RESEARCH ARTICLE Inflammatory mediators and dual depression: Potential biomarkers in plasma of primary and substance-induced major depression in cocaine and alcohol use disorders Nuria Garcı́a-Marchena1,2☯, Marta Barrera ID2☯, Joan Ignasi Mestre-Pintó2,3, Pedro Araos1,4, Antonia Serrano1, Clara Pérez-Mañá5,6, Esther Papaseit5,6, Francina Fonseca2,6,7, Juan Jesús Ruiz8, Fernando Rodrı́guez de Fonseca1, Magı́ Farré5,6, a1111111111 Francisco Javier Pavón1*, Marta Torrens2,6,7* a1111111111 1 Unidad Gestión Clı́nica de Salud Mental, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital a1111111111 Regional Universitario de Málaga/Universidad de Málaga, Málaga, Spain, 2 Addiction Research Group, a1111111111 Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, 3 Universitat Pompeu Fabra (UPF), a1111111111 Barcelona, Spain, 4 Departamento de Psicobiologı́a y Metodologı́a de las Ciencias del Comportamiento, Instituto de Investigación Biomédica de Málaga (IBIMA), Facultad de Psicologı́a, Universidad de Málaga (UMA), Málaga, Spain, 5 Hospital Universitari Germans Trias i Pujol (IGTP), Badalona, Spain, 6 Universitat Autònoma de Barcelona (UAB), Barcelona, Spain, 7 Institut de Neuropsiquiatria i Addiccions (INAD), Barcelona, Spain, 8 Centro Provincial de Drogodependencias, Diputación Provincial de Málaga, Málaga, Spain OPEN ACCESS Citation: Garcı́a-Marchena N, Barrera M, Mestre- ☯ These authors contributed equally to this work. Pintó JI, Araos P, Serrano A, Pérez-Mañá C, et al. * javier.pavon@ibima.eu (FJP); mtorrens@parcdesalutmar.cat (MT) (2019) Inflammatory mediators and dual depression: Potential biomarkers in plasma of primary and substance-induced major depression Abstract in cocaine and alcohol use disorders. PLoS ONE 14 (3): e0213791. https://doi.org/10.1371/journal. Major depressive disorder (MDD) is the most prevalent comorbid mental disorder among pone.0213791 people with substance use disorders. The MDD can be both primary and substance-induced Editor: Daimei Sasayama, Shinshu University and its accurate diagnosis represents a challenge for clinical practice and treatment School of Medicine, JAPAN response. Recent studies reported alterations in the circulating expression of inflammatory Received: September 25, 2018 mediators in patients with psychiatric disorders, including those related to substance use. Accepted: February 28, 2019 The aim of the study was to explore TNF-α, IL-1β, CXCL12, CCL2, CCL11 (eotaxin-1) and Published: March 14, 2019 CX3CL1 (fractalkine) as potential biomarkers to identify comorbid MDD and to distinguish primary MDD from substance-induced MDD in patients with substance disorders. Patients Copyright: © 2019 Garcı́a-Marchena et al. This is an open access article distributed under the terms diagnosed with cocaine (CUD, n = 64) or alcohol (AUD, n = 65) use disorders with/without of the Creative Commons Attribution License, MDD were recruited from outpatient treatment programs [CUD/non-MDD (n = 31); CUD/pri- which permits unrestricted use, distribution, and mary MDD (n = 18); CUD/cocaine-induced MDD (N = 15); AUD/non-MDD (n = 27); AUD/pri- reproduction in any medium, provided the original author and source are credited. mary MDD (n = 16) and AUD/alcohol-induced MDD (n = 22)]. Sixty-two healthy subjects were also recruited as control group. Substance and mental disorders were assessed Data Availability Statement: All relevant data are within the manuscript and its Supporting according to “Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revi- Information files. sion” (DSM-IV-TR) and a blood sample was collected for determinations in the plasma. The Funding: This work was supported by RETICS Red cocaine group showed lower TNF-α (p<0.05) and CCL11 (p<0.05), and higher IL-1β de Trastornos Adictivos (RD12/0028/0021, RD12/ (p<0.01) concentrations than the control group. In contrast, the alcohol group showed higher 0028/0009, RD16/0017/0001, RD16/0017/0010, IL-1β (p<0.01) and lower CXCL12 (p<0.01) concentrations than the control group. Regard- and RD 16/0017/003) funded by Instituto de Salud ing MDD, we only observed alterations in the cocaine group. Thus, CUD/MDD patients Carlos III (ISC-III) and European Regional and European Regional Development Funds-European showed lower IL-1β (p<0.05), CXCL12 (p<0.05) and CCL11 (p<0.05), and higher CXC3CL1 PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 1 / 17 Inflammatory mediators as biomarkers for dual depression Union (ERDF-EU); Research projects funded by (p<0.05) concentrations than CUD/non-MDD patients. Moreover, while CUD/primary MDD Ministerio de Economı́a y Competitividad and ISC- patients showed higher CCL11 (p<0.01) concentrations than both CUD/non-MDD and III (PI09/02121; PI12/01838, PI16/01698, PI16/ 01953); Research project funded by Ministerio de CUD/cocaine-induced MDD patients, CUD/cocaine-induced MDD patients showed lower Sanidad, Servicios Sociales e Igualdad and Plan CXCL12 (p<0.05) concentrations than CUD/non-MDD patients. Finally, a logistic regression Nacional sobre Drogas (043/2017 and 2012I054); model in the cocaine group identified CXCL12, CCL11 and sex to distinguish primary MDD Research project funded by Consejerı́a de from cocaine-induced MDD providing a high discriminatory power. The present data sug- Economı́a, Innovación y Ciencia, Junta de Andalucı́a and ERDF-EU (CTS-433); Suport Grups gest an association between changes in inflammatory mediators and the diagnosis of pri- de Recerca AGAUR Gencat 2017 SGR 316 and mary and substance-induced MDD, namely in CUD patients. SGR530). NGM received a training research grant funded by Consejerı́a de Salud y Bienestar Social, Junta de Andalucı́a (EF-0202-2017); AS and FJP hold a Miguel Servet research contract funded by ISC-III and ERDF-EU (CP14/00173 and CP14/ 00212, respectively); PA received a Plan Propio Introduction grant from Universidad de Málaga (Incorporación Doctores CI-17-415). These funding sources had Major depressive disorder (MDD) is the most prevalent comorbid mental condition in sub- no further role in study design; in the collection, jects with substance use disorders (SUD) and closely related to their poor prognosis. As analysis and interpretation of data; in the writing of expected, due to their marked clinical severity, these dual diagnosed patients also present con- the report; and in the decision to submit the paper siderable psychosocial acuteness and make greater use of health resources, including emer- for publication. gency services and psychiatric admissions [1–3]. The identification of MDD in substance Competing interests: The authors have declared users, however, is often complicated because of its inherent characteristics, that is to say, the that no competing interests exist. acute and chronic effects related to substance consumption/withdrawal can mimic depression. Currently, diagnosis is syndromic and established with clinical criteria using “Diagnostic and Statistical Manual of Mental Disorders 4th Edition Text Revision” (DSM-IV-TR), “DSM 5th Edition” (DSM-5) [4–5] or “International Classification of Diseases and Related Health Prob- lems 10th Edition” (ICD-10) [6], the availability of specific biomarkers to facilitate the diagno- sis of MDD in SUD are therefore required [7]. Among others, modulation of inflammatory mediators such as cytokines and chemokines has been recognized as a possible target. A grow- ing body of literature has established that cytokines can play a critical role in the pathogenesis of both MDD [8–9] and SUD [10–13]. Preclinical and clinical studies in MDD and SUD have reported alterations in the immune system and dysregulation of the hypothalamic-pituitary- adrenal axis (HPA) [14–15]. These changes result in a chronic inflammatory response and a disruption of brain integrity and homeostasis as consequence of neuroinflammation [16]. In the brain, glial activation results in the release of cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β), inflammatory