Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Cytokines interferon−γ− inducible protein 10 and granulocyte−macrophage colony−stimulating factor are associated with psychiatric symptoms in opioid−dependent patients: A cross− sectional study

  • Kristin Nygård−Odeh ,

    Contributed equally to this work with: Kristin Nygård−Odeh, Hedda Soløy−Nilsen

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

    Kristin.Nygard-Odeh@nordlandssykehuset.no

    Affiliations Nordland Hospital Trust, Bodø, Norway, Institute of Clinical Medicine, UIT − The Arctic University of Norway, Tromsø, Norway

    ⨯
  • Hedda Soløy−Nilsen ,

    Contributed equally to this work with: Kristin Nygård−Odeh, Hedda Soløy−Nilsen

    Roles Conceptualization, Data curation, Writing – review & editing

    Affiliations Nordland Hospital Trust, Bodø, Norway, Institute of Clinical Medicine, UIT − The Arctic University of Norway, Tromsø, Norway

    ⨯
  • Magnhild Gangsøy−Kristiansen,

    Roles Conceptualization, Writing – review & editing

    Affiliation Institute of Clinical Medicine, UIT − The Arctic University of Norway, Tromsø, Norway

    ⨯
  • Ole Lars Brekke,

    Roles Data curation, Resources, Writing – review & editing

    Affiliations Nordland Hospital Trust, Bodø, Norway, Institute of Clinical Medicine, UIT − The Arctic University of Norway, Tromsø, Norway

    ⨯
  • Tom Eirik Mollnes,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliations Research Laboratory, Nordland Hospital Trust, Bodø, Norway, Department of Immunology, Oslo University Hospital and University of Oslo, Oslo, Norway

    ⨯
  • Michael Berk

    Roles Methodology, Supervision, Writing – review & editing

    Affiliations Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia, Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia

    ⨯

Abstract

Background

Psychiatric disorders and chronic hepatitis virus C infection are known to alter blood cytokines levels. However, little is known about the association between cytokines and psychiatric symptoms in patients with chronic hepatitis C virus infection. This study aimed at exploring this association. Moreover, since nearly half of the patients receive opioid maintenance treatment, we also investigated if long−term opioid treatment had any impact on these associations.

Methods

We conducted a cross−sectional study on 120 outpatients referred for antiviral hepatitis C treatment. Serum level of 27 cytokines was measured using multiplex technology, and psychiatric symptom clusters were assessed using the Symptoms Check−List−90−R. Data on confounding factors including age, gender, weight, height, current medication and smoking habits were collected. Multiple linear regression analysis was performed to examine associations, adjusting for confounding factors.

Results

After adjusting for the most commonly known confounding factors, IP−10 and GM−CSF were negatively associated with depression, and GM−CSF was negatively associated with phobic anxiety. Subgroup analyses revealed that these associations were present only in patients receiving opioid maintenance treatment, as demonstrated by repeated regression analysis.

Conclusions

In patients with chronic hepatitis C viral infection, only IP−10 and GM−CSF were negatively associated with self−reported psychiatric symptom clusters. These associations were observed exclusively in patients receiving opioid maintenance treatment. Our study contributes to others investigations pointing to a possible immune dampening caused by long−term opioid treatment.

Citation: Nygård−Odeh K, Soløy−Nilsen H, Gangsøy−Kristiansen M, Brekke OL, Mollnes TE, Berk M (2025) Cytokines interferon−γ− inducible protein 10 and granulocyte−macrophage colony−stimulating factor are associated with psychiatric symptoms in opioid−dependent patients: A cross− sectional study. PLoS One 20(8): e0324365. https://doi.org/10.1371/journal.pone.0324365

Editor: Jason T. Blackard, University of Cincinnati College of Medicine, UNITED STATES OF AMERICA

Received: November 13, 2024; Accepted: April 23, 2025; Published: August 7, 2025

Copyright: © 2025 Nygård−Odeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data will be made available upon direct request to the following institution: Nordland hospital trust Department of research P.o. box 1480 N−8092 Bodø Norway forskning@nordlandssykehuset.no.

Funding: The study was in part funded by research grant from Northern Norway regional health authority (reference: RUS1303−16). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

Emerging evidence of immunological activation in psychiatric disorders has spurred research into the association between cytokines and various psychiatric disorders [1,2]. For instance, tumour necrosis factor−alpha (TNF) and interferon−gamma (IFN−γ) have been linked to generalized anxiety [2], while interleukin−6 (IL−6) are elevated in patients with major depressive disorder, suggesting that cytokines could serve as potential diagnostic biomarkers [1]. Similar to their use as diagnostic markers and therapeutic targets in inflammatory disorders [3], identifying associations between cytokines and psychiatric disorders may have important clinical implications. However, relying solely on diagnosis−based associations poses limitations, as psychiatric diagnoses are often co−occurring [4]. This leads to a potential overemphasis on one diagnosis while neglecting others. Moreover, inter−rater diagnostic variability challenges diagnostic accuracy [5]. Since symptoms frequently overlap and co−occur across many psychiatric diagnoses [6], investigating the relationship between cytokines and specific symptoms may offer a more accurate and clinically useful approach. Previous studies have explored associations between cytokines and symptoms such as anhedonia [7], fatigue [8], psychotic symptoms [9], agitation [10] and hostility [6].

