Association between sleep disturbance and hypertension: Mediation analysis involving Systemic Inflammatory Response Index in the NHANES
Description
We analyzed the data from the NHANES, a representative cross-sectional survey of all non-institutionalized civilian populations in the United States. The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. The NHANES data were released over 2-year cycles. To obtain a large sample for analysis, we combined eight cycles of continuous NHANES data from 2005 to 2020. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All participants provided written informed consent before participating in the study. More information about the NHANES could be obtained at: http://www.cdc.gov/nhanes. Of the 85,750 participants extracted from the NHANES database, we excluded those with missing information on the hypertension questionnaire (n = 30,855), SII data (n=5,361), trouble sleeping (n = 28), aged ≥ 20 years (44,285), and other covariates (n = 20,871). Finally, a total of 23,414 participants were included in this study. DecisionLinnc1.0 software(http://www.statsape.com.) was employed for data analysis. DecisionLinnc1.0 is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. P < 0.05 was considered as statistically significant. In descriptive statistics, continuous variables are expressed as means and standard deviations or medians and interquartile ranges, and categorical variables as proportions and percentages of the total. The χ2 test was used to compare the classified variables among the groups. For continuous variables, one-way ANOVA was used to compare normally distributed variables, and the Kruskal-Wallis H test was used to compare skewed distribution variables between groups. Multivariate logistic regression was used to analyze the correlation between sleep disorder and hypertension. The correlation between SII and hypertension was analyzed in the same way. Multivariate linear regression analysis was used to evaluate the correlation between sleep disturbance and the SII. In all analyses, the levels of SII were converted to natural logarithms. To explore the regulatory role of gender, we calculated the correlation intensity between hypertension, sleep disorders and SII in male and female subgroups. Finally, an intermediary analysis was conducted to examine whether the SII mediates the relationship between sleep disorders and hypertension. The magnitude of the indirect pathway effect, the proportion of the intermediary effect, and the p-value of the intermediary effect are all shown in the results.
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We analyzed the data from the NHANES, a representative cross-sectional survey of all non-institutionalized civilian populations in the United States. The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. The NHANES data were released over 2-year cycles. To obtain a large sample for analysis, we combined eight cycles of continuous NHANES data from 2005 to 2020. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All participants provided written informed consent before participating in the study. More information about the NHANES could be obtained at: http://www.cdc.gov/nhanes. Of the 85,750 participants extracted from the NHANES database, we excluded those with missing information on the hypertension questionnaire (n = 30,855), SII data (n=5,361), trouble sleeping (n = 28), aged ≥ 20 years (44,285), and other covariates (n = 20,871). Finally, a total of 23,414 participants were included in this study. DecisionLinnc1.0 software(http://www.statsape.com.) was employed for data analysis. DecisionLinnc1.0 is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. P < 0.05 was considered as statistically significant. In descriptive statistics, continuous variables are expressed as means and standard deviations or medians and interquartile ranges, and categorical variables as proportions and percentages of the total. The χ2 test was used to compare the classified variables among the groups. For continuous variables, one-way ANOVA was used to compare normally distributed variables, and the Kruskal-Wallis H test was used to compare skewed distribution variables between groups. Multivariate logistic regression was used to analyze the correlation between sleep disorder and hypertension. The correlation between SII and hypertension was analyzed in the same way. Multivariate linear regression analysis was used to evaluate the correlation between sleep disturbance and the SII. In all analyses, the levels of SII were converted to natural logarithms. To explore the regulatory role of gender, we calculated the correlation intensity between hypertension, sleep disorders and SII in male and female subgroups. Finally, an intermediary analysis was conducted to examine whether the SII mediates the relationship between sleep disorders and hypertension. The magnitude of the indirect pathway effect, the proportion of the intermediary effect, and the p-value of the intermediary effect are all shown in the results.