VEGF as a Potential Factor in the Cognitive Impairment Assessment of Major Depressive Disorder: A Case-Control Study

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Methods In this case-control study, we recruited 60 patients diagnosed with depression (33 males and 27 females, with a mean age of 41.17 years) from the outpatient or inpatient unit of Suzhou Guangji Hospital. Additionally, 60 healthy controls (28 males and 32 females, with a mean age of 37.20 years) were recruited from the local community in the Suzhou Xiangcheng District. Subsequently, we measured serum VEGF levels using the VEGF ELISA Kit and assessed cognitive performance using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Results This study has received approval from the Institutional Review Board of Suzhou Guangji Hospital, adhering to ethical guidelines and involving the handling of clinical biosamples. Following adjustment for variables such as gender, age, BMI, and other potential confounding factors, it was observed that the serum VEGF levels in individuals with depression were significantly reduced compared to those in the corresponding healthy control group (F = 4.55, p = 0.04). Within the depressive patient cohort, serum VEGF levels negatively correlated with attention scores (r=-0.32, p = 0.01) and RBANS total scores (r=-0.28, p = 0.03). Conversely, no such correlations were observed in the healthy control group (attention scores: r = 0.19, p = 0.15; RBANS total scores: r=-0.03, p = 0.82). Conclusions Our research findings suggest a potential association between serum VEGF levels and the physiological pathology of MDD. This association may have a corresponding impact on the cognitive function of individuals facing MDD. Vascular Endothelial Growth Factor Major depressive disorder Cognitive function Figures Figure 1 Figure 2 Figure 3 Background The incidence of Major Depressive Disorder (MDD) is gradually rising, becoming an increasingly prevalent and severe mental health condition[ 1 ]. Numerous studies have underscored cognitive impairment as a fundamental feature of this condition[ 2 – 4 ]. This impairment encompasses a range of cognitive functions, including learning ability, visual-spatial skills, memory, and attention. These cognitive deficits have a substantial influence on the well-being and treatment outcomes of those experiencing Major Depressive Disorder (MDD). Therefore, addressing these deficits is pivotal for the treatment and recovery of individuals experiencing MDD[ 5 ]. Given that the precise origins of MDD remain elusive, further research is imperative. Recently, a succession of investigations has suggested a potential correlation between the pathophysiological mechanisms of Major Depressive Disorder (MDD) and region-specific neurotrophic factors[ 6 – 8 ]. Based on this, the neurotrophic factor hypothesis proposes that abnormalities in the serum levels of neurotrophic factors lead to neuronal atrophy and reduced neurogenesis, thereby contributing to the onset of MDD[ 9 – 11 ]. Recent studies have honed in on the Vascular Endothelial Growth Factor (VEGF) as a distinctive neurotrophic element among these factors. This element enhances vascular permeability, promotes angiogenesis, and regulates neurogenesis and neural plasticity, playing a crucial role in the nervous system[ 12 – 16 ]. Studies suggest that VEGF is a critically important brain-derived neurotrophic factor. It provides neuroprotection against injuries such as hypoxia and ischemia and improves cognitive function by enhancing the plasticity of mature neurons[ 17 – 19 ]. At the gene expression level, Iga et al.'s clinical study found an elevated expression of VEGF mRNA associated with MDD. After antidepressant treatment, this expression level decreased, potentially indicating clinical improvement[ 20 ]. Nguyen et al.'s investigation found a correlation between the VEGF-related single nucleotide polymorphism (SNP) rs6921438 and subiculum atrophy in patients diagnosed with MDD[ 21 ]. Additionally, a body of research has identified that VEGF plays a role in influencing the initiation and advancement of Alzheimer's disease (AD) and other neurological disorders, including Amyotrophic Lateral Sclerosis (ALS), radiation brain necrosis, and Autism[ 22 – 25 ]. All of the studies mentioned above suggest that VEGF, functioning as a neurotrophic factor, may serve as a reliable biomarker for Major Depressive Disorder. Unfortunately, due to significant methodological variations in the studies, consistent results were not obtained throughout clinical investigations assessing VEGF levels in MDD patients. This poses a considerable obstacle to discussions regarding the potential involvement of VEGF in the pathophysiology of depression, as well as subsequent research on its role in antidepressant treatment[ 26 – 28 ]. Therefore, merely assessing VEGF levels in MDD patients may not be sufficient. In recent years, a series of studies on cerebral vasculature have revealed that blood-brain barrier (BBB) disruption plays a pivotal role in the development of cognitive impairment[ 29 ]. Similarly, cognitive deficits are closely associated with vascular abnormalities such as diffuse or focal ischemic changes, bleeding, and other vascular anomalies[ 30 , 31 ]. In this series of damaging processes, VEGF may play a significant role. These studies have prompted a renewed focus on the association between VEGF and cognitive impairment in MDD patients. However, to date, there have been no clinical studies reporting the association between VEGF and cognitive impairment in MDD patients. Therefore, this study aims to compare peripheral VEGF levels and their relationship with cognitive impairment between healthy controls and MDD patients, investigate the degree of heterogeneity, and further explore the impact of VEGF on cognitive function in MDD patients and its role in the onset and development of MDD. Methods Ethics Statement This study was conducted between May 2017 and May 2021. A research coordinator explained the study protocol and procedure to each participant, and then signed informed consent was obtained. The Medical Institutional Review Board of Suzhou Guangji Hospital approved the study protocol and informed consent. The ethical approval number is Suzhou Guangji Hospital Ethics Committee (2019-026). Participants Sixty MDD patients were enrolled in outpatient or inpatient units of Suzhou Guangji Hospital, a municipal-owned psychiatric hospital. All MDD patients met the following inclusion criteria: (1) Han Chinese, aged 18–60; (2) confirmation of unipolar depression according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) by two experienced psychiatrists; (3) had a minimum of 6 years of education; and (4) could participate in the assessment of depressive scale. Exclusion criteria: 1) significant comorbid psychiatric or medical illness, including substance abuse, diabetes mellitus, or hypertension, and 2) currently pregnant or lactating. In total, 60 HCs were enrolled from the local community