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Background <p>Obstructive sleep apnea (OSA) is recognized to increase the risk of dyslipidemia; however, the specific sleep traits in OSA that influence dyslipidemia are poorly understood. This study sought to determine which sleep traits are independently associated with dyslipidemia and serum lipid profiles in patients with OSA.</p> Methods <p>In this cohort study, 5239 participants were included from the Sleep Heart Health Study. Further, OSA was diagnosed via polysomnography with an AHI ≥ 5 events/h. Sleep traits were assessed using polysomnographic data and questionnaires. Then, logistic regression was used to identify sleep traits that predict dyslipidemia in OSA patients. Non-linear associations between sleep traits and dyslipidemia were evaluated using restricted cubic splines. The potential mediating effect of body mass index (BMI) was also calculated. Later, linear regression analysis identified sleep traits that were independently linked to lipid levels.</p> Results <p>After adjusting for confounding factors, logistic regression identified sleep latency (OR: 1.005, 95% CI: 1.002–1.009, <i>P</i> = 0.001), rapid eye movement (REM) stage (OR: 0.987, 95% CI: 0.977–0.998, <i>P</i> = 0.022), REM latency (OR: 1.001, 95% CI: 1.000–1.002, <i>P</i> = 0.027), mean oxygen saturation (meanSpO2) (OR: 0.961, 95% CI: 0.926–0.996, <i>P</i> = 0.031), percentage of time with oxygen saturation below 95% (T95) (OR: 1.003, 95% CI: 1.001–1.005, <i>P</i> = 0.005), and time to fall asleep (OR: 1.004, 95% CI: 1.000–1.007, <i>P</i> = 0.042) as variables independently associated with dyslipidemia. No significant non-linear associations were found (all <i>P</i> &gt;0.05). BMI mediated the association between REM stage, meanSpO2, T95, and dyslipidemia risk. Linear regression analysis identified T95 as a consistent independent determinant of all lipid parameters. Additionally, the meanSpO2 and sleep latency were significant independent determinants of most lipid parameters.</p> Conclusions <p>Sleep latency, sleep architecture, and nocturnal hypoxemia are key factors in dyslipidemia among patients with OSA. These insights suggest potential biomarkers and targeted interventions for the management of lipid-related complications of OSA.</p>

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Determinative sleep traits associated with dyslipidemia in obstructive sleep apnea patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月8日

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  • Longlong Wang,
  • Ping Gao,
  • Xinglin Gao

摘要

Background

Obstructive sleep apnea (OSA) is recognized to increase the risk of dyslipidemia; however, the specific sleep traits in OSA that influence dyslipidemia are poorly understood. This study sought to determine which sleep traits are independently associated with dyslipidemia and serum lipid profiles in patients with OSA.

Methods

In this cohort study, 5239 participants were included from the Sleep Heart Health Study. Further, OSA was diagnosed via polysomnography with an AHI ≥ 5 events/h. Sleep traits were assessed using polysomnographic data and questionnaires. Then, logistic regression was used to identify sleep traits that predict dyslipidemia in OSA patients. Non-linear associations between sleep traits and dyslipidemia were evaluated using restricted cubic splines. The potential mediating effect of body mass index (BMI) was also calculated. Later, linear regression analysis identified sleep traits that were independently linked to lipid levels.

Results

After adjusting for confounding factors, logistic regression identified sleep latency (OR: 1.005, 95% CI: 1.002–1.009, P = 0.001), rapid eye movement (REM) stage (OR: 0.987, 95% CI: 0.977–0.998, P = 0.022), REM latency (OR: 1.001, 95% CI: 1.000–1.002, P = 0.027), mean oxygen saturation (meanSpO2) (OR: 0.961, 95% CI: 0.926–0.996, P = 0.031), percentage of time with oxygen saturation below 95% (T95) (OR: 1.003, 95% CI: 1.001–1.005, P = 0.005), and time to fall asleep (OR: 1.004, 95% CI: 1.000–1.007, P = 0.042) as variables independently associated with dyslipidemia. No significant non-linear associations were found (all P >0.05). BMI mediated the association between REM stage, meanSpO2, T95, and dyslipidemia risk. Linear regression analysis identified T95 as a consistent independent determinant of all lipid parameters. Additionally, the meanSpO2 and sleep latency were significant independent determinants of most lipid parameters.

Conclusions

Sleep latency, sleep architecture, and nocturnal hypoxemia are key factors in dyslipidemia among patients with OSA. These insights suggest potential biomarkers and targeted interventions for the management of lipid-related complications of OSA.