Survival Outcomes Associated with Antidepressant Use in Glioblastoma: A Cohort Study

Survival Outcomes Associated with Antidepressant Use in Glioblastoma: A Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Survival Outcomes Associated with Antidepressant Use in Glioblastoma: A Cohort Study Yifei Sun, Mohammad Hamo, Travis Atchley, James M Markert, Burt Nabors, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7339610/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Journal of Neuro-Oncology → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose Glioblastoma is the most common primary brain malignancy and carries significant mortality. Preclinical studies have highlighted the efficacy of antidepressant therapy in inhibiting glioblastoma progression; however, real-world evidence remains conflicting. We sought to investigate the impact of different commonly utilized antidepressant therapies on survival in patients with glioblastoma. Methods In total, 1464 consecutive patients with glioblastoma treated at a single institution from 2008 to 2023 were included for analysis. Multivariate cox regression analysis with antidepressant usage modeled as a time varying covariate was used to assess the effect of antidepressants while controlling for a priori selected clinical variables with known relevance to survival. Results The median age at diagnosis was 62 (IQR 52-70) years with a median overall survival of 13.8 months. Of the cohort, 44% utilized antidepressants after diagnosis, with SSRIs as the most common class utilized (26%). The median duration of any antidepressant therapy was 111 (IQR 9-303) days. In a time varying, multivariate cox regression, usage of SSRIs (HR 1.4, 95%CI 1.21-1.62), SNRIs (HR 1.33, 95%CI 1.03-1.72), serotonin modulators (HR 1.61, 95%CI 1.40-1.86), and atypical antidepressants (HR 1.7, 95%CI 1.28-2.26) were associated with worse survival. Amongst SSRIs, only escitalopram (HR 1.33, 95%CI 1.10-1.60) and citalopram (HR 1.31, 95%CI 1.01-1.70) were associated with worse survival. Conclusions SSRIs, SNRIs, serotonin modulators, and atypical antidepressants are associated with worse survival in patients with glioblastoma. Careful selection of antidepressant medication in patients with glioblastoma may be necessary to optimize outcomes. Antidepressants Glioblastoma survival Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Glioblastoma is the most common primary central nervous system malignancy in adults, accounting for nearly half of primary brain tumors [ 1 ]. On the current standard of care of gross total surgical resection followed by radiation therapy and adjuvant chemotherapy, survival remains poor. Despite recent improvements in therapy delivery and innovations in treatment regimens, glioblastoma carries a poor prognosis, with median survival of around 15 months [ 1 , 2 ]. Thus, it remains of high interest to further create novel therapies to better patient survival. Disproportionally high rates of depression is a well-known comorbidity of glioblastoma, and is associated with poor patient outcomes [ 3 – 5 ]. Depression may occur in nearly 40% of patients with glioblastoma, and antidepressant therapy is frequently prescribed for management of these symptoms [ 4 ]. The potential ways in which antidepressant therapy my improve glioblastoma outcomes is many. Improvement of patient’s depressive symptoms may improve function, leading to decreased deterioration, increased adherence to treatment regimes, and improved activities of daily living (ADL) [ 6 , 7 ]. Many pre-clinical studies highlight the interplay between antidepressant therapy and glioblastoma signaling pathways. Several studies have demonstrated the ability of antidepressants to inhibit invasiveness and increase autophagy [ 8 , 9 ]. Some studies have demonstrated the ability of antidepressant medications to suppress transcription factors associated with glioblastoma progression in vitro [ 10 ]. Still others have demonstrated strong anti-glioblastoma effects in mice models as well [ 11 – 13 ]. However, the effect of antidepressant therapy on glioblastoma survival is inconclusive in literature. Analysis by Caudill et al. [ 14 ] found SSRI therapy to be associated with improved survival, while Seliger et al. [ 15 ] found antidepressant use to be associated with worse survival. In analysis by Edstrom et al. [ 16 ] using a multicenter registry, SSRI therapy and non-SSRI antidepressant therapy was found to be associated with worsened survival, while analysis by Otto-Meyer et al.[ 17 ] found non-significant results. Recent meta-analysis exploring this topic suggest inconclusive findings, limited studies, and high degrees of heterogeneity [ 18 , 19 ]. The effects of antidepressant therapy on glioblastoma survival remains unclear, and the effect of specific classes of antidepressants have not been explored. Furthermore, the association of antidepressants and glioblastoma has not been explored while taking into account socioeconomic and molecular factors associated with survival [ 20 , 21 ]. We sought to characterize the independent effect of antidepressants on glioblastoma survival while accounting for molecular and socioeconomic status. We additionally sought to understand the differential impact of different antidepressant classes on glioblastoma survival. METHODS This study was designed as a single center retrospective review with approval from the institutional review board (IRB-300005353). This manuscript was written in compliance with STROBE (Strengthening the Reporting of Observation Studies in Epidemiology) [ 22 ]. Participants and Data Collection We retrospectively identified all adult patients with histopathological confirmed glioblastoma who were treated at our institution between January 2008 and December 2023 with complete medication records. We reviewed the electronic medical record (EMR) for variables on patient demographics, treatment characteristics, and medication records. Patient consent was not sought due to the retrospective nature of this study. Defining Variables Variables were defined a priori with advice from the senior authors (DEO, JM, BN). The study variables included were age at diagnosis categorized according to standard groups (< 45, 45–54, 55–64, 65–74, and ≥ 75), race (white, African American, and other), gender (Male or Female), and insurance status, which was categorized as private, public (Medicare, Medicaid, Tricare), or indigent/self-pay, extent of resection, IDH mutation status, MGMT methylation status, treatment history such as history of chemotherapy and radiotherapy [ 23 ]. Patient addresses were extracted and geocoded and linked to federal information processing (FIPS) codes. Neighborhood deprivation, captured by Area Deprivation Index (ADI), was retrieved from the Neighborhood Atlas dataset produced by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health, with higher ADI indicating a higher level of socioeconomic disparity [ 24 ]. High ADI was defined as being in the top quartile of disadvantage nationally. Rural urban communicating area (RUCA) codes were extracted and categorized in accordance with the Economic Research Service (ERS) of the United States Department of Agriculture and divided into the 4 main categories of metropolitan, micropolitan, small town, and rural [ 25 ]. Patient medication records were reviewed for antidepressant usage after glioblastoma diagnosis. Usage was counted as date first prescribed to the end date on the prescription or censoring, whichever came first. Antidepressants were defined into 5 categories: selective serotonin reuptake inhibitors (SSRIs), serotonin/norepinephrine reuptake inhibitors (SNRIs), serotonin modulators (SMODs), tricyclic antidepressants (TCAs), and atypical antidepressants. The most common drugs for each category were selected for inclusion. Specific medications chosen for inclusion can be found in the supplementary content (Supplementary Digital Content, Supplementary Methods). Statistical Analysis Categorical, binary, and ordinal variables were summarized as counts and percentages, while continuous variables were summarized as the median and interquartile range (IQR). Univariable comparison analysis was performed via utilizing the one-way analysis of variance (ANOVA), log-rank test, Pearson’s chi-squared test, Wilcoxon rank sum test, or Fisher’s exact test. Simon-Makuch plots with Mantel-Byar method were utilized to visualize unadjusted time-varying survival curves [ 26 , 27 ]. To assess the independent effect of various antidepressants on survival, multivariate cox regression models were utilized with antidepressant usage modeled as a time varying covariate to assess the association of various antidepressant therapies with glioblastoma overall survival (OS) while controlling for age, insurance status, race, neighborhood disadvantage, MGMT methylation status, IDH mutation status, treatment with chemotherapy, treatment with radiotherapy, extent of resection, RUCA code status, and comorbid