The intrinsic association between somatic symptoms and psychiatric symptoms among Chinese COVID-19 inpatients: A network analysis

doi:10.21203/rs.3.rs-4311013/v1

Download PDF

Research Article

The intrinsic association between somatic symptoms and psychiatric symptoms among Chinese COVID-19 inpatients: A network analysis

https://doi.org/10.21203/rs.3.rs-4311013/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

COVID-19 stands as the most impactful global public health event in the 21st century, affecting both physical and mental well-being. However, the connection between various somatic and psychiatric symptoms remains unclear. The purpose of the survey is to investigate the relationship between somatic and psychiatric symptoms.

Methods

The study involved 534 Chinese hospitalized COVID-19 patients. Self-evaluation of somatic symptoms in COVID-19 inpatients, patient health questionnaire-9, insomnia severity index, and the PTSD checklist-civilian version were used to quantify the levels of somatic, depression, insomnia, and post-traumatic stress disorder, respectively. The network analysis method by the R program was used to judge the bridge symptoms and the network differences by gender.

Results

In this survey, the depression-somatic symptom network revealed seven robust edges, including “Anosmia”-“Ageusia”, “Headache”-“Muscle pain”, “Stuffy nose”-“Cough”, “Cough”-“Anhedonia”, “Cough” - “Sleep”, “Cough”-“Fatigue”, and “Muscle pain”-“Fatigue”. The insomnia-somatic symptom network highlighted five robust edges, including “Sleep onset”-“Maintenance”, “Noticeability”-“Distress”, “Ageusia”-“Anosmia”, “Headache”-“Muscle pain”, and “Stuffy nose”-“Cough”. The PTSD-somatic symptom network featured five prominent edges, including “Ageusia”-“Anosmia”, “Headache”-“Muscle pain”, “Reminders avoidance”-“Thoughts avoidance”, “Hypervigilance”-“Startle”, and “Stuffy nose”-“Cough”. Simultaneously, “Cough”、“Anhedonia”、 “Noticeability”、“Libido loss” and “Sleep” as bridge factors linked somatic symptoms with psychiatric symptoms. No gender differences in the somatic- psychiatric symptoms network.

Conclusions

This study provides new perspectives for assessing and intervening in COVID-19 and other multisystem diseases.

COVID-19 inpatients

Network analysis

Somatic symptoms

Depression

Insomnia

Post-traumatic stress disorder

The COVID-19 pandemic stands out as the most severe global public health crisis in the 21st century, profoundly impacting worldwide economic progress and the physical and mental well-being of individuals[1]-[2]. Despite the World Health Organization's declaration of the conclusion of the global public health emergency for COVID-19 [3], studies indicate that approximately 10–20% of SARS-CoV-2 patients may endure persistent symptoms[4] such as cough, difficulty breathing, fever, sore throat[5], chest pain, palpitations, fainting, fatigue [6], diarrhea, vomiting, anorexia [7], dizziness, headache, loss of taste, loss of smell, and cognitive impairment [8], nephritis[9], and impaired sexual function, among others[10].

Research has uncovered a bidirectional connection between COVID-19 and psychiatric disorders [11]. Individuals with COVID-19 frequently experience post-traumatic stress disorder (PTSD), depression, or insomnia [12], and studies have shown that 95%, 92.5%, and 70% of COVID-19 patients present with PTSD, insomnia, and depression, respectively [13]. Simultaneously, those with mental health conditions face an increased likelihood of infection or mortality during hospitalization, attributed to feelings of inferiority, stigma, cognitive deficits, and lack of insight [14]-[15]. Therefore, it is necessary to explore the interaction between somatic and psychiatric symptoms in patients' prognosis.

To delve into this interaction, we employed network analysis, a novel methodology that unveils the internal relationship between somatic and psychiatric symptoms. Diverging from traditional approaches, network analysis considers diseases as comprised of interconnected symptoms [16]-[17], where symptoms of one ailment can induce the development of another, and co-occurring symptoms form part of the same dynamic network [18]-[19]. According to network analysis theory, bridge symptoms play a pivotal role in linking two diseases [20]-[21], exerting a substantial influence on comorbidity regulation and the risk of disease metastasis [22].

