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Meta-analysis of effects of obstructive sleep apnea on the renin-angiotensin-aldosterone system
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Publication date (Print):
May 2016
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Science Press
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Abstract
Background
Obstructive sleep apnea (OSA) is the most common cause of resistant hypertension, which has been proposed to result from activation of the renin–angiotensin–aldosterone system (RAAS). We meta-analyzed the effects of OSA on plasma levels of RAAS components.
Methods
Full-text studies published on MEDLINE and EMBASE analyzing fasting plasma levels of at least one RAAS component in adults with OSA with or without hypertension. OSA was diagnosed as an apnea-hypopnea index or respiratory disturbance index ≥ 5. Study quality was evaluated using the Newcastle-Ottawa Scale, and heterogeneity was assessed using the I 2 statistic. Results from individual studies were synthesized using inverse variance and pooled using a random-effects model. Subgroup analysis, sensitivity analysis, and meta-regression were performed, and risk of publication bias was assessed.
Results
The meta-analysis included 13 studies, of which 10 reported results on renin ( n = 470 cases and controls), 7 on angiotensin II (AngII, n = 384), and 9 on aldosterone ( n = 439). AngII levels were significantly higher in OSA than in controls [mean differences = 3.39 ng/L, 95% CI: 2.00–4.79, P < 0.00001], while aldosterone levels were significantly higher in OSA with hypertension than OSA but not with hypertension (mean differences = 1.32 ng/dL, 95% CI: 0.58–2.07, P = 0.0005). Meta-analysis of all studies suggested no significant differences in aldosterone between OSA and controls, but a significant pooled mean difference of 1.35 ng/mL (95% CI: 0.88–1.82, P < 0.00001) emerged after excluding one small-sample study. No significant risk of publication bias was detected among all included studies.
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Most cited references55
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Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study.
José M. Marin, Santiago Carrizo, Eugenio Vicente … (2005)
The effect of obstructive sleep apnoea-hypopnoea as a cardiovascular risk factor and the potential protective effect of its treatment with continuous positive airway pressure (CPAP) is unclear. We did an observational study to compare incidence of fatal and non-fatal cardiovascular events in simple snorers, patients with untreated obstructive sleep apnoea-hypopnoea, patients treated with CPAP, and healthy men recruited from the general population. We recruited men with obstructive sleep apnoea-hypopnoea or simple snorers from a sleep clinic, and a population-based sample of healthy men, matched for age and body-mass index with the patients with untreated severe obstructive sleep apnoea-hypopnoea. The presence and severity of the disorder was determined with full polysomnography, and the apnoea-hypopnoea index (AHI) was calculated as the average number of apnoeas and hypopnoeas per hour of sleep. Participants were followed-up at least once per year for a mean of 10.1 years (SD 1.6) and CPAP compliance was checked with the built-in meter. Endpoints were fatal cardiovascular events (death from myocardial infarction or stroke) and non-fatal cardiovascular events (non-fatal myocardial infarction, non-fatal stroke, coronary artery bypass surgery, and percutaneous transluminal coronary angiography). 264 healthy men, 377 simple snorers, 403 with untreated mild-moderate obstructive sleep apnoea-hypopnoea, 235 with untreated severe disease, and 372 with the disease and treated with CPAP were included in the analysis. Patients with untreated severe disease had a higher incidence of fatal cardiovascular events (1.06 per 100 person-years) and non-fatal cardiovascular events (2.13 per 100 person-years) than did untreated patients with mild-moderate disease (0.55, p=0.02 and 0.89, p<0.0001), simple snorers (0.34, p=0.0006 and 0.58, p<0.0001), patients treated with CPAP (0.35, p=0.0008 and 0.64, p<0.0001), and healthy participants (0.3, p=0.0012 and 0.45, p<0.0001). Multivariate analysis, adjusted for potential confounders, showed that untreated severe obstructive sleep apnoea-hypopnoea significantly increased the risk of fatal (odds ratio 2.87, 95%CI 1.17-7.51) and non-fatal (3.17, 1.12-7.51) cardiovascular events compared with healthy participants. In men, severe obstructive sleep apnoea-hypopnoea significantly increases the risk of fatal and non-fatal cardiovascular events. CPAP treatment reduces this risk.
