Sleep Disturbances and Depression Are Co-morbid Conditions: Insights From Animal Models, Especially Non-human Primate Model

Li, Meng; Cui, Jieqiong; Xu, Bonan; Wei, Yuanyuan; Fu, Chenyang; Lv, Xiaoman; Xiong, Lei; Qin, Dongdong

doi:10.3389/fpsyt.2021.827541

Cited by 6 publications

(4 citation statements)

References 135 publications

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“…Sleep disorders were not only a common symptom of depression but could also be its rst presenting sign [52]. Prolonged lack of good sleep could cause physical and psychological fatigue, making individuals more susceptible to depressive moods [53]. Studies had revealed a statistically signi cant bidirectional correlation between insomnia and hypertension [54].…”

Section: Discussionmentioning

confidence: 99%

Association between sleep duration and depression in adults with hypertension: a cross-sectional study

Chen,

Lv,

Wang

et al. 2024

Preprint

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Background There is an increasing awareness of sleep duration and depression in hypertension. This study provided a theoretical basis for early screening of depression in patients with hypertension by analyzing the relationship between sleep time and depression in patients with hypertension. Methods The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2004 to 2015. Depressive symptoms were tested using the Patient Health Questionnaire (PHQ-9), and depression was diagnosed when PHQ-9 total score >10. The association between sleep duration and depression in hypertension was estimated using weighted multivariate logistic regression, and its nonlinearity was examined by restricted cubic spline (RCS) regression. Multivariate logistic regression analysis was used to identify independent risk factors for depression in hypertensive patients, a risk prediction nomogram model was established, and the receiver operating characteristic (ROC) curve was drawn to evaluate its ability to discriminate depression in hypertensive patients. Results A total of 8,696 hypertension participants were included in this study. Multivariate logistic regression showed that an increment of sleep duration was strongly associated with depression in hypertension (OR = 0.772, 95%CI = (0.705, 0.883). The RCS curve showed L-shaped relationships between sleep duration and depressive risk in hypertension. The nomogram model showed a favorable discriminatory power with an area under the curve (AUC) of 73.4%. Sleep duration at 7.22 hour might be most beneficial for the mental health of patients with hypertension. Conclusion Our study demonstrated the relationship between sleep duration and depression in patients with hypertension, Effective interventions for sleep disorders in patient with hypertension are needed for preventing depression.

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Section: Discussionmentioning

confidence: 99%

Association between sleep duration and depression in adults with hypertension: a cross-sectional study

Chen,

Lv,

Wang

et al. 2024

Preprint

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“…In fact, poor sleep may affect the emotional regulation ability of the individual through a physiological mechanism[ 68 ], and a lack of sleep increases the threat across multiple domains of dysfunction[ 69 ]. Sleep is associated with depression at the molecular and neurophysiological levels, and abnormalities in these neurotransmitter systems associated with sleep disorders may contribute to the exacerbation of depression[ 70 ]. In addition, sleep disorders affect students’ daytime function, resulting in daytime sleepiness, drowsiness, and inattention.…”

Section: Discussionmentioning

confidence: 99%

Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic

Gao¹,

Hu²,

Ji³

et al. 2023

World J Psychiatry

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BACKGROUND Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic. AIM To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates. METHODS Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version (SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep. RESULTS Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [ P < 0.01; 95% confidence interval (CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022 ( P < 0.01; 95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040 ( P < 0.01; 95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively. CONCLUSION The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

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“…The visualizations completed for the top four continuous covariates were concordant with current literature: there is strong epidemiological evidence that sleep problems are heavily linked with depression. Multiple papers have found difficulty falling asleep and decreased hours of sleep with increased depression [41][42][43][44][45][46][47][48][49][50][51][52][53]. Additionally, depression has bene linked to lower quality sleep and increased day time exhaustion [31,34,46,54,55].…”

Section: Plos Onementioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Sleep Disturbances and Depression Are Co-morbid Conditions: Insights From Animal Models, Especially Non-human Primate Model

Cited by 6 publications

References 135 publications

Association between sleep duration and depression in adults with hypertension: a cross-sectional study

Association between sleep duration and depression in adults with hypertension: a cross-sectional study

Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic

Use of machine learning to identify risk factors for insomnia

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