Symptom network analysis of anxiety and depression in epilepsy

Relationships among anxiety, depression, and health-related quality of life in adult epilepsy: A network analysis

OBJECTIVE

Comorbid depression and anxiety in patients with epilepsy (PWE) are common and frequently under-treated, thus, causing poor health-related quality of life (HRQoL). However, little is known regarding the interconnections between anxious/depressive symptoms and the dimensions of HRQoL. Therefore, we conducted a network analysis to explore these relationships in detail among Chinese adult PWE.

METHODS

A cohort of adult PWE was consecutively recruited from the First Affiliated Hospital of Chongqing Medical University. HRQoL, depression, and anxiety were measured with Quality of Life in Epilepsy Inventory-31, Neurological Disorders Depression Inventory for Epilepsy, and Generalized Anxiety Disorder 7-Item Scale, respectively. A regularized partial correlation network was constructed to investigate the interconnections between symptoms of anxiety/depression and the dimensions of HRQoL. We calculated expected influence (EI) and bridge expected influence (BEI) values to identify the most influential nodes.

RESULTS

A total of 396 PWE were enrolled in this study, 78.1% of whom had focal onset epilepsy. The prevalence of anxiety and depression was 30.3% and 28.8%, respectively. The symptoms "frustrated" and "uncontrollable worry" had the highest EI values, whereas "emotional well-being", "seizure worry", "difficulty finding pleasure", and "nervousness or anxiety" had the highest BEI values.

CONCLUSION

This study provides new insights into the relationships among anxiety, depression, and HRQoL. Critical central symptoms and bridge symptoms identified in the network might help to quickly identify PWE comorbid anxiety and depression in busy outpatient settings, thereby enabling early intervention and enhancing quality of life.

Network analysis of depression and anxiety symptoms and their associations with life satisfaction among Chinese hypertensive older adults: a cross-sectional study

Background

Hypertension is one of the most prevalent chronic diseases among the older adult population in China and older adults with hypertension are more susceptible to mental health problems. This study aimed to explore the network structure of depression and anxiety, and their association with life satisfaction (LS) in older adults with hypertension.

Methods

A total of 4,993 hypertensive individuals aged 60 and above were selected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017-2018). The design of the CLHLS study was approved by the Campus Institutional Review Board of Duke University (Pro00062871) and the Biomedical Ethics Committee of Peking University (IRB00001052-13,074). The Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depressive and anxiety symptoms. Central and bridge symptoms were identified via "Expected Influence" and "Bridge Expected Influence", respectively. Network stability was assessed using the case-dropping bootstrap technique.

Results

Network analysis identified CESD3 (Feeling blue/depressed), GAD4 (Trouble relaxing), and GAD2 (Uncontrollable worry) as the most influential central symptoms in the network of depression and anxiety. Concurrently, GAD1 (Nervousness or anxiety), CESD10 (Sleep disturbances), and CESD1 (Feeling bothered) stand as critical bridge symptoms between depression and anxiety disorders. Moreover, CESD7 (Lack of happiness) exhibited the strongest negative correlation with LS in Chinese hypertensive older adults.

Conclusion

This exploratory study represents the first investigation to examine the mutual relationship between depressive and anxiety symptoms among Chinese hypertensive older adults. Interventions addressing targeting bridge symptoms have the potential to alleviate depressive and anxiety symptoms. Furthermore, improving happiness, hope, and sleep quality in this population may mitigate the adverse effects of depression and anxiety on LS.

A network analysis of pain intensity and pain-related measures of physical, emotional, and social functioning in US military service members with chronic pain

OBJECTIVE

The purpose of this study was to apply network analysis methodology to better understand the relationships between pain-related measures among people with chronic pain.

METHODS

We analyzed data from a cross-sectional sample of 4614 active duty service members with chronic pain referred to 1 military interdisciplinary pain management center between 2014 and 2021. Using a combination of Patient-Reported Outcomes Measurement Information System measures and other pain-related measures, we applied the "EBICglasso" algorithm to create regularized partial correlation networks that would identify the most influential measures.

RESULTS

Pain interference, depression, and anxiety had the highest strength in these networks. Pain catastrophizing played an important role in the association between pain and other pain-related health measures. Bootstrap analyses showed that the networks were very stable and the edge weights accurately estimated in 2 analyses (with and without pain catastrophizing).

