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。

IF 7 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Ying Cui, Huimin Du
{"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}
引用次数: 0

摘要

背景:本研究旨在探讨绝经后妇女抑郁症状严重程度与睡眠障碍之间的关系。方法:这项观察性研究包括来自美国全国代表性样本的4808名绝经后妇女的数据。抑郁症状的严重程度通过患者健康问卷-9进行评估,而睡眠障碍则根据自我报告的医生诊断进行鉴定。采用加权多变量logistic回归模型分析抑郁症状严重程度与睡眠障碍之间的关系,并对潜在混杂因素进行校正。限制三次样条用于评估可能的非线性关系,并对关键的社会人口、健康和行为因素进行亚组分析。此外,使用Lasso回归和逻辑回归来确定睡眠障碍最具影响力的预测因素。使用替代睡眠结果和改良抑郁症状量表进行补充分析和敏感性分析,以检验稳健性和项目水平重叠。结果:抑郁症状严重程度与睡眠障碍呈正相关,显示出剂量-反应关系(P为趋势)。结论:本研究表明绝经后妇女抑郁症状严重程度与睡眠障碍呈线性剂量-反应关系,在心血管疾病患者中进一步放大。抑郁症状被认为是最关键的预测因素,强调了心理健康在管理睡眠健康中的重要性。这些发现强调了综合干预的必要性,包括心理健康筛查、生活方式改变和基于社区的护理方法,以减轻这一弱势群体抑郁症状和睡眠障碍的双重负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BMC Medicine
BMC Medicine 医学-医学:内科
CiteScore
13.10
自引率
1.10%
发文量
435
审稿时长
4-8 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信