In light of these considerations, this study exploring the inherent association between somatic and psychiatric symptoms in 534 Chinese hospitalized COVID-19 patients. A network analysis model was devised to elucidate the intrinsic links between somatic symptoms and depression, insomnia, and PTSD. Besides, a network analysis was also performed for subgroups of gender. According to the definition of bridge symptoms, the bridge symptoms are most likely the cross symptom with the characteristics of the two symptom communities. This study may provide information for understanding the interaction between somatic and psychiatric symptoms, thereby facilitating the development of intervention strategies.

Participants

Data were gathered from April 28 to May 26, 2022, through paper and pencil tests. A total of 534 COVID-19 patients without a history of mental disorders at the Shelter Hospital in Shanghai, China, were included in this study. The survey was explained to all participants by the researchers, and verbal consent was obtained. Participants completed all questionnaires in the ward. The study adopted a cross-sectional survey design. Approval for the study was granted by the Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University (project number: KY20222047-F-1).

Measures

Demographic statistics

Participants provided information on gender, age, educational level, the number of family contacts during hospitalization, type of vaccine received, and the number of vaccinations.

Self-evaluation of somatic symptoms in COVID-19 inpatients

Participants assessed eight somatic symptoms, including headache, stuffy nose, cough, muscle pain, chest distress, loss of smell, loss of taste, and loss of sexual desire. Ratings were on a scale of 0 (no feeling) to 10 (very strong feeling).

Assessment of depression symptoms

Depression symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9). This self-rating checklist consists of nine items, each graded on a scale of 0 (not at all) to 3 (nearly every day). The total score, ranging from 0 to 27, indicates the severity of depressive symptoms. Scores of 5–9 suggest mild depressive symptoms, 10–14 indicate moderate symptoms, and 15 or more indicate severe symptoms. The scale demonstrated a Cronbach's α coefficient of 0.875[23].

Assessment of insomnia symptoms

Insomnia symptoms were assessed using the Insomnia Severity Index (ISI), consisting of seven items graded on a 6-point scale from 0 (asymptomatic) to 5 (very severe). Total scores, ranging from 0 to 28, classified insomnia severity as mild (8–14), moderate (15–21), or severe (22–28). The Cronbach's α coefficient for the scale was 0.80 [24].

Assessment of PTSD symptoms

PTSD symptoms were evaluated using the PTSD Checklist-Civilian Version (PCL-C), comprising 17 items scored from 0 (asymptomatic) to 4 (very severe). Total scores, the sum of all 17 items, indicated the presence of PTSD symptoms at 38–49 and more significant symptoms at 50–85. The Cronbach's α coefficient for the scale was 0.94 [25].

Network analysis

The current network was appraised employing the Gaussian Graphical Model (GGM) [26]. GGM, falling under undirected networks, represents edges as the partial correlation between two nodes after statistically controlling for all other nodes in the network. Construction of the GGM was facilitated using the EBICglasso function in the qgraph package[26]-[27]. The graphical Least Absolute Shrinkage and Selection Operator (GLASSO) regularization algorithm, following the guidelines, was employed to yield a sparse network reflective of the authentic network structure [28]-[29]. In this regularization process, edges with small coefficients were eliminated, resulting in a network with the most robust edges and a sparse structure. The hyperparameter γ of EBIC was set to 0.5 [20]. The Spearman’s correlation method was employed for network structure estimation due to the continuous nature of all items [30]. The Fruchterman Reingold algorithm was utilized to arrange the network, placing nodes with stronger correlations in closer proximity. Positive correlations were represented as blue edges, and negative correlations as red edges. Edge thickness denoted the magnitude of correlations, with thicker edges indicating stronger correlations.

The bootnet package was employed to determine the precision of edge weights [31], and 95% confidence intervals (CI) were computed through non-parametric bootstrapping (2,000 bootstrapped samples). A narrower 95% CI signified more accurate edge weights and a more reliable network [26][32]. Bootstrapping (2,000 bootstrapped samples) using the bootnet package was conducted to examine significant differences between the edge weights of various node pairs [31].

