Abstract
The effects of age on the prevalence of sleep apnea in the general population remain unclear, because previous studies have focused on specific populations. The effects of age on the severity of apnea are unknown. This study was based on a two-stage general random sample of men (aged 20 to 100 yr), consisting of a telephone survey (n = 4,364) and a sleep laboratory evaluation of a survey subsample (n = 741). Obstructive sleep apnea (OSA), based on both sleep laboratory and clinical criteria (apnea/hypopnea index [AHI] ⩾ 10 and the presence of daytime symptoms) was found in 3.3% of the sample, with its maximum prevalence in the middle age group (45 to 64 yr). Also, based solely on laboratory criteria, the prevalence of OSA (obstructive AHI ⩾ 20) showed an age distribution similar to that of OSA diagnosed by laboratory and clinical criteria. The prevalence of any type of sleep apnea (central and obstructive) increased monotonically with age. However, central apnea appeared to account for this monotonic relationship with age. Severity of sleep apnea, as indicated by both number of events and minimum oxygen saturation, decreased with age when any sleep apnea criteria were used and when controlling for body mass index (BMI). The study shows that the prevalence of sleep apnea tends to increase with age but that the clinical significance (severity) of apnea decreases. On the basis of these findings, the sleep laboratory criteria used for diagnosis of sleep apnea should be adjusted for age.
Over the past two decades, sleep apnea has been the focus of extensive research. The effects of age on the prevalence of sleep apnea, however, remain unclear (1, 2). It has been observed, on the basis of laboratory criteria alone, that the elderly have a greater prevalence of sleep apnea than the young (3-7). On the basis of this finding, many investigators have focused their evaluation on the elderly. For example, in a comprehensive series of studies, Kripke and colleagues, using sleep laboratory criteria alone, observed a prevalence of sleep apnea ranging from 24% to 62% in various elderly populations (8-10). All of these findings appeared to establish the existence of a monotonic relationship between age and sleep apnea (i.e., that sleep apnea was age dependent). In contrast, the typical age distribution of sleep apnea observed within a clinical population based on sleep laboratory criteria plus clinical findings is not monotonic (11, 12). Specifically, it appears to peak around age 55 yr (i.e., it is age related). The apparent discrepancy in terms of the age distribution of sleep apnea derived from these two populations indicated the need for large scale studies covering a wide age range and with careful sampling.
The available large-scale epidemiologic studies of the prevalence of sleep apnea have used either subjects with a restricted age range (e.g., 30 to 60 yr, ⩾ 65 yr) and/or a selected population (e.g., state agency employees, industrial employees) (13-15). Further, these studies have not addressed the clinically relevant question of whether age affects the severity of obstructive sleep apnea (OSA). The purpose of this study was to evaluate the effects of age on the prevalence and severity of sleep apnea in a large sample of men randomly selected from the general population and with a wide age range.
In the first phase of this study, a sample of men aged 20 yr and older was randomly selected by telephone from households in two counties of southern Pennsylvania (Dauphin and Lebanon). The households were selected with the Mitofsky–Waksberg two-stage random-digit dialing procedure (16). Telephone interviews were conducted with 4,364 age-eligible men residing in the sample households; the response rate among age-eligible males was 73.5%. A within-household selection procedure described by Kish was used to obtain approximately equal numbers of subjects in each of three age groups (20 to 44 yr, 45 to 54 yr, and 55 yr and older) (17).
In the second phase of the study, a stratified random sample of the 4,364 age-eligible men interviewed by telephone was selected for study in our sleep laboratory for the purposes of assessing the presence and severity of sleep apnea. This sample was chosen by counting the number of four risk factors (snoring, daytime sleepiness, obesity, and hypertension) each interviewed subject reported (1, 12). Subjects with higher counts of risk factors were oversampled (3%, 9%, 32%, 45%, and 70% for 0, 1, 2, 3, and 4 symptoms reported, respectively). A total of 741 subjects were evaluated in the sleep laboratory.