Opioid maintenance treatment (OMT) is a well−established intervention for opioid dependence, involving the use of long−acting opioids. Notably, 43 percent of these patients have chronic hepatitis C viral infection (HCV) [11]. Both long−acting opioids [12–14] and chronic HCV [15,16] impact the immune system and alter cytokine levels. For example, 12 weeks of methadone treatment has been shown to reduce the levels of cytokines transforming growth factor−beta (TGF −β) and brain−derived neurotrophic factor (BDNF) [12], while prolonged methadone use (median 23.6 months) is associated with higher levels of interleukin−1 beta (IL−1β), IL−6 and interleukin−8 (IL−8) compared to healthy controls [13]. Similarly, chronic HCV infection leads to elevated levels of cytokines, including interleukin−1 alpha (IL−1α), IL−1β, IL−6 and IL−8 [17]. As chronic HCV progresses to liver cirrhosis, levels of IL−1α, interleukin−2 receptor (IL−2R), interleukin−12 (IL−12), interleukin−18 (IL−18) and C−X−C motif chemokine ligand 9 (CXCL9) increase, suggesting an association with disease progression [16]. Fifty percent of both HCV [18]− and OMT−patients [19] have psychiatric symptoms. This comorbidity is associated with poorer adherence to treatment in both groups [20,21]. Investigating the relationship between cytokines and psychiatric symptoms in this context may help identify diagnostic markers and pave the way for individualized, symptom−oriented treatment [22], ultimately improving treatment outcomes. Therefore, the aim of this study was to investigate the relationship between peripherally circulating cytokines and psychiatric symptoms in patients with chronic HCV infection. Additionally, we wanted to study the impact of opioid maintenance treatment on these associations by comparing non−OMT with OMT patients.

Materials and methods

Design, recruitment and participants

This study used data from a previously published cross−sectional study [23]. Patients referred for antiviral HCV treatment were recruited from the Department of infectious diseases at the Nordland Hospital Trust, Bodø, Norway in the period of April 23rd 2013−December 12th 2019. On the day of their appointment, a study nurse informed the patients about the study and invited them to participate. Interested individuals were provided with detailed verbal and written information by the main investigator (first author), and were asked to provide written consent [23]. Exclusion criteria included patients who did not understanding the Norwegian language, had obvious cognitive deficits, negative HCV−RNA, failure to obtain written consent and failure to provide blood samples. Of the 135 screened patients, 15 were excluded due to negative HCV−PCR (n = 9), failure to provide written consent (n = 4) and failure to provide blood−samples (n = 2). In the final sample of 120 patients, 53 were on OMT, and 67 patients were not [23]. The study was approved by the Regional ethics committee (notification 2015/1808/REK Nord and 2011/2024/REK Nord), and adhered to the principles of the Declaration of Helsinki.

Data collection

Upon recruitment, participants completed the self−administered form, the Symptoms Check−List−90−Revised (SCL−90−R). The SCL−90−R is a validated questionnaire investigating 90 items screening for psychological distress during the last week, each of which rated on a five−point scale of distress [24]. The 90 items are grouped into 9 subscales/clusters (somatization, obsessive−compulsive behaviour, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism) that have good reliability and replicability [25]. The scale ranges from “not at all” (0) to “extremely” (4). Mean scores across the clusters were calculated. Patients were interviewed by the main investigator using a modified adaptation of the multidimensional European Addiction Severity Index (Europ−ASI) questionnaire [26]. The version included sections general information, physical health, economy, education and employment−status and alcohol and other drugs−use. Interviewer’s severity assessment and reliance assessment were omitted. Data were collected on OMT drug type and dosage, gender, age, weight, height, body mass index (BMI = kg/m2), smoking habits, current medication, allergies, medical history and ongoing withdrawal symptoms [23].

Blood sampling and analyses

Blood samples were drawn by the study nurse on the day of inclusion between 11:00 a.m. and 2:50 p.m. [23]. Biochemical measures were performed at the Central laboratory, Department of Laboratory Medicine, Nordland Hospital Trust. For measurement of cytokines, blood was withdrawn in Vacuette serum tubes, left for 30 minutes before centrifugation 10 minutes at 2300 x g. Serum (2 x 1 mL) was stored in Matrix tubes on ice up to 2 hours before freezing at −80o C [23].