depression and/or anxiety. There was a high degree of missing values for MGMT methylation (39%) and IDH mutation (33%) status. Because most of the missing values were before 2016, we assumed that the data was missing at random (MAR) due to inconsistent biomolecular marker testing before the release of the 2016 WHO Guidelines on Tumors of the Central Nervous System [ 28 – 30 ]. We performed multiple imputations using the missForest random forest classifier, which resulted in an out of box (OOB) of 2%, demonstrating high imputation accuracy (Supplementary Digital Content, Figure S1 ). To conduct sensitivity analysis to demonstrate the robustness of our findings, we replicated the cox regression models using complete case analysis, and in a cohort of patients with comorbid or pre-existing depression and/or anxiety. Statistical significance was set at α = 0.05, and all tests for significance were two-sided. All statistical analyses were performed using R (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria) [ 31 ]. RESULTS Patient Characteristics and Demographics In total, 1464 patients were included for analysis. The median age at diagnosis was 62 [Interquartile range (IQR) 52–70], with 648 (44%) being female. Of these patients 155 (11%) were African American, and 49% had private insurance. Of these patients, 671 (46%) underwent gross total resection (GTR), 1219 (83%) had received chemotherapy, and 1235 (84%) had received radiation therapy. Of the cohort, 44% of patients had some form of antidepressant therapy, with the most common being SSRI therapy (26%) followed by serotonin modulator therapy (22%) and SNRI therapy (5.9%). Further details on patient characteristics can be found in Table 1 . Table 1 Patient Characteristics and Demographics Characteristic N = 1,464 1 Age (years) 62 (52, 70) Sex Female 648 (44%) Male 816 (56%) Race White 1,224 (84%) Black 155 (11%) Other 85 (5.8%) Insurance type Private 712 (49%) Public 701 (48%) Self-Pay/Indigent 51 (3.5%) RUCA code Metropolitan 1,062 (73%) Micropolitan 225 (15%) Rural 51 (3.5%) Small Town 126 (8.6%) ADI Rank 66 (46, 84) Vital Status at Last Follow-up Alive 249 (17%) Deceased 1,215 (83%) IDH Status IDH-Mut 92 (9.4%) IDH-WT 890 (91%) Unknown 482 MGMT status Methylated 344 (39%) Unmethylated 544 (61%) Unknown 576 Chemotherapy 1,219 (83%) Radiotherapy 1,235 (84%) Extent of Resection Biopsy 430 (29%) Gross Total Resection 671 (46%) Partial Resection 363 (25%) Comorbid Depression/Anxiety 432 (30%) Any Antidepressants 647 (44%) SSRI 377 (26%) Serotonin Modulators 316 (22%) SNRI 87 (5.9%) Atypical Antidepressants 69 (4.7%) TCAs 49 (3.3%) MAOI 3 (0.2%) 1 Median (Q1, Q3); n (%), SSRI: Selective Serotonin Receptor; SNRI: Serotonin/Norepinephrine Reuptake Inhibitors; TCA: Tricyclic antidepressants; MAOI: Mono-amine oxidase inhibitors; RUCA: Rural urban communicating area; ADI: Area Deprivation Index Univariable Comparison Patients who received antidepressant therapy were younger (61 vs 63 years, p = .016), more likely to be female (48% vs 41%, p = .009), more likely to be white (88% vs 80%, p < .001), more likely to have received chemotherapy (86% vs 81%, p = .01), radiotherapy (87% vs 82%, p = .039), and more likely to had undergone gross total resection (49% vs 43%, p < .001) (Table 2 ). Table 2 Comparison by Antidepressant Therapy Had Antidepressant Therapy Characteristic No Yes p-value 2 N = 817 1 N = 647 1 Age 63 (53, 71) 61 (51, 69) 0.016 Sex 0.009 Female 337 (41%) 311 (48%) Male 480 (59%) 336 (52%) Race < 0.001 White 657 (80%) 567 (88%) Black 91 (11%) 64 (9.9%) Other 69 (8.4%) 16 (2.5%) RUCA code 0.7 Metropolitan 593 (73%) 469 (72%) Micropolitan 130 (16%) 95 (15%) Rural 29 (3.5%) 22 (3.4%) Small Town 65 (8.0%) 61 (9.4%) Area Deprivation Index 67 (47, 84) 66 (44, 84) 0.2 IDH Status 0.3 IDH-Mut 52 (10%) 40 (8.4%) IDH-WT 451 (90%) 439 (92%) MGMT Status 0.7 Methylated 176 (39%) 168 (38%) Unmethylated 272 (61%) 272 (62%) Chemotherapy 662 (81%) 557 (86%) 0.01 Radiotherapy 674 (82%) 561 (87%) 0.028 Extent of Resection 0.039 Biopsy 261 (32%) 169 (26%) Gross Total Resection 355 (43%) 316 (49%) Partial Resection 201 (25%) 162 (25%) Comorbid Depression or Anxiety 78 (9.5%) 354 (55%) < 0.001 1 Median (Q1, Q3); n (%) 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test, RUCA: Rural urban communicating area; ADI: Area Deprivation Index Antidepressant prescribing patterns The most commonly prescribed category of antidepressants were SSRIs, followed by serotonin modulators and SNRIs (Table 1 ). The mean duration of time on antidepressant therapy amongst the cohort was 28.2 ± 128.2 days. Amongst the SSRIs, the mean daily dose was 28.3 ± 26.7 mg, escitalopram was the most commonly prescribed, followed by sertraline and citalopram. Of the SNRIs, the mean daily dose was 58.3 ± 42.7 mg, duloxetine was the most commonly prescribed followed by venlafaxine. Of the atypical antidepressants, the mean daily dose was 106 ± 101 mg, mirtazapine and bupropion were the most prescribed. Of the serotonin modulators, the mean daily dose was 69.5 ± 51.7 mg, and trazodone was the most prescribed. Of the MAOIs, the mean daily dose was 2.4 ± 3.7 mg, and rasagiline was the most prescribed (Table 3 , Supplementary Digital Content Table S1 ). Of the cohort, 137 patients had some form of antidepressant polytherapy, with the most common overlap being SSRIs and serotonin modulators, followed by SSRIs and atypical antidepressants (Supplementary Digital Content, Figure S2). Univariate Simon-Makuch plots showing unadjusted survival are shown in Fig. 1 . Table 3 Antidepressant usage patterns Drug Name N Mean duration (SD) days Daily dose (SD) mg Any Antidepressant 647 28.2 (128.2) 46.8 (50.6) SSRI 377 24.2 (112.8) 28.3 (26.7) SNRI 87 25.1 (140.5) 58.3 (42.7) Serotonin Modulator 316 42.4 (137.6) 69.5 (51.7) TCA 49 56.9 (221.8) 45.6 (24) MAOI 3 38.7 (81.1) 2.4 (3.7) Atypicals 69 27.1 (136.4) 106.4 (100.5) SSRI: Selective Serotonin Receptor; SNRI: Serotonin/Norepinephrine Reuptake Inhibitors; TCA: Tricyclic antidepressants; MAOI: Mono-amine oxidase inhibitors; Survival analysis On multivariate cox regression analysis adjusting for age, comorbid depression or anxiety, insurance payer type, race, neighborhood socioeconomic disadvantage, MGMT methylation status, IDH mutation status, treatment with chemotherapy, treatment with radiotherapy, extent of resection, and rurality, usage of any antidepressant (HR 1.57, 95%CI 1.38–1.78, p < .001) was associated with worse survival. In multivariate cox regression controlling for the same cofactors but investigating individual antidepressant classes, SSRI usage (HR 1.35, 95%CI 1.16–1.57, p < .001), SNRI usage (HR 1.35, 95%CI 1.05–1.74, p < .02), serotonin modulator usage (HR 1.63, 95%CI 1.42–1.88, p < .001), TCA utilization (HR 1.43, 95%CI 1.04–1.97, p = .027), and atypical antidepressant usage (HR 1.52, 95%CI 1.15–2.02, p < .004) were associated with worse survival. On complete case analysis, SSRI use (HR 1.25, 95%CI 1.02–1.54, p = .035), serotonin modulator use (HR 1.54, 95%CI 1.27–1.87, p < .001), and TCA use (HR 1.84, 95%CI 1.21–2.80, p = .005) were associated with worse survival (Fig. 1 , Supplementary Digital Content, Table S2). Polytherapy was similarly associated with worse overall survival (HR 1.61, 95%CI 1.31–1.98, p < .001) (Fig. 2 ). For increased robustness, in a subgroup analysis of patients with depression or anxiety, antidepressant use was associated with worse overall survival (HR 2.46, 95%CI 1.85–3.26, p < .001) (Supplemental Digital Content, Table S3). Further subgroup analysis within SSRI drugs were assessed due to the variation in prescribed SSRIs. Escitalopram (HR 1.33, 95%CI 1.10–1.60, p = .003) and citalopram (HR 1.31, 95%CI 1.01–1.70, p = .044) were associated with worse overall survival, while fluoxetine, paroxetine, and sertraline did not convey a survival disadvantage (Fig. 3 ). DISCUSSION Our findings suggest that utilization of antidepressants after glioblastoma diagnosis is associated with worse overall survival in patients with glioblastoma, with SSRI, serotonin modulator use, and TCA use were most strongly associated with decreased survival after adjusting for biochemical data, comorbid psychiatric conditions, treatment regimen, and other clinical and socioeconomic factors. With the disproportionally high rates of depression in glioblastoma patients, some patients may be placed on antidepressant therapy for symptomatic relief.[ 32 ] However, the effect of antidepressant therapy on survival outcomes in glioblastoma remains inconclusive [ 18 , 19 ]. In our study, we find that antidepressant therapy, specifically therapy with SSRIs, serotonin modulators, and TCAs, are associated with worse survival. This is supported by several studies in literature. Gramatski et al.[ 33 ] reported antidepressant usage to not be associated with any survival improvement in a review of a registry that included 404 patients. Similarly, an analysis by Otto-Meyer et al.