in the Suzhou Xiangcheng District. All HCs met the following inclusion criteria: (1) Han Chinese, aged 18–60; (2) had a minimum of 6 years of education; (3) could participate in the assessment of depressive symptoms; and (4) had a Zung Self-Rating Depression Scale (SDS) normal score < 50 that was assessed using the SDS Chinese version. In addition, none of the HCs had any history of MDD[ 32 , 33 ]. Cognition and VEGF Assessment Each participant provided detailed questionnaires, including a complete medical history, physical examination, and medical and psychological conditions information. Additional details, such as age, gender, education level, body mass index (BMI), smoking and drinking habits, suicide status, and age of onset, were collected from available medical records. The depressive symptoms of all patients were assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). This instrument comprises 12 test items categorized into 5 factors, namely immediate memory, visuospatial/constructional, language, attention, and delayed memory. Higher scores on the RBANS indicate a better psychological state for the test subjects. The measurement of RBANS was conducted by a research coordinator after patient recruitment [ 34 , 35 ]. Blood samples with coagulants were collected from all participants between 7 and 9 AM following an overnight fasting. The serum was separated, aliquoted, and stored at − 80◦C in a refrigerator before laboratory assays. Statistical Analysis Demographic and clinical data were compared between all MDD patients and the healthy control group (HCs). For continuous variables that met the assumptions of normality and homogeneity of variance, analysis of variance (ANOVA) was employed, while categorical variables were assessed using the chi-squared test. Subsequently, ANOVA was utilized to compare serum VEGF levels between all MDD patients and HCs. In cases where significance was observed in the ANOVA, potential confounding factors (gender, age, education, marital status, smoking, drinking, and BMI) were considered as covariates based on previous research experience. Further comparisons between MDD patients and HCs in terms of RBANS scores were conducted using ANOVA and analysis of covariance (ANCOVA). The relationship between serum VEGF levels and RBANS test scores in MDD patients and the healthy control group was assessed using Pearson's product-moment correlation coefficients. Continuous data were presented as mean and standard deviation (mean ± S.D.), and all p-values were two-tailed with a significance level of < 0.05. Results Demographic and Clinical Characteristics There were no notable distinctions noted in terms of gender, age, marital status, BMI, smoking, and drinking habits when comparing MDD patients to the healthy control group (Table 1). However, a significant distinction in education level was noted between the case and control groups (F=36.13, P<0.001). The mean age of onset for MDD patients was 36.51±13.85 years, and the illness duration was 5.07±6.24 years. Comparison of RBANS Scores and VEGF Levels between Two Groups The mean and standard deviation of RBANS test scores for 60 MDD patients and 60 healthy controls are outlined in Table 2. Significant distinctions were evident in all RBANS test scores between these two groups (all, p<0.001). Illustrated in Fig. 1, serum VEGF levels in MDD patients were lower than in the healthy control group (2.02±0.36 vs. 2.17±0.35, F=5.57, p=0.02). When considering gender, age, education, marital status, smoking, drinking, and BMI as covariates in ANOVA, a nominally significant difference between cases and controls was established (F=4.55, p=0.04). Table 1 Demographic and clinical data of MDD patients and healthy controls Variables Healthy Controls N=60, Mean (SD) MDD Patients N=60, Mean (SD) F or χ 2 p Gender(male/female) 28/32 33/27 0.53 0.47 Age(years) 37.20(11.93) 41.17(13.32) 2.95 0.09 Education(years) 12.80(2.49) 9.57(3.34) 36.13 <0.001 Marriage(Unmarried/Married/Divorced) 18/40/2 6/24/0 2.25 0.32 BMI(kg/m2) 22.90(2.69) 22.28(3.44) 1.2 0.27 Smoking(smoker/nonsmoker) 45/14 48/12 0.07 0.79 Drinking(drinker/nondrinker) 53/7 48/12 1 0.32 Firstage(years) 36.51(13.85) Duration of illness(years) 5.07(6.24) Abbreviations: MDD, Major depressive disorder, SD Standard deviation BMI, Body mass index Table 2 Comparisons of the RBANS test scores between MDD patients and healthy controls Variables Healthy Controls N=60, Mean (SD) MDD Patients N=60, Mean (SD) F or χ 2 p Effect Size Adjusted F value a Adjusted p-value a Adjusted Effect Size Immediate Memory 89.95(10.29) 66.62(18.03) 75.82 <0.001 0.39 27.90 <0.001 0.20 Visuospatial/Constructional 83.45(11.58) 72.78(16.07) 17.40 <0.001 0.13 4.93 0.03 0.04 Language 97.20(13.25) 74.53(14.90) 77.58 <0.001 0.40 32.11 <0.001 0.22 Attention 112.02(12.27) 88.75(19.03) 63.34 <0.001 0.35 21.80 <0.001 0.16 Delayed Memory 92.67(5.93) 74.70(17.39) 57.35 <0.001 0.33 18.25 <0.001 0.14 RBANS total score 92.63(8.07) 69.58(15.15) 108.20 <0.001 0.48 49.63 <0.001 0.31 Abbreviations: RBANS the repetitive battery for the assessment of neuropsychological status， an Adjusted p-value indicated the F and p-value after adjusting for gender, age, education, marital status, smoking, drinking, and BMI Association between RBANS Scores and VEGF Levels As shown in Figures 2 and 3, VEGF levels in the serum of MDD patients were negatively correlated with attention score (r=-0.32, p=0.01) and RBANS total score (r=-0.28, p=0.03), as determined by Pearson correlation analysis. However, significant correlations were not observed in the healthy control group (attention score: r=0.19, p=0.15; RBANS total score: r=-0.03, p=0.82). Discussion To the best of our knowledge, this represents the inaugural case-control study investigating the serum levels of VEGF, cognitive function, and their interrelation in individuals with major depressive disorder (MDD). The study revealed two primary outcomes: 1) Cognitive function in MDD patients was notably inferior compared to the healthy control group; 2) Within MDD patients, there was an inverse correlation observed between VEGF levels and both attention and RBANS total scores. Numerous studies have highlighted cognitive dysfunction as a fundamental aspect of MDD[2-4]. This impairment significantly impacts the quality of life for individuals with MDD and can even lead to occupational and functional disabilities, making it a critical focus for treatment and rehabilitation[5, 36]. While the pathophysiological mechanisms of MDD remain unclear, several previous studies have suggested that abnormalities in specific neurotrophic factor levels may be involved in the aetiology of MDD[6-11].VEGF is a crucial neurotrophic factor that regulates neurogenesis and neural plasticity[12-14, 18, 19]. Hence, abnormalities in VEGF may significantly contribute to the development of MDD. Our findings indicate that all MDD patients showed notably reduced RBANS test scores compared to the healthy control group. Additionally, serum VEGF levels negatively correlated with attention scores measured within the RBANS assessments. This experimental result cannot be explained by the neurotrophic hypothesis of depression, which posits that stress-induced reductions in neurotrophic factors, including VEGF, are a critical factor leading to the onset of depression and that restoring their levels is key to treating depression and alleviating symptoms[11]. Our findings do not align well with this hypothesis due to significant variability in the results of analyses on MDD and VEGF levels, stemming from differences in study design and detection methods across research[37]. For instance, studies by Tseng P.