0.001
Use of antihypertensive medications96 (6.2%)49 (8.3%)0.48041 (5.4%)24 (8.1%)0.22955 (6.8%)25 (8.5%)0.985
Fitness level765 (50.2%)266 (49.9%)0.642398 (52.9%)136 (45.6%)0.739367 (45.6%)130 (44.4%)0.719
Smoking§915 (58.8%)302 (51.1%)0.001544 (72.3%)221 (74.2%)0.550371 (46%)81 (27.7%)<0.001
Alcohol consumption718 (47.1%)226 (42.4%)0.059493 (65.6%)156 (52.3%)0.293225 (28%)70 (23.9%)0.180
Previous CVD136 (8.7%)47 (8%)0.56268 (9%)25 (8.4%)0.73768 (8.4%)22 (7.5%)0.616
Snorers415 (30%)162 (30.9%)0.711224 (29.8%)82 (27.5%)0.417191 (23.7%)80 (27.3%)0.230
Postmenopausal277 (34.4%)173 (59%)<0.001277 (34.4%)173 (59%)<0.001

Definition of abbreviations: BMI = body mass index; CVD = cardiovascular disease; DBP = diastolic blood pressure; EDS = excessive daytime sleepiness; IQR = interquartile range; RDI = respiratory disturbance index; SBP = systolic blood pressure; SH = systemic hypertension (SBP ≥ 140 or DBP ≥ 90 or use of antihypertensive medication).

EDS was defined as sleepiness of at least 3 or more days a week during the past 3 months in one or more of the following: after awakening, during free time (leisure time), at work or driving, or in the daytime in general.

Values are means and ± SD* or frequency and (percentage) unless otherwise specified.

Median and (IQR) and P values of the Mann-Whitney nonparametric test are provided.

§Current and former included.

Descriptive characteristics at baseline and follow-up of the subjects who completed the monitoring are presented in Table 2. At follow-up, both men and women had a significantly higher BMI, systolic BP, diastolic BP, and consequently a higher prevalence of SH. The percentage of subjects with ongoing antihypertensive medication is also shown in this table. The accumulated incidence of SH in the follow-up period was 30.5% in men and 20.8% in women.

TABLE 2. DESCRIPTIVE CHARACTERISTICS AT BASELINE (T0) AND FOLLOW-UP (T1) OF ALL THE SUBJECTS WHO COMPLETED THE MONITORING AND STRATIFIED BY SEX

All (n = 1557)
Men (n = 752)
Women (n = 805)
VariablesT0T1P ValueT0T1P ValueT0T1P Value
Time of follow-up, yr*7.6 ± 0.88 ± 0.87.1 ± 0.4
BMI, kg/m2*25.5 ± 3.726 ± 4.40.00726.2 ± 327.5 ± 3.3<0.00124.8 ± 4.325.5 ± 4<0.001
SBP, mm Hg*124 ± 17130 ± 19<0.001129 ± 16135 ± 16<0.001120 ± 16128 ± 15<0.001
DBP, mm Hg*79 ± 1080 ± 110.00381 ± 984 ± 10<0.00176 ± 1080 ± 9<0.001
SH377 (24.2%)775 (49.8%)<0.001226 (30.1%)456 (60.6%)<0.001151 (18.8%)319 (39.6%)<0.001
Use of antihypertensive medications96 (6.2%)256 (16.4%)<0.00141 (5.5%)161 (21.4%)<0.00155 (6.8%)95 (11.8%)<0.001

Definition of abbreviations: BMI = body mass index; DBP = diastolic blood pressure; SBP = systolic blood pressure; SH = systemic hypertension (SBP ≥ 140 or DBP ≥ 90 or use of antihypertensive medication).

Values are means ± SD* or frequency and (percentage).

The odds ratios for SH incidence after a follow-up of 7.5 ± 0.8 years, according to the RDI, are shown in Table 3. The RDI was divided into quartiles, using the first quartile (RDI 0–2.9 per hour) as reference. To assess the association between baseline RDI categories and the risk of developing SH, we used different adjusted models. The first model shows the crude odds ratio (unadjusted model), whereas the second is controlled for age; the third for age and sex; the fourth for age, sex, and BMI; and the fifth for age, sex, BMI, and neck circumference. The sixth model is controlled for all the preceding variables and for fitness level and cigarette, alcohol, and coffee consumption.