[ 17 ] found that no significant difference in survival between patients that had taken antidepressants. Edstrom et al.[ 16 ] demonstrated that SSRI therapy and SNRI were associated worsened survival. In an analysis of patients enrolled in clinical trials for glioblastoma, it was observed that antidepressant use during treatment for glioblastoma was associated with worsened survival [ 15 ]. This is supported by a wealth of preclinical data. A study by Bielecka et al.[ 34 ] demonstrated that imipramine and tranylcypromine reduced the cytotoxic efficacy of temozolomide, current standard of care for chemotherapy in glioblastoma [ 35 ]. Glioblastoma have been found to express serotonin receptors, of which agonism has been found to increase growth [ 36 , 37 ]. Serotoninergic medications may globally increase serotonin levels and increase the known autocrine signaling loops that drive glioblastoma proliferation, though the significant heterogeneity of glioblastoma serotonin receptor expression should be noted [ 38 ]. Serotonergic medications may modulate IL-6, activating STAT3 and NF-κB to promote glioblastoma proliferation [ 39 , 40 ]. Serotoninergic and psychotropic medications may significantly lower the seizure threshold in patients with glioblastoma, portending a poor long term prognosis as well. However, our results are in opposition to Caudill et al.[ 14 ] and Bi et al.[ 11 ] The mechanisms by which this may be occurring are many fold. Bi et al.[ 11 ] demonstrated that the ability of fluoxetine to inhibit sphingomyelin phosphodiesterase 1 (SMPD1), a key protein required for lipid synthesis, was a potential mechanism for the anti-glioblastoma effects of fluoxetine. There also is extensive preclinical literature highlighting these associations. Many other preclinical studies have demonstrated the ability of antidepressants to affect glioblastoma growth.[ 34 , 41 – 43 ] For example, studies have demonstrated the ability of fluoxetine to inhibit NF-κB signaling, inducing apoptosis in glioblastoma cells [ 10 ]. Others have demonstrated the ability of escitalopram to damage mitochondria and induce autophagy in cell models [ 13 , 44 ]. Several studies demonstrate the ability of tricyclics such as impramine in inhibiting glioblastoma cells proliferation as well [ 9 , 41 , 43 ]. Significantly, many of these clinical studies fail to discriminate between the major classes of antidepressants, such as SSRIs, SNRIs, TCAs, and more. Furthermore, many of these studies fail to adjust for known factors for glioblastoma survival such as biomolecular data and socioeconomic characteristics. Additionally, the sample size for glioblastoma in these studies may be a limiting factor as well. Our results offer evidence that these effects persist even after controlling for these important confounders, highlighting the need to focus on translating pre-clinical results to patient outcomes. Interestingly, we found that patients on escitalopram and citalopram had worsened survival, though this was not observed for the other SSRIs like sertraline. This may be due to lower sample sizes leading to difficulty detecting effects in the other types of SSRIs. Sertraline may exert a neuroprotective effect through its action on sigma-1 receptors, which may also account for our observations [ 45 ]. Similarly, fluoxetine has been shown to reduce MGMT expression via disruption of the NF-κB pathway, sensitizing cells to temozolomide (TMZ) in vitro and in vivo , which may account for our observations [ 46 ]. Paroxetine was also not significantly associated with worse survival. This may be due to slightly different mechanism of action of paroxetine on glioblastoma cells. Preclinical evidence has found that paroxetine induces intrinsic pathways of apoptosis in glioblastoma, which may prolong survival in some patients [ 47 ]. Polytherapy was also associated with worse survival. This may be due to similar mechanisms as previously described, with additional compounding of pro-survival effects due to polytherapy. Patients on polytherapy may also have worsened disease progression, as additions of polytherapy for depression suggests clinical states refractory to monotherapy. This is consistent with our observations that the most common polytherapy regimens are consistent with commonly prescribed add-on therapy for severe major depression [ 48 ]. This may reflect increasing disease progression and worsened state, which may be unaccounted for despite controlling for comorbid depression/anxiety in our survival models. These findings may also highlight an underlying interaction between antidepressant medication therapy and altered connectivity environments in glioblastoma. Recent studies have suggested that glioblastoma neural synapses are a driving force for glioblastoma growth and resistance to treatment [ 49 , 50 ]. It is possible that antidepressants may modulate these networks and increase glioblastoma growth. Our results highlight the importance of understanding the effect of pre-clinical study results in real patient populations, as clinical studies have significant heterogeneity, and findings are often not consistent with preclinical findings. This data suggests only certain classes of antidepressants are associated with poor survival in glioblastoma when considering all relevant clinical and socioeconomic factors, supporting careful selection of medications when treating depression in glioblastoma. Further research and higher-level evidence are necessary to better understand the impact of antidepressant therapy in glioblastoma survival. Limitations Our study is limited by its retrospective, single institution design. Due to this, we may not be able to control for unknown confounders. Furthermore, our study does not consider socioeconomic status, which has been shown to potentially significantly affect glioblastoma outcomes. However, we accounted for race and rurality in our analysis. A potential limitation is the fact that poor functional status may predict increased antidepressant usage, biasing our results. However, our adjustment for baseline mental health status as well as modeling exposure as a time varying covariate should account for this to some degree. There is also potential that our review of medication records may overestimate actual usage, as compliance with medication regimen is difficult to ensure. Though we included the most common drugs given for antidepressant therapy, it is possible that there are more rare antidepressant therapies that were not included for analysis. Though there may be risk of bias due to the single institution nation of this study, our center is the primary tertiary referral center for several states in the southeastern United States, and the only NCI-designated cancer center in the state. Thus, it may be reasoned that we have an adequate sampling of the glioblastoma patients in our region. Potential interactions with other psycho-effective medications were not investigated. Revised definition of the WHO Central Nervous System (CNS) Tumor guidelines have categorized IDH mutant, Grade IV astrocytoma as separate from glioblastoma. However, all IDH-mutant tumors were still included in this analysis to better understand the effect of antidepressant therapy and survival in high grade gliomas. We attempted to address this by controlling for biomolecular markers. There was significant missing data for IDH and MGMT marker status in the cohort, due to changes in patterns of practice prior to the 2016 WHO CNS guidelines. Thus, we were reasonably justified in assuming that data was missing in patterns that met criteria for missing-at-random (MAR), justifying the utilization of imputation methods at higher proportions of missingness [ 28 , 51 ]. Furthermore, we replicated our findings in several different cohorts, further reinforcing the robustness of our findings. CONCLUSION Utilization of SSRI, serotonin modulator use, and TCAs after glioblastoma diagnosis are associated with worse survival in patients, after adjusting for known factors with relevance to survival. Further studies should seek to validate this effect in a multicenter cohort and identify the precise biological effect of various antidepressant therapy on glioblastoma proliferation. Careful selection of antidepressant choice in patients with glioblastoma may be warranted. Declarations Funding: This project is supported in part by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R25NS079188 (DEO). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. DEO is also a Cornwall Clinical Scholar supported by the University of Alabama at Birmingham. Disclosures: The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. AUTHORSHIP STATEMENT All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yifei Sun and Dagoberto Estevez-Ordonez. Study supervision was performed by Dagoberto Estevez-Ordonez, Burt Nabors, and James Markert. Study support was performed by Dagoberto Estevez-Ordonez, Burt Nabors, and James Markert. 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Am J Epidemiol 187:576–584. 