Obsessive–compulsive disorder (OCD) is a widespread chronic mental illness characterized by obsessive thoughts and/or compulsive behaviors that consume the patient’s time and energy, cause distress, and severely impair social functioning. A 12-month prevalence rate of OCD is 0.7%~1.2%, and the lifetime prevalence is 1%~3%.[2–4] OCD is one of the most common mental illnesses, and its treatment would be expensive and financially and socially burdensome. It is accompanied by a great deal of distress and is frequently associated with functional impairment and suicide.[5,6] According to the World Health Organization, OCD is one of the leading causes of nonfatal diseases worldwide.
Psychopathological studies have found that comorbidities are a norm rather than the exception in OCD and that comorbidities can have a negative impact on OCD treatment.[2,8,9] Depression is one of the most common comorbid diagnoses.[10,11] Studies have shown that the longer patients with OCD are ill, the more likely they are to develop depression. Epidemiological and clinical studies have found that the lifetime prevalence of depression associated with OCD ranges from 40% to 80%.[10,13–15] Patients with OCD comorbid depression will have a lower response rate and a worser prognosis.[16,17]
The most commonly used model in traditional mental disorder research is the latent variable model. This model considers different symptoms to be observed variables and the individual’s diagnosable mental disorder to be the underlying variable that causes the symptoms to appear. According to this viewpoint, OCD can lead to obsessive thinking and/or compulsive behavior, and depression can lead to symptoms such as low spirits, pessimism, or sleep problems, and the comorbidity of OCD and depression is thought to be caused by the interaction of latent variables or the presence of common roots.
The network model is a novel and significant approach for conceptualizing mental disorders.[20,21] As a supplement to the latent variable model, it provides a new way to understand human psychological phenomena and is gradually applied to the study of psychiatry, clinical psychology, and other fields. The method views the interaction of symptoms as a network in which the symptoms are nodes and the connections between the nodes represent the interrelationships among the symptoms. It is data-driven and does not depend on prior assumptions of causality among variables. Rather than assuming that symptoms arise from an underlying disease entity, the complex network approach asserts that disorders exist as systems of interconnected network elements. This approach also provides a framework for comprehending therapeutic change. The network model offers a new viewpoint on comorbidities, and it believes that comorbidities may occur when certain specific symptoms of one mental disorder activate the symptoms of another. Activating these symptoms may result in the development and perpetuation of two diseases.[24,25] As a result, the network model provides a new perspective on OCD and depression research and practice. This suggests that identifying bridge connections and specific symptoms may help focus the problem and make the intervention more targeted; suppressing specific bridge connections and symptoms is expected to prevent the emergence of other obsessive–compulsive and depressive symptoms. Furthermore, the network model can assess the relative importance of each symptom in the symptom network, which may have a direct impact on understanding the clinical significance of each symptom.
This study is the first to apply the network model to investigate how obsessive–compulsive symptoms and depressive symptoms relate to patients with OCD in China. The pathways that connect obsessive–compulsive and depressive symptoms may provide insights into the prevention and treatment of OCD and depression comorbidities. Moreover, calculating the centrality index (expected influence) can help us better understand the clinical significance of specific symptoms.
SUBJECTS AND METHODS
The data were gathered from 840 people with OCD who visited the psychiatric clinic of a general hospital in Northwest China from August 2013 to July 2019. The patients included not only those who received treatment for the first time but also those who had relapsed. Patients were treated with clomipramine or serotonin selective reuptake inhibitor (SSRIs) (fluoxetine, fluvoxamine, paroxetine, sertraline, etc.). Some patients were treated with aripiprazole, mirtazapine, or SSRIs combined with low-dose antipsychotics. Before being enrolled in the group, the patients had undergone structured assessment and evaluation by psychiatrists. Inclusion criteria for patients are as follows: (a) over 16 years old; (b) meet the diagnostic criteria for OCD in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV); and (C) Yale-Brown Obsessive–Compulsive Scale (Y-BOCS) score ≥16 points. Exclusion criteria are as follows: (a) suffering from severe physical illness and (b) presence of psychoactive substance abuse. A total of 445 samples were included after screening. The patients in this sample had an average age of 28.23 (SD = 10.58) years. Female patients accounted for 49.44% (n = 221); patients with a bachelor’s degree or higher accounted for 49.89% (n = 222); and unmarried patients accounted for 57.75% (n = 257).