CONCLUSIONS

Our findings offer new insights into the relationships between symptoms using network analysis. Important findings highlight the strength of association between pain interference, depression and anxiety, which suggests that if pain is to be treated depression and anxiety must also be addressed. What was of specific importance was the role that pain catastrophizing had in the relationship between pain and other symptoms suggesting that pain catastrophizing is a key symptom on which to focus for treatment of chronic pain.

Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

Background

The move away from investigating mental disorders as whole using sum scores to the analysis of symptom-level interactions using network analysis has provided new insights into comorbidities. The current study explored the dynamic interactions between depressive and anxiety symptoms in older Chinese adults with diabetes mellitus (DM) and identified central and bridge symptoms in the depression-anxiety network to provide potential targets for prevention and intervention for depression and anxiety.

Methods

This study used a cross-sectional design with data from the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A regularized partial correlation network for depressive and anxiety symptoms was estimated based on self-reported scales completed by 1685 older adults with DM aged 65 years or older. Depressive and anxiety symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Seven-Item Generalized Anxiety Disorder Scale (GAD-7), respectively. Expected influence (EI) and bridge expected influence (BEI) indices were calculated for each symptom.

Results

According to cutoff scores indicating the presence of depression and anxiety, the prevalences of depression and anxiety in our sample were 52.9% and 12.8%, respectively. The comorbidity rate of depression and anxiety was 11.5%. The six edges with the strongest regularized partial correlations were between symptoms from the same disorder. "Feeling blue/depressed", "Nervousness or anxiety", "Uncontrollable worry", "Trouble relaxing", and "Worry too much" had the highest EI values. "Nervousness or anxiety" and "Everything was an effort" exhibited the highest BEI values.

Conclusion

Central and bridge symptoms were highlighted in this study. Targeting these symptoms may be effective in preventing the comorbidity of depressive and anxiety symptoms and facilitate interventions in older Chinese adults with DM who are at risk for or currently have depressive and anxiety symptoms.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Anxiolytic effects of N-(4,5-dihydro-5-oxo-1,2-dithiolo-[4,3,b]-pyrrole-6-yl)-N-methylformamide, a pyrroloformamide isolated from a marine Streptomyces sp., in adult zebrafish by the 5-HT system

General anxiety disorders are among the most prevalent mental health problems worldwide. The emergence and development of anxiety disorders can be due to genetic (30-50%) or non-genetic (50-70%) factors. Despite medical progress, available pharmacotherapies are sometimes ineffective or can cause undesirable side effects. Thus, it becomes necessary to discover new safe and effective drugs against anxiety. This study evaluated the anxiolytic effect in adult zebrafish (Danio rerio) of a natural pyrroloformamide (PFD), N-(4,5-dihydro-5-oxo-1,2-dithiolo-[4,3,b]-pyrrole-6-yl)-N-methylformamide, isolated from a Streptomyces sp. bacterium strain recovered from the ascidian Eudistoma vannamei. The complete structure of PFD was determined by a detailed NMR analysis, including 1H-13C and 1H-15N-HBMC data. In addition, conformational and DFT computational studies also were performed. A group of fishes (n = 6) was treated orally with PFD (0.1, 0.5 and 1.0 mg/mL; 20 μL) and subjected to locomotor activity and light/dark tests, as well as, acute toxicity 96 h. The involvement of the GABAergic and serotonergic (5-HT) systems was investigated using flumazenil (a silent modulator of GABA receptor) and 5-HT1, 5-HT2A/2C and 5-HTR3A/3B receptors antagonists, known as pizotifen, granisetron and cyproheptadine, respectively. PFD was nontoxic, reduced locomotor activity and promoted the anxiolytic effect in zebrafish. Flumazenil did not inhibit the anxiolytic effect of the PFD via the GABAergic system. This effect was reduced by a pretreatment with pizotifen and granisetron, and was not reversed after treatment with cyproheptadine. Molecular docking and dynamics studies confirmed the interaction of PFD with the 5-HT receptor.Communicated by Ramaswamy H. Sarma.

Seizure-related stress and arousal responses mediate a relationship between anxiety trait and state in epilepsy

BACKGROUND

Epilepsy causes substantial psychological distress and anxiety, primarily due to seizures. However, the impact of stress responses and changes in arousal and their association with anxiety patterns in patients with epilepsy (PWE) remains unclear. This study aimed to investigate the relationships among seizures, stress and arousal characteristics, and trait and state anxiety characteristics in PWE.