The sampling strategy of evaluating in the laboratory setting primarily those subjects who reported symptoms was strongly supported. As the number of symptoms increased from 0 to 4, the prevalence of an AHI ⩾ 5 increased from 8.8% to 44.0%, respectively (p < 0.001). When the AHI threshold was increased to 10, its prevalence increased from 1.6% to 34.0% for 0 to 4 symptoms, respectively (p < 0.001). When the AHI threshold was increased to 20, its prevalence increased from 0.0% to 31.8% for 0 to 4 symptoms, respectively (p < 0.001).
The sampling procedures used in the study departed from equal probability selection in order to improve the efficiency of subject identification and screening. Compensatory weights were developed to obtain estimates for the original target population of males in the two-county study area. Three weights were created. First, in the telephone sample, 32 of the 963 clusters of phone numbers in the first stage were “exhausted” before the target sample size was obtained. A compensatory weight was computed that corrected for this problem. A second weight was computed because the within-household screening deliberately introduced unequal probabilities of selection across the three age groups chosen for study in order to oversample the middle age group. The final weight was computed because the last stage of sampling of subjects for the sleep laboratory study also introduced unequal probabilities of selection, with substantially higher rates for those with larger counts of risk factors.
Each subject selected for laboratory evaluation completed a comprehensive sleep history, physical examination, and a psychometric battery assessing psychologic and cognitive function. The psychometric data will be reported in a future publication. The study was reviewed by the institutional review board of the Pennsylvania State University College of Medicine, and a signed informed consent was obtained from all subjects. All subjects were evaluated for one night in the sleep laboratory in sound-attenuated, light- and temperature-controlled rooms. During this evaluation, they were continuously monitored for 8 h, using 16-channel polygraphs (Model 78d; Grass Instrument, Quincy, MA). The electroencephalogram (EEG), three-channel electrooculogram (EOG), and an electromyogram (EMG) were recorded. The sleep records were subsequently scored independently, according to standardized criteria (18).
Respiration was monitored throughout the night with thermocouples at the nose and mouth (Model TCT 1R; Grass Instrument) and thoracic strain gauges. All-night recordings of hemoglobin oxygen saturation (SaO2 ) were obtained with an oximeter (Model 8800; Noonin Medical, Plymouth, MN) attached to the finger. Each 8-hr SaO2 recording was sampled by a computer at 2-s intervals, and the data stored for further evaluation.
An apnea was considered to have occurred if a breath cessation exceeded 10 s. Each apnea was categorized in terms of obstructive (chest-wall movement present) or central (chest-wall movement absent). In addition, hypopneas were considered to have occurred when a reduction in airflow of approximately 50% was indicated at the nose or mouth, and was associated with a 4% reduction in SaO2 .
For the purposes of data analysis, we calculated various apnea indices for the sleep laboratory-derived findings, based on the rate of the number of specific types of apneas per hour of sleep. Three indices were calculated for each subject: the AHI; an obstructive apnea/hypopnea index (OHI); and a central apnea index (CI). These indices are reported in terms of ⩾ 5, ⩾ 10, and ⩾ 20 except for CI, which is reported in terms of ⩾ 2.5 and ⩾ 20. The thresholds used for CI were chosen based on the underlying distribution (bimodal) observed for central apneas.
OSA was also diagnosed according to Sleep Disorders Clinic criteria, which employed laboratory plus clinical findings. This diagnosis was made by a sleep-disorders medicine specialist (A.N.V.), and was made if immediate treatment was considered appropriate. The diagnosis of OSA required an AHI ⩾ 10 plus the presence of symptoms (e.g., daytime sleepiness, hypertension, or another cardiovascular complication).
The severity of sleep apnea was indicated by both the AHI and the minimum value recorded for SaO2 during the 8-h sleep laboratory recording period. The association between these indices of severity of sleep apnea and age was assessed for various apnea thresholds. The thresholds used for this analysis were based on the AHI and included < 5, ⩾ 5, ⩾ 10, and ⩾ 20. For each apnea threshold, the relationship between continuous age and either minimum oxygen saturation or AHI was analyzed with weighted linear regression. Some higher order polynomial terms were significant at the 10% level, but were not clinically significant (i.e., less than 4% of the slope estimate), and were therefore not included. Confounding due to body mass index (BMI) was evaluated by assessing how much the slope of age versus severity measure changed upon moving BMI into the model.