10.1093/aje/kwx349 Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820. 10.1007/s00401-016-1545-1 Team RC (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria Mugge L, Mansour TR, Crippen M, Alam Y, Schroeder J (2020) Depression and glioblastoma, complicated concomitant diseases: a systemic review of published literature. Neurosurg Rev 43:497–511. 10.1007/s10143-018-1017-2 Gramatzki D, Rogers JL, Neidert MC, Hertler C, Le Rhun E, Roth P, Weller M (2020) Antidepressant drug use in glioblastoma patients: an epidemiological view. Neuro-Oncology Pract 7:514–521. 10.1093/nop/npaa022 Bielecka AM, Obuchowicz E (2017) Antidepressant drugs can modify cytotoxic action of temozolomide. Eur J Cancer Care (Engl) 26. 10.1111/ecc.12551 Stupp R, Mason WP, Bent MJvd, Weller M, Fisher B, Taphoorn MJB, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO (2005) Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. N Engl J Med 352:987–996. 10.1056/NEJMoa043330 Kast RE (2010) Glioblastoma chemotherapy adjunct via potent serotonin receptor-7 inhibition using currently marketed high-affinity antipsychotic medicines. Br J Pharmacol 161:481–487. 10.1111/j.1476-5381.2010.00923.x Hisaoka K, Nishida A, Takebayashi M, Koda T, Yamawaki S, Nakata Y (2004) Serotonin increases glial cell line-derived neurotrophic factor release in rat C6 glioblastoma cells. Brain Res 1002:167–170. 10.1016/j.brainres.2004.01.009 Yabo YA, Niclou SP, Golebiewska A (2022) Cancer cell heterogeneity and plasticity: A paradigm shift in glioblastoma. Neuro Oncol 24:669–682. 10.1093/neuonc/noab269 West AJ, Tsui V, Stylli SS, Nguyen HPT, Morokoff AP, Kaye AH, Luwor RB (2018) The role of interleukin-6-STAT3 signalling in glioblastoma. Oncol Lett 16:4095–4104. 10.3892/ol.2018.9227 Kubera M, Maes M, Kenis G, Kim YK, Lasoń W (2005) Effects of serotonin and serotonergic agonists and antagonists on the production of tumor necrosis factor alpha and interleukin-6. Psychiatry Res 134:251–258. 10.1016/j.psychres.2004.01.014 Bilir A, Erguven M, Oktem G, Ozdemir A, Uslu A, Aktas E, Bonavida B (2008) Potentiation of cytotoxicity by combination of imatinib and chlorimipramine in glioma. Int J Oncol 32:829–839 Parker KA, Pilkington GJ (2006) Apoptosis of human malignant glioma-derived cell cultures treated with clomipramine hydrochloride, as detected by Annexin-V assay. Radiol Oncol 40 Munson JM, Fried L, Rowson SA, Bonner MY, Karumbaiah L, Diaz B, Courtneidge SA, Knaus UG, Brat DJ, Arbiser JL, Bellamkonda RV (2012) Anti-invasive adjuvant therapy with imipramine blue enhances chemotherapeutic efficacy against glioma. Sci Transl Med 4:127ra136. 10.1126/scitranslmed.3003016 Dikmen M, Cantürk Z, Öztürk Y (2011) Escitalopram oxalate, a selective serotonin reuptake inhibitor, exhibits cytotoxic and apoptotic effects in glioma C6 cells. Acta neuropsychiatrica 23:173–178 Izumi Y, Reiersen AM, Lenze EJ, Mennerick SJ, Zorumski CF (2024) Sertraline modulates hippocampal plasticity via sigma 1 receptors, cellular stress and neurosteroids. Translational Psychiatry 14:474. 10.1038/s41398-024-03185-3 Song T, Li H, Tian Z, Xu C, Liu J, Guo Y (2015) Disruption of NF-κB signaling by fluoxetine attenuates MGMT expression in glioma cells. OncoTargets Therapy 8:2199–2208. 10.2147/OTT.S85948 Levkovitz Y, Gil-Ad I, Zeldich E, Dayag M, Weizman A (2005) Differential induction of apoptosis by antidepressants in glioma and neuroblastoma cell lines. J Mol Neurosci 27:29–42. 10.1385/JMN:27:1:029 Henssler J, Alexander D, Schwarzer G, Bschor T, Baethge C (2022) Combining Antidepressants vs Antidepressant Monotherapy for Treatment of Patients With Acute Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry 79:300–312. 10.1001/jamapsychiatry.2021.4313 Krishna S, Choudhury A, Keough MB, Seo K, Ni L, Kakaizada S, Lee A, Aabedi A, Popova G, Lipkin B, Cao C, Nava Gonzales C, Sudharshan R, Egladyous A, Almeida N, Zhang Y, Molinaro AM, Venkatesh HS, Daniel AGS, Shamardani K, Hyer J, Chang EF, Findlay A, Phillips JJ, Nagarajan S, Raleigh DR, Brang D, Monje M, Hervey-Jumper SL (2023) Glioblastoma remodelling of human neural circuits decreases survival. Nature 617:599–607. 10.1038/s41586-023-06036-1 Venkatesh HS, Morishita W, Geraghty AC, Silverbush D, Gillespie SM, Arzt M, Tam LT, Espenel C, Ponnuswami A, Ni L, Woo PJ, Taylor KR, Agarwal A, Regev A, Brang D, Vogel H, Hervey-Jumper S, Bergles DE, Suvà ML, Malenka RC, Monje M (2019) Electrical and synaptic integration of glioma into neural circuits. Nature 573:539–545. 10.1038/s41586-019-1563-y Lee JH, Huber JC Jr (2021) Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much? Iran J Public Health 50:1372–1380. 10.18502/ijph.v50i7.6626 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7339610","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":501603171,"identity":"244beb80-8402-41b3-8d94-c843d0165f5f","order_by":0,"name":"Yifei Sun","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Sun","suffix":""},{"id":501603172,"identity":"d522299b-b188-4d3f-b0ed-4881dd5fc39f","order_by":1,"name":"Mohammad Hamo","email":"","orcid":"","institution":"University of Alabama at 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System","correspondingAuthor":true,"prefix":"","firstName":"Dagoberto","middleName":"","lastName":"Estevez-Ordonez","suffix":""}],"badges":[],"createdAt":"2025-08-10 15:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7339610/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7339610/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11060-025-05288-3","type":"published","date":"2025-10-27T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89389110,"identity":"0ac64f52-a0ae-465c-a4cc-197ccf6ce250","added_by":"auto","created_at":"2025-08-19 12:46:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101041,"visible":true,"origin":"","legend":"\u003cp\u003eSimon Makuch plots showing unadjusted survival for \u003cstrong\u003eA.\u003c/strong\u003e Any antidepressant use, \u003cstrong\u003eB. \u003c/strong\u003eSSRI use, \u003cstrong\u003eC.\u003c/strong\u003eSMOD use, \u003cstrong\u003eD.\u003c/strong\u003e SNRI use, \u003cstrong\u003eE.\u003c/strong\u003e Tricyclic use, \u003cstrong\u003eF.\u003c/strong\u003e Atypical antidepressant use\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/567bb75c342edeaa704ba6e0.png"},{"id":89385441,"identity":"e40ec883-04e6-4951-a802-b2cf9b7e27c4","added_by":"auto","created_at":"2025-08-19 12:30:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":545345,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate cox regression model for impact of antidepressant usage and survival\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/7323192f2969cfd4aa2e645e.png"},{"id":89389106,"identity":"33885d5f-a289-4b7f-b85b-e055a7f4e578","added_by":"auto","created_at":"2025-08-19 12:46:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1081305,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate cox regression model for impact of polytherapy on glioblastoma survival\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/e22e68ecebd9a8bdd41728d7.png"},{"id":89385439,"identity":"23e54186-3429-42b2-acb7-935512519be7","added_by":"auto","created_at":"2025-08-19 12:30:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":491168,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate cox regression model for impact of most used SSRIs on glioblastoma survival\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/54f3a9630e6adc9d2d7f8f51.png"},{"id":95041391,"identity":"809e4b26-1fb8-4cae-86b1-223201220a41","added_by":"auto","created_at":"2025-11-03 16:11:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3477619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/50436bf7-1e26-46fd-b283-7b10e6d61945.pdf"},{"id":89389109,"identity":"502f1197-66a7-4934-8286-ccfc279a4cc5","added_by":"auto","created_at":"2025-08-19 12:46:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":272375,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementADgbm.docx","url":"https://assets-eu.researchsquare.com/files/rs-7339610/v1/66bb5e82f3b74911c8611263.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival Outcomes Associated with Antidepressant Use in Glioblastoma: A Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlioblastoma is the most common primary central nervous system malignancy in adults, accounting for nearly half of primary brain tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. On the current standard of care of gross total surgical resection followed by radiation therapy and adjuvant chemotherapy, survival remains poor. Despite recent improvements in therapy delivery and innovations in treatment regimens, glioblastoma carries a poor prognosis, with median survival of around 15 months [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, it remains of high interest to further create novel therapies to better patient survival.