This study also divided the participants into subgroups based on gender, and compared their internally constructed network structures to detect whether they differed.

Demographic statistics

A total of 534 patients (females = 271) participated in the study, ranging in age from 8 to 71 years (mean age = 38.97, SD = 13.53). Regarding depression, 143 (26.78%) had mild symptoms, 211 (39.51%) had moderate symptoms, and 180 (33.71%) had severe symptoms. Insomnia levels included 245 cases (45.88%) of mild, 133 (24.91%) of moderate, and 36 (6.74%) of severe. PTSD symptoms were present in 24 patients (4.49%), with 8 patients (1.50%) possibly having PTSD (Supplemental Material Table S1).

Network structure

Network nodes for PHQ9, ISI, and PCL-C, complete descriptions of each item, and node abbreviations are shown in Supplemental Material Table S2.

Depression-somatic symptom network

The depression-somatic symptom network, illustrated in Fig. 1, reveals seven robust edges, with notable associations such as “Anosmia” - “Ageusia” (weight = 0.46), “Headache”- “Muscle pain” (weight = 0.37), and “Stuffy nose”- “Cough” (weight = 0.32). “Cough” emerges as more strongly linked to depressive symptoms than other somatic symptoms, notably “Anhedonia” (weight = 0.07), “Sleep” (weight = 0.07), and “Fatigue” (weight = 0.01). Additionally, “Muscle pain” - “Fatigue” (weight = 0.11) exhibits a greater cross-community edge than others. The network's edge weights are detailed in Table S3 (Supplemental Material). The relatively narrow bootstrapped 95% confidence interval indicates the network's reliability (Fig. S1 Edge accuracy in Supplementary Material). Fig. S2 Edge Difference test (Supplementary Material) displays the bootstrapped difference test for edge weights.

Figure 2 illustrates the expected impact of the bridge on the depression-somatic symptom network. “Cough” (BEI = 0.20) and “Anhedonia” (BEI = 0.14) are identified as bridge symptoms, demonstrating their potent ability to elevate the risk of inter-community transmission in existing networks. The stability level of the BEI estimator, with a coefficient of stability at 0.52, suggests good stability (Fig. S3 Bridge EI stability in Supplementary Material). Additionally, a bootstrap differential test of the node's bridge expectation impact reveals that the "Cough" node's impact is significantly higher than 70% of other nodes in the current network (Figure S4. Bridge EI difference in Supplementary Material).

Insomnia-somatic symptom network

The insomnia-somatic symptom network, depicted in Fig. 3, highlights five robust edges, notably “Sleep onset”- “Maintenance” (weight = 0.59), “Noticeability”- “Distress” (weight = 0.46), “Ageusia”- “Anosmia” (weight = 0.46), “Headache”-“Muscle pain” (weight = 0.39), and “Stuffy nose”-“Cough” (weight = 0.33). “Cough” and “Headache” exhibit stronger associations with insomnia than with other somatic symptoms. Specifically, “Cough” is linked with “Sleep onset” (weight = 0.02), “Early wakening” (weight = 0.07), and “Noticeability” (weight = 0.02). Meanwhile, “Headache” is associated with “Early wakening” (weight = 0.01), “Noticeability” (weight = 0.02), and “Distress” (weight = 0.01). Refer to Table S4 (Supplemental Material) for all edge weights in the insomnia-somatic symptom network. The relatively narrow bootstrapped 95% confidence interval indicates the network's reliability (Fig. S5 Edge accuracy in Supplementary Material). Fig. S6 Edge Difference test (Supplementary Material) illustrates the bootstrapped difference test for edge weights.

Figure 4 illustrates the expected impact of the bridge on the insomnia-somatic symptom network. “Cough” (BEI = 0.12) and “Noticeability” (BEI = 0.10) are identified as bridge symptoms, indicating their potent ability to elevate the risk of inter-community contagion in existing networks. The stability level of the BEI estimator, with a coefficient of stability at 0.44, suggests good stability (Fig. S7 Bridge EI stability in Supplementary Material). Additionally, a bootstrap differential test of the node's bridge expectation impact reveals that the "Cough" node's impact is significantly higher than 64% of other nodes in the current network and“Noticeability” node's impact is significantly higher than 43% of other nodes in the current network (Figure S8. Bridge EI difference in Supplementary Material).