mediators that influence the central immune system, modulate neural activity, and regulate the HPA axis [14, 17–18]. Chemokines, small chemoattractant proteins, also act as modulators in neuronal transmission and partici- pate in the communication between glia and neurons [8, 19]. An increasing quantity of research has focused on the leading role of inflammatory media- tors in the pathophysiology of a variety of psychiatric problems including bipolar [20] and post-traumatic stress disorders [21], schizophrenia [22], and MDD [23]. With respect to MDD, patients show increased serum levels of TNF-α and it has been reported that elevated serum levels of CCL11 are associated with suicidal ideations in such patients [24]. Moreover, there are studies suggesting that inflammatory markers have the potential to predict antide- pressant treatment outcomes [25–26]. Regarding SUD, preclinical models of cocaine exposure have described that stromal derived factor-1/SDF-1 (CXCL12) participates in the modulation of cocaine-induced behavioral effects such as locomotion and stereotypes [27]. Previous stud- ies in abstinent subjects with CUD found a positive correlation between plasma levels of IL-1β, fractalkine (CX3CL1), and CXCL12 with a number of DSM-IV-TR criteria for CUD, and with increased prevalence of comorbid psychiatric disorders relative to those users with no other PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 2 / 17 Inflammatory mediators as biomarkers for dual depression psychiatric diagnosis [11]. With respect to alcohol, in preclinical models it has been reported to disrupt the cytokine profile during neuronal differentiation and influence adult neurogen- esis, thus providing potential mechanisms to understand alcohol effects on cerebral develop- ment [28]. Other clinical studies have indicated that alcohol dependence is characterized by enhanced glial IL-1β in the cerebral cortex [29] and elevated chemokine monocyte chemotac- tic protein 1/MCP-1 (CCL2) in different brain regions [10]. Furthermore, a recent study in patients diagnosed with alcohol use disorders (AUD) showed lower plasma levels of CXCL12 and CX3CL1 than in controls. Additionally, AUD patients with comorbid depressive and/or anxiety disorders have been reported to have alterations in the plasma levels of the chemokine eotaxin-1 (CCL11) [30]. Because the peripheral alterations of inflammatory molecules may modulate and/or reflect neuroinflammatory events in the brain, certain cytokines and chemokines could be reliable candidates to detect MDD in SUD patients. Changes in peripheral inflammatory mediators might thus contribute to improving diagnosis, treatment strategy, and medical approach for these psychiatric patients [7, 31]. The aim of the present study is to explore whether relevant proinflammatory cytokines such as TNF-α, and IL-1β, and chemokines such as CXCL12, CCL2, CCL11, and CX3CL1 could be potential biomarkers for the detection of co-occurrence of MDD in patients with CUD and/or AUD, and to distinguish primary MDD from sub- stance-induced MDD. Materials and methods Subjects The present cross-sectional study was conducted in white Caucasian population, and included SUD [cocaine (CUD) or alcohol (AUD)] patients diagnosed with/without comorbid MDD, and healthy control subjects. Patients were recruited at the addiction treatment facilities of the Instituto de Neuropsiquiatría y Adicciones-Parc Salut Mar (Barcelona, Spain), Hospital Univer- sitario 12 de Octubre (Madrid, Spain), and Centro Provincial de Drogodependencias (Málaga, Spain). Control participants were included from data bases of healthy subjects willing to par- ticipate in medical research projects at the Unidad de Farmacología del IMIM-Hospital del Mar Research Institute (Barcelona, Spain), Hospital Universitario 12 de Octubre (Madrid, Spain), and Hospital Regional Universitario de Málaga (Málaga, Spain). Cocaine (n = 64) and alcohol (n = 65) groups were classified into six different subgroups: CUD with no MDD (CUD/non-MDD, n = 31), CUD with primary MDD (CUD/primary MDD, n = 18); CUD with cocaine-induced MDD (CUD/cocaine-induced MDD, n = 15), AUD with no MDD (AUD/non-MDD, n = 27), AUD with primary MDD (AUD/primary MDD, n = 16); AUD with alcohol-induced MDD (AUD/alcohol-induced MDD, n = 22). All the subjects met the inclusion criteria, which were: both genders, age � 18 years old up to 65 years of age and at least 4 weeks of abstinence in the case of subjects with SUD. Those patients who were diagnosed with MDD had to be in remission and “Hamilton Depression Rating Scale” (HDRS) scores had to be lower than 6. We considered 4 weeks of abstinence (SUD) and remission phase (MDD) because patients have to be as pathophysiologically stable as possible for clinical assessments and biochemical determinations. The exclusion criteria included: cognitive or language limitations that precluded evaluations and pregnant or breast- feeding women. SUD patients suffering from any psychiatric disorder in Axis I (DSM-IV-TR) [4] other than MDD and/or CUD/AUD (except from nicotine use disorder) were excluded. In the control group, participants with psychiatric disorders in Axis I (DSM-IV-TR), SUD (except nicotine use disorder) or family history of MDD were also excluded. Other excluding factors for all participants were: use of anti-inflammatory drugs or MAOI’s, personal history of PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 3 / 17 Inflammatory mediators as biomarkers for dual depression cancer; major surgery within 6 months prior to the study; long-term inflammatory disease; any chronic illnesses that could interfere in the study such as cardiovascular, respiratory, renal, hepatic, endocrine, gastro-intestinal, hematological, neurological diseases; and infectious dis- eases (including HIV, hepatitis B, and hepatitis C). Clinical assessments and blood/urine anal- yses were performed to assure these participation criteria. Ethics statements Written informed consents were obtained from each participant after a complete description of the study. All the participants had the opportunity to discuss any questions or issues. The study and protocols for recruitment were approved by the Ethics Committee of the Hospital Regional Universitario de Málaga (CP14/00173, CP14/00212 and PI13/02261) and by the Research Ethical Committee from the CEIC-Parc de Salut Mar (2012/4903/I; 2012/4751/I; 2009/3494/I) in accordance with the Ethical Principles for Medical Research Involving Human Subjects adopted in the Declaration of Helsinki by the World Medical Association (64th WMA General Assembly, Fortaleza, Brazil, October 2013), Recommendation No. R (97) 5 of the Committee of Ministers to Member States on the Protection of Medical Data (1997), and Spanish data protection act (Ley Orgánica 15/1999 de Protección de Datos, LOPD). All collected data were given code numbers in order to maintain privacy and confidentiality. Clinical assessments Substance and non-substance use disorders were diagnosed according to the DSM-IV-TR cri- teria [4] using the Spanish version of the “Psychiatric Research Interview for Substance and Mental Disorders” (PRISM) [4, 32]. PRISM is a semi-structured interview that has demon- strated good psychometric properties in terms of test-retest reliability, inter-rater reliability, and validity for primary MDD and substance-induced MDD, with kappa ranging from 0.66 and 0.75 [32–33]. All the interviews were performed by trained and experienced psychologists. Collection of plasma samples Blood samples were obtained in the morning after fasting for 8–12 h by experienced nurses. Participants were comfortably seated in a chair for 5 min before the blood draw, and were asked whether they had allergies, phobias or a history of vaso-vagal response during previous injections or blood draws. The participants could be placed in the supine position by adjusting the chair (backrest) if necessary. After the blood