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Obstructive Sleep Apnea and Risk of Cardiovascular Events and All-Cause Mortality: A Decade-Long Historical Cohort Study
Introduction Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is characterized by repeated episodes of upper airway occlusion during sleep. OSA afflicts 3%–9% of women and 10%–17% of men in the United States [1]. Sleep apnea can increase the risk of developing cardiovascular (CV) disease through a number of mechanisms, including intermittent hypoxia, sleep fragmentation, chronic sympathetic activation, and systemic inflammation [2]–[6]. Although evidence exists for relationships between OSA and both all-cause mortality and various CV events, uncertainty surrounds the magnitude of these associations, and the contributions of different OSA-related variables to the development of long-term adverse outcomes [7]. The apnea-hypopnea index (AHI) is the most often-reported statistically significant predictor; however, a variety of non-clinically determined AHI thresholds are arbitrarily used to diagnose and categorize severity of OSA [7],[8]. By focusing almost exclusively on AHI, clinicians and researchers may have missed opportunities to better risk-stratify patients using other OSA-related variables [9]. A number of less-studied variables may be more pathophysiologically relevant and offer better predictive ability than AHI, which is only a crude measure of breathing stoppages: intermittent hypoxemia (e.g., level of oxygen saturation [SaO2]), sleep fragmentation or sleep deprivation (e.g., total sleep time [TST] or number of awakenings), sympathetic activation (e.g., heart rate during sleep), symptoms (daytime sleepiness, snoring), family history of snoring or OSA, and findings from physical examination (neck circumference) [7],[10]. A number of large, well-designed community-based studies have examined the relationship between OSA and CV disease and mortality [11]–[14]. However, these have been limited by the small number of individuals with severe OSA. On the other hand, in clinically based studies with higher disease severity, small numbers of events limit the number of variables that can be included in statistical models, a weak definition of CV events is often used, women are usually underrepresented, and inconsistency in polysomnographic scoring criteria over time means long-term follow-up is not possible [7]. Our study addresses the weaknesses described above by following up a large number of individuals with a wide spectrum of severity of OSA and evaluating the relationship between a comprehensive set of OSA-related variables and the development of CV outcomes and all-cause mortality after controlling for traditional CV risk factors. Finally, our study aims to resolve conflicting evidence on the impact of gender, age, body mass index (BMI), daytime sleepiness (DS), and comorbid CV disease on the strength of association between OSA and development of CV outcomes. We hypothesize that the AHI, currently used to establish severity of OSA, is not by itself sufficient to accurately predict CV outcomes in individuals with OSA. We also hypothesize that an expanded set of factors including patient demographic and clinical characteristics and physiologic indices will provide greater accuracy in predicting CV outcomes. Methods Study Design A historical cohort study was conducted using a clinical sleep database and provincial health administrative data. All adults who underwent a first diagnostic sleep study at St. Michael's Hospital (Toronto, Ontario, Canada) between September 1, 1994 and December 31, 2010 and were diagnosed or referred with OSA were included. Their clinical data were linked to health administrative data at the Institute for Clinical Evaluative Sciences (ICES, Toronto, Ontario, Canada) from July 1, 1991 to March 31, 2011. Ethics Statement The ethics committees of all institutions involved (St. Michael's Hospital, ICES, University of Toronto) approved the study. Data Sources Clinical data The St. Michael's Hospital database includes a large set of clinical, demographic, and polysomnographical (PSG) variables that have been collected consistently for research purposes since 1991 (Table S1). Each patient in the cohort underwent full in-laboratory PSG recording that was scored by a sleep technologist and reviewed by a sleep physician. Disease-specific symptoms and history were collected using standardized questionnaires; a physical examination was performed by sleep technicians. Health administrative data Residents of Ontario have universal public health insurance covering all medically necessary services. ICES houses high quality [15] administrative data on a wide variety of publicly funded services provided since 1991, including individual-level information on physician claims, acute care hospitalization, and emergency department visits within the province. For Ontario residents with diagnosed OSA, funding is provided for continuous positive airway pressure (CPAP) devices, and this funding is documented in the Assistive Devices Program (ADP) database from 2004 onwards [16]. Table S2 gives details of variables derived from administrative data sets. Study Sample Patients who had undergone a first diagnostic sleep study during the defined study period, and who had a diagnosis of OSA (AHI≥5), or suspected OSA (referred with sleep apnea, but with AHI 30) [18]. We assumed that CPAP treatment started at the time of the claim in the ADP data set. Outcome Variables The primary composite outcome was defined using health administrative data as the first of (i) hospitalization due to myocardial infarction (MI), stroke, or exacerbation of congestive heart failure (CHF); (ii) a revascularization procedure (percutaneous coronary intervention, coronary artery bypass graft surgery); or (iii) all-cause death (Table S2). Hospitalization was chosen as a well-defined, validated, and standardized measure of interest to patients, doctors, and policy-makers. Participants were followed from their first diagnostic sleep study to the end of March 2011, or the occurrence of a primary outcome, whichever occurred