-T, Becerril-Villanueva E, and others observed an increase in VEGF levels in patients with depression[10, 38]. In contrast, research by Ventriglia M, Carvalho AF, and others found no significant difference in VEGF levels between patients with depression and healthy control groups [39, 40]. Minelli reported an increase in VEGF levels following antidepressant treatment[41]. Consequently, researchers, including Clark-Raymond A, have proposed an alternative explanation, suggesting that the elevation in VEGF levels results from MDD patients' attempts to induce neuroprotective effects in response to the stress-related damage that has already occurred[21].VEGF plays a crucial role in neurotrophic and neuroprotective functions within the brain, as it can directly stimulate angiogenesis, enhancing the supply of oxygen and nutrients[42, 43]. Yang J et al.'s research suggests that VEGF may directly ameliorate cognitive deficits by enhancing neuronal activity and neurofunction through predominantly engaging VEGFR-2[44]. Moreover, previous animal studies have corroborated the significant role of VEGF in augmenting cognitive capabilities[45]. Research about cognitive functions in Alzheimer's disease patients has also unveiled a connection with VEGF[22, 46, 47]. The myriad of studies highlights a profound linkage between VEGF and cognitive function, and the negative correlation between VEGF levels and cognitive scores within the RBANS identified in this study contributes further to elucidating this link. It may also imply that individuals with MDD could ameliorate their cognitive deficits by elevating VEGF levels, thereby repairing impaired neuronal function. Similarly, cognitive impairments associated with VEGF may also be linked to changes in the permeability of the blood-brain barrier (BBB). Prior research has indicated that under chronic stress, VEGF/VEGFR2 may compromise the integrity of the BBB by increasing its permeability, thereby facilitating the development of neurovascular dysfunctions and the progression of major depressive disorder (MDD)[48-51]. This alteration in permeability allows inflammatory factors, including IL-6, to traverse the BBB, further exacerbating the development of MDD and related cognitive deficits[52-54]. This suggests that VEGF serves a dual role as a neuroprotective agent and a critical factor in the onset and progression of MDD, embodying both protective and damaging functions. Thus, we posit that the negative correlation observed between VEGF levels and RBANS scores, as well as cognitive function in this study, is influenced by two factors. On the one hand, patients with MDD may elevate VEGF levels as a neuroprotective response to mitigate potential or existing neural damage, such as impairment of neurogenesis in the hippocampus[55, 56]. On the other hand, excessively elevated VEGF levels can disrupt the BBB, thereby exacerbating the onset and progression of MDD, which in turn may lead to further decline in cognitive functions. In conclusion, our findings lend support to the significant role of VEGF in the onset and progression of cognitive decline in patients with MDD. However, to consider VEGF as a biomarker for cognitive decline or to use it as a measure to assess the degree of cognitive impairment, further specific studies are needed to elucidate its role and precise function in depression. Given the negative correlation between VEGF levels and RBANS scores observed in our study, future research should also focus on exploring the potential of VEGF as a target for antidepressant treatment. Lastly, the current study has certain limitations, and future research will need to control variables more rigorously to reduce heterogeneity in study design. This will aid in further investigation and exploration. Conclusion Our study observed a significant decline in cognitive functions among those with MDD compared to a healthy control group. This decrease was linked to a notable negative correlation between VEGF levels and scores on the overall RBANS and its attention subscales. Given the relative ease of measuring VEGF, our findings highlight the potential utility of VEGF as a biomarker for evaluating the extent of cognitive impairment in individuals with MDD. However, it's crucial to emphasize that due to limitations such as a relatively small sample size and the absence of longitudinal follow-up, the conclusions drawn from this study are considered preliminary. Declarations Ethical Approval: This study was approved by the Institutional Review Board of Suzhou Guangji Hospital (Ethical approval number: 2019-026). All participants provided written informed consent. Funding: This research was funded by the National Natural Science Foundation of China (grant numbers 82371508, 81771439), the Natural Science Foundation of Jiangsu Province (grant number BK20200210), Jiangsu Provincial Key Research and Development Program (grant number BE2020661), Jiangsu Provincial Administration of Traditional Chinese Medicine (grant number MS2022083), Suzhou Municipal Sci-Tech Bureau Program (grant numbers SKY2023225, SKY2022064, SKY2022065), and the Sample Bank of Suzhou Municipal Psychiatric Disorders with support from the Suzhou Municipal Finance Bureau. No additional financial support or compensation has been received for this work. Author Contribution Authors' Contributions:Zhenhua Zhu and Jingwei Yang were responsible for the study design and data analysis. Dongmei Dai, Liwan Zhang and Yili Zhang were responsible for data collection and preliminary analysis, ensuring the accuracy and completeness of the data.Xuyuan Yin was responsible for the literature review and preparation of background materials, providing a solid foundation for the study.Yuan Cai was responsible for drafting and revising the manuscript, ensuring its clarity and coherence.Weiwei Tao and Li Hui, as the corresponding authors, provided overall guidance and final approval of the manuscript. All authors read and approved the final manuscript. Acknowledgement Acknowledgements:We would like to thank all the staff at Suzhou Guangji Hospital and the Medical College of Soochow University for their support and assistance throughout the study. Availability of data and materials: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. 