TABLE 3. ODDS RATIO AND 95% CONFIDENCE INTERVAL FOR INCIDENCE OF SYSTEMIC HYPERTENSION IN MEN AND WOMEN AFTER A FOLLOW-UP OF 7.5 ± 0.8 YEARS, ACCORDING TO THE RDI*

RDI by Quartiles (n = 1180)No. of EventsModel 1Model 2Model 3§Model 4Model 5Model 6**
0–2.9 (n = 367)93 (25.3%)1.001.001.001.001.001.00
3–6.9 (n = 417)140 (33.6%)1.49 (1.09–2.03)1.18 (0.85–1.64)1.16 (0.83–1.61)1.09 (0.78–1.52)1.10 (0.78–1.53)1.08 (0.77–1.52)
7–13.9 (n = 247)95 (38.5%)1.84 (1.30–2.61)1.22 (0.84–1.77)1.10 (0.75–1.60)0.94 (0.64–1.39)0.95 (0.64–1.41)0.90 (0.61–1.34)
≥14 (n = 149)70 (47%)2.61 (1.75–3.89)1.57 (1.02–2.40)1.28 (0.83–1.99)1.00 (0.63–1.57)1.02 (0.65–1.61)0.98 (0.62–1.57)
P trend<0.0010.0510.3420.8030.8850.708

Definition of abbreviation: RDI = respiratory disturbance index per hour.

Systemic hypertension was defined as systolic blood pressure greater than or equal to 140 mm Hg or diastolic blood pressure greater than or equal to 90 mm Hg or use of antihypertensive medication.

*All subjects with systemic hypertension at baseline were excluded.

Model 1: Unadjusted model (crude odds ratio).

Model 2: Adjusted for age.

§Model 3: Adjusted for age and sex.

Model 4: Adjusted for age, sex, and body mass index.

Model 5: Adjusted for age, sex, body mass index, and neck circumference.

**Model 6: Adjusted for age, sex, body mass index, neck circumference, and alcohol, tobacco, and coffee consumption and fitness level.

The risk of developing SH significantly increased with higher RDI category in model 1 (unadjusted model) (P < 0.001) but was not statistically significant after adjustment for age (model 2) (P = 0.051). Adjustments for sex (P = 0.342), BMI (P = 0.803), neck circumference, (P = 0.885) and fitness level and alcohol, tobacco, and coffee consumption (P = 0.708) further reduced the strength of the association between RDI and SH.

The association between RDI and the incidence of SH was also evaluated, stratified by sex (Table 4). The relationship between both variables was no longer statistically significant after adjustment for age (model 2) in men (P = 0.365) and in women (P = 0.758). These results were also confirmed by the absence of statistical significance in the interaction between RDI and sex (P value for interaction = 0.562).

TABLE 4. ODDS RATIO AND 95% CONFIDENCE INTERVAL FOR INCIDENCE OF SYSTEMIC HYPERTENSION STRATIFIED BY SEX, ACCORDING TO THE RDI*

SexRDI by QuartilesNo. of EventsModel 1Model 2Model 3§Model 4Model 5
Men (n = 526)0–2.9 (n = 131)46 (35.1%)1.001.001.001.001.00
Follow-up: 8 ± 0.8 yr3–6.9 (n = 165)75 (45.5%)1.54 (0.96–2.47)1.25 (0.77–2.04)1.18 (0.72–1.96)1.27 (0.76–2.12)1.25 (0.74–2.10)
7–13.9 (n = 130)59 (45.4%)1.54 (0.93–2.53)1.17 (0.70–1.97)1.02 (0.60–1.74)1.04 (0.60–1.80)0.94 (0.53–1.65)
≥14 (n = 100)50 (50%)1.85 (1.09–3.14)1.36 (0.78–2.36)1.03 (0.58–1.83)0.97 (0.57–1.67)0.91 (0.52–1.57)
P trend0.0310.3650.9070.9070.486
Women (n = 654)0–2.9 (n = 236)47 (19.9%)1.001.001.001.001.00
Follow-up: 7.1 ± 0.4 yr3–6.9 (n = 252)65 (25.8%)1.40 (0.91–2.14)1.11 (0.70–1.75)1.05 (0.66–1.67)1.14 (0.70–1.84)1.12 (0.69–1.82)
7–13.9 (n = 117)36 (30.8%)1.79 (1.08–2.97)1.00 (0.57–1.77)0.88 (0.49–1.56)0.74 (0.42–1.30)0.74 (0.42–1.30)
≥14 (n = 49)20 (40.8%)2.77 (1.44–5.33)1.19 (0.56–2.53)0.98 (0.45–2.13)0.86 (0.43–1.74)0.86 (0.42–1.74)
P trend0.0010.7580.7720.3780.374

Definition of abbreviation: RDI = respiratory disturbance index per hour.