draw, participants were invited for breakfast prior to the psychiatric interviews. Venous blood was extracted into 10 mL K2 EDTA tubes (BD, Franklin Lakes, NJ, USA) and immediately processed to obtain plasma. Blood samples were centrifuged at 2,200 x g for 15 min (4˚C) and individually assayed to detect infectious diseases by 3 commercial rapid tests for HIV, hepatitis B, and hepatitis C (Strasbourg, Cedex, France). Finally, plasma samples were individually characterized, registered, and stored at -80˚C until further analyses. Multiplex immunoassays Proinflammatory cytokines and chemokines were chosen based on previous studies regarding relevant inflammatory mediators in psychiatric disorders and SUD using a Luminex Platform for quantification [11, 13, 34]. A Bio-Plex Suspension Array System 200 (Bio-Rad Laboratories, Hercules, CA, USA) was used to quantify cytokine and chemokine concentrations in plasma with a ProcartaPlex Immunoassay panel and an appropriate Plasma Standard Diluent Kit (Invitrogen, ThermoFisher Scientific, Waltham, MA, USA). A human cytokine/chemokine PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 4 / 17 Inflammatory mediators as biomarkers for dual depression 6-plex panel was used to simultaneously detect the following analytes: TNF-α, IL-1β, CXCL12; CCL2, CCL11, and CX3CL1. The measurements of these inflammatory mediators in plasma were performed following the manufacturer´s instructions [11]. Raw data (mean fluorescence intensity) were analyzed using the Bio-Plex Manager Software 4.1 (Bio-Rad laboratories, Her- cules, CA, USA). Plasma concentrations were expressed as pg/mL. According to manufactur- er’s specifications (ProcartaPlex Human Cytokine/Chemokine Simplex Kit), assay sensitivity and standard curve range for each analyte were as follows: 0.4 pg/mL and 8.54–35,000 pg/mL for TNF-α (catalog #: EPX01A-10223-901); 0.2 pg/mL and 2.44–10,000 pg/mL for IL-1β (cata- log #: EPX01A-10224-901); 20.5 pg/mL and 17.1–70,000 pg/mL for CXCL12 (catalog #: EPX01A-12138-901); unknown and 37.5–23,300 pg/mL for CCL2 (catalog #: EPX01B-10281- 901); 1.4 pg/mL and 0.61–2,500 pg/mL for CCL11 (catalog #: EPX01B-12120-901); and 0.5 pg/ mL and 2.08–8,500 pg/mL for CX3CL1 (catalog #: EPX01A-12121-901). Inter-assay and intra- assay (samples in duplicate) coefficients of variation (%CV) were as follows: 7.1% and 6.5% for TNF-α; <5% and 8.9% for IL-1β; 7.8% and 9.0% for CCL11; 7.2% and 6.4% for CX3CL1; <5% and 9.8% for CCL2; and 5% and 10.7% for CXCL12. Statistical analysis All data in the tables are expressed as number and percentage of subjects [n (%)] or mean and standard deviation [mean (SD)]. The significance of differences in categorical and normal con- tinuous variables was determined using Fisher’s exact test (chi-square test) and Student’s t- test, respectively. One-way analysis of covariance (ANCOVA) was performed to assess the effects of indepen- dent factors (i.e., grouping variables such as types of SUD, diagnosis of MDD, and types of MDD) on the plasma concentrations of cytokines and chemokines (dependent variables), con- trolling for additional independent variables and covariates [e.g., sex, age, body mass index (BMI), and antidepressant medication]. The post hoc tests for multiple comparisons were per- formed using Sidak’s correction test. Prior to performing one-way ANCOVA, logarithm (10)- transformation for dependent variables was used to ensure statistical assumptions for positive skewed distributions. Binary logistic models were employed to distinguish between primary MDD and sub- stance-induced MDD for SUD patients using full models that included cytokine and chemo- kine concentrations, sex, age, and BMI, and adjusted models (variables were chosen using a backward stepwise approach). Receiver operating characteristic (ROC) analysis was performed to determine the accuracy of these models comparing the area under the curve (AUC) and rep- resentative cut-off scores for the distinction between types of MDD, which included estima- tions of sensitivity and specificity. Statistical analysis was carried out with the GraphPad Prism version 5.04 (GraphPad Soft- ware, San Diego, CA, USA), and IBM SPSS Statistical version 22 (IBM, Armonk, NY, USA). A p-value<0.05 was considered statistically significant. Results Sociodemographic and clinical characteristics of the sample Table 1 shows the sociodemographic characteristics and depression-related variables of 191 participants in this cross-sectional study who were divided into 3 groups: cocaine, alcohol and control groups. The mean age of the cocaine group was 37.6 years, and 23.4% were women. The most com- mon marital status was single, mainly in cocaine-induced MDD patients, with elementary edu- cation (51.6%). As compared with the control group, we observed significant differences in the PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 5 / 17 Inflammatory mediators as biomarkers for dual depression Table 1. Baseline sociodemographic and clinical characteristics of the study sample. VARIABLES CONTROL COCAINE GROUP (CUD) (n = 64) ALCOHOL GROUP (AUD) (n = 65) GROUP (n = 62) Non-MDD Primary MDD Cocaine-Induced Non-MDD Primary MDD Alcohol-Induced (n = 31) (n = 18) MDD (n = 15) (n = 27) (n = 16) MDD (n = 22) SOCIODEMOGRAPHIC VARIABLES Age [Mean (SD)] Years 34.82 (6.23) 36.52 (6.47) 40.89 (10.11) 35.67 (9.34) 46.78 (6.71) 47.31 (6.03) aaa 46.64 (8.77) aaa aaa BMI [Mean (SD)] Kg/m2 24.89 (3.49) 24.64 (3.05) 26.69 (5.75) 26.16 (4.23) 25.63 (3.87) 27.68 (4.58) a 25.23 (3.57) Sex [n (%)] Women 29 (46.8) 6 (19.4) a 5 (27.8) 4 (26.7) 7 (25.9) 5 (31.3) 10 (45.5) Men 33 (53.2) 25 (80.6) 13 (72.2) 11 (73.3) 20 (74.1) 11 (68.8) 12 (54.5) Marital Status [n Single 28 (45.2) 12 (38.7) 6 (33.3) aa 11 (73.3) 9 (33.3) 3 (18.8) 8 (36.4) (%)] Cohabiting 24 (38.7) 11 (35.5) 3 (16.7) 1 (6.7) 13 (48.1) 7 (43.8) 9 (40.9) Separated 6 (9.7) 8 (25.8) 9 (50.0) 3 (20.0) 5 (18.5) 5 (31.3) 4 (18.2) Widow/er 4 (6.5) - - - - 1 (6.3) 1 (4.5) Education [n (%)] Elementary 1 (1.6) 16 (51.6) aaa 10 (55.6) aaa 7 (46.7) aaa 10 (37.0) aaa 4 (25.0) aaa 6 (27.3) aaa Secondary 22 (35.5) 11 (35.5) 4 (22.2) 4 (26.7) 14 (51.9) 8 (50.0) 7 (31.8) University 39 (62.9) 4 (12.9) 4 (22.2) 4 (26.7) 3 (11.1) 4 (25.0) 9 (40.9) aa Occupation [n (%)] Employed 41 (66.1) 15 (48.4) 10 (55.6) 4 (26.7) 12 (44.4) 6 (37.5) 9 (40.9) Unemployed 21 (33.9) 16 (51.6) 8 (44.4) 11 (73.3) 15 (55.6) 10 (62.5) 13 (59.1) DEPRESSION-RELATED VARIABLES Age of onset [Mean Years - - 33.33 (11.30) 31.40 (8.63) - 42.31 (7.67) 39.29 (9.21) (SD)] Number of episodes [Mean (SD)] - - 2.44 (1.89) 4.20 (6.47) - 3.54 (5.13) 4.00 (5.66) c Age at last episode Years - - 38.61 (10.25) 31.50 (5.17) - 47.23 (6.07) 46.64 (8.77) [Mean (SD)] Remission [Mean Months - - 15.00 (19.88) 29.56 (35.64) - 6.38 (4.05) 14.24 (18.24) (SD)] Current antidepressant treatment - - 10 (55.6) 4 (26.7) 5 (18.5) 11 (68.8) bb 9 (40.9) [n (%)] Family history of MDD [n (%)] - 5 (16.1) 9 (50.0) 4 (26.7) 15 (55.6) 9 (56.3) 12 (54.5) (a) p<0.05 (aa) p<0.01 and (aaa) p<0.001 denote significant differences versus control group (bb) p<0.01 denotes significant differences versus non-MDD subgroup (c) p<0.05 denotes significant differences versus primary MDD subgroup Abbreviations: AUD = alcohol use disorders; BMI = body mass index; CUD = cocaine use disorders MDD = major depressive disorders https://doi.org/10.1371/journal.pone.0213791.t001 percentage of women (CUD/non-MDD patients were 19% women; p<0.05), marital status (CUD/primary MDD patients were mainly separated; p<0.01), education (p<0.001), and occu- pation (CUD/cocaine-induced MDD patients were mainly unemployed; p<0.01). In the alcohol group, the mean age was 46.9 years, and 33.8% were women. In contrast, cohabiting was the most common marital status, and education at secondary or higher level reached 69.2%. The comparison with the control group revealed significant differences in the mean age (p<0.001), BMI (AUD/primary MDD had higher BMI