\u003c/p\u003e\u003cp\u003eDisproportionally high rates of depression is a well-known comorbidity of glioblastoma, and is associated with poor patient outcomes [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Depression may occur in nearly 40% of patients with glioblastoma, and antidepressant therapy is frequently prescribed for management of these symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The potential ways in which antidepressant therapy my improve glioblastoma outcomes is many. Improvement of patient\u0026rsquo;s depressive symptoms may improve function, leading to decreased deterioration, increased adherence to treatment regimes, and improved activities of daily living (ADL) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many pre-clinical studies highlight the interplay between antidepressant therapy and glioblastoma signaling pathways. Several studies have demonstrated the ability of antidepressants to inhibit invasiveness and increase autophagy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Some studies have demonstrated the ability of antidepressant medications to suppress transcription factors associated with glioblastoma progression \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Still others have demonstrated strong anti-glioblastoma effects in mice models as well [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the effect of antidepressant therapy on glioblastoma survival is inconclusive in literature. Analysis by Caudill et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found SSRI therapy to be associated with improved survival, while Seliger et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found antidepressant use to be associated with worse survival. In analysis by Edstrom et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] using a multicenter registry, SSRI therapy and non-SSRI antidepressant therapy was found to be associated with worsened survival, while analysis by Otto-Meyer et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found non-significant results. Recent meta-analysis exploring this topic suggest inconclusive findings, limited studies, and high degrees of heterogeneity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe effects of antidepressant therapy on glioblastoma survival remains unclear, and the effect of specific classes of antidepressants have not been explored. Furthermore, the association of antidepressants and glioblastoma has not been explored while taking into account socioeconomic and molecular factors associated with survival [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We sought to characterize the independent effect of antidepressants on glioblastoma survival while accounting for molecular and socioeconomic status. We additionally sought to understand the differential impact of different antidepressant classes on glioblastoma survival.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e This study was designed as a single center retrospective review with approval from the institutional review board (IRB-300005353). This manuscript was written in compliance with STROBE (Strengthening the Reporting of Observation Studies in Epidemiology) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Data Collection\u003c/h2\u003e\u003cp\u003eWe retrospectively identified all adult patients with histopathological confirmed glioblastoma who were treated at our institution between January 2008 and December 2023 with complete medication records. We reviewed the electronic medical record (EMR) for variables on patient demographics, treatment characteristics, and medication records. Patient consent was not sought due to the retrospective nature of this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefining Variables\u003c/h3\u003e\n\u003cp\u003eVariables were defined a priori with advice from the senior authors (DEO, JM, BN). The study variables included were age at diagnosis categorized according to standard groups (\u0026lt;\u0026thinsp;45, 45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, and \u0026ge;\u0026thinsp;75), race (white, African American, and other), gender (Male or Female), and insurance status, which was categorized as private, public (Medicare, Medicaid, Tricare), or indigent/self-pay, extent of resection, IDH mutation status, MGMT methylation status, treatment history such as history of chemotherapy and radiotherapy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Patient addresses were extracted and geocoded and linked to federal information processing (FIPS) codes. Neighborhood deprivation, captured by Area Deprivation Index (ADI), was retrieved from the Neighborhood Atlas dataset produced by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health, with higher ADI indicating a higher level of socioeconomic disparity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. High ADI was defined as being in the top quartile of disadvantage nationally.\u003c/p\u003e\u003cp\u003eRural urban communicating area (RUCA) codes were extracted and categorized in accordance with the Economic Research Service (ERS) of the United States Department of Agriculture and divided into the 4 main categories of metropolitan, micropolitan, small town, and rural [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePatient medication records were reviewed for antidepressant usage after glioblastoma diagnosis. Usage was counted as date first prescribed to the end date on the prescription or censoring, whichever came first. Antidepressants were defined into 5 categories: selective serotonin reuptake inhibitors (SSRIs), serotonin/norepinephrine reuptake inhibitors (SNRIs), serotonin modulators (SMODs), tricyclic antidepressants (TCAs), and atypical antidepressants. The most common drugs for each category were selected for inclusion. Specific medications chosen for inclusion can be found in the supplementary content (Supplementary Digital Content, Supplementary Methods).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eCategorical, binary, and ordinal variables were summarized as counts and percentages, while continuous variables were summarized as the median and interquartile range (IQR). Univariable comparison analysis was performed via utilizing the one-way analysis of variance (ANOVA), log-rank test, Pearson\u0026rsquo;s chi-squared test, Wilcoxon rank sum test, or Fisher\u0026rsquo;s exact test. Simon-Makuch plots with Mantel-Byar method were utilized to visualize unadjusted time-varying survival curves [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo assess the independent effect of various antidepressants on survival, multivariate cox regression models were utilized with antidepressant usage modeled as a time varying covariate to assess the association of various antidepressant therapies with glioblastoma overall survival (OS) while controlling for age, insurance status, race, neighborhood disadvantage, MGMT methylation status, IDH mutation status, treatment with chemotherapy, treatment with radiotherapy, extent of resection, RUCA code status, and comorbid depression and/or anxiety. There was a high degree of missing values for MGMT methylation (39%) and IDH mutation (33%) status. Because most of the missing values were before 2016, we assumed that the data was missing at random (MAR) due to inconsistent biomolecular marker testing before the release of the 2016 WHO Guidelines on Tumors of the Central Nervous System [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We performed multiple imputations using the \u003cem\u003emissForest\u003c/em\u003e random forest classifier, which resulted in an out of box (OOB) of 2%, demonstrating high imputation accuracy (Supplementary Digital Content, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo conduct sensitivity analysis to demonstrate the robustness of our findings, we replicated the cox regression models using complete case analysis, and in a cohort of patients with comorbid or pre-existing depression and/or anxiety. Statistical significance was set at α\u0026thinsp;=\u0026thinsp;0.05, and all tests for significance were two-sided. All statistical analyses were performed using R (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics and Demographics\u003c/h2\u003e\u003cp\u003eIn total, 1464 patients were included for analysis. The median age at diagnosis was 62 [Interquartile range (IQR) 52\u0026ndash;70], with 648 (44%) being female. Of these patients 155 (11%) were African American, and 49% had private insurance. Of these patients, 671 (46%) underwent gross total resection (GTR), 1219 (83%) had received chemotherapy, and 1235 (84%) had received radiation therapy. Of the cohort, 44% of patients had some form of antidepressant therapy, with the most common being SSRI therapy (26%) followed by serotonin modulator therapy (22%) and SNRI therapy (5.9%). Further details on patient characteristics can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient Characteristics and Demographics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,464\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (52, 