Department of Psychology, The Pennsylvania State University, University Park, PA, USA.
Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore.
BMC Psychiatry. 2024 Oct 5;24(1):656. doi: 10.1186/s12888-024-06074-7.
A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.
The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).
Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.
These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.
Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).
鉴于失眠在年轻人中普遍存在，需要更好地了解失眠与焦虑、情绪、饮食和酒精使用障碍之间的关系。有监督的机器学习提供了评估哪种精神障碍与美国大学生失眠程度升高最相关的能力。结合贝叶斯网络分析，可以阐明失眠与相互作用的症状之间可能存在的因果关系。
本研究利用在进入随机对照试验之前在 26 所美国高校进行的全国性大学生样本进行了探索性分析。我们采用了 4 步统计方法：（1）在疾病层面，弹性网络正则化模型检验了失眠与 7 种精神疾病（重度抑郁症、广泛性焦虑症、社交焦虑症、惊恐障碍、创伤后应激障碍、神经性厌食症和酒精使用障碍）之间关联的相对重要性；（2）在保留样本中评估该模型；（3）在症状层面，通过贝叶斯爬山算法计算完成的部分有向无环图（CPDAG），以估计失眠与其最相关疾病之间的潜在方向[基于 SHAP（SHapley Additive exPlanations）值]；（4）然后通过在保留样本中评估结构汉明距离（SHD）来检验 CPDAG 的可推广性。
在 31285 名参与者中，20597 名女性（65.8%）；平均（标准差）年龄为 22.96（4.52）岁。弹性网络模型在训练样本中对预测失眠严重程度具有临床意义[R=0.44（0.01）；RMSE=5.00（0.08）]，在保留样本中也具有类似的性能[R=0.33；RMSE=5.47]。SHAP 值表明任何精神障碍的存在都与较高的失眠评分相关，其中重度抑郁症是与失眠程度升高最相关的疾病（平均 |SHAP|=3.18）。训练的 CPDAG 和保留的 CPDAG（SHD=7）表明抑郁症状先于失眠，抑郁情绪、疲劳和自尊心是关键的父节点。
这些发现为了解大学生失眠与精神障碍之间的关系提供了新的视角，并值得进一步研究失眠与抑郁之间潜在的因果关系。
试验在国家卫生研究院报告网站上注册（R01MH115128||23/08/2018）。