PTSD-somatic symptom network

The PTSD-somatic symptom network, presented in Fig. 5, features five prominent edges, including “Ageusia”-“Anosmia” (weight = 0.46), “Headache”-“Muscle pain” (weight = 0.40), “Reminders avoidance”–“Thoughts avoidance” (weight = 0.39), “Hypervigilance”–“Startle” (weight = 0.38), and “Stuffy nose”-“Cough” (weight = 0.33). Notably, “Cough” demonstrates a stronger association with PTSD symptoms than other physical symptoms, such as “Physical reactivity” (weight = 0.0005), “Interest loss” (weight = 0.002), “Sleep” (weight = 0.07), “Irritable” (weight = 0.01), and “Concentration” (weight = 0.01). The edge strength (weight = 0.07) between “Cough” and “Sleep” surpasses any other cross-community edge. Refer to Table S5 (Supplemental Material) for all edge weights in the PTSD-somatic symptom network. The relatively narrow bootstrapped 95% confidence interval indicates the network's reliability (Fig. S9 Edge accuracy in Supplementary Material). Fig. S10 Edge Difference test (Supplementary Material) demonstrates the bootstrapped difference test for edge weights.

Figure6 illustrates the expected impact of the bridge on the PTSD-somatic symptom network. “Cough” (BEI = 0.10), “Libidoloss” (BEI = 0.08), and “Sleep” (BEI = 0.08) are identified as bridge symptoms, signifying their potent ability to elevate the risk of inter-community contagion in existing networks. The stability level of the BEI estimator, with a coefficient of stability at 0.28, suggests good stability (Fig. S11Bridge EI stability in Supplementary Material). Additionally, a bootstrap differential test of the node's bridge expectation impact reveals that the "Cough" node's impact is significantly higher than 29% of other nodes in the current network, “Libidoloss” node's impact is significantly higher than 29% of other nodes in the current network and “Sleep” node's impact is significantly higher than 21% of other nodes in the current network (Fig. S12 Bridge EI difference in Supplementary Material).

Subgroup network comparison

The network structures for two subpopulations are depicted in Fig.S13 subgroup network comparison. No significant difference in overall network strength and structure between different genders in the depression-somatic symptom network [Male group: 7.37, female group: 7.38, p = 0.997;network variance (p) = 0.782] indicates comparable interaction of nodes and network connectivity. Furthermore, no significant difference is observed between edge line weight and the replacement test of bridge expectation effect.

No significant difference in overall network strength and structure between different genders in insomnia-somatic symptom networks [Male group: 6.76, female group: 6.71, p = 0.826; Network variance (p) = 0.325] suggests comparable interaction of nodes and network connectivity. Furthermore, no significant difference is observed between the edge line weight and the replacement test of bridge expectation effect.

No significant difference in overall network strength in the PTSD-somatic symptom network between genders, and no significant difference in network structure [Male group: 11.46, female group: 11.90, p = 0.141; Network variance (p) = 0.766] indicates comparable interaction of nodes and network connectivity. Furthermore, no significant difference is observed between the edge line weight and the replacement test of bridge expectation effect.