70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e648 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e816 (56%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,224 (84%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (11%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e712 (49%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701 (48%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Pay/Indigent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRUCA code\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,062 (73%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (15%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall Town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADI Rank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (46, 84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVital Status at Last Follow-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249 (17%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,215 (83%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH-Mut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH-WT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e890 (91%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGMT status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethylated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e344 (39%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmethylated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e544 (61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,219 (83%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,235 (84%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtent of Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e430 (29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross Total Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e671 (46%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e363 (25%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbid Depression/Anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e432 (30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny Antidepressants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e647 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377 (26%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerotonin Modulators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316 (22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtypical Antidepressants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTCAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;Median (Q1, Q3); n (%), SSRI: Selective Serotonin Receptor; SNRI: Serotonin/Norepinephrine Reuptake Inhibitors; TCA: Tricyclic antidepressants; MAOI: Mono-amine oxidase inhibitors; RUCA: Rural urban communicating area; ADI: Area Deprivation Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUnivariable Comparison\u003c/h2\u003e\u003cp\u003ePatients who received antidepressant therapy were younger (61 vs 63 years, p\u0026thinsp;=\u0026thinsp;.016), more likely to be female (48% vs 41%, p\u0026thinsp;=\u0026thinsp;.009), more likely to be white (88% vs 80%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), more likely to have received chemotherapy (86% vs 81%, p\u0026thinsp;=\u0026thinsp;.01), radiotherapy (87% vs 82%, p\u0026thinsp;=\u0026thinsp;.039), and more likely to had undergone gross total resection (49% vs 43%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison by Antidepressant Therapy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHad Antidepressant Therapy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;817\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;647\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (53, 71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (51, 69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e480 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e657 (80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e567 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRUCA code\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e593 (73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e469 (72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall Town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea Deprivation Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (47, 84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (44, 84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH-Mut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH-WT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e439 (92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGMT Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethylated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmethylated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e662 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e557 (86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e674 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e561 (87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtent of Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross Total Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial Resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbid Depression or Anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e354 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Median (Q1, Q3); n (%)\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Wilcoxon rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test, RUCA: Rural urban communicating area; ADI: Area Deprivation Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAntidepressant prescribing patterns\u003c/h3\u003e\n\u003cp\u003eThe most commonly prescribed category of antidepressants were SSRIs, followed by serotonin modulators and SNRIs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean duration of time on antidepressant therapy amongst the cohort was 28.2 \u0026plusmn; 128.2 days. Amongst the SSRIs, the mean daily dose was 28.3 \u0026plusmn; 26.7 mg, escitalopram was the most commonly prescribed, followed by sertraline and citalopram. Of the SNRIs, the mean daily dose was 58.3 \u0026plusmn; 42.7 mg, duloxetine was the most commonly prescribed followed by venlafaxine. Of the atypical antidepressants, the mean daily dose was 106 \u0026plusmn; 101 mg, mirtazapine and bupropion were the most prescribed. Of the serotonin modulators, the mean daily dose was 69.5 \u0026plusmn; 51.7 mg, and trazodone was the most prescribed. Of the MAOIs, the mean daily dose was 2.4 \u0026plusmn; 3.7 mg, and rasagiline was the most prescribed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Digital Content Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Of the cohort, 137 patients had some form of antidepressant polytherapy, with the most common overlap being SSRIs and serotonin modulators, followed by SSRIs and atypical antidepressants (Supplementary Digital Content, Figure S2). Univariate Simon-Makuch plots showing unadjusted survival are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAntidepressant usage patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean duration (SD) days\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily dose (SD) mg\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny Antidepressant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.2 (128.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.8 (50.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.2 (112.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.3 (26.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.1 (140.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.3 (42.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerotonin Modulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.4 (137.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.5 (51.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.9 (221.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.6 (24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.7 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4 (3.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtypicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.1 (136.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.4 (100.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSSRI: Selective Serotonin Receptor; SNRI: Serotonin/Norepinephrine Reuptake Inhibitors; TCA: Tricyclic antidepressants; MAOI: Mono-amine oxidase inhibitors;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eOn multivariate cox regression analysis adjusting for age, comorbid depression or anxiety, insurance payer type, race, neighborhood socioeconomic disadvantage, MGMT methylation status, IDH mutation status, treatment with chemotherapy, treatment with radiotherapy, extent of resection, and rurality, usage of any antidepressant (HR 1.57, 95%CI 1.38\u0026ndash;1.78, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) was associated with worse survival. In multivariate cox regression controlling for the same cofactors but investigating individual antidepressant classes, SSRI usage (HR 1.35, 95%CI 1.16\u0026ndash;1.57, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), SNRI usage (HR 1.35, 95%CI 1.05\u0026ndash;1.74, p\u0026thinsp;\u0026lt;\u0026thinsp;.02), serotonin modulator usage (HR 1.63, 95%CI 1.42\u0026ndash;1.88, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), TCA utilization (HR 1.43, 95%CI 1.04\u0026ndash;1.97, p\u0026thinsp;=\u0026thinsp;.027), and atypical antidepressant usage (HR 1.52, 95%CI 1.15\u0026ndash;2.