first. Potential Confounders and Risk Factors The following potential confounders and risk factors were extracted from clinical data: age, sex, BMI, waist and hip circumferences, and self-reported smoking. Comorbidities at baseline (stroke, MI, CHF, hypertension [HTN], chronic obstructive pulmonary disease [COPD], depression, and diabetes) were identified from administrative data over a three-year period before the diagnostic sleep study. Neighbourhood income and rural status were derived from administrative data at the time of the diagnostic sleep study. Statistical Analysis Descriptive statistics were calculated for relevant data. Crude incidence rates of the composite end point per 100 person-years were calculated for each OSA severity group. Since AHI is the standard for defining of OSA and its severity [2],[18], and to allow comparisons with other research in this area, event-free survival in OSA severity groups was estimated using the Kaplan–Meier method and compared between groups with the log-rank test. We used univariate and multivariable Cox regression models to investigate the relationships between additional predictors and the CV outcome, and expressed the results as hazard ratios (HRs) and 95% CIs. To avoid choosing arbitrary cut points for PSG characteristics (e.g., AHI, TST), they were kept as continuous variables. We used restricted cubic spline transformations for continuous explanatory variables if non-linearity was observed (Figure S1), and the resulting standardized HRs compare the 75th and 25th percentiles, allowing comparison of the HRs on a common scale. The proportional hazards assumption for each variable was tested [19],[20]. Variables missing on more than 50% of individuals were excluded from all analyses (Table S1). For all others, we used multivariate imputation by chained equations to generate five complete data sets [21]. Eighty two variables including both the event status and the survival time were chosen for the imputation model. The following built-in imputation models were used in our analyses: for continuous variables, predictive mean matching; for binary variables, logistic regression; for unordered categorical variables, polytomous logistic regression; and for ordered categorical variables, proportional odds [22]. The separate estimates and standard errors from imputed data sets were pooled according to Rubin's rules [23]. For a unified presentation of all results and figures, the findings shown are for a single imputed data set. Pooled CIs across imputations were at most 2% wider than those presented. A systematic review [7] and expert opinion found age, sex, smoking status, BMI, AHI, TST, and DS to be clinically important, so these variables were forced into the models. Other variables were chosen for inclusion in the final model if they were selected by backward step-down variable deletion [24] in at least three imputed data sets. We investigated a priori-defined interactions between AHI or SaO2 and DS, BMI, age, sex, and CVD at baseline [7]. The final model was compared to a model with only traditional CV risk factors (age, sex, smoking status, BMI, prior HTN, diabetes, MI, stroke, and CHF [25]) using a likelihood ratio test [19]. To identify which outcomes were driving results, the final model was refitted to each separate component of the composite CV outcome. A clinical nomogram was constructed from the multivariable Cox model to estimate three- and five-year event-free survival probabilities and median survival times [26]. We used the bootstrap for internal validation (optimism for C-index and R2) and over-fitting-corrected calibration (predicted versus observed five-year survival). Discriminative ability was assessed using Harrell's C-index and predictive ability using the model likelihood ratio χ2 statistic [19]. Sensitivity analysis In the post-2004 cohort with information on CPAP claims, the final model was refitted with the addition of a time-dependent CPAP treatment variable. All statistical analyses were performed using R version 2.15.2 (http://www.r-project.org) and SAS 9.2. Results Sample Characteristics Between January 1, 1994 and December 31, 2010, 11,596 individuals underwent a first diagnostic sleep study and 10,149 (88%) were linked to administrative data sets and included in our analyses (Figure 1). Table 1 shows baseline characteristics of included and excluded patients. Excluded patients had similar OSA severity and demographic characteristics, but fewer CV comorbidities and greater DS. The included sample had 62% males, a mean age of 50 years, and a mean AHI of 25. The amount of missing data ranged from 0.69% (AHI) to10.1% (TST90SaO2). 10.1371/journal.pmed.1001599.g001 Figure 1 Flow diagram of the final cohort. *Split night, diagnostic study night when treatment was initiated due to severe OSA. 10.1371/journal.pmed.1001599.t001 Table 1 Comparison of included and excluded samples for patients with a full-night diagnostic sleep study. Variables Included (n = 10,149) Excluded (n = 929) Mean (SD) Unless Otherwise Indicated Missing, n (%) Mean (SD) Unless Otherwise Indicated Missing, n (%) Demographic characteristics Male 6,288 (62.0) — 544 (58.6) — Age, years 49. 9 (14.1) — 46.7 (13.2) 14 (1.5) Clinical symptoms and findings from physical examination “During the day, do you ever fall asleep unintentionally?” Yes, n (%) 3,657 (36.0) 688 (6.8) 382 (41.1) 54 (5.8) ESS total (0–24)a 8.6 (5.0) 615 (6.0) 10.3 (5.3) 40 (4.3) BMI, kg/m2 30.1 (7.0) 243 (2.4) 30.7 (6.9) 12 (1.3) History Smoking status, self-reported, n (%) 835 (8.2) 68 (7.3) Current 1,839 (18.1) — 174 (18.7) — Ex-smoker 1,869 (18.4) — 164 (17.7) — Never 5,606 (55.2) — 523 (56.3) — MI, self-reported, Yes, n (%)a 605 (6.0) 640 (6.3) 22 (2.4) 44 (4.7) CABG, self-reported, Yes, n (%)a 322 (3.2) 544 (5.4) 11 (1.2) 38 (4.1) Stroke, self-reported, Yes, n (%)a 301 (3.0) 546 (5.4) 16 (1.7) 38 (4.1) HTN, self-reported, Yes, n (%)a 3,097 (30.5) 585 (5.8) 250 (26.9) 44 (4.7) Lung disease, self-reported, Yes, n (%) 1,697 (16.7) 609 (6.0) 157 (16.9) 46 (5.0) PSG indexes TST, hours 5.5 (1.3) 241 (2.4) 5.9 (1.2) 14 (1.5) Sleep efficiency, % 76.5 (18.1) 211 (2.1) 78.6 (16.0) 23 (1.9) AHIO in TST, events/hour 21.1 (23.1) 70 (0.7) 21.6 (24.5) 7 (0.8) AHI in TST, events/hour 24.6 (25.3) 70 (0.7) 24.0 (26.0) 7 (0.8) OSA, severity, n (%) 70 (0.7) 7 (0.8) No OSA: AHI 30 2,960 (29.2) — 258 (27.8) — Arousals index, total, events/hour 29.9 (23.0) 511 (5.0) 30.7 (24.3) 34 (3.7) AWK in TST, number of events 28.8 (18.5) 214 (2.1) 28.7 (17.2) 11 (1.2) TST90SaO2, minutes 18.9 (49.7) 1,022 (10.1) 19.5 (51.9) 25 (2.7) Mean SaO2, % 94.4 (3.4) 60 (0.6) 94.4 (4.0) 5 (0.5) Heart rate, mean/TST, bpm 63.7 (10.3) 794 (7.8) 64.5 (10.7) 61 (6.6) a The differences between groups considered as clinically important as indicated by a systematic review [7] and expert opinion. AHIO, total and obstructive apnea-hypopnea index; ArI, total arousals index; AWK, total number of awakenings; bpm, beats per minute; CABG, coronary artery bypass graft surgery; ESS, Epworth Sleepiness Scale; TST90SaO2, sleep time spent with SaO2 less than 90%. Over a median follow-up of 68 months, 1,172 (11.5%) participants experienced a composite outcome, giving an incidence rate of 2 per 100 person-years. Event-free survival was 94% at three years and 90% at five years with CIs less than ±0.5%. Analyses of AHI AHI was significantly associated with event-free survival in univariate analyses when categorized (log-rank test; p 0.2) (Figure 3), although the association between AHI as a continuous variable and outcome of interest remained significant (HR = 1.12; 95% CI 1.05–1.2, p 30. 10.1371/journal.pmed.1001599.g003 Figure 3 Predicted survival by OSA severity, adjusted for traditional CV risk factors (BMI = 29, age = 50, sex = men, never smoked, without prior hypertension, diabetes, MI, stroke, or heart failure). Multivariable Cox Regression Models In the fully adjusted model, the following OSA-related predictors were significantly associated with occurrence of a composite outcome: TST90SaO2, TST, awakenings, periodic leg movements, mean heart rate, and presence of unintentional DS (Figures 4 and 5; Table 2). AHI was no longer a significant predictor. 10.1371/journal.pmed.1001599.g004 Figure 4 Results from multivariable Cox regression model presented as hazard ratios with shading representing confidence levels (99%, 95%, 90%, 80%, and 70%). AWK, number of awakenings in TST; TST90SaO2, sleep time spent with SaO2 less than 90%; PLMI, periodic leg movement index; HR, mean heart rate during sleep; day sleep, DS, identified by a positive answer to the question “During the day, do you ever fall asleep unintentionally?”. 10.1371/journal.pmed.1001599.g005 Figure 5 Predicted survival curves to show the effect of oxygen saturation (comparing 75th percentile [9 min] to 25th percentile [0 min]) controlling for potential confounders (BMI = 29, age = 50, sex = men, never smoked, without prior hypertension, diabetes, MI, stroke or heart failure, TST = 5.8, AWK = 25, PLMI, 1.2, mean heart rate, 63, without reporting excessive DS). 10.1371/journal.pmed.1001599.t002 Table 2 Model fitting and effect of predictors (n = 10,149, events = 1,172). Variable (X:Y) Indicates the Two Values Being Compared Univariate Models Multivariable Models HR (95% CI) LR χ2 (R2) CV Risk Factors Final Model (without Transformation) Final Model (with Transformationa) Demographic characteristics Sex (M:F) 1.71 (1.5–1.94) 69.6 (0.008) 1.62 (1.41–1.85) 1.60 (1.39–1.82) 1.55 (1.35–1.79) Age, years (59:40) 4.54 (4.18–4.94) 1,337.3 (0.143) 3.27 (2.95–3.62) 2.70 (2.42–3.01) 2.66 (2.38–2.97) BMI (33.6:25.4) 1.25 (1.18–1.33) 48.9 (0.006) 1.17 (1.09–1.25) — — History Smoking Status 109.3 (0.012) ex-smoker:never 2.03 (1.79–2.32) — 1.13 (0.99–1.30) 1.04 (0.91–1.20) 1.04 (0.90–1.19) current:never 1.13 (0.97–1.32) — 1.70 (1.45–1.99) 1.41 (1.20–1.66) 1.40 (1.19–1.65) Prior comorbidities HTN (Yes:No) 4.12 (3.65–4.65) 564.3 (0.062) 1.40 (1.22–1.61) 1.36 (1.18–1.55) 1.31 (1.15–1.51) Diabetes (Yes:No) 4.53 (4.01–5.11) 492.4 (0.055) 1.89 (1.66–2.16) 1.71 (1.50–1.95) 1.71 (1.50–1.94) Stroke (Yes:No) 4.96 (3.97–6.20) 130.7 (0.015) 1.61 (1.28–2.02) 1.71 (1.36–2.15) 1.71 (1.36–2.15) MI (Yes:No) 6.15 (5.23–7.24) 314.4 (0.035) 1.39 (1.16–1.66) 1.46 (1.21–1.74) 1.43 (1.19–1.72) CHF (Yes:No) 11.8 (10.39–13.4) 963.4 (0.105) 3.07 (2.64–3.57) 2.54 (2.17–2.96) 2.55 (2.19–2.97) COPD (Yes:No) 4.83 (4.28–5.45) 523.7 (0.058) — 1.37 (1.20–1.58) 1.37 (1.19–1.57) OSA-related variables Symptoms DS (Yes:No) 1.6 (1.41–1.81) 67.0 (0.008) — 1.15 (1.02–1.29) 1.13 (1.01–1.28) PSG indexes AHI, total, events/hr (35:6.3) 1.49 (1.42–1.57) 192.5 (0.022) — — — AWK, number (35:18) 1.35 (1.31–1.39) 243.6 (0.027) — 1.07 (1.03–1.11) 1.06 (1.02–1.10) TST90SaO2, minutes (9:0) 1.06 (1.05–1.07)2.99 (2.53–3.54)a 250.7 (0.028) — 1.02 (1.02–1.03) 1.50 (1.25–1.79) PLMI, events/hr (13.4:0.0) 1.18 (1.17–1.20) 232.2 (0.026) — 1.05 (1.03–1.07) 1.05 (1.03–1.07) TST, hours (4.9:6.4) 1.8 (1.7–1.9) 366.1 (0.041) — 1.19 (1.11–1.27) 1.20 (1.12–1.27) Heart rate, mean/TST, bpm (70:57) 1.38 (1.29–1.48) 69.9 (0.008) — 1.30 (1.21–1.40) 1.28 (1.19–1.37) LR χ2 (df) — — 2,025.15 (10) 2,227.6 (16) 2,246.0 (18) Bootstrap-corrected R2 — — 0.209 0.224 0.226 Bootstrap-corrected Harrell's C-index — — 0.843 0.856 0.857 For a unified presentation of all results and figures, the findings shown are for a single imputed data set (the third data set). For OSA-related variables, HRs and 95% CIs in this data set were all within 2% of coefficients pooled across five imputed data sets. a TST90SaO2 variable was transformed using restricted cubic spline transformation with 4 knots at 0.5, 4, 9, and 100 minutes. AWK, total number of awakenings in TST; bpm, beats per minute; df, degree of freedom; HR, hazard ratio; LR, likelihood ratio; PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%. All models were well calibrated (all observed and predicted five-year survival within 5%) (Figure S2) and validated (optimism<0.004 for final model R2). The fit for the model with only traditional CV risk factors was statistically significantly worse than the final model (χ2 (8) = 220; p<0.0001), indicating that OSA-related predictors independently contribute to the development of the composite outcome. Nomogram Clinical nomograms estimating median survival