Respiratory medicine · 2025
Association of sleep apnea syndrome with respiratory infections: insights from NHANES and Mendelian randomization.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Yang Yingqi, Tang Xia, Zhuang Tianchi, You Chang, Yu Hongkai, Hu Lu, Mei Haokun, Xu Ting, Ren Haili, Yu Hairong, Gu Yibo, Duan Xiaoying, Li Ning, Ji Minghui
Аннотация
BACKGROUND: Sleep apnea syndrome (SAS) affects one billion people worldwide and is a potential risk factor for a range of diseases. However, its association with respiratory infections is currently unknown.
METHODS: We first employed a multi-model logistic regression approach to control confounding factors and explore the association between SAS and respiratory infections with data from NHANES. Subsequently, a Mendelian randomization (MR) analysis was employed to investigate the causal relationship between SAS and 15 respiratory infections outcomes. Heterogeneity tests were conducted and MR-Egger and MR-PRESSO were used to test for horizontal pleiotropy. Leave-one-out analysis was performed to identify influential SNPs. The study adhered to STROBE-MR guidelines for reporting MR results.
RESULTS: In data from NHANES, we observed that SAS was an independent risk factor for respiratory infections (Model 2: P = 0.031, OR = 1.147, 95 % CI = 1.012-1.299). MR analysis showed that SAS was a risk factor for acute upper respiratory infections (P = 0.014, OR = 1.049, 95 % CI = 1.010-1.090) and other acute lower respiratory infections (P = 0.001, OR = 1.128, 95 % CI = 1.052-1.210). In the reverse MR analysis, COVID-19 (very severe respiratory confirmed vs. population) (P = 0.013, OR = 0.969, 95 % CI = 0.945-0.993), anti-influenza virus subtype H3N2 IgG levels (P = 0.028, OR = 0.974, 95 % CI = 0.952-0.997) and respiratory tuberculosis (P = 0.047, OR = 0.972, 95 % CI = 0.946-1.000) were protective factors for SAS.
CONCLUSION: SAS is a causal risk factor for respiratory infections and certain infections or specific immune responses may in turn reduce the risk of SAS, revealing a complex bidirectional relationship between them. Future studies are needed to validate the underlying mechanisms.
METHODS: We first employed a multi-model logistic regression approach to control confounding factors and explore the association between SAS and respiratory infections with data from NHANES. Subsequently, a Mendelian randomization (MR) analysis was employed to investigate the causal relationship between SAS and 15 respiratory infections outcomes. Heterogeneity tests were conducted and MR-Egger and MR-PRESSO were used to test for horizontal pleiotropy. Leave-one-out analysis was performed to identify influential SNPs. The study adhered to STROBE-MR guidelines for reporting MR results.
RESULTS: In data from NHANES, we observed that SAS was an independent risk factor for respiratory infections (Model 2: P = 0.031, OR = 1.147, 95 % CI = 1.012-1.299). MR analysis showed that SAS was a risk factor for acute upper respiratory infections (P = 0.014, OR = 1.049, 95 % CI = 1.010-1.090) and other acute lower respiratory infections (P = 0.001, OR = 1.128, 95 % CI = 1.052-1.210). In the reverse MR analysis, COVID-19 (very severe respiratory confirmed vs. population) (P = 0.013, OR = 0.969, 95 % CI = 0.945-0.993), anti-influenza virus subtype H3N2 IgG levels (P = 0.028, OR = 0.974, 95 % CI = 0.952-0.997) and respiratory tuberculosis (P = 0.047, OR = 0.972, 95 % CI = 0.946-1.000) were protective factors for SAS.
CONCLUSION: SAS is a causal risk factor for respiratory infections and certain infections or specific immune responses may in turn reduce the risk of SAS, revealing a complex bidirectional relationship between them. Future studies are needed to validate the underlying mechanisms.