This study enrolled 534 Chinese COVID-19 inpatients revealed that COVID-19 inpatients were frequently accompanied by psychiatric manifestations such as depression, insomnia, and PTSD, with incidence rates of 100%, 77.63%, and 5.99%, respectively—significantly surpassing those in the general population [33] -[34]. A network model illustrating the intrinsic connections between somatic symptoms and depression, insomnia, and PTSD. In the depression-somatic symptom network revealed seven robust edges, including “Anosmia”-“Ageusia”, “Headache”-“Muscle pain”, “Stuffy nose”-“Cough”, “Cough”-“Anhedonia”, “Cough” - “Sleep”, “Cough”-“Fatigue”, and “Muscle pain”-“Fatigue”. The insomnia-somatic symptom network highlighted five robust edges, including “Sleep onset”-“Maintenance”, “Noticeability”-“Distress”, “Ageusia”-“Anosmia”, “Headache”-“Muscle pain”, and “Stuffy nose”-“Cough”. The PTSD-somatic symptom network featured five prominent edges, including “Ageusia”-“Anosmia”, “Headache”-“Muscle pain”, “Reminders avoidance”-“Thoughts avoidance”, “Hypervigilance”-“Startle”, and “Stuffy nose”-“Cough”. Simultaneously, “Cough”、“Anhedonia”、 “Noticeability”、“Libido loss” and “Sleep” play a crucial role as bridge factors linking somatic symptoms with psychiatric symptoms. Meanwhile, “Cough”、“Anhedonia”、 “Noticeability”、“Libido loss” and “Sleep” play a crucial role as bridge factors linking somatic symptoms with psychiatric symptoms.

Prior investigations have indicated the prevalence of depressive symptoms and major depressive disorder in patients with "Cough" [35]-[36]. In line with these findings, this study identifies "Cough" as a crucial bridge factor between somatic symptoms and depressive symptoms in hospitalized COVID-19 patients. This connection plays a role in regulating key nodes associated with depressive symptoms, including "Anhedonia," "Sleep," "Appetite," "Concentration," and "Fatigue," aligning with previous literature reports [37]. Examining the neurobiological mechanism underlying the mutual regulation of cough symptoms and depressive symptoms in COVID-19, cough induces an increase in gray matter nerve electrical activity around the aqueduct in the midbrain [38]-[39]. This activity is involved in regulating stress, pain, and depression, among other emotions [37]. Conversely, elevated blood cortisol concentrations and abnormalities in the hypothalamic-pituitary-adrenal cortical axis (HPA axis) following SARS-CoV-2 infection are identified as crucial contributors to depression [40]-[41]. Past studies have established that the severity of cough serves as an independent risk factor for depressive symptoms, show casing a bidirectional moderating relationship between cough and psychiatric symptoms such as depression and insomnia[42]-[43]. Besides, this study founds "Cough" as a crucial bridge factor between somatic symptoms and insomnia and PTSD symptoms in hospitalized COVID-19 patients. This relationship may be associated with pathophysiological processes such as abnormal gene expression in the prefrontal cortex and limbic system of the brain, increased synapse formation, and dendritic growth in the amygdala, along with dendrite contraction in the hippocampus. Additionally, inflammatory responses and autonomic nerve damage, resulting from physical diseases, psychological stress, and emotional trauma, could contribute to this intricate relationship [44]. The finding that "Anhedonia" serves as an additional connecting bridge factor between somatic symptoms and depressive symptoms in hospitalized COVID-19 patients is consistent with previous research. "Anhedonia" is an essential symptom in the diagnosis of depression, and previous research has demonstrated a strong relationship between headache, pleasure deficit, and depression[45].

"Noticeability" as another bridging symptom between insomnia and somatic symptoms, consistent with previous studies that insomnia is associated with headache[46], the present study further found "Noticeability" to be associated with headache, cough, muscle pain, and loss of libido.

"Libido loss" emerges as a pivotal bridging factor connecting somatic symptoms and PTSD symptoms, exhibiting associations with PTSD-related nodes such as "Intrusive thoughts," "Nightmares," "Interest loss," and "Startle." This finding aligns with existing literature suggesting that PTSD is linked to a heightened incidence of libido loss[47]. Earlier research has indicated that libido loss in individuals with PTSD may be linked to diminished plasma levels of dehydroepiandrosterone and cortisol, coupled with a weakened response to the dexamethasone inhibition test[48]-[49]. Consequently, the correlation between libido loss and PTSD could represent specific clinical manifestations of adaptive changes in response to stress [50]. Moreover, PTSD has been associated with the upregulation of pro-inflammatory factors like IL-1β, IL-6, and TNF-α, leading to immune dysfunction[51]. However, the potential relationship between this process and libido loss, as well as increased acute cardiovascular risk, warrants further investigation. In addition, the present study found "Sleep " to be another bridging symptom between PTSD and somatic symptoms, which is consistent with previous research that sleep in PTSD is associated with headache and loss of libido.

