Van Den Noortgate Minne, Morrens Manuel, Van Hemert Albert M, Schoevers Robert A, Penninx Brenda W J H, Giltay Erik J
Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Scientific Initiative of Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Centre Duffel, Duffel, Belgium.
Depress Anxiety. 2024 Jul 16;2024:4393070. doi: 10.1155/2024/4393070. eCollection 2024.
Major depressive disorder (MDD) and anxiety disorders (AD) have high degrees of comorbidity and show great overlap in symptoms. The analysis of covarying depressive- and anxiety symptoms in longitudinal, sparse data panels has received limited attention. Dynamic time warping (DTW) analysis may help to provide new insights into symptom network properties based on diagnostic- and disease-state stability criteria.
In the Netherlands Study of Depression and Anxiety depressive-, anxiety-, and worry symptoms were assessed four or five times over the course of 9 years using self-report questionnaires. The sample included 1,649 participants at baseline, comprising controls ( = 360), AD patients ( = 158), MDD patients ( = 265), and comorbid AD-MDD patients ( = 866). With DTW, 1,649 distance matrices were calculated, which yielded symptom networks and enabling comparison of network densities among subgroups.
The mean age of the sample was 41.5 years (standard deviations, 13.2), of whom 66.4% were female. The largest distance was between worry symptoms and physiological arousal symptoms, implicating the most dissimilar dynamics over time. The network density in the groups, from lowest to highest, followed the order: controls, AD, MDD, and comorbid AD-MDD. The comorbid group showed strongly connected mood and cognitive symptoms, which contrasted with the more strongly connected somatic and arousal symptoms in the AD and MDD groups. Groups that showed more transitions in disease states over follow-up, regardless of the diagnoses, had the highest network density compared to more stable states of health or disease (beta for quadratic term = -0.095; < 0.001).
Symptom networks over time can be visualized by applying DTW methods on sparse panel data. Network density was highest in patients with comorbid anxiety and depressive disorders and those with more instability in disease states, suggesting that a stronger internal connectivity may facilitate "critical transitions" within the complex systems framework.
重度抑郁症（MDD）和焦虑症（AD）具有高度共病性，且症状有很大重叠。对纵向稀疏数据面板中抑郁和焦虑症状的共变分析受到的关注有限。动态时间规整（DTW）分析可能有助于基于诊断和疾病状态稳定性标准，为症状网络特性提供新的见解。
在荷兰抑郁症与焦虑症研究中，使用自我报告问卷在9年期间对抑郁、焦虑和担忧症状进行了4次或5次评估。样本在基线时包括1649名参与者，包括对照组（n = 360）、AD患者（n = 158）、MDD患者（n = 265）和AD - MDD共病患者（n = 866）。通过DTW计算了1649个距离矩阵，得出症状网络并能够比较亚组间的网络密度。
样本的平均年龄为41.5岁（标准差为13.2），其中66.4%为女性。最大距离出现在担忧症状和生理唤醒症状之间，这意味着随时间变化的动态差异最大。各组的网络密度从低到高依次为：对照组、AD组、MDD组和AD - MDD共病组。共病组显示出情绪和认知症状的强连接，这与AD组和MDD组中躯体和唤醒症状的更强连接形成对比。在随访期间疾病状态有更多转变的组，无论诊断如何，与更稳定的健康或疾病状态相比，网络密度最高（二次项的β = -0.095；P < 0.001）。
通过对稀疏面板数据应用DTW方法，可以直观显示随时间变化的症状网络。焦虑和抑郁障碍共病患者以及疾病状态更不稳定的患者网络密度最高，这表明更强的内部连接性可能在复杂系统框架内促进“关键转变”。