time and the probability of 3 and 5 years event-free survival were based on the final transformed model (Figure 6). Variables with the widest point range in the nomogram provide the greatest discrimination. The points assigned to each predictor are presented in Table S3. To estimate the role of OSA-related variables using the nomogram, as an example we compared two different patients (Table 3). First, the total number of points was calculated for a relatively healthy person: male, 50 years old without signs of OSA and without comorbidities, never smoked, with mean heart rate of 70, and 7 hours of sleep. The total number of points for such a person is 75, indicating 5-year event-free survival of 0.95–0.99. The total number of points for a person with the same characteristics except with moderate-severe OSA is 99, indicating 5-year event-free survival of 0.95. The 24-point difference between the first and second person due to OSA-related predictors leads to a relatively small difference in 5-year event-free survival. However, the total number of points for a person of the same age, gender, and sleep time with comorbid illnesses, who smoked, and had a mean heart rate of 80 is 150, indicating 5-year event-free survival of 0.4–0.5 and median survival time of 4 years. For this person, a 24-point difference (attributed to OSA-related predictors) gives a total of 174, which corresponds to a 5-year event-free survival of about 0.1 and median survival time between 1 and 2 years, a clinically significant decrease. 10.1371/journal.pmed.1001599.g006 Figure 6 Clinical nomogram for obstructive sleep apnea patients. To obtain the nomogram predicted probability of three- and five-year event-free survival and to estimate median event-free survival, locate patient values at each axis, then draw a vertical line to the “Point” scale (axis) to determine how many points are attributed for each predictor. Sum the points for all predictors. Locate the sum on the “Total Points” scale. Draw a vertical line towards the “3-year Survival,” “5-year Survival,” and “Median Survival Time” axes to determine the three-year composite CV outcome-free survival, the five-year event-free survival, and to estimate median survival respectively. PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%. 10.1371/journal.pmed.1001599.t003 Table 3 Example of clinical nomogram (point system) usage. Characteristics Person 1 Person 2 Person 3 Person 4 Value Points Value Points Value Points Value Points Sex male 10 male 10 male 10 male 10 Age, years old 50 44 50 44 50 44 50 44 Smoking status never 0 never 0 current 7 current 7 Prior HTN No 0 No 0 Yes 6 Yes 6 Prior MI No 0 No 0 Yes 8 Yes 8 Prior stroke No 0 No 0 Yes 12 Yes 12 Prior CHF No 0 No 0 Yes 20 Yes 20 Prior diabetes No 0 No 0 Yes 12 Yes 12 Prior COPD No 0 No 0 Yes 7 Yes 7 Mean heart rate, bpm 70 16 70 16 80 19 80 19 TST, hours 7 5 5 10 7 5 5 10 Presence of DS No 0 Yes 3 No 0 Yes 3 AWK per TST 0 0 50 4 0 0 50 4 TST90SaO2, min 0 0 50 8 0 0 50 8 PLMI, per hr 0 0 50 4 0 0 50 4 Total number of pointsa — 75 — 99 — 150 — 174 Proportion with 5-year event-free survivalb — 0.95–0.99 — 0.95 — 0.4–0.5 — 0.1 Person 1, male, 50 years old without signs of OSA and without comorbidities, never smoked, with mean heart rate of 70 and 7 hours of TST; Person 2, person 1+ moderate-severe OSA; Person 3, male, 50 years old with comorbidities, smoked, mean heart rate of 80 and 7 hours of TST; Person 4, person 3+ moderate-severe OSA. a The sum of points attributable for each patient's characteristic. b Corresponds to the total number of points. AWK, total number of awakenings in TST; bpm, beats per minute; PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%. Separate Components of Composite CV Outcome When the final model was applied to each component event, OSA-related variables were predictive of all-cause mortality (762 events), hospitalization for CHF (414 events), and stroke (100 events), but not for MI (145 events) (Table 4). 10.1371/journal.pmed.1001599.t004 Table 4 Final model re-fitted for separate components of cardiovascular composite outcome (n = 10,149). Variable (X:Y) Indicates the Two Values Being Compared All-Cause Mortality (n events = 762) MI (n events = 145) Stroke (n events = 100) CHF (n events = 414) Demographic characteristics Sex (M:F) 1.38 (1.17–1.64)a 1.86 (1.23–2.82)a 1.63 (1.01–2.64)a 1.12 (0.89–1.42) Age, years (59:40) 3.50 (3.04–4.02)a 2.57 (1.88–3.52)a 3.03 (2.07–4.44)a 2.44 (2.00–2.98)a History Smoking Status Ex-smoker:never 1.03 (0.87–1.22) 0.91 (0.61–1.36) 0.84 (0.52–1.35) 0.99 (0.79–1.25) Current:never 1.52 (1.24–1.86)a 1.60 (1.03–2.48)a 1.56 (0.90–2.72) 1.39 (1.04–1.85)a Prior comorbidities HTN (Yes:No) 1.22 (1.03–1.44)a 1.61 (1.09–2.38)a 2.24 (1.35–3.70)a 1.52 (1.18–1.94)a Diabetes (Yes:No) 1.67 (1.42–1.96)a 2.43 (1.69–3.50)a 1.52 (0.98–2.38) 1.83 (1.48–2.26)a Stroke (Yes:No) 1.57 (1.18–2.07)a 1.39 (0.67–2.88) 3.18 (1.74–5.82)a 1.56 (1.11–2.20)a MI (Yes:No) 1.27 (1.01–1.59)a 2.44 (1.50–3.98)a 1.00 (0.51–1.93) 1.39 (1.07–1.79)a CHF (Yes:No) 2.12 (1.76–2.55)a 0.95 (0.57–1.57) 1.24 (0.72–2.15) 6.56 (5.15–8.37)a COPD (Yes:No) 1.53 (1.30–1.81)a 1.34 (0.90–2.01) 1.31 (0.82–2.08) 1.46 (1.16–1.83)a OSA-related variables Symptoms DS (Yes:No) 1.05 (0.91–1.22) 1.15 (0.83–1.61) 1.46 (0.97–2.19) 1.19 (0.98–1.46) PSG indexes AWK, number (35:18) 1.06 (1.02–1.11)a 0.96 (0.85–1.09) 1.14 (1.02–1.27)a 1.08 (1.02–1.15)a TST90SaO2, minutes (9:0)b 1.58 (1.26–1.98)a 1.31 (0.79–2.18) 1.24 (0.68–2.25) 1.92 (1.39–2.64)a PLMI, events/hr (13.4:0.0) 1.05 (1.02–1.07)a 0.98 (0.91–1.05) 1.01 (0.94–1.09) 1.05 (1.02–1.09)a TST, hours (4.9:6.4) 1.16 (1.07–1.27)a 1.05 (0.86–1.28) 1.54 (1.23–1.93)a 1.28 (1.14–1.43)a Heart rate, mean/TST, bpm (70:57) 1.39 (1.28–1.52)a 0.92 (0.74–1.15) 0.96 (0.74–1.24) 1.22 (1.08–1.37)a LR χ2 (df) 1,673.95 (18) 210.03 (18) 218.30 (18) 1,371.66 (18) R2 0.21 0.09 0.14 0.25 a Significant association (p < 0.05). b TST90SaO2 was transformed using a restricted cubic spline with 4 knots at 0.5, 4, 9, and 100 minutes. AWK, total number of awakenings in TST; bpm, beats per minute; df, degree of freedom; LR, likelihood ratio; PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%. Interactions The association between TST90SaO2 (9 versus 0 minutes) and the composite outcome was significantly (p = 0.04) stronger for females (HR = 2.21, 95% CI 1.56–3.11) than for males (HR = 1.29, 95% CI 1.05–1.57). CPAP Treatment Among 4,733 individuals who underwent a diagnostic sleep study between 2004 and 2010, 762 (16%) submitted a CPAP claim and 333 (7%) experienced a composite event. In our final model, a CPAP claim was not associated with risk of an event (HR = 0.85, 95% CI 0.64–1.14, p = 0.3). To assess the final model on an untreated sample, patients were censored at the time of a CPAP claim; all predictors except DS remained significantly associated with outcome (Table 5). 