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Ye G, Yin GZ, Tang Z, Fu JL, Chen J, Chen SS, Li J, Fu T, Yu X, Xu DW et al : Association between increased serum interleukin-6 levels and sustained attention deficits in patients with major depressive disorder . Psychological medicine 2018, 48 (15):2508-2514. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, Lanctôt KL: A meta-analysis of cytokines in major depression . Biological psychiatry 2010, 67 (5):446-457. Goldsmith DR, Rapaport MH, Miller BJ: A meta-analysis of blood cytokine network alterations in psychiatric patients: comparisons between schizophrenia, bipolar disorder and depression . Molecular psychiatry 2016, 21 (12):1696-1709. Zhang SQ, Deng Q, Zhu Q, Hu ZL, Long LH, Wu PF, He JG, Chen HS, Yue Z, Lu JH et al : Cell type-specific NRBF2 orchestrates autophagic flux and adult hippocampal neurogenesis in chronic stress-induced depression . Cell discovery 2023, 9 (1):90. Boldrini M, Santiago AN, Hen R, Dwork AJ, Rosoklija GB, Tamir H, Arango V, John Mann J: Hippocampal granule neuron number and dentate gyrus volume in antidepressant-treated and untreated major depression . Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 2013, 38 (6):1068-1077. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 03 Mar, 2025 Editor assigned by journal 28 Feb, 2025 Submission checks completed at journal 28 Feb, 2025 First submitted to journal 05 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5766380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422246796,"identity":"b5ed791f-c644-4177-8f78-c2d4090b5127","order_by":0,"name":"Zhenhua Zhu","email":"","orcid":"","institution":"Suzhou Guangji Hospital, Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Zhu","suffix":""},{"id":422246797,"identity":"7bbbbbf5-4d21-492d-a6e4-30a51a36aa4b","order_by":1,"name":"Jingwei Yang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingwei","middleName":"","lastName":"Yang","suffix":""},{"id":422246798,"identity":"b220c1eb-320b-437e-9246-533b52f1221d","order_by":2,"name":"Dongmei Dai","email":"","orcid":"","institution":"Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Dai","suffix":""},{"id":422246799,"identity":"12e6b31a-7272-46ea-b520-7895f9c2d64a","order_by":3,"name":"Liwan Zhang","email":"","orcid":"","institution":"Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Liwan","middleName":"","lastName":"Zhang","suffix":""},{"id":422246800,"identity":"81ecfc3a-3fd7-4363-9262-df2d918ae033","order_by":4,"name":"Yili Zhang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yili","middleName":"","lastName":"Zhang","suffix":""},{"id":422246801,"identity":"49a19154-65e9-4210-9280-d3e4511c6be3","order_by":5,"name":"Xuyuan Yin","email":"","orcid":"","institution":"Suzhou Guangji Hospital, Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xuyuan","middleName":"","lastName":"Yin","suffix":""},{"id":422246802,"identity":"cdbe3efe-f077-4d3b-8055-b9d095ff8bee","order_by":6,"name":"Yuan Cai","email":"","orcid":"","institution":"Suzhou Guangji Hospital, Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Cai","suffix":""},{"id":422246803,"identity":"cdfc760b-0989-4a57-85b0-ab32dae3dba9","order_by":7,"name":"Li Hui","email":"","orcid":"","institution":"Suzhou Guangji Hospital, Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Hui","suffix":""},{"id":422246804,"identity":"184a662a-1265-43c3-b278-f1cc27294966","order_by":8,"name":"Weiwei Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYNCCCgZmCIONCMU8YPIMyVoY22BcYrTYsx8+Js077w67wfHTCQwfyg4z8M9uIGALT1qaNO+2Z8wGZ3I3MM44d5hB4s4BQg7LMbudu+0ws8EN3g3MvG2HGQwkEgho4X8D1DIHquUvUVokQLY0QLUwEqXlxrP033+OHWaWBPrlYM+5dB6JGwS0sPcnHzacUXM4me/42Y0PfpRZy/HPIKAFBpJBxAEGWNwSA+yIVjkKRsEoGAUjDwAABe1Bl3f/6TgAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-01-05 06:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5766380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5766380/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-07612-7","type":"published","date":"2025-11-15T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77684399,"identity":"ac24ca4c-95e9-46d4-8940-758d992cdb3d","added_by":"auto","created_at":"2025-03-04 08:58:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37038,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of serum VEGF levels between MDD patients and healthy controls.\u003c/p\u003e\n\u003cp\u003eAbbreviations: VEGF, Vascular Endothelial Growth Factor; ANOVA, Analysis of Variance; ANCOVA, Analysis of Covariance; HCs, Healthy Controls.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5766380/v1/28ad61d4231a289ec3843704.png"},{"id":77684398,"identity":"dd756476-93c9-4de3-8e66-21079c2611a2","added_by":"auto","created_at":"2025-03-04 08:58:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45199,"visible":true,"origin":"","legend":"\u003cp\u003eA negative correlation between serum VEGF levels and attention score in MDD patients (r=-0.32, p=0.01), but this correlation was not observed in the healthy control group (r=0.19, p=0.15).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5766380/v1/8113647844d2d384a4b23af7.png"},{"id":77684401,"identity":"983d4c2c-b8c9-4049-b838-fbe588ab69fa","added_by":"auto","created_at":"2025-03-04 08:58:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51602,"visible":true,"origin":"","legend":"\u003cp\u003eA negative correlation between serum VEGF levels and RBANS total score in MDD patients (r=-0.28, p=0.03), but this correlation was not observed in the healthy control group (r=-0.03, p=0.82).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5766380/v1/70d103cff5307c8ff304b4eb.png"},{"id":96105177,"identity":"e5b3034c-1461-4c9b-8094-f10342c27c05","added_by":"auto","created_at":"2025-11-17 16:09:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2817256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5766380/v1/50278011-b9ee-446a-bd25-483b2faafbef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"VEGF as a Potential Factor in the Cognitive Impairment Assessment of Major Depressive Disorder: A Case-Control Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe incidence of Major Depressive Disorder (MDD) is gradually rising, becoming an increasingly prevalent and severe mental health condition[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Numerous studies have underscored cognitive impairment as a fundamental feature of this condition[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This impairment encompasses a range of cognitive functions, including learning ability, visual-spatial skills, memory, and attention. These cognitive deficits have a substantial influence on the well-being and treatment outcomes of those experiencing Major Depressive Disorder (MDD). Therefore, addressing these deficits is pivotal for the treatment and recovery of individuals experiencing MDD[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given that the precise origins of MDD remain elusive, further research is imperative.