than controls; p<0.05), and education (p<0.001). Regarding MDD-related variables in the cocaine group, the mean age of onset was 32.5 years and 3.2 episodes. However, there were significant differences in the age of the last epi- sode when primary and cocaine-induced patients were compared (p<0.05). In the alcohol group, the mean age of onset was 40.6 years and 3.81 episodes. No differences were found in the age of the last episode in AUD patients. Finally, although remission and current treatment with antidepressants were apparently different as compared both primary and substance- PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 6 / 17 Inflammatory mediators as biomarkers for dual depression induced MDD subgroups in the cocaine and alcohol groups, statistical analysis found no sig- nificant differences. Inflammatory mediators in relation to substance use disorders The impact of SUD on the plasma concentrations of proinflammatory cytokines and chemo- kines was investigated using a one-way ANCOVA [cocaine group (CUD/non-MDD patients), alcohol group (AUD/non-MDD patients), and control group], and controlling for sex, age, and BMI. Plasma concentrations and statistical analysis of inflammatory mediators according to SUD are described in Table 2. One-way ANCOVA revealed a main effect of SUD on TNF-α (p<0.05) concentrations. While the cocaine group showed significantly lower TNF-α concentrations than the control (p<0.05), there were no differences in TNF-α concentrations between the alcohol group and the control. In the case of IL-1β, there was also a main effect of SUD (p<0.01) and both cocaine and alcohol groups reported higher IL-1β concentrations than the control (p<0.01). Regarding chemokine concentrations, the statistical analysis revealed a main effect of SUD on CXCL12 (p<0.01) and CCL11 (p<0.05) concentrations. The multiple comparisons test showed signifi- cantly lower CXCL12 concentrations in the alcohol group than in the cocaine (p<0.05) and control (p<0.01) groups, but also significantly lower CCL11 (p<0.05) in the cocaine group than in the control (p<0.05). In contrast, no main effects of SUD were detected on plasma concentrations of CCL2 and CX3CL1. Inflammatory mediators in relation to comorbid major depressive disorders We investigated whether comorbid MDD was associated with alterations in the plasma con- centrations of these inflammatory mediators in the two groups of SUD patients. A one-way ANCOVA was performed for the cocaine (CUD/non-MDD and CUD/MDD patients) and Table 2. Plasma concentrations of inflammatory mediators in participants grouped according to the type of substance use disorder. VARIABLE CONTROL GROUP COCAINE GROUP (CUD) Non-MDD ALCOHOL GROUP (AUD) Non-MDD ANCOVA (Statistics)(1) (n = 62) (n = 31) (n = 27) F-value df p-value Mean ± SD Mean ± SD Mean ± SD TNF-α (pg/mL) 11.32±4.91 8.00±5.11 a 10.27±5.97 4.540 2, 0.013 113 IL-1β (pg/mL) 0.724±0.76 1.346±0.87 aa 1.764±1.13 aa 7.891 2,103 0.001 CXCL12 (pg/ 287.2±44.53 287.9±60.34 240.1±77.69 aa, b 5.662 2,114 0.005 mL) CCL2 (pg/mL) 35.29±16.37 38.70±9.303 39.01±16.28 2.198 2, 0.116 114 CCL11 (pg/mL) 148.5±61.24 126.5±54.00 a 128.8±37.10 3.737 2, 0.027 114 CX3CL1 (pg/ 2.606±1.37 1.987±1.67 1.839±1.09 1.886 2, 0.156 mL) 113 (1) Statistical analysis was conducted on the logarithmic transformed values to ensure statistical assumptions. Data were analyzed by ANCOVA controlling for age, sex, and BMI (a) p<0.05 and (aa) p<0.01 denote significant differences versus control group after post hoc comparisons (b) p<0.05 denotes significant differences versus cocaine non-MDD group after post hoc comparisons Missing participants: TNF-α (n = 1, control); IL-1β (n = 4, control; n = 7, CUD); CX3CL1 (n = 1, CUD) Abbreviations: ANCOVA = analysis of covariance; AUD = alcohol use disorders; CUD = cocaine use disorders; df = degree of freedom; Non-MDD = non-major depressive disorders https://doi.org/10.1371/journal.pone.0213791.t002 PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 7 / 17 Inflammatory mediators as biomarkers for dual depression alcohol groups (AUD/non-MDD and AUD/MDD patients) controlling for sex, age, BMI, and antidepressant medication. Plasma concentrations and statistical analysis of inflammatory mediators according to MDD in the cocaine and alcohol groups are described in Table 3. One-way ANCOVA showed a main effect of MDD on the plasma concentrations of IL-1β (p<0.05), CXCL12 (p<0.05), CCL11 (p<0.05), and CX3CL1 (p<0.05) in the cocaine group, but no significant effects on any of these inflammatory mediators in the alcohol one. CUD/ MDD patients showed significantly lower IL-1β and CXCL12 concentrations than CUD/non- MDD patients, and significantly higher CCL11 and CX3CL1 concentrations than CUD/non- MDD patients. Inflammatory mediators in relation to primary and substance-induced major depressive disorders SUD patients who were diagnosed with MDD were divided into primary and substance- induced MDD according to DSM-IV-TR criteria. We investigated the impact of the type of comorbid MDD in CUD and AUD patients using one-way ANCOVA for the cocaine (CUD/ non-MDD, CUD/primary MDD, and CUD/cocaine-induced MDD patients) and alcohol groups (AUD/non-MDD, AUD/primary MDD, and AUD/cocaine-induced MDD patients), controlling for sex, age, BMI, and antidepressant medication. Plasma concentrations and sta- tistical analysis of inflammatory mediators according to type of MDD in the cocaine and alco- hol groups are described in Table 4. In a similar manner to previous results with comorbid MDD diagnosis, one-way ANCOVA revealed a main effect of the type of MDD on the plasma concentrations of CXCL12 (p<0.05) and CCL11 (p<0.01) in the cocaine group, but not in the alcohol one. The multiple compari- sons test showed that CXCL12 concentrations were significantly lower in CUD/cocaine- induced MDD patients than in CUD/non-MDD patients (p<0.05). The post hoc test also revealed that CCL11 concentrations were significantly higher in CUD/primary MDD patients than in CUD/non-MDD (p<0.01) and CUD/cocaine-induced MDD (p<0.01) patients. Inflammatory mediators in cocaine use disorders as potential biomarkers of comorbid major depressive disorders Logistic regression models were generated to evaluate whether these cytokine and chemokine concentrations could serve as explanatory variables in order to discriminate between primary Table 3. Plasma concentrations of inflammatory mediators in the cocaine and alcohol groups according to comorbid depression. VARIABLE COCAINE GROUP (CUD) (n = 64) ANCOVA (Statistics) (1) ALCOHOL GROUP (AUD) (n = 65) ANCOVA (Statistics) (1) Non-MDD (n = 31) MDD (n = 33) F-value df p-value Non-MDD (n = 27) MDD (n = 38) F-value df p-value Mean ± SD Mean ± SD Mean ± SD Mean ± SD TNF-α (pg/mL) 8.00±5.11 9.80±5.58 0.992 1,57 0.319 10.27±5.97 9.67±5.03 0.077 1,59 0.782 IL-1β (pg/mL) 1.346±0.868 0.699±0.327 4.211 1,46 0.046 1.764±1.129 1.646±1.137 0.105 1,59 0.747 CXCL12 (pg/mL) 287.9±60.34 252.5±41.04 5.222 1,58 0.026 240.1±77.69 241.8±110.3 0.053 1,59 0.819 CCL2 (pg/mL) 38.70±9.303 43.36±12.89 0.853 1,58 0.360 39.01±16.28 34.89±16.14 1.568 1,59 0.215 CCL11 (pg/mL) 126.5±54.00 171.10±98.14 4.778 1,58 0.033 128.8±37.10 111.9±35.3 2.899 1,59 0.094 CX3CL1 (pg/mL) 1.987±1.666 2.342±1.252 5.807 1,55 0.019 1.839±1.090 2.212±1.298 1.742 1,59 0.192 (1) Statistical analysis was conducted on the logarithmic transformed values to ensure statistical assumptions. Data were analyzed by ANCOVA controlling for age, sex, BMI, and antidepressant medication Missing participants: TNF-α (n = 1, CUD/MDD); IL-1β (n = 7, CUD/non-MDD; n = 5, CUD/MDD); CX3CL1 (n = 1, CUD/non-MDD; n = 2, CUD/MDD) Abbreviations: ANCOVA = analysis of covariance; AUD = alcohol use disorders; CUD = cocaine use disorders; df = degree of freedom; MDD = major depressive disorder https://doi.org/10.1371/journal.pone.0213791.t003 PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 8 / 17 Inflammatory mediators as biomarkers