10.1371/journal.pmed.1001599.t005 Table 5 Association between OSA-related variables and the composite CV outcome on untreated subsample versus entire cohort. Variable (X:Y) Indicates the Two Values Being Compared Untreated Subsamplea (≥2,004) (n Total = 4,733, n Events = 270) For Entire Cohort (n Total = 10,149, n Events = 1,172) Demographic characteristics Sex (M:F) 1.34 (1.01–1.78) 1.55 (1.35–1.79) Age, years (59:40) 2.48 (1.97–3.13) 2.66 (2.38–2.97) History Smoking Status Ex-smoker:never 1.10 (0.83–1.46) 1.04 (0.90–1.19) Current:never 1.61 (1.15–2.26) 1.40 (1.19–1.65) Prior comorbidities HTN (Yes:No) 1.72 (1.25–2.36) 1.31 (1.15–1.51) Diabetes (Yes:No) 1.36 (1.03–1.79) 1.71 (1.50–1.94) Stroke (Yes:No) 1.17 (0.70–1.93) 1.71 (1.36–2.15) MI (Yes:No) 1.46 (1.04–2.06) 1.43 (1.19–1.72) CHF (Yes:No) 3.03 (2.23–4.12) 2.55 (2.19–2.97) COPD (Yes:No) 1.13 (0.85–1.51) 1.37 (1.19–1.57) OSA-related variables Symptoms DS (Yes:No) 1.12 (0.88–1.44) 1.13 (1.01–1.28) PSG indexes AWK, number (35:18) 1.17 (1.09–1.26) 1.06 (1.02–1.10) TST90SaO2, minutes (9:0)b 1.70 (1.18–2.47) 1.50 (1.25–1.79) PLMI, events/h (13.4:0.0) 1.07 (1.02–1.11) 1.05 (1.03–1.07) TST, hours (4.9:6.4) 1.22 (1.06–1.39) 1.20 (1.12–1.27) Heart rate, mean/TST, bpm (70:57) 1.22 (1.05–1.43) 1.28 (1.19–1.37) a Subcohort of patients who underwent a diagnostic sleep study since 2004; patients who claimed CPAP through ADP data set before event of interested occurred were censored at the time of a CPAP claim. b TST90SaO2 was transformed using a restricted cubic spline with 4 knots at 0.5, 4, 9, and 100 minutes. AWK, total number of awakenings in TST; bpm, beats per minute; PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%. Discussion In a large clinical cohort, 11.5% of individuals experienced a composite CV outcome over a median 68 months of follow-up. Our incidence rate of 2 per 100 person-years was similar to rates reported in other clinically-based sleep studies [27],[28]. While AHI was found to predict CV events in a univariate analysis, there was no significant association in a multivariable model adjusted for potential confounders. In contrast, the multivariable models identified other OSA-related factors as independent and significant predictors of the occurrence of CV events and all-cause mortality. The results obtained were driven by all-cause mortality, hospitalized heart failure and stroke, and were replicated on a subsample of untreated patients. Using a nomogram, we demonstrated that OSA-related variables independently contribute to predicted three- and five-year event-free survival and median survival time, over and above traditional risk factors. Though the difference attributed to OSA-related predictors for a relatively healthy person seems small, for individuals with high baseline risk, even a small increase in risk due to OSA will place a patient in considerably higher risk group. The significant association between AHI and the composite CV outcome in our univariate analyses is consistent with findings of other studies. However, the persistent significance of this association in multivariable models in other studies, but not in ours, could be explained by methodological differences between our study and other studies. For example, large, community-based studies may not include potentially more important OSA-related predictors or selectively report findings from subgroup analyses [13]. Variations in the definition of hypopnea may be important. We used a definition of hypopnea that does not demand the occurrence of oxygen desaturation. The ability of AHI to predict CV disease was found to improve if 3% or 4% oxygen desaturation is required for the scoring of hypopneas [29], indicating the probable importance of hypoxia in mediating CV risk from OSA and raising the question of whether a more direct measure of hypoxia might be more predictive than AHI. In fact, the strongest OSA-related predictor of CV events in our study was the sleep time spent with SaO2<90%. The association between TST90SaO2 and the outcome of interest remains significant for both sexes, and across a spectrum of age, BMI, DS, and comorbid CV diseases. Our finding that a measure of hypoxia predicts CV risk is consistent with emerging evidence in both animal models and humans that intermittent hypoxia may be a crucial mechanistic link whereby OSA causes oxidative stress, metabolic derangement, and endothelial damage [30],[31]. The remainder of the OSA-related variables predictive of CV risk were mainly reflective of either sleep fragmentation or sympathetic activity. Increases in the number of awakenings or periodic leg movements or decreases in TST may represent the direct effects of OSA on sleep fragmentation and deprivation that can increase risk by inducing endothelial dysfunction and sympathetic activation [32]. Despite limited evidence elsewhere on the association between the number of awakenings and CV events, the evidence is growing regarding the relationship of PLM to CV events [7],[33],[34]. Low TST may indicate insomnia, which by itself can lead to increased risk of all-cause mortality and development of CV events [35]. However, in patients with chronic insomnia, most awakenings may be caused by sleep-disordered breathing [36]. Many of the CV consequences of OSA may be mediated by activation of sympathetic activity, and represented by increased heart rate [5]. Finally, available data suggest an association between mortality (all-cause