The cytokine analysis was performed by multiplex technology with a predefined kit Bio−Plex Human Cytokine 27−Plex Panel (Bio−Rad Laboratories Inc., Hercules, CA) according to the instructions of the manufacturer [23] The assay detected the following interleukins, chemokines, and growth factors: tumour necrosis factor (TNF), interferon (IFN)−gamma, IL‑1β, IL‑1 receptor antagonist (IL‑1ra), IL‑2, IL‑4, IL‑5, IL‑6, IL‑7, IL‑8 (C−X−C motif chemokine ligand 8; CXCL8), IL‑9, IL−10, IL‑12, IL‑13, IL−15, IL−17, monocyte chemotactic protein (MCP−1/CCL2), interferon−γ−inducible protein 10 (IP−10/C−X−CL chemokine 10; CXCL10), eotaxin−1 (C−C motif chemokine ligand 11; CCL11), macrophage inflammatory protein−1α (MIP−1α/ CCL3), macrophage inflammatory protein−1−β (MIP−1β/ CCL4), regulated upon activation T cell expressed and secreted (RANTES), granulocyte macrophage colony stimulating factor (GM−CSF), vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), granulocyte−colony stimulating factor (G−CSF) and platelet derived growth factor−BB (PDGF−BB) [23].

Statistical analyses

Tests for cytokine level−normality was performed using Q−Q plots and found to be normal with lg10 transformation. Extreme outliers defined as lying outside the third quartile + 3 x interquartile range were identified by boxplots. Their impact was tested using independent samples t−test before and after their removal. No impact was found. Cytokine values below lower limit of detection (LLOD) were assigned a value randomly drawn by Excel between LLOD and zero [23]. The cytokines with more than 50% randomly drawn numbers were excluded from further analyses, excluding β−FGF, IL−7, IL−10, and G−CSF. Cytokines in which more than 50% had values below lower standard value were also excluded from further analysis, eliminating IL−5, IL−10 and IL−12. Multiple linear regression analysis was performed with cytokines as dependent variables and symptom clusters and confounding factors (age, gender, BMI, smoking) as independent variables. Hierarchical linear regression analysis was used in the further testing of IP−10 and GM− CSF. Testing with variance inflation factor below 10, showed no problems with collinearity [23]. Comparisons of the variables between the groups were performed using Mann−Whitney U test for continuous vs. binary variables and non−normal distributed variables, and chi−square tests for categorical variables. P−values <0.05 were deemed statistically significant [23]. Statistical analyses were performed using IBM SPSS Statistics viewer version 29.0.1.0.

Inclusivity in global research

Additional information regarding the ethical, cultural and scientific considerations specific to inclusivity in global research is included in the Supporting Information (SX Checklist).

Results

In the 120 included patients, the median age was 45 years, 39 were women and 108 were Norwegian nationals. Fifty−three were on OMT (Table 1).

thumbnail
Download:
Table 1. Sociodemographic, clinical and psychometric characteristics of study participants (N = 120).

https://doi.org/10.1371/journal.pone.0324365.t001

Data on smoking were missing for 30 patients, while data on ongoing psychotropic drug administration were missing for 58 patients. Cytokines values presented in Table 2 are the non−lg10 transformed values.

thumbnail
Download:
Table 2. Median cytokines values of all study participants (N = 120).

https://doi.org/10.1371/journal.pone.0324365.t002

From a previous study, cytokine levels in a group of healthy controls were used to validate our levels and can be found in the Supporting information (S1 Table) [27]. Most cytokine levels were low both in in the participants and in the healthy controls.

The multiple linear regression analysis with cytokines as dependent variable showed significant negative associations in two symptom clusters after adjusting for age, gender, BMI and smoking (Table 3): IP−10 (β = −0.23, p = 0.04) and GM−CSF (β = −0.26, p = 0.04) were negatively associated with depression, whereas GM−CSF (β = −0.26, p = 0.03) was negatively associated with phobic anxiety. Neither of these two cytokines had any association with HCV−infection status (IP−10: HCV−RNA: β = −0.02, p = 0.88. Ratio ASAT/ALAT: β = 0.17, p = 0.14. GM−CSF: HCV−RNA: β = −0.01, p = 0.91. Ratio ASAT/ALAT: β = 0.05, p = 0.64).

thumbnail
Download:
Table 3. Regression analysis between all the cytokines and symptom clusters. Cytokines are dependent variables, and symptom clusters and confounding factors (age, gender, BMI and smoking) independent variables.

https://doi.org/10.1371/journal.pone.0324365.t003

To investigate if opioids had any impact on these associations, participants were stratified into non−OMT and OMT groups. Sociodemographic characteristics differed minimally between the groups, with age being the only difference (Table 4). Median age in the non−OMT−group was 47 years and 42 years in the OMT group (p = 0.043).

thumbnail
Download:
Table 4. Difference between non−OMT and OMT patients in demographic characteristics, virus load, psychiatric symptom−cluster and substance use last 30 days.