Limitations and strengths

While this study offers novel insights for disease assessment interventions, certain limitations must be acknowledged. Firstly, the cross-sectional nature of this study impedes the determination of a regulatory causal relationship between somatic symptoms and neuropsychiatric symptoms. Secondly, reliance on internationally accepted classical questionnaire scales introduces inherent subjectivity in patient responses.

By investigating the somatic and psychiatric symptoms of 534 COVID-1919 inpatients, this study found, using network analysis, that connections between somatic symptoms and psychiatric symptoms, and identified key bridge factors in COVID-19 patients. Specifically, "Cough" and "Anhedonia" have emerged as a pivotal bridge connecting somatic symptoms with depression. "Cough" and "Noticeability" have become known as a crucial bridge connecting somatic symptoms with insomnia, while "Cough", " Libido loss " and "Sleep " play a crucial role as bridge factors linking somatic symptoms with PTSD symptoms. In summary, these findings offer novel perspectives for the assessment and intervention of COVID-19 and other multisystem disorders.

COVID-19: coronavirus disease-2019; PTSD: posttraumatic stress disorder; HPA axis : Hypothalamic-pituitary-adrenal cortical axis.

Acknowledgements

We would like to thank all the individuals who participated in the study.

Authors’ contributions

Xing Gao: Visualization, Data curation, Formal analysis, Writing – original draft. Lei Ren: Data curation, Formal analysis, Writing – original draft. Mengyuan Yang: Visualization, Formal analysis. Lingdi Chang: Data curation, Investigation. Jinliang Zhang: Formal analysis, Investigation. Yongcai Yu: Data curation, Investigation. Chao Zhang: Formal analysis, Investigation. Xiangliang Meng: Formal analysis. Xunmei Huang: Data curation. Sikai Li: Investigation. Zhaokun Shi: Investigation. Jing Xu: Investigation. Weijun Qin: Data curation, Formal analysis, Investigation. Keying Zhang: Writing - review & editing, Formal analysis, Investigation. Rui Cheng: Methodology, Data curation, Formal analysis, Investigation.

Funding

This study was supported by the First Affiliated Hospital of Air Force Military Medical University

(2022XJZT-YH09) and the National Natural Science Foundation of China (No. 82201774, 82202933, 82203633, 82173204, and 82220108004), and Young Talent Support Program of Xi'an (No. 959202313006).

Availability of data and materials

The quantitative dataset supporting the conclusions of this article are included within the article. Data is provided within the manuscript or supplementary information files. The qualitative dataset used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethical approval and consent to participate

