Effect of obstructive sleep apnea hypopnea syndrome on lipid profile: a meta-regression analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is associated with obesity, metabolic syndrome, and dyslipidemia, which may be related to decrease androgen levels found in OSA patients. Dyslipidemia may contribute to atherosclerosis leading to increasing risk of heart disease. METHODS: Systematic review was conducted using PubMed and Cochrane library by utilizing different combinations of key words; sleep apnea, obstructive sleep apnea, serum lipids, dyslipidemia, cholesterol, total cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL), and triglyceride (TG). Inclusion criteria were: English articles, and studies with adult population in 2 groups of patients (patients with OSA and without OSA). A total 96 studies were reviewed for inclusion, with 25 studies pooled for analysis. RESULTS: Sixty-four studies were pooled for analysis; since some studies have more than one dataset, there were 107 datasets with 18,116 patients pooled for meta-analysis. All studies measured serum lipids. Total cholesterol pooled standardized difference in means was 0.267 (p = 0.001). LDL cholesterol pooled standardized difference in means was 0.296 (p = 0.001). HDL cholesterol pooled standardized difference in means was -0.433 (p = 0.001). Triglyceride pooled standardized difference in means was 0.603 (p = 0.001). Meta-regression for age, BMI, and AHI showed that age has significant effect for TC, LDL, and HDL. BMI had significant effect for LDL and HDL, while AHI had significant effect for LDL and TG. CONCLUSION: Patients with OSA appear to have increased dyslipidemia (high total cholesterol, LDL, TG, and low HDL).

Serum inflammatory markers in obstructive sleep apnea: a meta-analysis.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) has been linked to and is associated with increased cardiovascular and cerebrovascular morbidity. Ongoing inflammatory responses play an important role in this association. Multiple small size studies addressing the profile of the inflammatory markers in OSA are available therefore we performed a meta-analysis. METHODS: Systematic review of medical literature was conducted using PubMed, Cochrane, and EMBASE databases from 1968 to 2011 by utilizing the key words obstructive sleep apnea, C-Reactive protein, tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), interleukin 8 (IL-8), intercellular adhesion molecule (ICAM), vascular cell adhesion molecule (VCAM) and Selectins. Inclusion criteria were: full text English articles; studies with adult population; reported values for at least one of the markers of interest; with at least two separate groups (subjects with OSA and control group); OSA was defined as AHI of ≥ 5/h. RESULTS: Five hundred and twelve studies were reviewed for inclusion with 51 studies pooled for analysis (30 studies for CRP, 19 studies for TNF-α, 8 studies for ICAM, 18 studies for IL-6, six studies for VCAM and 5 studies for Selectins). The levels of inflammatory markers were higher in patients with OSA compared to control group. Standardized pooled Mean differences were calculated to be 1.77 for CRP, 1.03 for TNF-α, 2.16 for IL-6, 4.22 for IL-8, 2.93 for ICAM, 1.45 for Selectins and 2.08 for VCAM. CONCLUSIONS: In this meta-analysis, the levels of systemic inflammatory markers were found to be higher in OSA patients compared to control subjects.