https://doi.org/10.1371/journal.pone.0324365.t004

We ran a hierarchical regression analysis between the associated symptom cluster and cytokines that displayed significance in the linear regression analysis (Table 5). The significance was lost in the non−OMT group and remained significant throughout all the adjusting steps (age, gender, BMI and smoking) in the OMT−group with a negative association (depression/IP−10: β = −0.49, p = 0.001, depression/GM−CSF: β = −0.37, p = 0.02, phobic anxiety/GM−CSF: −0.32, p = 0.03).

thumbnail
Download:
Table 5. Hierarchical linear regression analysis of the relationship between the cytokines IP−10 and GM−CSF and their relationships to SCL−90−R symptom clusters previously displaying significant association, stratified into non−OMT and OMT patients. Cytokines are dependant variable.

https://doi.org/10.1371/journal.pone.0324365.t005

Discussion

In this study on patients with chronic HCV, we found that in the OMT patients IP−10 and GM−CSF were negatively associated with depression, and GM−CSF with phobic anxiety. No other association between nine symptom clusters and 27 cytokines were found in the entire study population.

IP−10, a member of the CXC chemokine family, mediates chemotaxis, apoptosis and angiostasis upon binding to the CXCR3 receptor [28]. It has been implicated in autoimmune diseases [29], infections [30] and tumours [31]. Studies on IP−10 and depressive symptoms show various and conflicting results. Elevated IP−10 levels have been observed in depressed adolescents compared to healthy controls [32], while no difference was reported in a geriatric population [33]. Consistent with our finding, IP−10 was lower in depressed patients with Takayasus arteritis compared to their non−depressed counterparts [34]. Studies investigating IP−10 as a potential prognostic therapeutic marker show mixed outcomes. While pharmaceutical antidepressant treatment did not have lasting impact on IP−10 levels [32,35], levels increased with symptom improvement following electroconvulsive treatment [36]. These results, coupled with our findings, suggest an inverse relationship between IP−10 levels and depressive symptoms, particularly in OMT patient.

GM−CSF drives the differentiation of progenitor cells into granulocytes and macrophages [37] and influences mature, differentiated cells [38] and non−hematopoietic cells [39]. It is implicated in inflammatory− and autoimmune disorders [40], and used in cancer immunotherapy [41]. Studies exploring the inflammatory hypothesis of depression, have shown higher baseline GM−CSF levels in depressed patients compared to healthy controls [42–44]. However, antidepressant treatment did not alter GM−CSF levels or have any association with clinical improvement [42–46]. The number of studies investigating the association between cytokines, depressive symptoms and OMT are scarce. One study measured levels of TNF, IL−1β and IL−2 [14]. As the patients transitioned from opioid addiction to treatment with either methadone or buprenorphine, levels of cytokines increased with subsequent lowering of depressive symptoms scores [14]. The negative association is consistent with our finding, albeit with different cytokines. These findings highlight the complexity of GM−CSF’s role in depression and suggest that its relationship with symptoms may vary depending on the context, such as OMT.

While the number of studies linking GM−CSF to phobic anxiety are limited, associations with generalized anxiety disorders (GAD) have been documented, showing higher GM−CSF levels in GAD−patients compared to healthy controls [47]. This aligns with findings investigating GAD with other cytokines [48]. Our study is the first to suggest a potential relationship between GM−CSF and phobic anxiety in opioid dependent patients.

Both IP−10 and GM−CSF have pro−inflammatory properties [29,49], and the negative associations in a group of OMT patients with both infection and substance use as inflammatory drivers, were unexpected. This suggests that opioids may modulate these relationships in an immune−dampening way. The immunomodulatory effects of opioids can vary depending on the specific opioid [50], and might be explained by the various opioids’ ability to activate different signalling pathways upon binding to the μ−opioid receptor on the cells of the immune system [51]. Moreover, one opioid does not exert the same effect on the level of the same cytokine [14,52], suggesting that the immunomodulatory properties of opioids are subject to the milieu, such as concomitant infection [53] and duration of opioid treatment [13,23]. Additionally, opioids exert their immunomodulatory effects not only directly on cells of the immune system, but also indirectly through the hypothalamic−pituitary−adrenal axis [54,55]. Lastly, cytokines act in an autocrine and paracrine manner, complicating their interactions with opioids [56].

Limitations

Our study recorded ongoing psychotropic medication dichotomously (yes/no) without accounting for the type or dosage. Different psychotropic drugs have various effects on cytokines [57,58], which may have influenced our results. However, the prevalence of ongoing psychotropic medication did not differ significantly between the non−OMT and OMT groups. The HCV−genotype was not taken into consideration, and HIV status was unknown, both of which are known to affect cytokine levels [59,60]. Many recordings were missing for smoking [61] and on−going administration of anti−inflammatory drugs [62] and might have been contributed to the results. The lack of a healthy control group limits comparisons and contextualization of cytokine levels. The cross−sectional design precludes examination of longitudinal changes in cytokines levels linked to duration of opioid treatment, which may influence cytokine−symptom relationships [13,23]. Because of the number of cytokines analysed, there is a risk of type 1 error. Therefore, these data should be regarded as exploratory and hypothesis−generating, rather than definitive.