for dual depression Table 4. Plasma concentrations of inflammatory mediators in the cocaine and alcohol groups according to the type of depression. VARIABLE COCAINE GROUP (CUD) (n = 64) ANCOVA ALCOHOL GROUP (AUD) (n = 65) ANCOVA (Statistics) (1) (Statistics) (1) Non-MDD Primary MDD Cocaine-Induced- F- df p- Non-MDD Primary MDD Alcohol-Induced- F- df p- (n = 31) (n = 18) MDD (n = 15) value value (n = 27) (n = 16) MDD (n = 22) value value Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD TNF-α (pg/ 8.00±5.11 11.63±6.38 7.45±3.25 0.751 2,56 0.477 10.27±5.97 9.62±5.17 9.70±5.05 0.102 2,58 0.875 mL) IL-1β (pg/ 1.346±0.868 0.660±0.389 0.760±0.202 2.411 2,45 0.101 1.764±1.129 1.343±0.692 1.867±1.347 0.217 2,58 0.806 mL) CXCL12 (pg/ 287.9±60.34 265.5±42.56 236.9±34.26 a 4.453 2,57 0.016 240.1±77.69 248.7±66.69 236.8±134.8 0.415 2,58 0.662 mL) CCL2 (pg/ 38.70±9.303 45.40±14.15 40.91±11.16 0.501 2,57 0.609 39.01±16.28 30.96±13.71 37.74±17.45 1.444 2,58 0.244 mL) CCL11 (pg/ 126.5±54.00 211.7±111.4 aa 124.3±49.9 bb 6.445 2,57 0.003 128.8±37.10 115.4±39.4 109.3±32.8 1.401 2,58 0.255 mL) CX3CL1 (pg/ 1.987±1.666 2.272±1.195 2.439±1.371 0.134 2,54 0.875 1.839±1.090 2.116±1.387 2.283±1.259 0.864 2,58 0.427 mL) (1) Statistical analysis was conducted on the logarithmic transformed values to ensure statistical assumptions. Data were analyzed by ANCOVA controlling for age, sex, BMI, and antidepressant medication. (a) p<0.05 and (aa) p<0.01 denote significant differences versus cocaine non-MDD group after post hoc comparisons (bb) p<0.01 denotes significant differences versus CUD primary MDD group after post hoc comparisons Missing participants: TNF-α (n = 1, CUD/cocaine-induced MDD); IL-1β (n = 7, CUD/non-MDD; n = 1, CUD/primary MDD; n = 4, CUD/cocaine-induced MDD); CX3CL1 (n = 1, CUD/non-MDD; n = 2, CUD/cocaine-induced MDD) Abbreviations: ANCOVA = analysis of covariance; AUD = alcohol use disorders; CUD = cocaine use disorders; df = degree of freedom; MDD = major depressive disorders https://doi.org/10.1371/journal.pone.0213791.t004 MDD and substance induced-MDD in the cocaine group. The regression analyses included additional variables (age, sex, and BMI) using different approaches and the resulting models are represented in Table 5. The full model included plasma concentrations of TNF-α, IL-1β, CXCL12, CCL2, CCL11, and CX3CL1, and age, sex and BMI as independent variables in the logistic regression analysis in the cocaine group. As depicted in Fig 1A, the ROC curve analysis showed an AUC = 0.914 (p<0.001), which indicates a high discriminative power. A representative cut-off value was 0.452 [sensitivity 88.24 (95%CI = 63.56–98.54) and specificity 81.82 (95% CI = 48.22–97.72)]. Regarding the scatter plot of the predictive probabilities for the type of MDD, their means were significantly different between primary MDD and cocaine-induced MDD patients (U = 16.00, p<0.001). To identify the most discriminative variables, a backward stepwise approach was used to generate a logistic regression model in the cocaine group. This model was restricted to 3 explanatory variables in the equation: CXCL12 and CCL11 concentrations and sex. As depicted in Fig 1B, the ROC analysis showed an AUC = 0.867 (p<0.001), maintaining a high discriminative power. A representative cut-off value was 0.311 [sensitivity 77.78 (95% CI = 52.36–93.59) and specificity 93.33 (95% CI = 68.05–99.83)]. The predictive probabilities for the type of MDD also differed between primary MDD and cocaine-induced MDD patients (U = 36.00, p<0.001). Discussion The main findings of this observational study are as follows: (a) There were differences in the plasma concentrations of some inflammatory mediators between CUD (TNF-α, IL-1β and PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 9 / 17 Inflammatory mediators as biomarkers for dual depression Table 5. Binary logistic regression models for predicting cocaine-induced depression through plasma concentrations of inflammatory mediators, sex, age, and BMI. MODEL VARIABLE B SEM W df p value Exp(B) 95% CI for Exp(B) Lower Upper LOGISTIC REGRESSION FULL MODEL 1 TNF-α -0.204 0.144 2.005 1 0.157 0.816 0.616 1.081 Method: Enter IL-1β 2.809 2.493 1.270 1 0.260 16.597 0.125 2198 CXCL12 -0.021 0.017 1.401 1 0.237 0.980 0.947 1.014 CCL2 0.070 0.073 0.921 1 0.337 1.073 0.930 1.238 CCL11 -0.017 0.012 1.890 1 0.169 0.983 0.959 1.007 CX3CL1 0.308 0.602 0.262 1 0.609 1.361 0.418 4.426 Age -0.019 0.106 0.033 1 0.856 0.981 0.797 1.208 Sex 2.788 1.503 3.443 1 0.064 16.25 0.855 308.8 BMI -0.084 0.170 0.243 1 0.622 0.920 0.659 1.283 Constant 5.369 5.991 0.803 1 0.370 214.7 - - LOGISTIC REGRESSION MODEL 2 CXCL12 -0.022 0.013 2.829 1 0.093 0.978 0.954 1.004 Method: Backward Stepwise CCL11 -0.015 0.007 4.567 1 0.033 0.985 0.971 0.999 Sex 2.037 0.976 4.354 1 0.037 7.669 1.132 51.973 Constant 6.610 3.506 3.555 1 0.059 742.329 Abbreviations: B = coefficient; SEM = standard error; W = Wald test; df = degrees of Freedom; CI = confidence interval https://doi.org/10.1371/journal.pone.0213791.t005 CCL11) and AUD (IL-1β and CXCL12) patients when compared with control subjects; (b) CUD patients diagnosed with comorbid MDD showed significant differences in plasma con- centrations of IL-1β, CXCL12, CCL11 and CX3CL1. In contrast, there were no differences in the alcohol group; (c) Plasma CCL11 concentrations were found to be higher in CUD patients with primary MDD than those with cocaine-induced MDD. Evidence has revealed that the immune system participates in the mechanisms involved in long-term adaptation and neurotoxicity associated with the substance use [35]. As conse- quence of microglia activation, blood-brain-barrier disruption and neuroinflammation, proin- flammatory mediators such as cytokines and chemokines are altered in the brain [36–37]. However, these changes are not restricted to the Central Nervous System, and alterations in these inflammatory signals have been also found in the blood of SUD patients and proposed as biomarkers of such disorders [38]. Thus, our results revealed that abstinent CUD patients had lower concentrations of TNF-α and CCL11, and higher concentrations of IL-1β relative to controls. Accordingly, a previous study in abstinent patients with a pathological use of cocaine reported lower concentrations of TNF-α than controls and elevated concentrations of IL-1β in those patients with a high number of DMS-IV-TR criteria for CUD [11]. These peripheral alterations of relevant cytokines and CCL11 in the plasma of cocaine users could be associated with changes in the expression of immune blood cells. In fact, acute cocaine administration in rats produces increased levels of neutrophils and decreased levels of leukocytes and lympho- cytes [39]. Interestingly, a study with cocaine-dependent volunteers reported a decrease in the capacity of monocytes to express TNF-α and IL-6 compared with controls [40]. Unlike the archetypal proinflammatory cytokines TNF-α and IL-1β, there is no literature linking cocaine exposure with changes in plasma levels of CCL11. In contrast, a cross-sectional study has sug- gested that current cannabis use is linked to increased plasma levels of CCL11 [41]. Regarding AUD patients, significant alterations in the plasma were observed for IL-1β and CXCL12. Sim- ilar to CUD patients, high concentrations of IL-1β were found in AUD patients, which has been associated with an altered pattern of production of inflammatory cytokines in peripheral blood monocytes from chronic alcohol patients [42]. In addition to IL-1β, a specific decrease PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 10 / 17 Inflammatory mediators as biomarkers for dual depression Fig 1. ROC analysis and scatter dots for two logistic models to distinguish primary MDD from cocaine-induced MDD in the cocaine group. (A) ROC analysis of a full model: TNF-α, IL-1β, CXCL12, CCL2, CCL11 CX3CL1, sex, age and BMI; and a scatter plot for the predictive probabilities. (B) ROC