Treatment for sleep apnea by continuous positive airway pressure improves levels of inflammatory markers - a meta-analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is associated with coronary artery disease (CAD). Intermittent hypoxia associated with OSA increases sympathetic activity and may cause systemic inflammation, which may contribute to CAD in patients with OSA. Treatment with continuous positive airway pressure (CPAP) has been shown to change levels of inflammatory markers. We analyzed data from published studies by a systematic meta-analysis. OBJECTIVE: To asses if treatment for sleep apnea by CPAP will affect levels of inflammatory markers. DATA RESOURCES: PubMed, Embase and Cochrane library. METHODS: Study eligibility criteria full text English studies of adult, human subjects, addressing values of at least one of the inflammatory markers before and after CPAP treatment. We used the definition of OSA as an apnea-hypopnea index (AHI) of ≥ 5/h, reported values in mean and standard deviation or median with range. PARTICIPANTS: Adult, human. INTERVENTIONS: CPAP treatment for OSA. STUDY APPRAISAL AND SYNTHESIS METHOD: A total of 3835 studies were reviewed for inclusion, while 23 studies pooled for analysis. A total of 14 studies with 771 patients were pooled for C-reactive protein (CRP); 9 studies with 209 patients were pooled for tumor necrosis factor-alpha (TNF-α); and 8 studies with 165 patients were pooled for interleukin-6 (IL-6). ENDPOINT DEFINITIONS: THE FOLLOWING INFLAMMATORY MARKERS WERE CHOSEN: CRP, TNF-α, and IL-6. RESULTS: C-reactive protein: Study level means ranged from 0.18 to 0.85 mg/dl before CPAP treatment and 0.10 to 0.72 mg/dl after CPAP treatment. Mean differences, at a study level, ranged from -0.05 to 0.50. The pooled mean difference was 0.14 [95% confidence interval 0.08 to 0.20, p < 0.00001]. There was heterogeneity in this endpoint (df = 13, p < 0.00001, I(2) = 95%). Tumor necrosis factor-α: Study level means ranged from 1.40 to 50.24 pg/ml before CPAP treatment and 1.80 to 28.63 pg/ml after CPAP treatment. Mean differences, at a study level, ranged from -1.23 to 21.61. The pooled mean difference was 1.14 [95% confidence interval 0.12 to 2.15, p = 0.03]. There was heterogeneity in this endpoint (df = 8, p < 0.00001, I2 = 89%). Interleukin-6: Study level means ranged from 1.2 to 131.66 pg/ml before CPAP treatment and 0.45 to 66.04 pg/ml after CPAP treatment. Mean differences, at a study level, ranged from -0.40 to 65.62. The pooled mean difference was 1.01 [95% confidence interval -0.00 to 2.03, p = 0.05]. There was heterogeneity in this endpoint (df = 7, p < 0.00001, I(2) = 95%). LIMITATIONS: Only published data. Studies pooled were mainly small, non-randomized trials. CONCLUSION: Sleep apnea treatment with CPAP improves levels of inflammatory markers.

Effect of visualization of raw graphic polysomnography data by sleep apnea patients on adherence to CPAP therapy.

BACKGROUND: CPAP is considered to be the cornerstone of therapy for obstructive sleep apnea. However, adherence to this treatment is frequently poor, which may lead to ongoing symptoms, including daytime sleepiness and poor cognitive function. We aimed to determine the efficacy of showing patients their raw graphic polysomnography (PSG) data in increasing their CPAP adherence. METHODS: The subjects were patients with obstructive sleep apnea (n = 37, diagnosed on prior PSG), who were prospectively randomized into an experimental arm or a control arm. The patients in the experimental arm (n = 18) were shown detailed PSG data, including graphic data from PSG prior to prescription of CPAP. The patients in the control arm (n = 19) were shown the non-graphic paper report of the PSG. Adherence data, collected using CPAP devices with internal microprocessors (adherence cards), was read at 4 weeks after treatment initiation. RESULTS: There was no difference in age (57.3 ± 11.8 y vs 55.5 ± 11.6 y, P = .64), body mass index (BMI) (32.7 ± 6.3 kg/m(2) vs 32.3 ± 6.6 kg/m(2), P = .85), and apnea-hypopnea index (36.0 ± 27.8 events/h vs 30.5 ± 19.1 events/h, P = .48) between the experimental and control arms. There was no difference in percent of days CPAP was used (58% vs 64%, P = .59) and average number of hours each night CPAP was used (3.9 ± 2.1 h vs 4.1 ± 2.5 h, P = .76) between the experimental and control arms, respectively. In multi logistic regression models, which included age, BMI > 30 kg/m(2), apnea-hypopnea index, and experimental intervention, only BMI was found to increase likelihood of improved adherence (odds ratio = 13.3, P = .007). CONCLUSIONS: Showing patients raw graphic PSG data does not seem to improve adherence to CPAP. BMI is a very strong predictor of CPAP adherence.