Strengths

All interviews were conducted by the first author, reducing inter−rater variability [5]. The examination of 27 cytokines provides a comprehensive view of potential immune dysregulation, accounting for the complex interaction between cytokines [56]. We recorded data concerning illicit drug use during the last 30 days prior to blood sampling and found that there were no inter−group differences. This reduces the probability that polydrug use had a role in the differing cytokine levels [63–65]. The study design accounted for the high prevalence of HCV among OMT−patients [11], reducing confounding effects from infection. All cytokine measurements were performed by the same experienced technician using a standardized protocol, and blood samples were collected at consistent times, minimizing diurnal variation [66].

Conclusions

This study investigated the association between 27 cytokines and self−reported psychiatric symptom clusters in a population with chronic hepatitis C virus infection. We found that only in the opioid−dependent group were IP−10 and GM−CSF negatively associated with depression, while GM−CSF was negatively associated with phobic anxiety. The negative associations may indicate that long−term opioid administration dampens the immunological response to psychiatric symptoms.

Supporting information

S1 Table.

Median cytokine values of healthy controls.

https://doi.org/10.1371/journal.pone.0324365.s001

(DOCX)

Acknowledgments

We sincerely thank Elin Malin for her invaluable contribution to the patient inclusion process. Our gratitude extends to Judith Krey Ludviksen and Evy Eide for conducting the cytokine analyses, and to Lill Magna Lekanger for her meticulous SPSS data preparation. Your efforts were essential to the success of this study.