\u003c/p\u003e \u003cp\u003eRecently, a succession of investigations has suggested a potential correlation between the pathophysiological mechanisms of Major Depressive Disorder (MDD) and region-specific neurotrophic factors[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Based on this, the neurotrophic factor hypothesis proposes that abnormalities in the serum levels of neurotrophic factors lead to neuronal atrophy and reduced neurogenesis, thereby contributing to the onset of MDD[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have honed in on the Vascular Endothelial Growth Factor (VEGF) as a distinctive neurotrophic element among these factors. This element enhances vascular permeability, promotes angiogenesis, and regulates neurogenesis and neural plasticity, playing a crucial role in the nervous system[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies suggest that VEGF is a critically important brain-derived neurotrophic factor. It provides neuroprotection against injuries such as hypoxia and ischemia and improves cognitive function by enhancing the plasticity of mature neurons[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. At the gene expression level, Iga et al.'s clinical study found an elevated expression of VEGF mRNA associated with MDD. After antidepressant treatment, this expression level decreased, potentially indicating clinical improvement[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nguyen et al.'s investigation found a correlation between the VEGF-related single nucleotide polymorphism (SNP) rs6921438 and subiculum atrophy in patients diagnosed with MDD[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, a body of research has identified that VEGF plays a role in influencing the initiation and advancement of Alzheimer's disease (AD) and other neurological disorders, including Amyotrophic Lateral Sclerosis (ALS), radiation brain necrosis, and Autism[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All of the studies mentioned above suggest that VEGF, functioning as a neurotrophic factor, may serve as a reliable biomarker for Major Depressive Disorder.\u003c/p\u003e \u003cp\u003eUnfortunately, due to significant methodological variations in the studies, consistent results were not obtained throughout clinical investigations assessing VEGF levels in MDD patients. This poses a considerable obstacle to discussions regarding the potential involvement of VEGF in the pathophysiology of depression, as well as subsequent research on its role in antidepressant treatment[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, merely assessing VEGF levels in MDD patients may not be sufficient. In recent years, a series of studies on cerebral vasculature have revealed that blood-brain barrier (BBB) disruption plays a pivotal role in the development of cognitive impairment[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, cognitive deficits are closely associated with vascular abnormalities such as diffuse or focal ischemic changes, bleeding, and other vascular anomalies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this series of damaging processes, VEGF may play a significant role. These studies have prompted a renewed focus on the association between VEGF and cognitive impairment in MDD patients.\u003c/p\u003e \u003cp\u003eHowever, to date, there have been no clinical studies reporting the association between VEGF and cognitive impairment in MDD patients. Therefore, this study aims to compare peripheral VEGF levels and their relationship with cognitive impairment between healthy controls and MDD patients, investigate the degree of heterogeneity, and further explore the impact of VEGF on cognitive function in MDD patients and its role in the onset and development of MDD.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eEthics Statement\u003c/p\u003e\u003cp\u003eThis study was conducted between May 2017 and May 2021. A research coordinator explained the study protocol and procedure to each participant, and then signed informed consent was obtained. The Medical Institutional Review Board of Suzhou Guangji Hospital approved the study protocol and informed consent. The ethical approval number is Suzhou Guangji Hospital Ethics Committee (2019-026).\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eSixty MDD patients were enrolled in outpatient or inpatient units of Suzhou Guangji Hospital, a municipal-owned psychiatric hospital. All MDD patients met the following inclusion criteria: (1) Han Chinese, aged 18–60; (2) confirmation of unipolar depression according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) by two experienced psychiatrists; (3) had a minimum of 6 years of education; and (4) could participate in the assessment of depressive scale. Exclusion criteria: 1) significant comorbid psychiatric or medical illness, including substance abuse, diabetes mellitus, or hypertension, and 2) currently pregnant or lactating.\u003c/p\u003e\u003cp\u003eIn total, 60 HCs were enrolled from the local community in the Suzhou Xiangcheng District. All HCs met the following inclusion criteria: (1) Han Chinese, aged 18–60; (2) had a minimum of 6 years of education; (3) could participate in the assessment of depressive symptoms; and (4) had a Zung Self-Rating Depression Scale (SDS) normal score \u0026lt; 50 that was assessed using the SDS Chinese version. In addition, none of the HCs had any history of MDD[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCognition and VEGF Assessment\u003c/p\u003e\u003cp\u003eEach participant provided detailed questionnaires, including a complete medical history, physical examination, and medical and psychological conditions information. Additional details, such as age, gender, education level, body mass index (BMI), smoking and drinking habits, suicide status, and age of onset, were collected from available medical records.\u003c/p\u003e\u003cp\u003eThe depressive symptoms of all patients were assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). This instrument comprises 12 test items categorized into 5 factors, namely immediate memory, visuospatial/constructional, language, attention, and delayed memory. Higher scores on the RBANS indicate a better psychological state for the test subjects. The measurement of RBANS was conducted by a research coordinator after patient recruitment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBlood samples with coagulants were collected from all participants between 7 and 9 AM following an overnight fasting. The serum was separated, aliquoted, and stored at − 80◦C in a refrigerator before laboratory assays.