METHODS

Our sample consisted of 159 outpatients with epilepsy recruited from five institutions in Japan in 2020. Participants completed the State-Trait Anxiety Inventory-Form JYZ (STAI) and the Japanese-Stress Arousal Check List (J-SACL). We analyzed the correlations between inventory scores and clinical information. Using principal component analysis (PCA), we derived epilepsy-specific stress/arousal characteristics, which accounted for high arousal and low-stress levels, termed epilepsy-specific stress or arousal response (ESAR), from the J-SACL scores. We conducted a mediation analysis to assess the mediating role of ESAR in the relationship between traits and state anxiety.

RESULTS

We found significant correlations between J-SACL stress and arousal factors (r = -0.845, p < 0.001), ESAR and seizure frequency (r = -0.29, p < 0.001), ESAR and trait anxiety scores on the STAI (r = -0.77, p < 0.0001), and ESAR and state anxiety scores on the STAI (r = -0.60, p < 0.0001). Mediation analysis supported by the Monte Carlo method revealed that ESAR significantly mediated the association between trait and state anxiety.

CONCLUSIONS

These findings elucidate the epilepsy-specific stress and arousal characteristics and their roles in mediating traits and state anxiety. These results may reflect the long-term clinical course and unique emotion recognition tendencies in epilepsy.

Baseline depressive symptoms as predictors of efficacy and tolerability of the treatment with duloxetine: a network analysis approach

Introduction

Depression is considered one of the most prevalent and burdensome mental disorders. Only 50-60% of patients respond to first-line treatment. Individuals with depression might benefit from personalized treatment, tailored to the individual needs of the patient. In this study, we aimed to explore the baseline characteristics of depressive symptoms associated with a good response to duloxetine treatment using a network analysis. Additionally, the relationship between baseline psychopathological symptoms and treatment tolerability was assessed.

Methods

The sample of 88 drug-free patients with active depressive episode, who started monotherapy with increasing doses of duloxetine were evaluated. The Hamilton Depression Rating Scale (HAM-D) was used to assess depression severity and the UKU side effect rating scale to monitor adverse drug reactions (ADRs). A network analysis that explored interactions of specific baseline depression symptoms, treatment efficacy and tolerability was performed.

Results

The node representing duloxetine treatment efficacy was directly connected to the nodes representing the first HAM-D item ("depressed mood") (edge weight = 0.191) and duloxetine dose (edge weight = 0.144). The node representing ADRs was directly connected to only one node representing the baseline score of the HAM-D anxiety (psychic) item (edge weight = 0.263).

Discussion

Our findings indicate that individuals with depression presenting greater levels of depressed mood and lower levels of anxiety symptoms might better respond to the treatment with duloxetine in terms of efficacy and tolerability.

Network analysis of anxiety and depression in the functionally impaired elderly

Background

Evidence from previous studies has confirmed that functionally impaired elderly individuals are susceptible to comorbid anxiety and depression. Network theory holds that the comorbidity emerges from interactions between anxiety and depression symptoms. This study aimed to investigate the fine-grained relationships among anxiety and depression symptoms in the functionally impaired elderly and identify central and bridge symptoms to provide potential targets for intervention of these two comorbid disorders.

Methods

A total of 325 functionally impaired elderly individuals from five communities in Xi'an, China, were recruited for our investigation. The GAD-7 and PHQ-9 were used to measure anxiety and depression, respectively. SPSS 22.0 software was used for descriptive statistics, and R 4.1.1 software was used for network model construction, expected influence (EI) evaluation and bridge expected influence (BEI) evaluation.

Results

In the network, there were 35 edges (indicating partial correlations between symptoms) across the communities of anxiety and depression, among which the strongest edge was A1 "Nervousness or anxiety"-D2 "Depressed or sad mood." A2 "Uncontrollable worry" and D2 "Depressed or sad mood" had the highest EI values in the network, while A6 "Irritable" and D7 "Concentration difficulties" had the highest BEI values of their respective community. In the flow network, the strongest direct edge of D9 "Thoughts of death" was with D6 "Feeling of worthlessness."

Conclusion

Complex fine-grained relationships exist between anxiety and depression in functionally impaired elderly individuals. "Uncontrollable worry," "depressed or sad mood," "irritable" and "concentration difficulties" are identified as the potential targets for intervention of anxiety and depression. Our study emphasizes the necessity of suicide prevention for functionally impaired elderly individuals, and the symptom "feeling of worthlessness" can be used as an effective target.