References

  1. 1. Min X, Wang G, Cui Y, Meng P, Hu X, Liu S, et al. Association between inflammatory cytokines and symptoms of major depressive disorder in adults. Front Immunol. 2023;14:1110775. pmid:36860860
  2. 2. Hou R, Garner M, Holmes C, Osmond C, Teeling J, Lau L, et al. Peripheral inflammatory cytokines and immune balance in generalised anxiety disorder: case−controlled study. Brain Behav Immun. 2017;62:212–8. pmid:28161475
  3. 3. Brodeur KE, Liu M, Ibanez D, de Groot MJ, Chen L, Du Y, et al. Elevation of IL−17 cytokines distinguishes kawasaki disease from other pediatric inflammatory disorders. Arthritis Rheumatol. 2024;76(2):285–92.
  4. 4. Hyman SE. Grouping diagnoses of mental disorders by their common risk factors. Am J Psychiatry. 2011;168(1):1–3. pmid:21205810
  5. 5. Oiesvold T, Nivison M, Hansen V, Skre I, Ostensen L, Sørgaard KW. Diagnosing comorbidity in psychiatric hospital: challenging the validity of administrative registers. BMC Psychiatry. 2013;13:13. pmid:23297686
  6. 6. Soloey−Nilsen H, Nygaard−Odeh K, Kristiansen MG, Kvig EI, Brekke OL, Mollnes TE, et al. Transdiagnostic associations between anger hostility and chemokine interferon−gamma inducible protein 10. Clin Psychopharmacol Neurosci. 2024;22(2):285–94.
  7. 7. Freed RD, Mehra LM, Laor D, Patel M, Alonso CM, Kim−Schulze S, et al. Anhedonia as a clinical correlate of inflammation in adolescents across psychiatric conditions. World J Biol Psychiatry. 2019;20(9):712–22. pmid:29843560
  8. 8. Groven N, Fors EA, Reitan SK. Patients with fibromyalgia and chronic fatigue syndrome show increased hsCRP compared to healthy controls. Brain Behav Immun. 2019;81:172–7. pmid:31176728
  9. 9. Herniman SE, Wood SJ, Khandaker G, Dazzan P, Pariante CM, Barnes NM, et al. Network analysis of inflammation and symptoms in recent onset schizophrenia and the influence of minocycline during a clinical trial. Transl Psychiatry. 2023;13(1):297. pmid:37723153
  10. 10. Larsen JB, Stunes AK, Vaaler A, Reitan SK. Cytokines in agitated and non−agitated patients admitted to an acute psychiatric department: a cross−sectional study. PLoS One. 2019;14(9):e0222242. pmid:31509578
  11. 11. Schulte B, Schmidt CS, Strada L, Rosenkranz M, Schäfer I, Verthein U, et al. Hepatitis C virus prevalence and incidence in a large nationwide sample of patients in opioid substitution treatment in germany: a prospective cohort study. Clin Infect Dis. 2020;70(10):2199–205. pmid:31631215
  12. 12. Lu R−B, Wang T−Y, Lee S−Y, Chen S−L, Chang Y−H, See Chen P, et al. Correlation between interleukin−6 levels and methadone maintenance therapy outcomes. Drug Alcohol Depend. 2019;204:107516. pmid:31513981
  13. 13. Chan Y−Y, Yang S−N, Lin J−C, Chang J−L, Lin J−G, Lo W−Y. Inflammatory response in heroin addicts undergoing methadone maintenance treatment. Psychiatry Res. 2015;226(1):230–4. pmid:25660662
  14. 14. Neri S, Bruno CM, Pulvirenti D, Malaguarnera M, Italiano C, Mauceri B, et al. Randomized clinical trial to compare the effects of methadone and buprenorphine on the immune system in drug abusers. Psychopharmacology (Berl). 2005;179(3):700–4. pmid:15806416
  15. 15. Capone F, Guerriero E, Colonna G, Maio P, Mangia A, Castello G, et al. Cytokinome profile evaluation in patients with hepatitis C virus infection. World J Gastroenterol. 2014;20(28):9261–9. pmid:25071319
  16. 16. Costantini S, Capone F, Guerriero E, Marfella R, Sorice A, Maio P, et al. Cytokinome profile of patients with type 2 diabetes and/or chronic hepatitis C infection. PLoS One. 2012;7(6):e39486. pmid:22745767
  17. 17. Costantini S, Capone F, Guerriero E, Maio P, Colonna G, Castello G. Serum cytokine levels as putative prognostic markers in the progression of chronic HCV hepatitis to cirrhosis. Eur Cytokine Netw. 2010;21(4):251–6. pmid:21081303
  18. 18. Faccioli J, Nardelli S, Gioia S, Riggio O, Ridola L. Neurological and psychiatric effects of hepatitis C virus infection. World J Gastroenterol. 2021;27(29):4846–61. pmid:34447230
  19. 19. Kidorf M, King VL, Peirce J, Gandotra N, Ghazarian S, Brooner RK. Substance use and response to psychiatric treatment in methadone−treated outpatients with comorbid psychiatric disorder. J Subst Abuse Treat. 2015;51:64–9. pmid:25468006
  20. 20. Al−Khazraji A, Patel I, Saleh M, Ashraf A, Lieber J, Malik R. Identifying barriers to the treatment of chronic hepatitis C infection. Dig Dis. 2020;38(1):46–52. pmid:31422405
  21. 21. Havard A, Teesson M, Darke S, Ross J. Depression among heroin users: 12−Month outcomes from the Australian Treatment Outcome Study (ATOS). J Subst Abuse Treat. 2006;30(4):355–62. pmid:16716851
  22. 22. Miller AH, Haroon E, Raison CL, Felger JC. Cytokine targets in the brain: impact on neurotransmitters and neurocircuits. Depress Anxiety. 2013;30(4):297–306. pmid:23468190
  23. 23. Nygaard−Odeh K, Soloy−Nilsen H, Kristiansen MG, Brekke OL, Mollnes TE, Berk M. Cytokines in hepatitis C−infected patients with or without opioid maintenance therapy. Acta Neuropsychiatr. 2024;:1–7.
