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Abstract：
OBJECTIVE: Obstructive sleep apnea (OSA) is associated with abnormal glucose and lipid metabolism. However, whether there is an independent association between Sleep Apnea-Specific Hypoxic Burden (SASHB) and glycolipid metabolism disorders in patients with OSA is unknown.
METHODS: We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles.
RESULTS: The SASHB was independently associated with fasting blood glucose (FBG) (β = 0.058, P = 0.016), fasting insulin (FIN) (β = 0.073, P < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (β = 0.058, P = 0.011), total cholesterol (TC) (β = 0.100, P < 0.001), total triglycerides (TG) (β = 0.063, P = 0.011), low-density lipoprotein cholesterol (LDL-C) (β = 0.075, P = 0.003), apolipoprotein A-I (apoA-I) (β = 0.051, P = 0.049), apolipoprotein B (apoB) (β = 0.136, P < 0.001), apolipoprotein E (apoE) (β = 0.088, P < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile (P < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile (P < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile (P < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles.
CONCLUSIONS: We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA.
BACKGROUND: ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.
METHODS: We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles.
RESULTS: The SASHB was independently associated with fasting blood glucose (FBG) (β = 0.058, P = 0.016), fasting insulin (FIN) (β = 0.073, P < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (β = 0.058, P = 0.011), total cholesterol (TC) (β = 0.100, P < 0.001), total triglycerides (TG) (β = 0.063, P = 0.011), low-density lipoprotein cholesterol (LDL-C) (β = 0.075, P = 0.003), apolipoprotein A-I (apoA-I) (β = 0.051, P = 0.049), apolipoprotein B (apoB) (β = 0.136, P < 0.001), apolipoprotein E (apoE) (β = 0.088, P < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile (P < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile (P < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile (P < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles.
CONCLUSIONS: We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA.
BACKGROUND: ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.
摘要：
目的：阻塞性睡眠呼吸暂停（OSA）与糖脂代谢异常有关。然而,睡眠呼吸暂停特异性低氧负担(SASHB)与OSA患者糖脂代谢紊乱之间是否存在独立关联尚不清楚.
方法：在这项研究中，我们从2019年1月至2023年7月招募了2,173名疑似OSA的参与者。多导睡眠图变量，生化指标,并收集每位参与者的身体测量值.多元线性回归分析用于评估SASHB，AHI,CT90和葡萄糖以及脂质分布。此外，逻辑回归用于确定各种SASHB中异常葡萄糖和脂质代谢的比值比（OR），AHI,CT90四分位数。
结果：SASHB与空腹血糖（FBG）独立相关（β=0.058，P=0.016），空腹胰岛素（FIN）（β=0.073，P<0.001），胰岛素抵抗的稳态模型评估（HOMA-IR）（β=0.058，P=0.011），总胆固醇(TC)(β=0.100,P<0.001),总甘油三酯（TG）（β=0.063，P=0.011），低密度脂蛋白胆固醇(LDL-C)(β=0.075,P=0.003),载脂蛋白A-I（apoA-I）（β=0.051，P=0.049），载脂蛋白B(apoB)(β=0.136,P<0.001),校正混杂因素后的载脂蛋白E（apoE）（β=0.088，P<0.001）。此外，在较高的SASHB四分位数中，高胰岛素血症的OR分别为1.527、1.545和2.024，与最低四分位数相比(线性趋势P<0.001);与最低四分位数相比,高SASHB四分位数中高总胆固醇血症的OR分别为1.762、1.998和2.708(线性趋势P<0.001),与低四分位数相比,高SASHB四分位数中高LDL胆固醇血症的OR分别为1.663、1.695和2.值得注意的是,高甘油三酯血症的OR{1.471,1.773,2.099}和异常HOMA-IR{1.510,1.492,1.937}在SASHB四分位数中保持了一致的趋势。
结论：我们发现SASHB与高胰岛素血症独立相关，异常HOMA-IR，高总胆固醇血症,中国汉族人群高甘油三酯血症和高LDL胆固醇血症。需要进一步的前瞻性研究来证实SASHB可以作为OSA患者糖脂代谢异常的预测因子。
背景：ChiCTR1900025714{http://www.chictr.org.cn/}；2019年9月6日提前注册；中国。
方法：在这项研究中，我们从2019年1月至2023年7月招募了2,173名疑似OSA的参与者。多导睡眠图变量，生化指标,并收集每位参与者的身体测量值.多元线性回归分析用于评估SASHB，AHI,CT90和葡萄糖以及脂质分布。此外，逻辑回归用于确定各种SASHB中异常葡萄糖和脂质代谢的比值比（OR），AHI,CT90四分位数。
结果：SASHB与空腹血糖（FBG）独立相关（β=0.058，P=0.016），空腹胰岛素（FIN）（β=0.073，P<0.001），胰岛素抵抗的稳态模型评估（HOMA-IR）（β=0.058，P=0.011），总胆固醇(TC)(β=0.100,P<0.001),总甘油三酯（TG）（β=0.063，P=0.011），低密度脂蛋白胆固醇(LDL-C)(β=0.075,P=0.003),载脂蛋白A-I（apoA-I）（β=0.051，P=0.049），载脂蛋白B(apoB)(β=0.136,P<0.001),校正混杂因素后的载脂蛋白E（apoE）（β=0.088，P<0.001）。此外，在较高的SASHB四分位数中，高胰岛素血症的OR分别为1.527、1.545和2.024，与最低四分位数相比(线性趋势P<0.001);与最低四分位数相比,高SASHB四分位数中高总胆固醇血症的OR分别为1.762、1.998和2.708(线性趋势P<0.001),与低四分位数相比,高SASHB四分位数中高LDL胆固醇血症的OR分别为1.663、1.695和2.值得注意的是,高甘油三酯血症的OR{1.471,1.773,2.099}和异常HOMA-IR{1.510,1.492,1.937}在SASHB四分位数中保持了一致的趋势。
结论：我们发现SASHB与高胰岛素血症独立相关，异常HOMA-IR，高总胆固醇血症,中国汉族人群高甘油三酯血症和高LDL胆固醇血症。需要进一步的前瞻性研究来证实SASHB可以作为OSA患者糖脂代谢异常的预测因子。
背景：ChiCTR1900025714{http://www.chictr.org.cn/}；2019年9月6日提前注册；中国。