This study has been reviewed and approved by the Medical Ethics Committee of Xijing Hospital Affiliated with the Air Force Military Medical University (No. KY20202063-F-2). All subjects or their families were informed about the study’s content and purpose and signed an informed consent form.
Demographic information: The questionnaire is used to collect basic information such as age, gender, and years of education.
Mini International Neuropsychiatric Interview (MINI): MINI is a short-structured interview designed by Professors Sheehan and Lecrubier, targeting 16 types of axis I mental illnesses in DSM-IV and International Classification of Diseases, Tenth Revision (ICD-10).[27,28] Studies have shown that MINI has good reliability, validity, and high consistency among investigators. Additionally, it has a good correlation with the Structured Clinical Interview Instrument for DSM-IV-Text Revision (TR) and the Composite International Diagnostic Interview.
Obsessive–Compulsive symptoms: The obsessive–compulsive symptoms were assessed by a modified version of the Chinese-translated ten-item Y-BOCS, which is considered the gold standard for evaluating the severity and symptoms of OCD. Participants responded on a 5-point Likert scale ranging from 0 (“no symptoms”) to 4 (“extremely severe”). The higher the total score, the more severe the patient’s obsessive–compulsive symptoms are. Cronbach’α for the current scale was 0.64.
Depression symptoms: The depressive symptoms were assessed by the Self-Rating Depression Scale (SDS), which is a commonly used screening tool for depressive symptoms. It contains 20 items, and participants were asked to choose the frequency of each symptom in the previous week. Each item received a score ranging from 1 (“has no or very little time”) to 4 (“has most or all of the time”). The higher the total score is, the more severe the depression would be. We used the Chinese version of the SDS, which has a cutoff of 53 for depressive symptoms. Cronbach’α of SDS in this study was 0.56.
The graphical Gaussian model (GGM) is used to fit the data. GGM is an undirected network in which the edge represents the partial correlation between two nodes following statistical control of other symptoms in the network. The GGM is estimated based on the nonparametric Spearman correlation matrices. In addition, a graphical Lasso algorithm is used to regularize the GGM. This process limits the number of edges by assigning penalties to reduce the small correlation to zero, resulting in a more stable and interpretable sparse network.[33,34] Based on previous studies, we set the tuning parameter to 0.5 to balance the sensitivity and specificity of finding the true edge of the network.[33,35] The fruchterman reingold algorithm layout is used to display the network. Nodes with stronger connections are closer to the network’s center, while nodes with weaker connections are closer to the network’s periphery. The blue line indicates a positive correlation, whereas the red line indicates a negative correlation. The thicker the edge, the greater the association between the two nodes; the thinner the edge, the smaller the association between the two nodes. The network was constructed and visualized in R 4.0.0 using the qgraph package.
We used R-package qgraph to calculate the expected influence for each node. Compared with the traditional centrality index (e.g., strength centrality), this indicator is more appropriate for the network because it has both positive and negative edges. The expected influence of a given node is the sum of all edges connecting the node’s weights (not absolute values). The greater a node’s expected influence, the closer its relationship with other nodes in the network, indicating a greater degree of importance in the network.
We used the R-package bootnet to evaluate the accuracy and stability of the network.[39,40] First, the nonparametric bootstrap method (2000 bootstrap samples) was used to calculate the 95% confidence intervals to evaluate the accuracy of the edge weights and computing bootstrapped difference tests for edge weights (α = 0.05). Second, we assessed the stability of the node’s expected influences by computing bootstrapped difference tests for node expected influences (α = 0.05) and calculating correlation stability coefficients using a case-dropping bootstrap approach. A previous study has shown that the relative stability coefficient should preferably be greater than 0.50 and should not be less than 0.25.