Differences in risk of apnea among the three age groups defined previously were analyzed with age-specific prevalence estimates and weighted logistic regression. Confounding due to BMI was not present because BMI decreases with age among subjects with apnea and increases with age among those without apnea.
All estimates of means, prevalence, slopes, and odds ratios (ORs) are presented with corresponding 95% confidence intervals (CIs) in parentheses. The three sets of weights were incorporated into the analyses with the SAS software package (SAS Institute, Cary, NC). Analysis with the SUDAAN software package (Research Triangle Institute, NC) was not needed, because cluster sampling was not performed, thus precluding the need for the robust standard errors in SUDAAN.
The prevalence of OSA diagnosed according to Sleep Disorders Clinic criteria was found to be 3.3% (2.2, 4.8) in the study sample. The pattern of age-specific prevalence of OSA for the three age groups was found to be quadratic (Tables 1 and 2). The age-specific prevalence of OSA increased from 1.2% (0.4, 3.8) in men aged 20 to 44 yr to 4.7% (3.1, 7.1) in those aged 45 to 64 yr (OR = 4.0 [1.2, 14.1], p = 0.03), and then to 1.7% (0.3, 9.1) in those aged ⩾ 65 yr (OR = 2.8 [0.5, 16.9], p = 0.3). A similar pattern was observed when the age-specific prevalence of OSA was evaluated by decade (Figure 1). The prevalence increased monotonically from 20 to 29 yr through 50 to 59 yr (0.4%, 1.5%, 2.8%, and 5.4%, respectively). The age-specific prevalence subsequently decreased in the 60- to 69-yr and ⩾ 70-yr groups (4.2% and 2.5%, respectively).
| Age | Total (n = 741) | |||||||
|---|---|---|---|---|---|---|---|---|
| 20–44 (n = 236 ) | 45–64 (n = 430) | 65–100 (n = 75) | ||||||
| Sleep apnea* | ||||||||
| Obstructive | 1.2 (0.4, 3.8) | 4.7 (3.1, 7.1) | 1.7 (0.3, 9.1) | 3.3 (2.2, 4.8) | ||||
| Central | — | 0.4 (0.1, 1.8) | 1.1 (0.1, 8.9) | 0.4 (0.1, 1.2) | ||||
| Apnea/hypopnea index | ||||||||
| ⩾ 5 | 7.9 (5.0, 12.1) | 19.7 (16.2, 23.7) | 30.5 (21.1, 41.7) | 17.0 (14.5, 19.9) | ||||
| ⩾ 10 | 3.2 (1.6, 6.4) | 11.8 (9.1, 15.3) | 23.9 (15.7, 34.9) | 10.5 (8.3, 12.7) | ||||
| ⩾ 20 | 1.7 (0.6, 4.4) | 6.4 (4.3, 8.9) | 13.3 (7.3, 23.0) | 5.6 (4.0, 7.4) | ||||
| Obstructive apnea/hypopnea index | ||||||||
| ⩾ 5 | 7.9 (5.0, 12.1) | 18.8 (15.4, 22.8) | 24.8 (16.3, 35.7) | 15.9 (13.5, 18.7) | ||||
| ⩾ 10 | 3.2 (1.6, 6.4) | 11.3 (8.5, 14.5) | 18.1 (10.9, 28.4) | 9.4 (7.4, 11.6) | ||||
| ⩾ 20 | 1.7 (0.6, 4.4) | 6.3 (4.2, 8.8) | 5.1 (1.9, 13.0) | 4.7 (3.3, 6.3) | ||||
| Central apnea index | ||||||||
| ⩾ 2.5 | — | 1.7 (0.8, 3.4) | 12.1 (6.5, 21.6) | 2.2 (1.3, 3.5) | ||||
| ⩾ 20 | — | — | 5.2 (2.0, 13.2) | 0.5 (0.2, 1.4) | ||||
| Apnea Group | Age Groups | |||||
|---|---|---|---|---|---|---|
| 20–44 versus 45–64 | 45–64 versus 65–100 | 20–44 versus 65–100 | ||||