  24. 24. Dinning WD, Evans RG. Discriminant and convergent validity of the SCL−90 in psychiatric inpatients. J Pers Assess. 1977;41(3):304–10. pmid:16367234
  25. 25. Preti A, Carta MG, Petretto DR. Factor structure models of the SCL−90−R: Replicability across community samples of adolescents. Psychiatry Res. 2019;272:491–8. pmid:30611969
  26. 26. Ahmad−Nielsen EB, Andersen NG, Andersen K, Nielsen AS, Mellentin AI. The degree of multidimensional severity of alcohol use disorder among treatment−seeking patients: is there an additive effect of parental alcohol use disorder? Eur Addict Res. 2019;25(4):191–7. pmid:30999312
  27. 27. Hennø LT, Storjord E, Christiansen D, Bergseth G, Ludviksen JK, Fure H, et al. Effect of the anticoagulant, storage time and temperature of blood samples on the concentrations of 27 multiplex assayed cytokines − Consequences for defining reference values in healthy humans. Cytokine. 2017;97:86–95. pmid:28595117
  28. 28. Liu M, Guo S, Hibbert JM, Jain V, Singh N, Wilson NO, et al. CXCL10/IP−10 in infectious diseases pathogenesis and potential therapeutic implications. Cytokine Growth Factor Rev. 2011;22(3):121–30. pmid:21802343
  29. 29. Torres−Vázquez J, Uriel Vázquez−Medina M, Comoto−Santacruz DA, Pradillo−Macias ME, Muñoz−Monroy OE, Martínez−Cuazitl A. Relationship of IP−10 gene expression to systemic lupus erythematosus activity. Reumatol Clin (Engl Ed). 2022;18(2):91–3. pmid:34090843
  30. 30. Strzelak A, Komorowska−Piotrowska A, Borowa A, Krasińska M, Feleszko W, Kulus M. IP−10 for the diagnosis and treatment monitoring of tuberculosis in children. Diagnostics (Basel). 2024;14(2):177. pmid:38248054
  31. 31. Li Y, Wang C, Yin X, Jiang L, Li X, Yang J. Profile and clinical significance of interferon gamma−inducible protein−10 (IP−10) and its receptor in patients with hepatocellular carcinoma. J Cancer Res Clin Oncol. 2023;149(16):14879–88. pmid:37599316
  32. 32. de la Peña FR, Cruz−Fuentes C, Palacios L, Girón−Pérez MI, Medina−Rivero E, Ponce−Regalado MD, et al. Serum levels of chemokines in adolescents with major depression treated with fluoxetine. World J Psychiatry. 2020;10(8):175–86. pmid:32874955
  33. 33. Fanelli G, Benedetti F, Wang S−M, Lee S−J, Jun T−Y, Masand PS, et al. Reduced CXCL1/GRO chemokine plasma levels are a possible biomarker of elderly depression. J Affect Disord. 2019;249:410–7. pmid:30826620
  34. 34. Zhang Y, Yang S, Fan A, Du J, Gao N, Pan L, et al. Decreased IL−4 is the risk factor of depression in patients with Takayasu arteritis. Front Med (Lausanne). 2024;11:1337206. pmid:38426163
  35. 35. Milenkovic VM, Sarubin N, Hilbert S, Baghai TC, Stöffler F, Lima−Ojeda JM, et al. Macrophage−derived chemokine: a putative marker of pharmacological therapy response in major depression? Neuroimmunomodulation. 2017;24(2):106–12. pmid:28898872
  36. 36. Mindt S, Neumaier M, Hoyer C, Sartorius A, Kranaster L. Cytokine−mediated cellular immune activation in electroconvulsive therapy: a CSF study in patients with treatment−resistant depression. World J Biol Psychiatry. 2020;21(2):139–47. pmid:31081432
  37. 37. Burgess AW, Metcalf D. The nature and action of granulocyte−macrophage colony stimulating factors. Blood. 1980;56(6):947–58. pmid:7002232
  38. 38. Yong K, Rowles P, Patterson K, Linch D. Granulocyte−macrophage colony−stimulating factor induces neutrophil adhesion to pulmonary vascular endothelium in vivo: role of beta 2 integrins. Blood. 1992;80(6):1565–75.
  39. 39. Robson M, Kucukcelebi A, Carp SS, Hayward PG, Hui PS, Cowan WT, et al. Effects of granulocyte−macrophage colony−stimulating factor on wound contraction. Eur J Clin Microbiol Infect Dis. 1994;13(Suppl 2):S41–6. pmid:7875152
  40. 40. Kitamura T, Tanaka N, Watanabe J, Uchida , Kanegasaki S, Yamada Y, et al. Idiopathic pulmonary alveolar proteinosis as an autoimmune disease with neutralizing antibody against granulocyte/macrophage colony−stimulating factor. J Exp Med. 1999;190(6):875–80.
  41. 41. Dranoff G, Jaffee E, Lazenby A, Golumbek P, Levitsky H, Brose K, et al. Vaccination with irradiated tumor cells engineered to secrete murine granulocyte−macrophage colony−stimulating factor stimulates potent, specific, and long−lasting anti−tumor immunity. Proc Natl Acad Sci U S A. 1993;90(8):3539–43.
  42. 42. Zhou Y, Wang C, Lan X, Li H, Chao Z, Ning Y. Plasma inflammatory cytokines and treatment−resistant depression with comorbid pain: improvement by ketamine. J Neuroinflammation. 2021;18(1):200. pmid:34526064
  43. 43. Kiraly DD, Horn SR, Van Dam NT, Costi S, Schwartz J, Kim−Schulze S, et al. Altered peripheral immune profiles in treatment−resistant depression: response to ketamine and prediction of treatment outcome. Transl Psychiatry. 2017;7(3):e1065. pmid:28323284
  44. 44. Chen C−Y, Yeh Y−W, Kuo S−C, Liang C−S, Ho P−S, Huang C−C, et al. Differences in immunomodulatory properties between venlafaxine and paroxetine in patients with major depressive disorder. Psychoneuroendocrinology. 2018;87:108–18. pmid:29055264
