{"title":"一项基于人群的观察性研究,使用统计模型评估绝经后妇女抑郁症状严重程度与睡眠障碍之间的关系。","authors":"Ying Cui, Huimin Du","doi":"10.1186/s12916-025-04248-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study aimed to investigate the association between depressive symptom severity and sleep disorders in postmenopausal women.</p><p><strong>Methods: </strong>This observational study included data from 4808 postmenopausal women derived from a nationally representative sample in the USA. Depressive symptom severity was assessed using the Patient Health Questionnaire-9, while sleep disorders were identified based on self-reported physician diagnoses. Weighted multivariable logistic regression models were used to analyze the association between depressive symptom severity and sleep disorders, adjusting for potential confounders. Restricted cubic splines were applied to evaluate possible nonlinear relationships, and subgroup analyses were conducted across key sociodemographic, health, and behavioral factors. Additionally, Lasso regression and logistic regression were used to identify the most influential predictors of sleep disorders. Supplementary and sensitivity analyses were performed using alternative sleep outcomes and modified depressive symptom scales to test robustness and item-level overlap.</p><p><strong>Results: </strong>Depressive symptom severity was positively associated with sleep disorders, demonstrating a dose-response relationship (P for trend < 0.001). Each unit increase in depressive symptom score was associated with a 10% higher risk of sleep disorders (OR = 1.10, 95% CI: 1.07-1.13). RCS analysis confirmed a linear association (P for nonlinear = 0.4696). Subgroup analyses identified CVD as a significant effect modifier (P for interaction = 0.019), with a stronger association in individuals with CVD (OR = 1.11, 95% CI: 1.09-1.13) compared to those without (OR = 1.07, 95% CI: 1.03-1.11). Lasso and logistic regression analyses consistently ranked depressive symptoms as the strongest predictor of sleep disorders. The association remained robust and specific across both supplementary outcomes and sensitivity models using modified depressive symptom scales.</p><p><strong>Conclusions: </strong>This study demonstrated a linear dose-response association between depressive symptom severity and sleep disorders in postmenopausal women, which was further amplified among individuals with CVD. Depressive symptoms were identified as the most critical predictor, underscoring the importance of mental health in managing sleep health. These findings highlight the need for integrated interventions combining mental health screening, lifestyle modifications, and community-based care approaches to mitigate the dual burden of depressive symptoms and sleep disorders in this vulnerable population.</p>","PeriodicalId":9188,"journal":{"name":"BMC Medicine","volume":"23 1","pages":"424"},"PeriodicalIF":7.0000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261605/pdf/","citationCount":"0","resultStr":"{\"title\":\"A population-based observational study using statistical modeling to assess the association between depressive symptom severity and sleep disorders in postmenopausal women.\",\"authors\":\"Ying Cui, Huimin Du\",\"doi\":\"10.1186/s12916-025-04248-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>This study aimed to investigate the association between depressive symptom severity and sleep disorders in postmenopausal women.</p><p><strong>Methods: </strong>This observational study included data from 4808 postmenopausal women derived from a nationally representative sample in the USA. Depressive symptom severity was assessed using the Patient Health Questionnaire-9, while sleep disorders were identified based on self-reported physician diagnoses. Weighted multivariable logistic regression models were used to analyze the association between depressive symptom severity and sleep disorders, adjusting for potential confounders. Restricted cubic splines were applied to evaluate possible nonlinear relationships, and subgroup analyses were conducted across key sociodemographic, health, and behavioral factors. Additionally, Lasso regression and logistic regression were used to identify the most influential predictors of sleep disorders. Supplementary and sensitivity analyses were performed using alternative sleep outcomes and modified depressive symptom scales to test robustness and item-level overlap.