| Sleep apnea | ||||||
| Obstructive | 4.0 (1.2, 14.1)* | 2.8 (0.5, 16.9) | 1.4 (0.2, 11.7) | |||
| Central | — | 2.9 (0.05, 56.0) | — | |||
| Apnea/hypopnea index | ||||||
| ⩾ 5 | 2.9 (1.7, 4.9)† | 1.8 (1.0, 3.1)* | 5.1 (2.6, 10.2)† | |||
| ⩾ 10 | 4.0 (1.8, 8.7)† | 2.3 (1.3, 4.3)† | 9.4 (3.9, 23.1)† | |||
| ⩾ 20 | 3.9 (1.3, 11.5)† | 2.3 (1.1, 5.1)* | 9.1 (2.8, 30.4)† | |||
| Obstructive apnea/hypopnea index | ||||||
| ⩾ 5 | 2.7 (1.6, 4.6)† | 1.4 (0.8, 2.5) | 3.9 (1.9, 7.8)† | |||
| ⩾ 10 | 3.7 (1.7, 8.2)† | 1.8 (0.9, 3.4) | 6.6 (2.6, 16.7)† | |||
| ⩾ 20 | 3.9 (1.3, 11.3)† | 1.2 (0.4, 3.7) | 3.2 (0.8, 13.4) | |||
| Central apnea index | ||||||
| ⩾ 2.5 | — | 8.3 (3.0, 23.0)† | — | |||
Fig. 1. Age-specific prevalence of obstructive sleep apnea diagnosed according to Sleep Disorders Clinic criteria.
[More] [Minimize]The overall prevalence of an AHI ⩾ 5 was 17.0% (14.5, 19.9) (Tables and ). The age-specific prevalence of an AHI ⩾ 5 increased from 7.9% (5.0, 12.1) to 30.5% (21.1, 41.7) for the 20- to 44-yr and ⩾ 65-yr age groups, respectively (OR = 5.1 [2.6, 10.2], p < 0.001). The overall prevalence of an AHI ⩾ 10 was 10.5% (8.3, 12.7). The age-specific prevalence of an AHI ⩾ 10 again increased monotonically with age. The age-specific prevalence increased from 3.2% (1.6, 6.4) to 23.9% (15.7, 34.9) for the young and older age groups (OR = 9.4 [3.9, 23.1], p < 0.001). The overall prevalence of an AHI ⩾ 20 was 5.6% (4.0, 7.4). The age-specific prevalence based on this threshold again increased monotonically with age from 1.7% (0.6, 4.4) to 13.3% (7.3, 23.0) (OR = 9.1 [2.8, 30.4], p < 0.001).
The overall prevalence of an OHI ⩾ 5 was 15.9% (13.5, 18.7) (Tables and ). The age-specific prevalence increased monotonically with age from 7.9% (5.0, 12.1) to 24.8% (16.3, 35.7) (OR = 3.9 [1.9, 7.8], p < 0.001). The overall prevalence of an OHI ⩾ 10 was 9.4% (7.4, 11.6). The corresponding age-specific prevalence increased with age from 3.3% (1.6, 6.4) to 18.1% (10.9, 28.4) (OR = 6.6 [2.6, 16.7], p < 0.001). The overall prevalence of an OHI ⩾ 20 was 4.7 (3.3, 6.3). The age-specific prevalence did not increase with age. There was a significant increase in the age-specific prevalence of an OHI ⩾ 20 from age 20 to 44 yr (1.7% [0.6, 4.4]) through 45 to 64 yr (6.3% [4.2, 8.8]) (OR = 3.9 [1.3, 11.3], p = 0.01). The prevalence in the ⩾ 65-yr group (5.1% [1.9, 13.0]) decreased from the 45- to 64-yr group and did not differ significantly from that in the 20- to 44-yr group (OR = 3.2 [0.8, 13.4], p = 0.1) or the 45- to 64-yr group (OR = 1.2 [0.4, 3.7], p = 0.7).
The prevalence of central apnea diagnosed according to Sleep Disorders Clinic criteria was 0.4% (0.1, 1.2) (Table ). An age-specific prevalence of central apnea was observed primarily in the oldest age group (1.1% [0.1, 8.9]) and less frequently in the middle age group (0.4% [0.1, 1.8]).