  45. 45. Atake K, Hori H, Kageyama Y, Koshikawa Y, Igata R, Tominaga H, et al. Pre−treatment plasma cytokine levels as potential predictors of short−term remission of depression. World J Biol Psychiatry. 2022;23(10):785–93. pmid:35193470
  46. 46. Ricken R, Busche M, Schlattmann P, Himmerich H, Bopp S, Bschor T, et al. Cytokine serum levels remain unchanged during lithium augmentation of antidepressants in major depression. J Psychiatr Res. 2018;96:203–8. pmid:29101798
  47. 47. Tang Z, Ye G, Chen X, Pan M, Fu J, Fu T, et al. Peripheral proinflammatory cytokines in Chinese patients with generalised anxiety disorder. J Affect Disord. 2018;225:593–8. pmid:28886500
  48. 48. Hou R, Garner M, Holmes C, Osmond C, Teeling J, Lau L, et al. Peripheral inflammatory cytokines and immune balance in Generalised Anxiety Disorder: case−controlled study. Brain Behav Immun. 2017;62:212–8. pmid:28161475
  49. 49. Bhattacharya P, Budnick I, Singh M, Thiruppathi M, Alharshawi K, Elshabrawy H, et al. Dual role of GM−CSF as a pro−inflammatory and a regulatory cytokine: implications for immune therapy. J Interferon Cytokine Res. 2015;35(8):585–99. pmid:25803788
  50. 50. Franchi S, Moschetti G, Amodeo G, Sacerdote P. Do all opioid drugs share the same immunomodulatory properties? A review from animal and human studies. Front Immunol. 2019;10:2914.
  51. 51. Borner C, Lanciotti S, Koch T, Hollt V, Kraus J. mu opioid receptor agonist−selective regulation of interleukin−4 in T lymphocytes. J Neuroimmunol. 2013;263(1–2):35–42.
  52. 52. Sacerdote P, Franchi S, Gerra G, Leccese V, Panerai AE, Somaini L. Buprenorphine and methadone maintenance treatment of heroin addicts preserves immune function. Brain Behav Immun. 2008;22(4):606–13. pmid:18294814
  53. 53. Beck M, Mirmohammadsadegh A, Franz B, Blanke J, Hengge UR. Opioid receptors on white blood cells: effect of HIV infection and methadone treatment. Pain. 2002;98(1–2):187–94. pmid:12098631
  54. 54. Mohamed Khair A. Long−term opiate therapy−induced secondary adrenal insufficiency: a distinct differential diagnosis that should be considered. Cureus. 2023;15(12):e49955.
  55. 55. Policola C, Stokes V, Karavitaki N, Grossman A. Adrenal insufficiency in acute oral opiate therapy. Endocrinol Diabetes Metab Case Rep. 2014;2014:130071. pmid:24683482
  56. 56. Codarri L, Gyülvészi G, Tosevski V, Hesske L, Fontana A, Magnenat L. RORγt drives production of the cytokine GM−CSF in helper T cells, which is essential for the effector phase of autoimmune neuroinflammation. Nature Immunol. 2011;12(6):560–7.
  57. 57. Hinze−Selch D, Deuschle M, Weber B, Heuser I, Pollmächer T. Effect of coadministration of clozapine and fluvoxamine versus clozapine monotherapy on blood cell counts, plasma levels of cytokines and body weight. Psychopharmacology (Berl). 2000;149(2):163–9. pmid:10805611
  58. 58. Hori H, Yoshimura R, Yamada Y, Ikenouchi A, Mitoma M, Ida Y, et al. Effects of olanzapine on plasma levels of catecholamine metabolites, cytokines, and brain−derived neurotrophic factor in schizophrenic patients. Int Clin Psychopharmacol. 2007;22(1):21–7. pmid:17159456
  59. 59. Baharlou R, Romani B, Kiani SJ, Sadeghi K, Shadmand E, Fazel H, et al. Genotype−related variations in proinflammatory and regulatory cytokine levels in treated and treatment−naive HCV−infected patients. Med Microbiol Immunol. 2018;207(1):65–74. pmid:29147974
  60. 60. Liu S−W, Liu Y−L, Hwang L−L, Wang S−C, Kuo H−W, Wu S−L, et al. Chemokine IP−10 is correlated with cardiac responses and status of infection with HIV and HCV in methadone maintenance patients. Int J Cardiol. 2015;194:36–8. pmid:26011262
  61. 61. D’Esposito V, Di Tolla MF, Lecce M, Cavalli F, Libutti M, Misso S, et al. Lifestyle and dietary habits affect plasma levels of specific cytokines in healthy subjects. Front Nutr. 2022;9:913176. pmid:35811952
  62. 62. Koj A. Termination of acute−phase response: role of some cytokines and anti−inflammatory drugs. Gen Pharmacol. 1998;31(1):9–18.
  63. 63. Araos P, Pedraz M, Serrano A, Lucena M, Barrios V, García−Marchena N. Plasma profile of pro−inflammatory cytokines and chemokines in cocaine users under outpatient treatment: influence of cocaine symptom severity and psychiatric co−morbidity. Addict Biol. 2015;20(4):756–72.
  64. 64. Ajrawat P, Yang Y, Wasilewski E, Leroux T, Ladha KS, Bhatia A, et al. Medical cannabis use and inflammatory cytokines and chemokines among adult chronic pain patients. Cannabis Cannabinoid Res. 2024;9(1):267–81. pmid:36342776
  65. 65. Qin L, He J, Hanes RN, Pluzarev O, Hong JS, Crews FT. Increased systemic and brain cytokine production and neuroinflammation by endotoxin following ethanol treatment. J Neuroinflammation. 2008;5:10.
  66. 66. Liu J, Malkani G, Shi X, Meyer M, Cunningham−Runddles S, Ma X, et al. The circadian clock Period 2 gene regulates gamma interferon production of NK cells in host response to lipopolysaccharide−induced endotoxic shock. Infect Immun. 2006;74(8):4750–6.