</p><p><strong>Results: </strong>Depressive symptom severity was positively associated with sleep disorders, demonstrating a dose-response relationship (P for trend < 0.001). Each unit increase in depressive symptom score was associated with a 10% higher risk of sleep disorders (OR = 1.10, 95% CI: 1.07-1.13). RCS analysis confirmed a linear association (P for nonlinear = 0.4696). Subgroup analyses identified CVD as a significant effect modifier (P for interaction = 0.019), with a stronger association in individuals with CVD (OR = 1.11, 95% CI: 1.09-1.13) compared to those without (OR = 1.07, 95% CI: 1.03-1.11). Lasso and logistic regression analyses consistently ranked depressive symptoms as the strongest predictor of sleep disorders. The association remained robust and specific across both supplementary outcomes and sensitivity models using modified depressive symptom scales.</p><p><strong>Conclusions: </strong>This study demonstrated a linear dose-response association between depressive symptom severity and sleep disorders in postmenopausal women, which was further amplified among individuals with CVD. Depressive symptoms were identified as the most critical predictor, underscoring the importance of mental health in managing sleep health. These findings highlight the need for integrated interventions combining mental health screening, lifestyle modifications, and community-based care approaches to mitigate the dual burden of depressive symptoms and sleep disorders in this vulnerable population.</p>\",\"PeriodicalId\":9188,\"journal\":{\"name\":\"BMC Medicine\",\"volume\":\"23 1\",\"pages\":\"424\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261605/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12916-025-04248-y\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12916-025-04248-y","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
A population-based observational study using statistical modeling to assess the association between depressive symptom severity and sleep disorders in postmenopausal women.
Background: This study aimed to investigate the association between depressive symptom severity and sleep disorders in postmenopausal women.
Methods: This observational study included data from 4808 postmenopausal women derived from a nationally representative sample in the USA. Depressive symptom severity was assessed using the Patient Health Questionnaire-9, while sleep disorders were identified based on self-reported physician diagnoses. Weighted multivariable logistic regression models were used to analyze the association between depressive symptom severity and sleep disorders, adjusting for potential confounders. Restricted cubic splines were applied to evaluate possible nonlinear relationships, and subgroup analyses were conducted across key sociodemographic, health, and behavioral factors. Additionally, Lasso regression and logistic regression were used to identify the most influential predictors of sleep disorders. Supplementary and sensitivity analyses were performed using alternative sleep outcomes and modified depressive symptom scales to test robustness and item-level overlap.
Results: Depressive symptom severity was positively associated with sleep disorders, demonstrating a dose-response relationship (P for trend < 0.001). Each unit increase in depressive symptom score was associated with a 10% higher risk of sleep disorders (OR = 1.10, 95% CI: 1.07-1.13). RCS analysis confirmed a linear association (P for nonlinear = 0.4696). Subgroup analyses identified CVD as a significant effect modifier (P for interaction = 0.019), with a stronger association in individuals with CVD (OR = 1.11, 95% CI: 1.09-1.13) compared to those without (OR = 1.07, 95% CI: 1.03-1.11). Lasso and logistic regression analyses consistently ranked depressive symptoms as the strongest predictor of sleep disorders. The association remained robust and specific across both supplementary outcomes and sensitivity models using modified depressive symptom scales.
Conclusions: This study demonstrated a linear dose-response association between depressive symptom severity and sleep disorders in postmenopausal women, which was further amplified among individuals with CVD. Depressive symptoms were identified as the most critical predictor, underscoring the importance of mental health in managing sleep health. These findings highlight the need for integrated interventions combining mental health screening, lifestyle modifications, and community-based care approaches to mitigate the dual burden of depressive symptoms and sleep disorders in this vulnerable population.
期刊介绍:
BMC Medicine is an open access, transparent peer-reviewed general medical journal. It is the flagship journal of the BMC series and publishes outstanding and influential research in various areas including clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities. In addition to research articles, the journal also publishes stimulating debates, reviews, unique forum articles, and concise tutorials. All articles published in BMC Medicine are included in various databases such as Biological Abstracts, BIOSIS, CAS, Citebase, Current contents, DOAJ, Embase, MEDLINE, PubMed, Science Citation Index Expanded, OAIster, SCImago, Scopus, SOCOLAR, and Zetoc.