The prevalence of a CI ⩾ 2.5 was 2.2% (1.3, 3.5) (Tables and ). No men in the youngest age group met this threshold. The age-specific prevalence of a CI ⩾ 2.5 increased from 1.7% (0.8, 3.4) in the middle age group to 12.1% (6.5, 21.6) in the older age group (OR = 8.3 [3.0, 23.0], p < 0.001). The overall prevalence of a CI ⩾ 20 was 0.5% (0.2, 1.4). All the men with this latter CI were in the ⩾ 65-yr age group, for whom the age-specific prevalence of a CI ⩾ 20 was 5.2% (2.0, 13.2).
The severity of OSA, as indicated by both minimum SaO2 and AHI, was found to decrease with age (i.e., the lowest minimum SaO2 and the highest AHI were observed in the young). Further, the rate of change between severity of OSA and age increased with the threshold of OSA in a dose–response manner. For example, the slope of the association between minimum SaO2 and age for subjects without sleep apnea (AHI < 5) was −0.08 (−0.11, −0.05) (p < 0.001), demonstrating a slightly decreasing minimum SaO2 with age (Figure 2). In other words, as men without OSA get older, they tend to desaturate more. In contrast, the slope of the minimum SaO2 –age association for subjects with mild sleep apnea (AHI ⩾ 5) was 0.04 (−0.06, 0.15) (p = 0.2), demonstrating a significant increase in minimum SaO2 with age as compared with subjects without sleep apnea (p < 0.001). In other words, younger men with sleep apnea tend to desaturate more than men without sleep apnea. Further, the relationship between age and minimum SaO2 became increasingly positive and significant as the apnea threshold was increased. Specifically, the slope for AHI ⩾ 10 was 0.13 (0.002, 0.26) (p = 0.001), whereas the slope for AHI ⩾ 20 was 0.25 (0.05, 0.45) (p < 0.001).
Fig. 2. Severity of sleep apnea as indicated by slope of minimum oxygen saturation (SaO2 ) in relation to age for four different apnea hypopnea indices. Note, that the lower the minimum SaO2 the greater the severity of apnea.
[More] [Minimize]The decreasing severity of OSA with age cannot be explained by corresponding changes in BMI. The average BMI did increase with age for subjects with OSA. For example, for subjects with an AHI ⩾ 20, the BMI was 35.7 (30.1, 41.3), 30.6 (28.8, 32.4), and 29.4 (28.4, 30.4), (p = 0.07) for the three age groups (20 to 44, 45 to 64, and ⩾ 65 yr), respectively. However, the slope of the association between age and minimum SaO2 , with adjustment for BMI, was found to be very similar to the slope without adjustment for BMI. For those subjects with an AHI ⩾ 20, the slope for age alone was 0.25 (0.05, 0.45), and for age with adjustment for BMI the slope was 0.22 (0.01, 0.43). Thus, there appears to be some confounding due to BMI, but it appears to be only slight.
The prevalence of sleep apnea diagnosed according to Sleep Disorders Clinic criteria (AHI ⩾ 10 plus daytime sleepiness, hypertension or other cardiovascular complication) was estimated to be 3.3% (2.2, 4.8) in the adult male population ranging in age from 20 to 100 yr. The age distribution of sleep apnea based on this criterion did not increase monotonically with age. Rather, the prevalence of this diagnosis changed with age in a quadratic fashion, increasing from over 1% in the youngest age group to almost 5% in the middle age group and then returning to less than 2% in the older subjects. This age distribution is consistent with the age distribution typically observed in a Sleep Disorders Clinic setting (11, 12).
The age-specific prevalence of sleep apnea based solely on laboratory criteria (AHI ⩾ 20) demonstrated a monotonic increase with age, increasing from about 2% in the younger to over 13% in the older age groups. However, when central apneas were not included in the index, the age-specific prevalence of OSA based solely on laboratory criteria (OHI ⩾ 20) was no longer monotonically associated with age. The age-specific prevalence of this index increased from about 2% in the young age group to over 6% in the middle age group, and then decreased to about 5% in the older age group. Thus, this age-specific distribution of OSA apnea based solely on laboratory criteria appeared to be somewhat similar to the age-specific distribution of sleep apnea based on Sleep Disorders Clinic criteria.