The present investigation obtained ethics approval from the Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University (project number: KY20222047-F-1), and written informed consent was obtained from all participants prior to being involved.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Li M, Wang H, Tian L, Pang Z, Yang Q, Huang T, Fan J, Song L, Tong Y, Fan H (2022) COVID-19 vaccine development: milestones, lessons and prospects. Signal Transduction and Targeted Therapy 7:146
Wang W, Tang J, Wei F (2020) Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol 92:441–447
WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int. Accessed 29 Jun 2023
Bellan M, Soddu D, Balbo PE, et al (2021) Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open 4:e2036142
Mo P, Xing Y, Xiao Y, et al (2021) Clinical Characteristics of Refractory Coronavirus Disease 2019 in Wuhan, China. Clin Infect Dis 73:e4208–e4213
Davis HE, Assaf GS, McCorkell L, Wei H, Low RJ, Re’em Y, Redfield S, Austin JP, Akrami A (2021) Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. Eclinicalmedicine 38:101019
Dong Z-Y, Xiang B-J, Jiang M, Sun M-J, Dai C (2021) The Prevalence of Gastrointestinal Symptoms, Abnormal Liver Function, Digestive System Disease and Liver Disease in COVID-19 Infection: A Systematic Review and Meta-Analysis. J Clin Gastroenterol 55:67–76
Baig AM (2020) Deleterious Outcomes in Long-Hauler COVID-19: The Effects of SARS-CoV-2 on the CNS in Chronic COVID Syndrome. ACS Chem Neurosci 11:4017–4020
Bowe B, Xie Y, Xu E, Al-Aly Z (2021) Kidney Outcomes in Long COVID. J Am Soc Nephrol 32:2851–2862
Wang Z, Xu X (2020) scRNA-seq Profiling of Human Testes Reveals the Presence of the ACE2 Receptor, A Target for SARS-CoV-2 Infection in Spermatogonia, Leydig and Sertoli Cells. Cells 9:920
Liu L, Ni S-Y, Yan W, et al (2021) Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action. EClinicalMedicine 40:101111
Huang C, Huang L, Wang Y, et al (2023) 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet 401:e21–e33
Yuan K, Zheng Y-B, Wang Y-J, et al (2022) A systematic review and meta-analysis on prevalence of and risk factors associated with depression, anxiety and insomnia in infectious diseases, including COVID-19: a call to action. Mol Psychiatr 27:3214–3222
Bhanot D, Singh T, Verma SK, Sharad S (2020) Stigma and Discrimination During COVID-19 Pandemic. Front Public Health 8:577018
Ceban F, Ling S, Lui LMW, et al (2022) Fatigue and cognitive impairment in Post-COVID-19 Syndrome: A systematic review and meta-analysis. Brain Behav Immun 101:93–135
Bai W, Zhao Y, An F, Zhang Q, Sha S, Cheung T, Cheng CP-W, Ng CH, Xiang Y-T (2021) Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study. Nat Sci Sleep 13:1921–1930
Bringmann LF, Lemmens LHJM, Huibers MJH, Borsboom D, Tuerlinckx F (2015) Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychol Med 45:747–757
Borsboom D, Cramer AOJ (2013) Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psycho 9:91–121
Kendler KS, Zachar P, Craver C (2011) What kinds of things are psychiatric disorders? Psychol Med 41:1143–1150
Epskamp S, Kruis J, Marsman M (2017) Estimating psychopathological networks: Be careful what you wish for. PLoS One 12:e0179891
Wang Y, Ma Z, Wilson A, Hu Z, Ying X, Han M, Cui Z, Chen R (2021) Psychopathological symptom network structure in transgender and gender queer youth reporting parental psychological abuse: a network analysis. BMC Med 19:215
Jones PJ, Ma R, McNally RJ (2021) Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivar Behav Res 56:353–367
Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16:606–13
Bastien CH, Vallieres A, Morin CM (2001) Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2:297–307
Dobie DJ, Kivlahan DR, Maynard C, Bush KR, McFall M, Epler AJ, Bradley KA (2002) Screening for post-traumatic stress disorder in female Veteran’s Affairs patients: validation of the PTSD checklist. Gen Hosp Psychiat 24:367–374
Epskamp S, Fried EI (2018) A tutorial on regularized partial correlation networks. Psychol Methods 23:617–634
Chen J, Chen Z (2008) Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95:759–771
Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9:432–441
Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc B 73:273–282
Liu D, Epskamp S, Isvoranu A-M, Chen C, Liu W, Hong X (2021) Network analysis of physical and psychiatric symptoms of hospital discharged patients infected with COVID-19. J Affect Disord 294:707–713