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDemographic and clinical data were compared between all MDD patients and the healthy control group (HCs). For continuous variables that met the assumptions of normality and homogeneity of variance, analysis of variance (ANOVA) was employed, while categorical variables were assessed using the chi-squared test. Subsequently, ANOVA was utilized to compare serum VEGF levels between all MDD patients and HCs. In cases where significance was observed in the ANOVA, potential confounding factors (gender, age, education, marital status, smoking, drinking, and BMI) were considered as covariates based on previous research experience. Further comparisons between MDD patients and HCs in terms of RBANS scores were conducted using ANOVA and analysis of covariance (ANCOVA). The relationship between serum VEGF levels and RBANS test scores in MDD patients and the healthy control group was assessed using Pearson's product-moment correlation coefficients. Continuous data were presented as mean and standard deviation (mean ± S.D.), and all p-values were two-tailed with a significance level of \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic and Clinical Characteristics\u003c/p\u003e\n\u003cp\u003eThere were no notable distinctions noted in terms of gender, age, marital status, BMI, smoking, and drinking habits when comparing MDD patients to the healthy control group (Table 1). However, a significant distinction in education level was noted between the case and control groups (F=36.13, P\u0026lt;0.001). The mean age of onset for MDD patients was 36.51\u0026plusmn;13.85 years, and the illness duration was 5.07\u0026plusmn;6.24 years.\u003c/p\u003e\n\u003cp\u003eComparison of RBANS Scores and VEGF Levels between Two Groups\u003c/p\u003e\n\u003cp\u003eThe mean and standard deviation of RBANS test scores for 60 MDD patients and 60 healthy controls are outlined in Table 2. Significant distinctions were evident in all RBANS test scores between these two groups (all, p\u0026lt;0.001). Illustrated in Fig. 1, serum VEGF levels in MDD patients were lower than in the healthy control group (2.02\u0026plusmn;0.36 vs. 2.17\u0026plusmn;0.35, F=5.57, p=0.02). When considering gender, age, education, marital status, smoking, drinking, and BMI as covariates in ANOVA, a nominally significant difference between cases and controls was established (F=4.55, p=0.04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic and clinical data of MDD patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eHealthy Controls\u003c/p\u003e\n \u003cp\u003eN=60, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eMDD Patients\u003c/p\u003e\n \u003cp\u003eN=60, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eF or \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eGender(male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e28/32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e33/27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e37.20(11.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e41.17(13.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eEducation(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e12.80(2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e9.57(3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e36.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eMarriage(Unmarried/Married/Divorced)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e18/40/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6/24/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e22.90(2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e22.28(3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eSmoking(smoker/nonsmoker)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e45/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e48/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eDrinking(drinker/nondrinker)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e53/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e48/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eFirstage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e36.51(13.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eDuration of illness(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.07(6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MDD, Major depressive disorder, SD Standard deviation BMI, Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Comparisons of the RBANS test scores between MDD patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eHealthy Controls\u003c/p\u003e\n \u003cp\u003eN=60, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eMDD Patients\u003c/p\u003e\n \u003cp\u003eN=60, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eF or \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAdjusted F value \u003csup\u003ea\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eAdjusted p-value \u003csup\u003ea\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eAdjusted Effect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eImmediate Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e89.95(10.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e66.62(18.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e75.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e27.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eVisuospatial/Constructional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e83.45(11.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e72.78(16.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLanguage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e97.20(13.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e74.53(14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e77.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e32.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e112.02(12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e88.75(19.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e63.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eDelayed Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e92.67(5.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e74.70(17.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e57.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRBANS total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e92.63(8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e69.58(15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e108.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e49.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: RBANS the repetitive battery for the assessment of neuropsychological status， \u003csup\u003ean\u0026nbsp;\u003c/sup\u003eAdjusted p-value indicated the F and p-value after adjusting for gender, age, education, marital status, smoking, drinking, and BMI\u003c/p\u003e\n\u003cp\u003eAssociation between RBANS Scores and VEGF Levels\u003c/p\u003e\n\u003cp\u003eAs shown in Figures 2 and 3, VEGF levels in the serum of MDD patients were negatively correlated with attention score (r=-0.32, p=0.01) and RBANS total score (r=-0.28, p=0.03), as determined by Pearson correlation analysis. However, significant correlations were not observed in the healthy control group (attention score: r=0.19, p=0.15; \u0026nbsp; RBANS total score: r=-0.03, p=0.82).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this represents the inaugural case-control study investigating the serum levels of VEGF, cognitive function, and their interrelation in individuals with major depressive disorder (MDD). The study revealed two primary outcomes: 1) Cognitive function in MDD patients was notably inferior compared to the healthy control group; 2) Within MDD patients, there was an inverse correlation observed between VEGF levels and both attention and RBANS total scores.