Descriptive Statistics of Y-BOCS and SDS.
The average and standard deviation of Y-BOCS and SDS items are shown in Table 1.
Figure 1a depicts the obsessive–compulsive and depressive symptom network in patients with OCD. This network has some characteristics. First of all, 53 of the 435 possible edges are not zero (12%). Second, we find that there are two important bridge symptoms between OCD and depressive symptoms: Time consumed by obsessions (Y1) linked to uneasiness (D13) (weight = 0.09), and distress caused by obsessions (Y3) linked to low spirit (D1) (weight = 0.06). Third, we discover two other closely related edges: interference due to obsessions (Y2) and interference due to compulsions (Y7) (weight = 0.32), and difficulty resisting obsessions (Y4) and difficulty resisting compulsions (Y9) (weight = 0.30). All edge weight values can be found in Supplementary Material 1. The bootstrapped 95% confidence interval indicates that the accuracy of edge weights was relatively reliable and accurate [Figure S1 in Supplementary Material 2]. Figure S2 in the supplementary material 2 shows the bootstrapped difference test for edge weights.
Figure 1b. depicts the expected influences’ nodes of obsessive–compulsive and depressive symptoms. Interference due to compulsions (Y7) (raw score = 0.81), distress caused by obsessions (Y3) (raw score = 0.69), time consumed by compulsions (Y6) (raw score = 0.60), and uneasiness (D13) (raw score = 0.59) have the highest expected influences, indicating that these four symptoms are the most associated in the current network. Loss of appetite (D5) (raw score = -0.07) and weight loss (D7) (raw score = -0.08) have the lowest expected influences, indicating that these two symptoms are the least associated symptoms in the current network. Table 1 displays all expected influences (raw score) of symptoms. The correlation stability coefficient of node expected influence is 0.36, indicating that the node expected influence estimations meet the requirement [Figure S3 in Supplementary Material 2]. Figure S4 in Supplementary Material 2 shows the bootstrapped difference test for node expected influences.
Network theory has a particularly unique understanding of comorbidities: Preventing and treating bridge symptoms may be more beneficial to patients’ disease recovery. In our research, we discovered two significant bridge edges between “uneasiness” (D13) and “time consumed by obsessions” (Y1) and between “low spirits” (D1) and “distress caused by obsessions” (Y3). In fact, for people with OCD, beliefs and actions are decoupled to some extent and with a loss of connection, which leads them to display a series of behaviors that contradict their cognition. In contrary to healthy persons, patients with OCD have trouble managing their conduct by cognitive confidence in their surroundings. OCD patients may be aware of the truths and the rules, yet it is difficult for them to control their obsessions, which heightens their uneasiness and anxiety. “Low spirit” (D1) is a bridge symptom that connects the OCD and depression symptom clusters. This finding supports previous related research. From a cognitive perspective, OCD patients with obsessive thoughts frequently take action to control potential consequences, which can exacerbate feelings of responsibility and guilt, leading to depression.[42–44] These results suggest that if we focus on preventing and treating the symptoms that link the two diseases, we may be able to effectively cut off the “bridge edges” between the diseases and reduce comorbidities. For instance, the edge between “low spirit” (D1) and “distress caused by obsessions” (Y3) serves as a link between OCD and depression. It would therefore be wise for clinicians to target these bridge edges (i.e., D13-Y1 and D1-Y3) or nodes (i.e., Y1 and Y3) therapeutically to prevent the emergence of comorbidity.
