Introduction: Few studies have compared the effect of surgical and conservative weight loss strategies on obstructive sleep apnea (OSA). We hypothesized that Roux-en-Y gastric bypass (RYGB) would be more effective than intensive lifestyle intervention (ILI) at reducing the prevalence and severity of OSA (apnea-hypopnea-index [AHI] ≥ 5 events/hour).
Methods: A total of 133 morbidly obese subjects (93 females) were treated with either a 1-year ILI-program (n = 59) or RYGB (n = 74) and underwent repeated sleep recordings with a portable somnograph (Embletta).
Results: Participants had a mean (SD) age of 44.7(10.8) years, BMI 45.1(5.7) kg/m(2), and AHI 17.1(21.4) events/hour. Eighty-four patients (63%) had OSA. The average weight loss was 8% in the ILI-group and 30% in the RYGB-group (p < 0.001). The mean (95%CI) AHI reduced in both treatment groups, although significantly more in the RYGB-group (AHI change -6.0 [ILI] vs -13.1 [RYGB]), between group difference 7.2 (1.3, 13.0), p = 0.017. Twenty-nine RYGB-patients (66%) had remission of OSA, compared to 16 ILI-patients (40%), p = 0.028. At follow-up, after adjusting for age, gender, and baseline AHI, the RYGB-patients had significantly lower adjusted odds for OSA than the ILI-patients-OR (95% CI) 0.33 (0.14, 0.81), p = 0.015. After further adjustment for BMI change, treatment group difference was no longer statistically significant-OR (95% CI) 1.31 (0.32, 5.35), p = 0.709.
Conclusion: Our study demonstrates that RYGB was more effective than ILI at reducing the prevalence and severity of OSA. However, our analysis also suggests that weight loss, rather than the surgical procedure per se, explains the beneficial effects.
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http://dx.doi.org/10.5664/jcsm.2656 | DOI Listing |
Int J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
California State University Monterey Bay, Seaside, California, USA.
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EClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
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View Article and Find Full Text PDFPatient Prefer Adherence
January 2025
College of Medicine, King Faisal University, Al Hofuf, Saudi Arabia.
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J Clin Transl Endocrinol
December 2024
Division of Endocrinology Diabetes and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
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