\u003c/p\u003e\n\u003cp\u003eNumerous studies have highlighted cognitive dysfunction as a fundamental aspect of MDD[2-4]. This impairment significantly impacts the quality of life for individuals with MDD and can even lead to occupational and functional disabilities, making it a critical focus for treatment and rehabilitation[5, 36].\u003c/p\u003e\n\u003cp\u003eWhile the pathophysiological mechanisms of MDD remain unclear, several previous studies have suggested that abnormalities in specific neurotrophic factor levels may be involved in the aetiology of MDD[6-11].VEGF is a crucial neurotrophic factor that regulates neurogenesis and neural plasticity[12-14, 18, 19]. Hence, abnormalities in VEGF may significantly contribute to the development of MDD. Our findings indicate that all MDD patients showed notably reduced RBANS test scores compared to the healthy control group. Additionally, serum VEGF levels negatively correlated with attention scores measured within the RBANS assessments.\u003c/p\u003e\n\u003cp\u003eThis experimental result cannot be explained by the neurotrophic hypothesis of depression, which posits that stress-induced reductions in neurotrophic factors, including VEGF, are a critical factor leading to the onset of depression and that restoring their levels is key to treating depression and alleviating symptoms[11]. Our findings do not align well with this hypothesis due to significant variability in the results of analyses on MDD and VEGF levels, stemming from differences in study design and detection methods across research[37]. For instance, studies by Tseng P.-T, Becerril-Villanueva E, and others observed an increase in VEGF levels in patients with depression[10, 38]. In contrast, research by Ventriglia M, Carvalho AF, and others found no significant difference in VEGF levels between patients with depression and healthy control groups [39, 40]. Minelli reported an increase in VEGF levels following antidepressant treatment[41].\u003c/p\u003e\n\u003cp\u003eConsequently, researchers, including Clark-Raymond A, have proposed an alternative explanation, suggesting that the elevation in VEGF levels results from MDD patients' attempts to induce neuroprotective effects in response to the stress-related damage that has already occurred[21].VEGF plays a crucial role in neurotrophic and neuroprotective functions within the brain, as it can directly stimulate angiogenesis, enhancing the supply of oxygen and nutrients[42, 43]. Yang J et al.'s research suggests that VEGF may directly ameliorate cognitive deficits by enhancing neuronal activity and neurofunction through predominantly engaging VEGFR-2[44]. Moreover, previous animal studies have corroborated the significant role of VEGF in augmenting cognitive capabilities[45]. Research about cognitive functions in Alzheimer's disease patients has also unveiled a connection with VEGF[22, 46, 47]. The myriad of studies highlights a profound linkage between VEGF and cognitive function, and the negative correlation between VEGF levels and cognitive scores within the RBANS identified in this study contributes further to elucidating this link. It may also imply that individuals with MDD could ameliorate their cognitive deficits by elevating VEGF levels, thereby repairing impaired neuronal function.\u003c/p\u003e\n\u003cp\u003eSimilarly, cognitive impairments associated with VEGF may also be linked to changes in the permeability of the blood-brain barrier (BBB). Prior research has indicated that under chronic stress, VEGF/VEGFR2 may compromise the integrity of the BBB by increasing its permeability, thereby facilitating the development of neurovascular dysfunctions and the progression of major depressive disorder (MDD)[48-51]. This alteration in permeability allows inflammatory factors, including IL-6, to traverse the BBB, further exacerbating the development of MDD and related cognitive deficits[52-54]. This suggests that VEGF serves a dual role as a neuroprotective agent and a critical factor in the onset and progression of MDD, embodying both protective and damaging functions.\u003c/p\u003e\n\u003cp\u003eThus, we posit that the negative correlation observed between VEGF levels and RBANS scores, as well as cognitive function in this study, is influenced by two factors. On the one hand, patients with MDD may elevate VEGF levels as a neuroprotective response to mitigate potential or existing neural damage, such as impairment of neurogenesis in the hippocampus[55, 56]. On the other hand, excessively elevated VEGF levels can disrupt the BBB, thereby exacerbating the onset and progression of MDD, which in turn may lead to further decline in cognitive functions.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings lend support to the significant role of VEGF in the onset and progression of cognitive decline in patients with MDD. However, to consider VEGF as a biomarker for cognitive decline or to use it as a measure to assess the degree of cognitive impairment, further specific studies are needed to elucidate its role and precise function in depression. Given the negative correlation between VEGF levels and RBANS scores observed in our study, future research should also focus on exploring the potential of VEGF as a target for antidepressant treatment.\u003c/p\u003e\n\u003cp\u003eLastly, the current study has certain limitations, and future research will need to control variables more rigorously to reduce heterogeneity in study design. This will aid in further investigation and exploration.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study observed a significant decline in cognitive functions among those with MDD compared to a healthy control group. This decrease was linked to a notable negative correlation between VEGF levels and scores on the overall RBANS and its attention subscales. Given the relative ease of measuring VEGF, our findings highlight the potential utility of VEGF as a biomarker for evaluating the extent of cognitive impairment in individuals with MDD. However, it\u0026apos;s crucial to emphasize that due to limitations such as a relatively small sample size and the absence of longitudinal follow-up, the conclusions drawn from this study are considered preliminary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval:\u003c/h2\u003e \u003cp\u003e This study was approved by the Institutional Review Board of Suzhou Guangji Hospital (Ethical approval number: 2019-026). All participants provided written informed consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Natural Science Foundation of China (grant numbers 82371508, 81771439), the Natural Science Foundation of Jiangsu Province (grant number BK20200210), Jiangsu Provincial Key Research and Development Program (grant number BE2020661), Jiangsu Provincial Administration of