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.004) were associated with worse survival. On complete case analysis, SSRI use (HR 1.25, 95%CI 1.02\u0026ndash;1.54, p\u0026thinsp;=\u0026thinsp;.035), serotonin modulator use (HR 1.54, 95%CI 1.27\u0026ndash;1.87, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and TCA use (HR 1.84, 95%CI 1.21\u0026ndash;2.80, p\u0026thinsp;=\u0026thinsp;.005) were associated with worse survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Digital Content, Table S2). Polytherapy was similarly associated with worse overall survival (HR 1.61, 95%CI 1.31\u0026ndash;1.98, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For increased robustness, in a subgroup analysis of patients with depression or anxiety, antidepressant use was associated with worse overall survival (HR 2.46, 95%CI 1.85\u0026ndash;3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Supplemental Digital Content, Table S3). Further subgroup analysis within SSRI drugs were assessed due to the variation in prescribed SSRIs. Escitalopram (HR 1.33, 95%CI 1.10\u0026ndash;1.60, p\u0026thinsp;=\u0026thinsp;.003) and citalopram (HR 1.31, 95%CI 1.01\u0026ndash;1.70, p\u0026thinsp;=\u0026thinsp;.044) were associated with worse overall survival, while fluoxetine, paroxetine, and sertraline did not convey a survival disadvantage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings suggest that utilization of antidepressants after glioblastoma diagnosis is associated with worse overall survival in patients with glioblastoma, with SSRI, serotonin modulator use, and TCA use were most strongly associated with decreased survival after adjusting for biochemical data, comorbid psychiatric conditions, treatment regimen, and other clinical and socioeconomic factors. With the disproportionally high rates of depression in glioblastoma patients, some patients may be placed on antidepressant therapy for symptomatic relief.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] However, the effect of antidepressant therapy on survival outcomes in glioblastoma remains inconclusive [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, we find that antidepressant therapy, specifically therapy with SSRIs, serotonin modulators, and TCAs, are associated with worse survival. This is supported by several studies in literature. Gramatski et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported antidepressant usage to not be associated with any survival improvement in a review of a registry that included 404 patients. Similarly, an analysis by Otto-Meyer et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found that no significant difference in survival between patients that had taken antidepressants. Edstrom et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] demonstrated that SSRI therapy and SNRI were associated worsened survival. In an analysis of patients enrolled in clinical trials for glioblastoma, it was observed that antidepressant use during treatment for glioblastoma was associated with worsened survival [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis is supported by a wealth of preclinical data. A study by Bielecka et al.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] demonstrated that imipramine and tranylcypromine reduced the cytotoxic efficacy of temozolomide, current standard of care for chemotherapy in glioblastoma [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Glioblastoma have been found to express serotonin receptors, of which agonism has been found to increase growth [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Serotoninergic medications may globally increase serotonin levels and increase the known autocrine signaling loops that drive glioblastoma proliferation, though the significant heterogeneity of glioblastoma serotonin receptor expression should be noted [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Serotonergic medications may modulate IL-6, activating STAT3 and NF-κB to promote glioblastoma proliferation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Serotoninergic and psychotropic medications may significantly lower the seizure threshold in patients with glioblastoma, portending a poor long term prognosis as well.\u003c/p\u003e\u003cp\u003eHowever, our results are in opposition to Caudill et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Bi et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] The mechanisms by which this may be occurring are many fold. Bi et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated that the ability of fluoxetine to inhibit sphingomyelin phosphodiesterase 1 (SMPD1), a key protein required for lipid synthesis, was a potential mechanism for the anti-glioblastoma effects of fluoxetine. There also is extensive preclinical literature highlighting these associations. Many other preclinical studies have demonstrated the ability of antidepressants to affect glioblastoma growth.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] For example, studies have demonstrated the ability of fluoxetine to inhibit NF-κB signaling, inducing apoptosis in glioblastoma cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Others have demonstrated the ability of escitalopram to damage mitochondria and induce autophagy in cell models [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Several studies demonstrate the ability of tricyclics such as impramine in inhibiting glioblastoma cells proliferation as well [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSignificantly, many of these clinical studies fail to discriminate between the major classes of antidepressants, such as SSRIs, SNRIs, TCAs, and more. Furthermore, many of these studies fail to adjust for known factors for glioblastoma survival such as biomolecular data and socioeconomic characteristics. Additionally, the sample size for glioblastoma in these studies may be a limiting factor as well. Our results offer evidence that these effects persist even after controlling for these important confounders, highlighting the need to focus on translating pre-clinical results to patient outcomes.\u003c/p\u003e\u003cp\u003eInterestingly, we found that patients on escitalopram and citalopram had worsened survival, though this was not observed for the other SSRIs like sertraline. This may be due to lower sample sizes leading to difficulty detecting effects in the other types of SSRIs. Sertraline may exert a neuroprotective effect through its action on sigma-1 receptors, which may also account for our observations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Similarly, fluoxetine has been shown to reduce MGMT expression via disruption of the NF-κB pathway, sensitizing cells to temozolomide (TMZ) \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e, which may account for our observations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Paroxetine was also not significantly associated with worse survival. This may be due to slightly different mechanism of action of paroxetine on glioblastoma cells. Preclinical evidence has found that paroxetine induces intrinsic pathways of apoptosis in glioblastoma, which may prolong survival in some patients [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePolytherapy was also associated with worse survival. This may be due to similar mechanisms as previously described, with additional compounding of pro-survival effects due to polytherapy. Patients on polytherapy may also have worsened disease progression, as additions of polytherapy for depression suggests clinical states refractory to monotherapy. This is consistent with our observations that the most common polytherapy regimens are consistent with commonly prescribed add-on therapy for severe major depression [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This may reflect increasing disease progression and worsened state, which may be unaccounted for despite controlling for comorbid depression/anxiety in our survival models.\u003c/p\u003e\u003cp\u003eThese findings may also highlight an underlying interaction between antidepressant medication therapy and altered connectivity environments in glioblastoma. Recent studies have suggested that glioblastoma neural synapses are a driving force for glioblastoma growth and resistance to treatment [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. It is possible that antidepressants may modulate these networks and increase glioblastoma growth.