4.1 Baseline characteristics
Among 65,101,139 participants from the NHANES database, 8696 participants diagonosed with hypertension and without exclusion criteria was included in this study. Sleep duration was divided into four categories: short sleep duration (<6 hours), normal sleep duration (6-8 hours), mild excessive sleep time (8-9 hours), and excessive sleep time (>9 hours).Table 1 presents the baseline characteristics according to the sleep duration and depression status. The analysis showed that participants with depression were typically younger, female, with DM, stroke, CHD, smoking habits, alcohol use, and increased urine creatinine and BMI. They also had lower BUN, shorter sleep duration, and lower education levels (Table 1, all P<0.05). However, Hyperlipidemia, CKD, alanine aminotransferase (ALT), and creatinine(CR) levels showed no significant differences across the sleep duration categories.
4.2 Association between sleep duration and depression in Hypertension
The sleep duration was analyzed both as a continuous variable and as a categorized variable (four groups).When sleep duration was analyzed as a continuous variable, it was positively correlated with the risk of depression in patients with hypertension. In model 1 was OR = 0.72, 95%CI: 0.66, 0.79, P < 0.0001). After adjusting for age and gender in model 2, sleep duration reminded positively correlated with the risk of depression in patients with hypertension (OR = 0.73, 95%C: 0.67, 0.79, P < 0.0001). Further adjustment for age, sex, BMI, education, ethnicity, DM, blood urea nitrogen (BUN), hyperlipidemia, smoking history, stroke, ALT, chronic kidney disease estimated by CKD-EPI Scr, and alcohol use in model 3 still showed a positive correlation.(OR = 0.77, 95%CI: 0.71, 0.88, P < 0.0001).
When analyzed as a categorized variable, participants with short sleep duration had an increased risk of depression compared to those with normal sleep duration. In Model 1(unadjusted), the odds ratio(OR) was 3.52(95%CI: 2.84, 4.36, P< 0.0001); In Model 2 adjused for age and sex, the OR was 3.44(95%CI: 2.80, 4.23, P = 0.001); In Model 3,which was adjusted for age, sex, BMI, education, ethnicity, DM, BUN, hyperlipidemia, smoking history, stroke, Alt, CKD-EPI Scr, and alcohol use, the OR was 2.87(95%CI: 2.31, 3.55), P= 0.006).The association between sleep duration and depression in hypertension was detailed in Table 2.
4.3 Nonlinear association between sleep duration and depression in Hypertension
Using the retricted cubic alpine(RCS) model, it was found that the relationship between sleep duration and depression presents an L-shaped curve in patients with Hypertension (P = 0.000 for nonlinearity).When sleep duration was less than 7.22 hours, it was positively correlated with the risk of depression in hypertensive patients, with an OR value of 0.60(95%CI: 0.55, 0.66; P< 0.0001)(Figure 2).For sleep durations greater than 7.22 hours, the correlation was no longer statistically significant (Table 3).
4.4 Subgroup analysis of the relationship between sleep duration and depression in hypertensive patients from different populations
We also conducted subgroup analyses stratified by sex (female, male), age (<60, ≥60years), poverty-income ratio (PIR<1.5, ≥1.5), BMI (<25, 25-30, ≥30) to further explore the association between sleep duration and depression in hypertension patients across different populations. The results showed no differences in the association between sleep duration and depression in hypertension patients across the subgroups of sex, BMI, PIR, and age, indicating that the conclusion of the present study was not affected by these confounders (Table 4).
4.5 Logistic regression and the nomogram model
Multivariate logistic regression analysis (Model 2) was applied to identify the key factors most closely related to depression in hypertension patients. A total of 11 variables (age, sex, sleep duration, stroke, BMI, education, DM, BUN, smoking status, serum creatinine (SCR), alcohol use) were included to establish the crude nomogram model (Figure 3). The receiver operating characteristic (ROC) curve of the test set was used to evaluate the discriminative ability of the model. The ROC curve results demonstrated that the nomogram model established in this study had good discriminative power, and an area under the curve (AUC) was 73.2% (Figure 4), indicating good stability and prediction accuracy.
The decision curve analysis (DCA) for the nomogram was conducted to measure the risks and benefits. According to the results of multiple regression analysis, predictive factors such as age, gender, sleep duration, stroke, BMI, education level, diabetes, blood urea nitrogen, smoking, serum creatinine (SCR), and alcohol use were identified, Based on these factors, a nomogram model for predicting the risk of depression in hypertensive individuals was constructed (Figure 5)..