Epskamp S, Borsboom D, Fried EI (2018) Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods 50:195–212
Liang S, Liu C, Rotaru K, Li K, Wei X, Yuan S, Yang Q, Ren L, Liu X (2022) The relations between emotion regulation, depression and anxiety among medical staff during the late stage of COVID-19 pandemic: a network analysis. Psychiat Res 317:114863
Krämer HU, Rüter G, Schöttker B, Rothenbacher D, Rosemann T, Szecsenyi J, Brenner H, Raum E (2012) Gender differences in healthcare utilization of patients with diabetes. Am J Manag Care 18:362–369
Wang C, Pan R, Wan X, et al (2020) A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav Immun 87:40–48
Dicpinigaitis PV, Tso R, Banauch G (2006) Prevalence of depressive symptoms among patients with chronic cough. Chest 130:1839–1843
French CL, Crawford SL, Bova C, Irwin RS (2017) Change in Psychological, Physiological, and Situational Factors in Adults After Treatment of Chronic Cough. Chest 152:547–562
Arinze JT, Hofman A, de Roos EW, de Ridder MAJ, Verhamme KMC, Stricker B, Brusselle GG, Luik AI (2022) The interrelationship of chronic cough and depression: a prospective population-based study. ERJ Open Res 8:00069–02022
Ando A, Smallwood D, McMahon M, Irving L, Mazzone SB, Farrell MJ (2016) Neural correlates of cough hypersensitivity in humans: evidence for central sensitisation and dysfunctional inhibitory control. Thorax 71:323–329
Buhle JT, Kober H, Ochsner KN, Mende-Siedlecki P, Weber J, Hughes BL, Kross E, Atlas LY, McRae K, Wager TD (2013) Common representation of pain and negative emotion in the midbrain periaqueductal gray. Soc Cogn Affect Neur 8:609–616
Misiak B, Łoniewski I, Marlicz W, Frydecka D, Szulc A, Rudzki L, Samochowiec J (2020) The HPA axis dysregulation in severe mental illness: Can we shift the blame to gut microbiota? Prog Neuro-psychoph 102:109951
Tan T, Khoo B, Mills EG, et al (2020) Association between high serum total cortisol concentrations and mortality from COVID-19. Lancet Diabetes Endocrinol 8:659–660
Hari G, Naunheim M, Kallogjeri D, Huston M (2023) Anxiety and Depression Diagnoses and the Cough Severity Index: A Retrospective Study. Ear Nose Throat J 1455613231180336
White KS, Craft JM, Gervino EV (2010) Anxiety and hypervigilance to cardiopulmonary sensations in non-cardiac chest pain patients with and without psychiatric disorders. Behav Res Ther 48:394–401
Li Y, Cao C, Ji Y, Xu S (2022) Anxiety and depression are associated with reduced quality of life and increased cough severity in chronic cough. Asian Pac J Allergy. https://doi.org/10.12932/AP-110721-1184
Lovati C, Bernasconi G, Capogrosso C, Molteni L, Giorgetti F, Dell’Osso B, Pantoni L (2022) Personality traits and efficacy of anti-CGRP monoclonal antibodies in migraine prevention. Neurol Sci 43:5765–5767
Sharif S, Amin F, Hafiz M, Benzel E, Peev N, Dahlan RH, Enchev Y, Pereira P, Vaishya S, World Spinal Column Society Executive Board (2020) COVID 19-Depression and Neurosurgeons. World Neurosurg 140:e401–e410
Castillo O, Chen IK, Amini E, Yafi FA, Barham DW (2022) Male Sexual Health Related Complications Among Combat Veterans. Sex Med Rev 10:691–697
Bird ER, Piccirillo M, Garcia N, Blais R, Campbell S (2021) Relationship Between Posttraumatic Stress Disorder and Sexual Difficulties: A Systematic Review of Veterans and Military Personnel. J Sex Med 18:1398–1426
Lehrner A, Flory JD, Bierer LM, Makotkine I, Marmar CR, Yehuda R (2016) Sexual dysfunction and neuroendocrine correlates of posttraumatic stress disorder in combat veterans: Preliminary findings. Psychoneuroendocrino 63:271–275
O’Loughlin JI, Brotto LA (2020) Women’s Sexual Desire, Trauma Exposure, and Posttraumatic Stress Disorder. J Trauma Stress 33:238–247
Hori H, Kim Y (2019) Inflammation and post-traumatic stress disorder. Psychiat Clin Neuros 73:143–153

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

The intrinsic association between somatic symptoms and psychiatric symptoms among Chinese COVID-19 inpatients: A network analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Participants

Measures

Demographic statistics

Self-evaluation of somatic symptoms in COVID-19 inpatients

Assessment of depression symptoms

Assessment of insomnia symptoms

Assessment of PTSD symptoms

Network analysis

Results

Demographic statistics

Network structure

Depression-somatic symptom network

Insomnia-somatic symptom network

PTSD-somatic symptom network

Subgroup network comparison

Discussion

Limitations and strengths

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1