\u003c/p\u003e\u003cp\u003eOur results highlight the importance of understanding the effect of pre-clinical study results in real patient populations, as clinical studies have significant heterogeneity, and findings are often not consistent with preclinical findings. This data suggests only certain classes of antidepressants are associated with poor survival in glioblastoma when considering all relevant clinical and socioeconomic factors, supporting careful selection of medications when treating depression in glioblastoma. Further research and higher-level evidence are necessary to better understand the impact of antidepressant therapy in glioblastoma survival.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOur study is limited by its retrospective, single institution design. Due to this, we may not be able to control for unknown confounders. Furthermore, our study does not consider socioeconomic status, which has been shown to potentially significantly affect glioblastoma outcomes. However, we accounted for race and rurality in our analysis. A potential limitation is the fact that poor functional status may predict increased antidepressant usage, biasing our results. However, our adjustment for baseline mental health status as well as modeling exposure as a time varying covariate should account for this to some degree. There is also potential that our review of medication records may overestimate actual usage, as compliance with medication regimen is difficult to ensure. Though we included the most common drugs given for antidepressant therapy, it is possible that there are more rare antidepressant therapies that were not included for analysis. Though there may be risk of bias due to the single institution nation of this study, our center is the primary tertiary referral center for several states in the southeastern United States, and the only NCI-designated cancer center in the state. Thus, it may be reasoned that we have an adequate sampling of the glioblastoma patients in our region. Potential interactions with other psycho-effective medications were not investigated. Revised definition of the WHO Central Nervous System (CNS) Tumor guidelines have categorized IDH mutant, Grade IV astrocytoma as separate from glioblastoma. However, all IDH-mutant tumors were still included in this analysis to better understand the effect of antidepressant therapy and survival in high grade gliomas. We attempted to address this by controlling for biomolecular markers. There was significant missing data for IDH and MGMT marker status in the cohort, due to changes in patterns of practice prior to the 2016 WHO CNS guidelines. Thus, we were reasonably justified in assuming that data was missing in patterns that met criteria for missing-at-random (MAR), justifying the utilization of imputation methods at higher proportions of missingness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, we replicated our findings in several different cohorts, further reinforcing the robustness of our findings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eUtilization of SSRI, serotonin modulator use, and TCAs after glioblastoma diagnosis are associated with worse survival in patients, after adjusting for known factors with relevance to survival. Further studies should seek to validate this effect in a multicenter cohort and identify the precise biological effect of various antidepressant therapy on glioblastoma proliferation. Careful selection of antidepressant choice in patients with glioblastoma may be warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This project is supported in part by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R25NS079188 (DEO). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. DEO is also a Cornwall Clinical Scholar supported by the University of Alabama at Birmingham.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORSHIP STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yifei Sun and Dagoberto Estevez-Ordonez. Study supervision was performed by Dagoberto Estevez-Ordonez, Burt Nabors, and James Markert. Study support was performed by Dagoberto Estevez-Ordonez, Burt Nabors, and James Markert. The first draft of the manuscript was written by Yifei Sun and all authors critically reviewed and edited previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement:\u003c/strong\u003e Data is available upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHanif F, Muzaffar K, Perveen K, Malhi SM, Simjee Sh U (2017) Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pac J Cancer Prev 18:3\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22034/APJCP.2017.18.1.3\u003c/span\u003e\u003cspan address=\"10.22034/APJCP.2017.18.1.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrochans S, Cybulska AM, Simińska D, Korbecki J, Kojder K, Chlubek D, Baranowska-Bosiacka I (2022) Epidemiology of Glioblastoma Multiforme-Literature Review. 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Iran J Public Health 50:1372\u0026ndash;1380. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18502/ijph.v50i7.6626\u003c/span\u003e\u003cspan address=\"10.18502/ijph.v50i7.6626\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Antidepressants, Glioblastoma, survival","lastPublishedDoi":"10.21203/rs.3.rs-7339610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7339610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlioblastoma is the most common primary brain malignancy and carries significant mortality. Preclinical studies have highlighted the efficacy of antidepressant therapy in inhibiting glioblastoma progression; however, real-world evidence remains conflicting. We sought to investigate the impact of different commonly utilized antidepressant therapies on survival in patients with glioblastoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 1464 consecutive patients with glioblastoma treated at a single institution from 2008 to 2023 were included for analysis. Multivariate cox regression analysis with antidepressant usage modeled as a time varying covariate was used to assess the effect of antidepressants while controlling for a priori selected clinical variables with known relevance to survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median age at diagnosis was 62 (IQR 52-70) years with a median overall survival of 13.8 months. Of the cohort, 44% utilized antidepressants after diagnosis, with SSRIs as the most common class utilized (26%). The median duration of any antidepressant therapy was 111 (IQR 9-303) days. In a time varying, multivariate cox regression, usage of SSRIs (HR 1.4, 95%CI 1.21-1.62), SNRIs (HR 1.33, 95%CI 1.03-1.72), serotonin modulators (HR 1.61, 95%CI 1.40-1.86), and atypical antidepressants (HR 1.7, 95%CI 1.28-2.26) were associated with worse survival. Amongst SSRIs, only escitalopram (HR 1.33, 95%CI 1.10-1.60) and citalopram (HR 1.31, 95%CI 1.01-1.70) were associated with worse survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSRIs, SNRIs, serotonin modulators, and atypical antidepressants are associated with worse survival in patients with glioblastoma. Careful selection of antidepressant medication in patients with glioblastoma may be necessary to optimize outcomes.\u003c/p\u003e","manuscriptTitle":"Survival Outcomes Associated with Antidepressant Use in Glioblastoma: A Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:30:44","doi":"10.21203/rs.3.rs-7339610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T11:08:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T03:01:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T16:57:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203626215120798604947101089268457371088","date":"2025-08-18T00:16:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310481140413258739404082247001179609341","date":"2025-08-16T14:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283852162927691876853038879289459197633","date":"2025-08-14T15:28:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237560511905023835392609015028330137615","date":"2025-08-11T18:26:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T14:26:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T13:57:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T13:55:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2025-08-10T14:59:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fe7389c7-c740-4d84-ae84-7251429aff7d","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:09:56+00:00","versionOfRecord":{"articleIdentity":"rs-7339610","link":"https://doi.org/10.1007/s11060-025-05288-3","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2025-10-27 15:57:40","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-08-19 12:30:44","video":"","vorDoi":"10.1007/s11060-025-05288-3","vorDoiUrl":"https://doi.org/10.1007/s11060-025-05288-3","workflowStages":[]},"version":"v1","identity":"rs-7339610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7339610","identity":"rs-7339610","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}