A prevalence for subjects with central events (CI ⩾ 2.5) was observed exclusively in the middle- and older-age groups. The age-specific prevalence for this CI threshold was about 2% of the middle age group and over 13% of the elderly group. In addition, when we increased the threshold to a CI ⩾ 20, central apnea was present in only about 5% of the oldest age group, and not in any of the younger or middle age subjects. Thus, from these data it is clear that central apnea is primarily observed in the elderly, and may in part reflect a normal aging process.
Bliwise has proposed a model that can account for these apparently competing age distributions (1). He speculates that sleep apnea is both an age-related and an age-dependent disorder. He suggests that two types of sleep apnea may exist. The first type of sleep apnea would have an age-related distribution. This type of apnea would peak around age 55 yr, and would account for the type of sleep apnea that is treated in a typical sleep disorders clinic. A second type of sleep apnea would occur primarily in the elderly and would be age dependent. This type of sleep apnea is less frequently seen in sleep disorders clinics, and would not have the clinical consequences of the age-related type. This proposed model was based primarily on data reported by Young and colleagues, which were derived from the largest random sample for the prevalence of sleep apnea reported to date (13). This sample, however, could not adequately test this model, because the age range of the sample was restricted (age ranged only from 30 to 60 yr in a state-agency-employed population).
An important additional finding observed in our study, which further supports the model of two types of sleep apnea, is that the most severe apnea tends to occur in the young, whereas apnea in the elderly appears to be less severe. Although the minimum SaO2 tended to be lower in older nonapneic subjects, it was found to be lowest in young subjects with apnea. In addition, the slope of the relationship between age and minimum SaO2 became more positive as the threshold for apnea was raised (i.e., as the severity of apnea was increased, the strength of the association with age increased). This relationship held even when controlling for BMI. A similar relationship was observed between AHI and age in association with OSA threshold.
A growing body of evidence suggests that the elderly with mild to moderate sleep apnea based on laboratory criteria may not be at any more risk for mortality or morbidity than those without sleep apnea (19-28). For example, in one study of untreated sleep apnea patients in which mortality was used as an outcome, only those untreated patients under 50 yr of age with an AI > 20 were observed to have a higher mortality rate than similarly aged patients who were treated (21). In another study, evaluating mortality rates in 1,620 patients with diagnosed sleep apnea, excess mortality was observed only in the 4th and 5th decades (25). Thus, our epidemiologic finding that sleep apnea is more severe in the young is consistent with these clinical data, which indicate that the clinical impact of apneic activity is weaker in the elderly. All of these data also tend to support the idea that the elderly population might be considered the “survivor” population (29).
The findings in the present study also support the hypothesis that there exists a genetic predisposition for sleep-disordered breathing (SDB) (30-32). In medical diseases with genetic influence, the most severe form tends to be expressed in the young. Our finding that SDB is more severe in the young tends to underscore the significance of the role of the genetic factors in sleep apnea.
The finding that severity of sleep apnea is greater in the young supports the proposal that the apnea/hypopnea threshold should be increased in the elderly (33). However, as has previously been stated by Bliwise (2), Berry and colleagues (34), and Ancoli-Israel and associates (35), our data do not imply that sleep apnea should be dismissed in the elderly. Rather, its presence should be interpreted in a more conservative manner. Sleep apnea in any age group, if severe and accompanied by symptoms, should be treated.
In conclusion, our data support the hypotheses that OSA increases in prevalence to about age 55 yr, after which it fails to increase or decreases, depending on the diagnostic criteria employed. In addition, our findings on the relationship between age and severity of sleep apnea clearly support the hypothesis that sleep apnea in older patients is less severe than sleep apnea in the young. These findings support the position that sleep laboratory criteria employed for the diagnosis of sleep apnea should be age adjusted.
We wish to gratefully acknowledge the strong support in terms of the design of this project by Professor J. Richard Landis, Ph.D., Director of Biostatistics & Epidemiology, The Pennsylvania State University College of Medicine. In addition, James M. Lepkowski, Ph.D., Senior Study Director, University of Michigan Survey Research Center, Institute for Social Research, contributed strong guidance for the sampling strategy as well as the establishment of the sample weights.
Supported by the National Institute of Health Grant #5 ROI HL40916.
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