analysis of an adjusted model: CXCL12, CCL11 and sex; and a scatter plot for the predictive probabilities. Lines on the scatter plots are means and SD. https://doi.org/10.1371/journal.pone.0213791.g001 in CXCL12 concentrations in AUD patients was found, which is in agreement with a previous study from our group examining plasma chemokines in abstinent AUD patients in treatment [30]. Among mood disorders, MDD is the comorbid psychiatric disorder more prevalent in SUD patients [7]. Because cytokines play a role in neuronal integrity, neurogenesis and synap- tic remodeling, growing evidence suggests that inflammatory mediators are involved in the development of MDD [43]. Moreover, numerous studies have reported changes in circulating proinflammatory cytokines in patients with MDD [44]. In the present study, we explored PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 11 / 17 Inflammatory mediators as biomarkers for dual depression circulating cytokines and chemokines in SUD patients diagnosed with MDD. Notably, while the diagnosis of comorbid MDD was associated with alterations in plasma concentrations of some cytokines and chemokines in the CUD group, no differences were found in the AUD group. Thus, CUD patients with comorbid MDD showed lower concentrations of IL-1β and CXCL12, and higher concentrations of CX3CL1 and CCL11 as compared with than those CUD patients without this condition. These results suggest that the type of SUD could have a prominent effect on the detection of alterations in inflammatory mediators associated with comorbid MDD. In addition to the type of substance, differences in other clinical and sociode- mographic variables between both groups could be affecting the expression of circulating inflammatory mediators, for example age and sex. Previously, we reported that plasma concen- trations of IL-1β, CXCL12 and CX3CL1 are positively associated with the number of DSM-IV-TR criteria for cocaine abuse and dependence. However, although cocaine users with severe CUD displayed an elevated prevalence of mood disorders, no differences in the plasma concentrations of these inflammatory mediators were reported [11]. In contrast, another recent study in the same cohort with CUD patients reported no changes of CCL11 concentra- tions relative to addiction-related variables or diagnosis of psychiatric comorbidity [13]. It is important to point out that in both studies we explored the association of these inflammatory mediators with psychiatric comorbidity at a level more general (mood disorders [11] or psy- chiatric DSM-IV-TR Axis I disorders and personality disorders [13]). While we observed lower plasma concentrations of IL-1β and CXCL12 in CUD patients with comorbid MDD, other studies conducted in psychiatric patients with no interference of substances have showed opposite results. For example, while increased IL-1β concentrations have been reported in depressed individuals compared with controls [45], another clinical study showed increased plasma levels of CXCL12 in MDD patients with different severities of depressive conditions compared with a control group [46]. These data suggest that the co-occurrence of CUD and MDD could prevent increased plasma concentrations of IL-1β and/or CXCL12 concentrations when CUD or MDD are not comorbid. Unlike IL-1β and CXCL12, chemokines CX3CL1 and CCL11 were found increased in CUD patients with MDD. Accordingly, transient elevated serum levels of CX3CL1 have been described in colorectal cancer patients with anxiety and depression [47]. Regarding CCL11, considerable evidence exists on the role of this chemokine in the progression of neurodegenerative diseases (e.g., schizophrenia or dementia patients) [22, 48], but contradictory results have been reported in relation to MDD. Thus, while changes in CCL11 are observed in women with recurrent MDD with suicidal ideation [24] and in young adults with mood disorders [49], another study in elderly patients with MDD showed no changes [50]. Because there was a strong association between alterations in the plasma expression of cer- tain inflammatory mediators and comorbid MDD, we extended our study in CUD patients diagnosed with primary and cocaine-induced MDD. In fact, the differentiation between pri- mary and substance-induced MDD in dual pathology is a field of study to be explored, and a recent research has showed that platelet IRAS/nischarim (I1-Imidazoline receptor) could dis- criminate between primary MDD and cocaine-induced MDD in CUD patients [51]. In our study, we have observed differences in the plasma expression of CCL11 and CXCL12 accord- ing to the type of MDD. Thus, CCL11 was increased in CUD patients with primary MDD rela- tive to those with cocaine-induced MDD and non-comorbid patients. In the case of CXCL12, CUD patients with cocaine-induced MDD showed decreased CXCL12 concentrations relative to those with primary MDD and non-comorbid patients. Then, the ROC analysis of regression models confirmed that CXCL12 and CCL11, in combination with sex, had a strong discrimi- native power to discriminate between both types of MDD. PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 12 / 17 Inflammatory mediators as biomarkers for dual depression Limitations and strengths Although our findings support the clinical importance of a differential diagnosis for MDD in CUD and AUD, we are aware of important limitations. First, there is a growing number of additional social, environmental and addiction-related variables (covariates) that could influ- ence our data. Second, because the participants were under current treatment for SUD at the moment of evaluation, we cannot exclude an influence of other psychiatric medications (e.g., anxiolytics and antipsychotics) on these inflammatory signals. Third, the impact of sex/gender in the expression of inflammatory mediators is critical and, therefore, it is required a larger sample size and a better characterization including other variables (e.g., sex hormones). Fourth, a cross-validation of the proposed discriminative models is necessary to ensure reli- ability and stability of the estimations. Fifth, the inclusion of patients diagnosed with MDD and no history of SUD and healthy controls are needed for future research, even though these psychiatric individuals have been previously described in the literature [45]. The strengths of the study are that we measured inflammatory mediators in a well-pheno- typed cohort of subjects characterized by a marked presence of comorbid depressive disorders. Furthermore, we employed a diagnostic procedure with good test-retest reliability, validity, and inner rater reliability in the differentiation between primary and substance-induced MDD [32]. Conclusions Patients with dual depression have worse clinical evolution and worse therapeutic response. Namely, the efficacy of antidepressant medication and/or psychological therapies in patients with AUD or CUD and comorbid MDD can be influenced by the type of depression (i.e. pri- mary and substance-induced MDD) (DSM-IV-TR). However, there are no biological sub- strates that can reveal the difference between both mental conditions for a better clinical management of these patients. The present study represents an attempt at the identification of inflammatory biomarkers for the differentiation between primary and substance-induced dis- orders. Although our findings indicate that plasma concentrations of chemokine CCL11 could act as a potential biological marker to differentiate between primary and substance-induced MDD in CUD patients, further clinical research is necessary to confirm the role of inflamma- tory mediators in dual depression. Supporting information S1 File. Dataset. Dataset includes: Sex (man/woman); SUD (no/yes), SUD type (no/AUD/ CUD), MDD (no/yes), MDD type (no/primary MDD/induced MDD), and concentrations (pg/mL) of TNF-, IL-, CXCL12, CCL2, CCL11 and CX3CL1. (PDF) Acknowledgments The authors are grateful