Systemic hypertension was defined as systolic blood pressure greater than or equal to 140 mm Hg or diastolic blood pressure greater than or equal to 90 mm Hg or use of antihypertensive medication.

*All subjects with systemic hypertension at baseline were excluded.

Model 1: Unadjusted model (crude odds ratio).

Model 2: Adjusted for age.

§Model 3: Adjusted for age and body mass index.

Model 4: Adjusted for age, body mass index, and neck circumference.

Model 5: Adjusted for age, body mass index, neck circumference, and alcohol, tobacco, and coffee consumption and fitness level.

The findings do not endorse an association between OSA and the incidence of SH in the middle-aged general population.

The potential causal relationship between OSA and SH has been supported by three meta-analyses (1719), a systematic review (20), and recently published intervention studies (32, 33), which have shown a decrease in BP values by approximately 2 mm Hg after CPAP treatment. Some studies have also reported an association between OSA and more severe forms of SH (34, 35), and data from other studies also suggest that CPAP reduces BP values in refractory hypertension, a condition with a high prevalence in OSA subjects (36, 37). A significant association between OSA and SH was found in our previous cross-sectional study (1), in agreement with findings observed elsewhere (48).

To date, there are only two observational longitudinal studies that have evaluated OSA as an independent risk factor for SH: the WSCS (4) and the SHHS (16). Peppard and coworkers (4) demonstrated that even low RDI levels were an independent risk factor for developing SH. O'Connor and coworkers (16), in a recently published longitudinal study from the SSHS, reported elevated risks of developing hypertension in subjects with an RDI greater than 30, but this association was attenuated and no longer statistically significant after adjustment for BMI. The differences observed among the WSCS, the SHHS, and our study might be related to the differences in the population sample, the different OSA-assessment techniques that were used, and the RDI cut-off point adopted as reference. The SHHS sample was older than our population (60 vs. 47 yr); more obese (BMI = 28 vs. 26 kg/m2); more hypertensive (51% baseline hypertensive subjects vs. 24%); and more geographically and, to a certain extent, racially diverse than ours. The WSCS population was also more obese than ours (BMI = 29 vs. 26 kg/m2); more hypertensive (28% baseline hypertensive subjects vs. 24%); with a higher male prevalence (56% men vs. 48%); and from a working population and not from the general population. As regards the diagnostic procedures used, the SHHS used unattended at-home polysomnography, the WSCS used attended in-laboratory polysomnography, and we used unattended at-home polygraphy. As regards the RDI cut-off point, we divided the RDI into quartiles, using the first quartile (RDI 0–2.9) as reference, whereas Peppard and coworkers (4) took an apnea–hypopnea index of 0 as reference. The decision to use this range of values (0–2.9) was based on the fact that few patients have an RDI of 0 in real life. O'Connor and coworkers (16) used an apnea–hypopnea index range of 0–4.9 as reference in their study.

The association between OSA and SH may be confounded by variables that could cause both disorders. Therefore, we controlled for age; sex; BMI; neck circumference; fitness level; and alcohol, tobacco, and coffee consumption. In our study, in contrast with the results of the WSCS (4), age was a strong potential confounder and was the variable that most attenuated the relationship between OSA and SH. The differences observed between both studies might be explained by the differences in the population sample and the methods used. The remaining analyzed variables (sex; BMI; neck circumference; fitness level; and alcohol, tobacco, and coffee consumption) further reduced the strength of the association between RDI and SH. Regarding BMI, it has been suggested that obesity might be one of the causal pathways for the cardiovascular effects of OSA (38), in which case the reduced odds ratio after adjustment for BMI might be the result of an overadjustment of the association between OSA and SH. In our results, however, BMI had no significant effect on the estimates after controlling for age.

Analyses stratified by sex suggested that there were no significant sex differences in the association between OSA and SH. Our data are not consistent with those observed in one of the few studies that assessed the influence of sex on the association between OSA and SH, performed by Hedner and coworkers (12). After adjusting for the confounding variables, they found an independent association between OSA and SH, with a dose–response effect in men but not in women. Because there are few data available about the association between OSA and SH in women, longitudinal studies are needed to better determine this association.