Traditional Chinese Medicine (grant number MS2022083), Suzhou Municipal Sci-Tech Bureau Program (grant numbers SKY2023225, SKY2022064, SKY2022065), and the Sample Bank of Suzhou Municipal Psychiatric Disorders with support from the Suzhou Municipal Finance Bureau. No additional financial support or compensation has been received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors' Contributions:Zhenhua Zhu and Jingwei Yang were responsible for the study design and data analysis. Dongmei Dai, Liwan Zhang and Yili Zhang were responsible for data collection and preliminary analysis, ensuring the accuracy and completeness of the data.Xuyuan Yin was responsible for the literature review and preparation of background materials, providing a solid foundation for the study.Yuan Cai was responsible for drafting and revising the manuscript, ensuring its clarity and coherence.Weiwei Tao and Li Hui, as the corresponding authors, provided overall guidance and final approval of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements:We would like to thank all the staff at Suzhou Guangji Hospital and the Medical College of Soochow University for their support and assistance throughout the study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMonroe SM, Harkness KL: \u003cstrong\u003eMajor Depression and Its Recurrences: Life Course Matters\u003c/strong\u003e. \u003cem\u003eAnnual review of clinical psychology \u003c/em\u003e2022, \u003cstrong\u003e18\u003c/strong\u003e:329-357.\u003c/li\u003e\n\u003cli\u003eVarghese S, Frey BN, Schneider MA, Kapczinski F, de Azevedo Cardoso T: \u003cstrong\u003eFunctional and cognitive impairment in the first episode of depression: A systematic review\u003c/strong\u003e. \u003cem\u003eActa psychiatrica Scandinavica \u003c/em\u003e2022, \u003cstrong\u003e145\u003c/strong\u003e(2):156-185.\u003c/li\u003e\n\u003cli\u003eKnight MJ, Baune BT: \u003cstrong\u003eCognitive dysfunction in major depressive disorder\u003c/strong\u003e. \u003cem\u003eCurrent opinion in psychiatry \u003c/em\u003e2018, \u003cstrong\u003e31\u003c/strong\u003e(1):26-31.\u003c/li\u003e\n\u003cli\u003eFrampton JE: \u003cstrong\u003eVortioxetine: A Review in Cognitive Dysfunction in Depression\u003c/strong\u003e. \u003cem\u003eDrugs \u003c/em\u003e2016, \u003cstrong\u003e76\u003c/strong\u003e(17):1675-1682.\u003c/li\u003e\n\u003cli\u003eMacQueen GM, Memedovich KA: \u003cstrong\u003eCognitive dysfunction in major depression and bipolar disorder: Assessment and treatment options\u003c/strong\u003e. \u003cem\u003ePsychiatry and clinical neurosciences \u003c/em\u003e2017, \u003cstrong\u003e71\u003c/strong\u003e(1):18-27.\u003c/li\u003e\n\u003cli\u003eSharma AN, da Costa e Silva BF, Soares JC, Carvalho AF, Quevedo J: \u003cstrong\u003eRole of trophic factors GDNF, IGF-1 and VEGF in major depressive disorder: A comprehensive review of human studies\u003c/strong\u003e. \u003cem\u003eJournal of affective disorders \u003c/em\u003e2016, \u003cstrong\u003e197\u003c/strong\u003e:9-20.\u003c/li\u003e\n\u003cli\u003eNeto FL, Borges G, Torres-Sanchez S, Mico JA, Berrocoso E: \u003cstrong\u003eNeurotrophins role in depression neurobiology: a review of basic and clinical evidence\u003c/strong\u003e. \u003cem\u003eCurrent neuropharmacology \u003c/em\u003e2011, \u003cstrong\u003e9\u003c/strong\u003e(4):530-552.\u003c/li\u003e\n\u003cli\u003eCastr\u0026eacute;n E, V\u0026otilde;ikar V, Rantam\u0026auml;ki T: \u003cstrong\u003eRole of neurotrophic factors in depression\u003c/strong\u003e. \u003cem\u003eCurrent opinion in pharmacology \u003c/em\u003e2007, \u003cstrong\u003e7\u003c/strong\u003e(1):18-21.\u003c/li\u003e\n\u003cli\u003eDuman RS, Li N: \u003cstrong\u003eA neurotrophic hypothesis of depression: role of synaptogenesis in the actions of NMDA receptor antagonists\u003c/strong\u003e. \u003cem\u003ePhilosophical transactions of the Royal Society of London Series B, Biological sciences \u003c/em\u003e2012, \u003cstrong\u003e367\u003c/strong\u003e(1601):2475-2484.\u003c/li\u003e\n\u003cli\u003eTseng PT, Lee Y, Lin PY: \u003cstrong\u003eAge-associated decrease in serum glial cell line-derived neurotrophic factor levels in patients with major depressive disorder\u003c/strong\u003e. \u003cem\u003eProgress in neuro-psychopharmacology \u0026amp; 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Additionally, 60 healthy controls (28 males and 32 females, with a mean age of 37.20 years) were recruited from the local community in the Suzhou Xiangcheng District. Subsequently, we measured serum VEGF levels using the VEGF ELISA Kit and assessed cognitive performance using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e This study has received approval from the Institutional Review Board of Suzhou Guangji Hospital, adhering to ethical guidelines and involving the handling of clinical biosamples. Following adjustment for variables such as gender, age, BMI, and other potential confounding factors, it was observed that the serum VEGF levels in individuals with depression were significantly reduced compared to those in the corresponding healthy control group (F\u0026thinsp;=\u0026thinsp;4.55, p\u0026thinsp;=\u0026thinsp;0.04). Within the depressive patient cohort, serum VEGF levels negatively correlated with attention scores (r=-0.32, p\u0026thinsp;=\u0026thinsp;0.01) and RBANS total scores (r=-0.28, p\u0026thinsp;=\u0026thinsp;0.03). Conversely, no such correlations were observed in the healthy control group (attention scores: r\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.15; RBANS total scores: r=-0.03, p\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur research findings suggest a potential association between serum VEGF levels and the physiological pathology of MDD. This association may have a corresponding impact on the cognitive function of individuals facing MDD.\u003c/p\u003e","manuscriptTitle":"VEGF as a Potential Factor in the Cognitive Impairment Assessment of Major Depressive Disorder: A Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 08:58:27","doi":"10.21203/rs.3.rs-5766380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T09:35:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-28T10:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-28T10:37:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-01-05T06:23:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c3ffe26-d3aa-4c69-836e-27d405ee7ab5","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:04:50+00:00","versionOfRecord":{"articleIdentity":"rs-5766380","link":"https://doi.org/10.1186/s12888-025-07612-7","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-11-15 15:57:52","publishedOnDateReadable":"November 15th, 2025"},"versionCreatedAt":"2025-03-04 08:58:27","video":"","vorDoi":"10.1186/s12888-025-07612-7","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07612-7","workflowStages":[]},"version":"v1","identity":"rs-5766380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5766380","identity":"rs-5766380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}