to J. Mateus, R. Rodrı́guez-Minguela, E. Menoyo, M. Pérez, S. Martı́n, C. Gibert and G. Rubio for their valuable assistance throughout the clinical part of the study. The authors thank the participants and the psychiatry research support staff for their generos- ity and interest, which made this study possible. Author Contributions Conceptualization: Nuria Garcı́a-Marchena, Marta Torrens. PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 13 / 17 Inflammatory mediators as biomarkers for dual depression Data curation: Joan Ignasi Mestre-Pintó, Pedro Araos, Francina Fonseca, Juan Jesús Ruiz. Formal analysis: Nuria Garcı́a-Marchena, Joan Ignasi Mestre-Pintó. Investigation: Clara Pérez-Mañá, Esther Papaseit. Methodology: Nuria Garcı́a-Marchena, Marta Barrera, Francisco Javier Pavón. Project administration: Antonia Serrano, Francina Fonseca, Fernando Rodrı́guez de Fonseca, Marta Torrens. Resources: Antonia Serrano, Fernando Rodrı́guez de Fonseca, Marta Torrens. Supervision: Antonia Serrano, Juan Jesús Ruiz, Fernando Rodrı́guez de Fonseca, Magı́ Farré, Francisco Javier Pavón. Validation: Francisco Javier Pavón. Visualization: Fernando Rodrı́guez de Fonseca, Magı́ Farré. Writing – original draft: Nuria Garcı́a-Marchena, Marta Barrera. Writing – review & editing: Francisco Javier Pavón, Marta Torrens. References 1. Pettinati HM, O’Brien CP, Dundon WD. Current status of co-occurring mood and substance use disor- ders: a new therapeutic target. Am J Psychiatry. 2013; 170(1):23–30. Epub 2012/12/12. https://doi.org/ 10.1176/appi.ajp.2012.12010112 PMID: 23223834; PubMed Central PMCID: PMCPMC3595612. 2. Samet S, Fenton MC, Nunes E, Greenstein E, Aharonovich E, Hasin D. Effects of independent and sub- stance-induced major depressive disorder on remission and relapse of alcohol, cocaine and heroin dependence. Addiction. 2013; 108(1):115–23. Epub 2012/07/11. https://doi.org/10.1111/j.1360-0443. 2012.04010.x PMID: 22775406; PubMed Central PMCID: PMCPMC3767419. 3. Torrens M, Mestre-Pinto JI, Montanari L, Vicente J, Domingo-Salvany A. Dual diagnosis: an European perspective. Adicciones. 2017; 29(1):3–5. Epub 2017/02/09. https://doi.org/10.20882/adicciones.933 PMID: 28170050. 4. APA. DSM-IV-TR: Diagnostic and statistical manual of mental disorders, text revision. Washington, DC: American Psychiatric Association. 2000; 75:78–85. 5. APA. Diagnostic and statistical manual of mental disorders (DSM-5®): American Psychiatric Associa- tion; 2013. 6. WHO. The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research: World Health Organization; 1993. 7. Tirado Munoz J, Farre A, Mestre-Pinto J, Szerman N, Torrens M. Dual diagnosis in Depression: treat- ment recommendations. Adicciones. 2018; 30(1):66–76. Epub 2017/05/12. https://doi.org/10.20882/ adicciones.868 PMID: 28492960. 8. Rostene W, Kitabgi P, Parsadaniantz SM. Chemokines: a new class of neuromodulator? Nat Rev Neu- rosci. 2007; 8(11):895–903. Epub 2007/10/20. https://doi.org/10.1038/nrn2255 PMID: 17948033. 9. Teixeira AL, Gama CS, Rocha NP, Teixeira MM. Revisiting the Role of Eotaxin-1/CCL11 in Psychiatric Disorders. Front Psychiatry. 2018; 9:241. Epub 2018/07/03. https://doi.org/10.3389/fpsyt.2018.00241 PMID: 29962972; PubMed Central PMCID: PMCPMC6010544. 10. He J, Crews FT. Increased MCP-1 and microglia in various regions of the human alcoholic brain. Exp Neurol. 2008; 210(2):349–58. Epub 2008/01/15. https://doi.org/10.1016/j.expneurol.2007.11.017 PMID: 18190912; PubMed Central PMCID: PMCPMC2346541. 11. Araos P, Pedraz M, Serrano A, Lucena M, Barrios V, Garcia-Marchena N, et al. 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. Epub 2014/ 05/24. https://doi.org/10.1111/adb.12156 PMID: 24854157. 12. Pedraz M, Araos P, Garcia-Marchena N, Serrano A, Romero-Sanchiz P, Suarez J, et al. Sex differ- ences in psychiatric comorbidity and plasma biomarkers for cocaine addiction in abstinent cocaine- addicted subjects in outpatient settings. Front Psychiatry. 2015; 6:17. Epub 2015/03/13. https://doi.org/ 10.3389/fpsyt.2015.00017 PMID: 25762940; PubMed Central PMCID: PMCPMC4329735. PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 14 / 17 Inflammatory mediators as biomarkers for dual depression 13. Maza-Quiroga R, Garcia-Marchena N, Romero-Sanchiz P, Barrios V, Pedraz M, Serrano A, et al. Eval- uation of plasma cytokines in patients with cocaine use disorders in abstinence identifies transforming growth factor alpha (TGFalpha) as a potential biomarker of consumption and dual diagnosis. PeerJ. 2017; 5:e3926. Epub 2017/10/19. https://doi.org/10.7717/peerj.3926 PMID: 29038767; PubMed Cen- tral PMCID: PMCPMC5641428. 14. Cui C, Shurtleff D, Harris RA. Neuroimmune mechanisms of alcohol and drug addiction. Int Rev Neuro- biol. 2014; 118:1–12. Epub 2014/09/02. https://doi.org/10.1016/B978-0-12-801284-0.00001-4 PMID: 25175859; PubMed Central PMCID: PMCPMC4804710. 15. Sinha R. New findings on biological factors predicting addiction relapse vulnerability. Curr Psychiatry Rep. 2011; 13(5):398–405. Epub 2011/07/28. https://doi.org/10.1007/s11920-011-0224-0 PMID: 21792580; PubMed Central PMCID: PMCPMC3674771. 16. Herrera MI, Kolliker-Frers R, Barreto G, Blanco E, Capani F. Glial Modulation by N-acylethanolamides in Brain Injury and Neurodegeneration. Frontiers in aging neuroscience. 2016; 8:81. Epub 2016/05/21. https://doi.org/10.3389/fnagi.2016.00081 PMID: 27199733; PubMed Central PMCID: PMCPMC4844606. 17. Hueston CM, Deak T. The inflamed axis: the interaction between stress, hormones, and the expression of inflammatory-related genes within key structures comprising the hypothalamic-pituitary-adrenal axis. Physiol Behav. 2014; 124:77–91. Epub 2013/11/05. https://doi.org/10.1016/j.physbeh.2013.10.035 PMID: 24184413. 18. Mora C, Zonca V, Riva MA, Cattaneo A. Blood biomarkers and treatment response in major depression. Expert Rev Mol Diagn. 2018. Epub 2018/04/28. https://doi.org/10.1080/14737159.2018.1470927 PMID: 29701114. 19. Rossi D, Zlotnik A. The biology of chemokines and their receptors. Annu Rev Immunol. 2000; 18:217– 42. Epub 2000/06/03. https://doi.org/10.1146/annurev.immunol.18.1.217 PMID: 10837058. 20. Goldstein BI, Kemp DE, Soczynska JK, McIntyre RS. Inflammation and the phenomenology, patho- physiology, comorbidity, and treatment of bipolar disorder: a systematic review of the literature. J Clin Psychiatry. 2009; 70(8):1078–90. Epub 2009/06/06. https://doi.org/10.4088/JCP.08r04505 PMID: 19497250. 21. Acosta SA, Diamond DM, Wolfe S, Tajiri N, Shinozuka K, Ishikawa H, et al. Influence of post-traumatic stress disorder on neuroinflammation and cell proliferation in a rat model of traumatic brain injury. PLoS One. 2013; 8(12):e81585. Epub 2013/12/19. https://doi.org/10.1371/journal.pone.0081585 PMID: 24349091; PubMed Central PMCID: PMCPMC3857205. 22. Teixeira AL, Reis HJ, Nicolato R, Brito-Melo G, Correa H, Teixeira MM, et al. Increased serum levels of CCL11/eotaxin in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2008; 32(3):710–4. Epub 2007/12/22. https://doi.org/10.1016/j.pnpbp.2007.11.019 PMID: 18096286. 23. Stuart MJ, Singhal G, Baune BT. Systematic Review of the Neurobiological Relevance of Chemokines to Psychiatric Disorders. Front Cell Neurosci. 2015; 9:357. Epub 2015/10/07. https://doi.org/10.3389/ fncel.2015.00357 PMID: 26441528; PubMed Central PMCID: PMCPMC4564736. 24. Grassi-Oliveira R, Brieztke E, Teixeira A, Pezzi JC, Zanini M, Lopes RP, et al. Peripheral chemokine levels in women with recurrent major depression with suicidal ideation. Rev Bras Psiquiatr. 2012; 34 (1):71–5. Epub 2012/03/07. PMID: 22392392. 25. Hashimoto K. Inflammatory Biomarkers as Differential Predictors of Antidepressant Response. Interna- tional Journal of Molecular Sciences. 2015; 16(4):7796. https://doi.org/10.3390/ijms16047796 PMID: 25856677 26. Zhang WQ, Smolik CM, Barba-Escobedo PA, Gamez M, Sanchez JJ, Javors MA, et al. Acute dietary tryptophan manipulation differentially alters social behavior, brain serotonin and plasma corticosterone in three inbred mouse strains. Neuropharmacology. 