and from CV disease) and excessive DS [37]. Despite strong evidence from randomized controlled trials of a causal relationship between OSA and markers of risk of CV events, it remains to be proven whether OSA-induced HTN translates into increased morbidity and mortality [38]. A recent randomized controlled trial found that in patients with OSA without DS, the prescription of CPAP did not result in a statistically significant reduction in the incidence of a combined outcome of HTN or CV events, compared with usual care [39]. In the same trial, OSA severity, assessed by the AHI and sleep time spent with SaO2 less than 90%, was not related to the combined outcome. However, in a post hoc analysis, patients with worse oxygen desaturation at night and with CPAP adherence of less than four hours per night showed a higher rate of HTN or CV events than the control group. Although evidence has been inconsistent on the association between OSA-related predictors and CV events in females and elderly individuals [7], we found that the significant association extended to these groups, in part due to the composition of our cohort. Our cohort included a large number of women over 50 years of age, who we assume are postmenopausal and therefore at increased risk of CV events [40],[41]. Our research addresses many limitations of previously published observational studies. Our study includes over 10,000 patients from a clinically-based sleep cohort and consequently has a large number of events, allowing us to assess many variables in the model and control for all available confounders. Data were consistently collected and used the same PSG scoring criteria over time. We included patients with a wide range of OSA severity and a relatively large number of females. We matched a high percentage of patients (89%) to administrative data, had long and complete follow-up through health administrative data, used validated algorithms to define CV outcomes and comorbidities at baseline, and, finally, used rigorous methods for missing data, model selection, calibration, and validation. The format of the nomogram used as a predictive tool allows predictions based on any combination of patient characteristics, not only from categorical, but also from continuous (possibly transformed) variables and interactions between variables. The nomogram allows the use of a complicated model in clinical prediction, improving accuracy of predictions over simpler points-based scores that require categorization of risk using arbitrary categories [42],[43]. As with any observational study, there are limitations related to availability of data. Some important confounders (e.g., level of cholesterol, race, treatment of hypertension) were not available. However, our model with CV risk factors had high predictive and discriminative ability, reflecting that the majority of important predictors were included. Assessing the sensitivity of obtained results to unmeasured confounders using approach recommended by Lin and colleagues [44], we found that unmeasured confounders (e.g., treatment for hypertension) should be really strong (i.e., increased both the hazard of composite CV outcome and the probability of severe SaO2 [75th percentile] 4-fold) to change the association between OSA and time spent with SaO2 less than 90% to non-significant. Our findings are based on patients referred to a single centre, which may reduce the generalizability of our findings. Validation of our results in other patient populations is recommended. Furthermore, there is no accepted definition of intermittent hypoxemia that includes both SaO2 variability and severity. Although oxygen desaturation index (ODI), the hourly average number of desaturation episodes, could be a useful predictor, it was not available in our study. However, while ODI reflects frequency of oxygen desaturation, time spent with SaO2<90% reflects its severity. The non-significant relationship between treatment and development of composite CV outcome that we found in the post-2004 cohort could be explained by lack of information about CPAP adherence, treatment approaches other than CPAP, and reduced power in this analysis. Moreover, the association between CPAP treatment and CV events can be attenuated because some patients were treated unnecessarily. Finally, it is naive to assume that all predictor levels will remain constant during the follow-up time [45]. Their trajectories (e.g., weight gain, OSA progression over time) were not known for our patients and not assessable at baseline when risk prediction was made. Though we have developed a nomogram for prediction to show how the baseline predictors relate to 3- and 5-year outcomes, we cannot strongly recommend its use in a clinical setting before it has undergone external validation. Rather, we used the nomogram to show the strength of the various factors on absolute risks of the composite CV outcome. Further, although the increments observed in c-statistics between final model and CV risk factors were small (0.857–0.843 = 0.014), it has been shown that the increase in c-statistics is very small when the baseline model's c-index is large: “good models are harder to improve upon” [46],[47]. We showed that OSA-related factors other than AHI are important predictors of the composite CV outcome. We believe a revision of the operative definition of OSA may be necessary, to reflect not simply the frequency of apneas and hypopneas, but the actual physiologic consequences that result—the severity of oxygen desaturation, sleep fragmentation, sleep deprivation, and sympathetic activation. It is these “downstream” phenomena that we have found to be more predictive of CV risk. The OSA-related predictors identified in our study could be collected using more limited recordings than PSG, potentially in the home setting. Conclusions AHI was significantly associated with a composite CV outcome in univariate analyses; however, this association became non-significant after controlling for potential confounders. Other OSA-related predictors, such as sleep time spent with SaO2 less than 90%, the number of awakenings, mean heart rate, TST, or presence of