This study has several strengths that lend confidence to the findings. First, it is a prospective study, which lends support to the evidence of causality, in comparison with previous cross-sectional studies (58). Second, the sample is from the general population and not from sleep-disorder clinics (39, 40) or from a working population (4), making it possible to obtain results more representative of the general population by minimizing the selection bias. Third, the data are adjusted and also disaggregated by sex and the monitoring period was prolonged (8 ± 0.8 yr in men and 7.1 ± 0.4 yr in women). Fourth, the final sample size is large (752 men and 805 women). Fifth, the response rate was very high (>80%). Finally, the dropout rate was low (26%). This latter point could be explained by the fact that in Spain in general, and more particularly in the Basque Country, there is a low percentage of family mobility, lending stability and favorable conditions to monitoring studies.

Some considerations that may be relevant to the accuracy of the results should be mentioned. The association between OSA and SH could be confounded by variables that might cause both disorders and we therefore controlled for age; sex; BMI; neck circumference; fitness level; and alcohol, tobacco and coffee consumption. However, there are other possible confounding variables that could produce a bias and have not been taken into account in the study (e.g., glycemia, cholesterol, lipid and hormone levels, and C-reactive protein), not to mention others that are still largely unexplored (e.g., gene expression and proteins). Another potential weakness is related to the method for BP measurement. We used office BP measurements instead of 24-hour ambulatory BP monitoring (ABPM), which has proved better than a mercury sphygmomanometer in assessing circadian BP patterns, minimizing the white-coat effect and predicting cardiovascular risk (41). The use of office BP measurements has, however, been validated in various studies and they are recommended as the screening method for SH (15), whereas ABPM is costly and time-consuming when performed on a large population. Moreover, as suggested by the PAMELA study (42) performed under similar conditions (general population longitudinally study), BP values measured by a sphygmomanometer have a similar diagnostic and predictive capacity to those measured by ABPM. Another potential limitation is that polysomnography was not performed on all the subjects. However, the portable home recording device used in this study (MESAM IV), which records oxygen saturation, heart rate, snoring sounds, and body position, is a validated tool for OSA diagnosis (29), which allowed the inclusion of a large sample of subjects. Furthermore, the data obtained for participants who underwent both polygraphy and polysomnography did not show any significant differences in the association between OSA and SH (1).

In conclusion, the results do not support an association between OSA and SH in the middle-aged general population. Nevertheless, we have assessed this association in the general population and not in the clinical population, where other studies have found a relationship between OSA and SH (7, 39, 40). In light of these findings, and of the differences observed among the WSCS, the SHHS, and our study, longitudinal studies with a long-term follow-up are needed to better ascertain the effect of OSA on SH in the general population, and to assess whether treatment with CPAP might be considered in asymptomatic patients with an abnormal RDI.

The authors thank the Alava Research Unit for advice on data analysis and preparation of the report, and the Spanish Respiratory and Sleep Society for its help and support.

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Correspondence and requests for reprints should be addressed to Joaquín Durán-Cantolla, M.D., Ph.D., Unidad de Trastornos del Sueño, Hospital Txagorritxu, Facultad de Medicina, Universidad del País Vasco, José Atxotegui s/n, 01009 Vitoria-Gasteiz, Spain. E-mail:

*Joint first authors of this paper.

‡Members of the Spanish Sleep and Breathing Group.

Supported by Fondo de Investigación Sanitaria (FIS 01/1577); Departamento de Sanidad del Gobierno Vasco (2001 1037); and FEPAR 2001.

Author Contributions: I.C.-P. and J.D.-C. contributed to the design and coordination of the study, to carrying out the recruitment, to compiling the database, and to the statistical analysis, and also prepared the manuscript and read and approved the final manuscript; F.A. and E.M.-S. contributed to the statistical analysis and read and approved the final manuscript; R.R. and F.B. contributed to the design of the study, revised the article, and read and approved the final manuscript; C.M.-N. contributed to performing the database, recruited the subjects, and read and approved the final manuscript; J.M. contributed to preparing the manuscript and read and approved the final manuscript; C.E. and M.F.-B. contributed to compiling the database, recruited the subjects, and read and approved the final manuscript; and L.C. and A.A. contributed to carrying out the recruitment and clinical care of subjects in this study and read and approved the final manuscript.

Originally Published in Press as DOI: 10.1164/rccm.201101-0130OC on August 25, 2011

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