Consistent with previous studies, this study discovered a connection between the symptoms “interference due to obsessions” (Y2) and “interference due to compulsions” (Y7). According to some researchers, if people believe that their negative thoughts are important or even dangerous, they will try to control them to protect themselves or others from harm or danger. Individuals with OCD are always plagued by intrusive, repetitive, and disturbing thoughts, which can result in negative emotions, after which the individual will engage in ritualized behaviors (e.g., compulsive counting) to alleviate intrusive thinking. Studies have shown that patients with OCD score higher than healthy people in controlling their thinking. This is consistent with the metacognitive model theory of OCD, which holds that people with the disorder seem to misinterpret the significance and consequences of normal, distressing intrusive thoughts, leading them to engage in compulsions and thus perpetuating the cycle of obsessions and compulsions.[46,51–53]
The expected influence centrality of the node may play an important role in identifying symptoms that are more significant in the network. The results indicate that the obsessive–compulsive symptoms “distress caused by obsessions” (Y3) and “interference due to compulsions” (Y7) are the hallmark symptoms of patients with OCD, which is consistent with previous research findings.[41,45,54] The cognitive-behavioral model of OCD believes that patients with OCD often regard normal intrusive thoughts as potential threats. To avoid danger, patients will engage in compulsive behaviors,[42,55] which will temporarily relieve the patient’s inner anxiety. The study also revealed the importance of the symptom “uneasiness” (D13) in the network. It could be due to OCD patients’ tendency to overestimate the likelihood of general risks, as well as their low personal tolerance to adverse events, both of which have been linked to anxiety and restlessness.[57,58] This discovery may serve as a guiding principle in the selection of psychotherapy strategies such as reactive preventive exposure therapy. The symptoms “loss of appetite” (D5) and “weight loss” (D7) are marginal in the network, indicating that the physical symptoms included in SDS are not at the center of the obsessive–compulsive and depressive symptom network. This finding also demonstrates the potential clinical utility of network analysis, implying that treatments aimed at controlling these symptoms are unlikely to have a significant impact on the rest of the symptoms of OCD.
The findings of this study are consistent with previous research. Meanwhile, there are some distinctions between our research and that of others. The variations in these results could be attributed to the number of participants and the evaluation tools used. For example, the results of studies on adult OCD and depression show that symptoms of “fatigue” and “distress caused by obsessive–compulsive thoughts” are the most important symptoms. A study of adolescents found that “concentration impairment” is the core symptom. These findings illustrate that various groups may have a significant impact on the development of mental illness networks.
There are some limitations in our study. First, the cross-sectional data used to construct the network structure of obsessive–compulsive and depression symptoms cannot reveal how symptoms appear one after the other over time. To gain a better understanding of the temporal dynamic relationship between individual-level symptoms, longitudinal research is required. Second, the network structure in this study is unique to the questionnaires that we used. There are differences among questionnaires used to assess the symptoms of obsession–compulsion and depression. As a result, different questionnaires may produce different network structures. Third, the network structure constructed here investigated between-subject effects on a group level. This means that the network structure may not be replicated in the same way within a single individual.
The purpose of this study is to investigate the network structure of obsessive–compulsive and depression symptoms in patients with OCD. There are two bridge edges between symptoms of “uneasiness” and “time consumed by obsessions” and between “low spirit” and “distress caused by obsessions.” The symptom “interference due to compulsions” has the greatest expected influence of centrality. Clinical implications such as treating these symptoms as targets for prevention and interventions are discussed.
Data availability statement
The datasets used and/or analyzed during this study are available from the corresponding author upon reasonable request.
Zhujing Ma, Lei Ren, Li Guo, Fengzhan Li, Yinchuan Jin, Wei Liang, Qintao Zhang, Huiling Yuan, and Qun Yang conceived and designed the study and interpreted the study results. Zhujing Ma, Lei Ren, and Fengzhan Li analyzed the data. Zhujing Ma and Lei Ren wrote the manuscript. Li Guo, Qintao Zhang, and Qun Yang critically reviewed drafts of the manuscript. All authors approved the final version of the manuscript.
Financial support and sponsorship
This study was supported by the National Logistics Research Key Project (No. BWS17J018) and the Key Research and Development Program of Shaanxi Province (No. 2020SF-245). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Conflicts of interest
There are no conflicts of interest.
The authors express our heartfelt thanks to all patients who participated in this study. The authors would also like to thank the doctors who helped screen patients for this study. There is no support, financial or otherwise that has been received from any organization that may have an interest in the submitted work; there are no additional relationships or activities that have influenced the submitted work.
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