2015; 90:1–8. https://doi.org/10.1016/j. neuropharm.2014.10.024 PMID: 25445490 27. Trecki J, Unterwald EM. Modulation of cocaine-induced activity by intracerebral administration of CXCL12. Neuroscience. 2009; 161(1):13–22. Epub 2009/03/24. https://doi.org/10.1016/j.neuroscience. 2009.03.027 PMID: 19303923; PubMed Central PMCID: PMCPMC2680459. 28. Crews FT, Nixon K. Mechanisms of neurodegeneration and regeneration in alcoholism. Alcohol Alco- hol. 2009; 44(2):115–27. Epub 2008/10/23. https://doi.org/10.1093/alcalc/agn079 PMID: 18940959; PubMed Central PMCID: PMCPMC2948812. 29. Liu L, Hutchinson MR, White JM, Somogyi AA, Coller JK. Association of IL-1B genetic polymorphisms with an increased risk of opioid and alcohol dependence. Pharmacogenetics and genomics. 2009; 19 (11):869–76. Epub 2009/10/06. https://doi.org/10.1097/FPC.0b013e328331e68f PMID: 19801958. 30. Garcia-Marchena N, Araos PF, Barrios V, Sanchez-Marin L, Chowen JA, Pedraz M, et al. Plasma Che- mokines in Patients with Alcohol Use Disorders: Association of CCL11 (Eotaxin-1) with Psychiatric PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 15 / 17 Inflammatory mediators as biomarkers for dual depression Comorbidity. Front Psychiatry. 2016; 7:214. Epub 2017/02/06. https://doi.org/10.3389/fpsyt.2016. 00214 PMID: 28149283; PubMed Central PMCID: PMCPMC5242327. 31. Nunes EV, Liu X, Samet S, Matseoane K, Hasin D. Independent versus substance-induced major depressive disorder in substance-dependent patients: observational study of course during follow-up. J Clin Psychiatry. 2006; 67(10):1561–7. Epub 2006/11/17. PMID: 17107247. 32. Torrens M, Serrano D, Astals M, Perez-Dominguez G, Martin-Santos R. Diagnosing comorbid psychiat- ric disorders in substance abusers: validity of the Spanish versions of the Psychiatric Research Inter- view for Substance and Mental Disorders and the Structured Clinical Interview for DSM-IV. Am J Psychiatry. 2004; 161(7):1231–7. Epub 2004/07/02. https://doi.org/10.1176/appi.ajp.161.7.1231 PMID: 15229056. 33. Hasin D, Samet S, Nunes E, Meydan J, Matseoane K, Waxman R. Diagnosis of comorbid psychiatric disorders in substance users assessed with the Psychiatric Research Interview for Substance and Men- tal Disorders for DSM-IV. Am J Psychiatry. 2006; 163(4):689–96. Epub 2006/04/06. https://doi.org/10. 1176/ajp.2006.163.4.689 PMID: 16585445. 34. Dahl J, Ormstad H, Aass HC, Malt UF, Bendz LT, Sandvik L, et al. The plasma levels of various cyto- kines are increased during ongoing depression and are reduced to normal levels after recovery. Psy- choneuroendocrinology. 2014; 45:77–86. Epub 2014/05/23. S0306-4530(14)00115-2 [pii] https://doi. org/10.1016/j.psyneuen.2014.03.019 PMID: 24845179. 35. Crews FT. Immune function genes, genetics, and the neurobiology of addiction. Alcohol Res. 2012; 34 (3):355–61. Epub 2012/11/09. PMID: 23134052; PubMed Central PMCID: PMCPMC3860409. 36. da Fonseca AC, Matias D, Garcia C, Amaral R, Geraldo LH, Freitas C, et al. The impact of microglial activation on blood-brain barrier in brain diseases. Front Cell Neurosci. 2014; 8:362. Epub 2014/11/19. https://doi.org/10.3389/fncel.2014.00362 PMID: 25404894; PubMed Central PMCID: PMC4217497. 37. Varatharaj A, Galea I. The blood-brain barrier in systemic inflammation. Brain Behav Immun. 2017; 60:1–12. Epub 2016/03/21. S0889-1591(16)30055-1 [pii] https://doi.org/10.1016/j.bbi.2016.03.010 PMID: 26995317. 38. Achur RN, Freeman WM, Vrana KE. Circulating cytokines as biomarkers of alcohol abuse and alcohol- ism. J Neuroimmune Pharmacol. 2010; 5(1):83–91. Epub 2009/12/19. https://doi.org/10.1007/s11481- 009-9185-z PMID: 20020329; PubMed Central PMCID: PMCPMC2862254. 39. Jankowski MM, Ignatowska-Jankowska B, Glac W, Swiergiel AH. Cocaine administration increases CD4/CD8 lymphocyte ratio in peripheral blood despite lymphopenia and elevated corticosterone. Int Immunopharmacol. 2010; 10(10):1229–34. Epub 2010/07/20. https://doi.org/10.1016/j.intimp.2010.07. 003 PMID: 20637837. 40. Irwin MR, Olmos L, Wang M, Valladares EM, Motivala SJ, Fong T, et al. Cocaine dependence and acute cocaine induce decreases of monocyte proinflammatory cytokine expression across the diurnal period: autonomic mechanisms. J Pharmacol Exp Ther. 2007; 320(2):507–15. Epub 2006/10/28. https://doi.org/10.1124/jpet.106.112797 PMID: 17068203. 41. Fernandez-Egea E, Scoriels L, Theegala S, Giro M, Ozanne SE, Burling K, et al. Cannabis use is asso- ciated with increased CCL11 plasma levels in young healthy volunteers. Prog Neuropsychopharmacol Biol Psychiatry. 2013; 46:25–8. Epub 2013/07/04. https://doi.org/10.1016/j.pnpbp.2013.06.011 PMID: 23820464. 42. Laso FJ, Vaquero JM, Almeida J, Marcos M, Orfao A. Production of inflammatory cytokines by periph- eral blood monocytes in chronic alcoholism: relationship with ethanol intake and liver disease. Cytome- try B Clin Cytom. 2007; 72(5):408–15. Epub 2007/02/03. https://doi.org/10.1002/cyto.b.20169 PMID: 17266151. 43. Yirmiya R, Goshen I. Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain Behav Immun. 2011; 25(2):181–213. Epub 2010/10/26. https://doi.org/10.1016/j.bbi.2010.10.015 PMID: 20970492. 44. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, et al. A meta-analysis of cytokines in major depression. Biol Psychiatry. 2010; 67(5):446–57. Epub 2009/12/18. https://doi.org/10.1016/j. biopsych.2009.09.033 PMID: 20015486. 45. Simon NM, McNamara K, Chow CW, Maser RS, Papakostas GI, Pollack MH, et al. A detailed examina- tion of cytokine abnormalities in Major Depressive Disorder. Eur Neuropsychopharmacol. 2008; 18 (3):230–3. Epub 2007/08/08. https://doi.org/10.1016/j.euroneuro.2007.06.004 PMID: 17681762; PubMed Central PMCID: PMCPMC2267745. 46. Oglodek EA, Szota A, Just MJ, Mos D, Araszkiewicz A. Comparison of chemokines (CCL-5 and SDF- 1), chemokine receptors (CCR-5 and CXCR-4) and IL-6 levels in patients with different severities of depression. Pharmacol Rep. 2014; 66(5):920–6. Epub 2014/08/26. https://doi.org/10.1016/j.pharep. 2014.06.001 PMID: 25150002. PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 16 / 17 Inflammatory mediators as biomarkers for dual depression 47. Miranda DO, Anatriello E, Azevedo LR, Santos JC, Cordeiro JFC, Peria FM, et al. Fractalkine (C-X3-C motif chemokine ligand 1) as a potential biomarker for depression and anxiety in colorectal cancer patients. Biomed Rep. 2017; 7(2):188–92. Epub 2017/08/15. https://doi.org/10.3892/br.2017.937 PMID: 28804633; PubMed Central PMCID: PMCPMC5525586. 48. Bettcher BM, Fitch R, Wynn MJ, Lalli MA, Elofson J, Jastrzab L, et al. MCP-1 and eotaxin-1 selectively and negatively associate with memory in MCI and Alzheimer’s disease dementia phenotypes. Alzhei- mers Dement (Amst). 2016; 3:91–7. Epub 2016/07/28. https://doi.org/10.1016/j.dadm.2016.05.004 PMID: 27453930; PubMed Central PMCID: PMCPMC4941041. 49. Magalhaes PV, Jansen K, Stertz L, Ferrari P, Pinheiro RT, da Silva RA, et al. Peripheral eotaxin-1 (CCL11) levels and mood disorder diagnosis in a population-based sample of young adults. J Psychiatr Res. 2014; 48(1):13–5. Epub 2013/11/05. https://doi.org/10.1016/j.jpsychires.2013.10.007 PMID: 24182965. 50. Myung W, Lim SW, Woo HI, Park JH, Shim S, Lee SY, et al. Serum Cytokine Levels in Major Depres- sive Disorder and Its Role in Antidepressant Response. Psychiatry Investig. 2016; 13(6):644–51. Epub 2016/12/03. https://doi.org/10.4306/pi.2016.13.6.644 PMID: 27909456; PubMed Central PMCID: PMCPMC5128353. 51. Keller B, Mestre-Pinto J-I, Álvaro-Bartolomé M, Martinez-Sanvisens D, Farre M, Garcı́a-Fuster MJ, et al. A Biomarker to Differentiate between Primary and Cocaine-Induced Major Depression in Cocaine Use Disorder: The Role of Platelet IRAS/Nischarin (I1-Imidazoline Receptor). Frontiers in psychiatry. 2017; 8:258. https://doi.org/10.3389/fpsyt.2017.00258 PMID: 29326609 PLOS ONE | https://doi.org/10.1371/journal.pone.0213791 March 14, 2019 17 / 17