excessive DS, were significantly and independently associated with a 5% to 50% increased risk of development of the composite CV outcome even after controlling for known CV risk factors. Supporting Information Alternative Language Abstract S1 Spanish translation of the abstract. Translation by Romina Brignardello-Petersen. (DOCX) Click here for additional data file. Alternative Language Abstract S2 Russian translation of the abstract. Translation by Tetyana Kendzerska. (DOCX) Click here for additional data file. Figure S1 Effect of sleep time spent with SaO2 less than 90%, on the log hazard of composite CV outcome. A restricted cubic spline transformation with 4 knots was used to model the non-linearity in this relationship. The shaded area is a 95% confidence band. (TIF) Click here for additional data file. Figure S2 Calibration plot of the final model (predicted versus observed five-year survival). All observed and predicted 5-year survival values were within 5%. The final model was well calibrated: for 14 of 17 groups by 500 patients prediction was good. X, resampling optimism added, B = 150; Based on observed–predicted. Each group is 500 individuals; gray is ideal. (TIF) Click here for additional data file. Table S1 List of variables collected in sleep laboratory (each patient in the cohort underwent an overnight full standard PSG recording which was scored manually by a sleep technologist and reviewed by a board-certified sleep physician). *These variables were excluded from the main analyses as more than 50% of individuals had missing values. (DOCX) Click here for additional data file. Table S2 Information about variables derived from administrative data. List of data sets used: The Registered Persons Database (RPDB); Canadian Institute for Health Information Discharge Abstract database (CIHI-DAD) and the Same Day Surgery (CIHI-SDS); Ontario Health Insurance Plan Physician Services Claims database (OHIP); NACRS (National Ambulatory Care Reporting System); the Ontario Diabetes Database (ODD); HYPERTENSION; Ontario Congestive Heart Failure database (CHF); Ontario Chronic Obstructive Pulmonary Disease database; Ontario Mental Health Reporting System (OMHRS) stand-alone admissions data set; Ontario Cancer Registry Data (OCRD); Ontario Registrar General Death (ORGD) data; Assistive Devices Program data set (ADP). CABG, coronary artery bypass graft surgery; PCI, percutaneous coronary intervention. *OHIP diagnostic codes except written OHIP fee codes. (DOCX) Click here for additional data file. Table S3 A simple points system based on the beta-coefficients from the final transformed model. Points per predictors, total points and corresponding three- and five-year event-free survival and median survival time are presented. Points per unit of linear predictor: 21.59161. Linear predictor units per point: 0.04631428. (DOCX) Click here for additional data file.
- Record: found
- Abstract: found
- Article: not found
Plasma aldosterone is related to severity of obstructive sleep apnea in subjects with resistant hypertension.
Obstructive sleep apnea (OSA) and primary aldosteronism are common in subjects with resistant hypertension; it is unknown, however, if the two disorders are causally related. This study relates plasma aldosterone and renin levels to OSA severity in subjects with resistant hypertension, and in those with equally severe OSA but without resistant hypertension serving as control subjects. Seventy-one consecutive subjects referred to the University of Alabama at Birmingham (UAB) for resistant hypertension (BP uncontrolled on three medications) and 29 control subjects referred to UAB Sleep Disorders Center for suspected OSA were prospectively evaluated by an early morning plasma aldosterone concentration (PAC) and renin level, and by overnight, attended polysomnography. OSA (apnea-hypopnea index [AHI] > or = 5/h) was present in 85% of subjects with resistant hypertension. In these subjects, PAC correlated with AHI (rho = 0.44, p = 0.0002) but not renin concentration. Median PAC was significantly lower in control subjects compared to subjects with resistant hypertension (5.5 ng/dL vs 11.0 ng/dL, p < 0.05) and not related to AHI. In male subjects compared to female subjects with resistant hypertension, OSA was more common (90% vs 77%) and more severe (median AHI, 20.8/h vs 10.8/h; p = 0.01), and median PAC was significantly higher (12.0 ng/dL vs 8.8 ng/dL, p = 0.006). OSA is extremely common in subjects with resistant hypertension. A significant correlation between PAC and OSA severity is observed in subjects with resistant hypertension but not in control subjects. While cause and effect cannot be inferred, the data suggest that aldosterone excess may contribute to OSA severity.
Author and article information
Journal
Journal ID (nlm-ta): J Geriatr Cardiol
Journal ID (iso-abbrev): J Geriatr Cardiol
Journal ID (publisher-id): JGC
Title:
Journal of Geriatric Cardiology : JGC
Publisher:
Science Press
ISSN
(Print):
1671-5411
Publication date
(Print):
May
2016
Volume: 13
Issue: 4
Pages: 333-343
Affiliations
Author notes
Correspondence to: Yong-Xiang WEI, MD, Department of Otolaryngology, Beijing Anzhen Hospital, Capital
Medical University, 2
th Anzhen Road, Chaoyang District, Beijing 100029, China. E-mail:
Drweiyx@
123456hotmail.com
Telephone:+86-10-84005301
Fax:+86-10-84005301
Article
Publisher ID: jgc-13-04-333DOI: 10.11909/j.issn.1671-5411.2016.03.020
PMC ID: 4921546
PubMed ID: 27403143
SO-VID: b31f23c3-f621-4193-a7ad-4db8c1073cb9
Copyright © Institute of Geriatric Cardiology
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License, which allows readers to alter, transform, or build upon the article and then distribute the resulting work under the same or similar license to this one. The work must be attributed back to the original author and commercial use is not permitted without specific permission.
History
Date
received
: 13
January
2016
Date
revision received
: 22
February
2016
Date
accepted
: 24
February
2016
Categories
Subject:
Research Article
ScienceOpen disciplines: Cardiovascular Medicine
Data availability:
ScienceOpen disciplines: Cardiovascular Medicine