Prevalence of Cognitive Impairment among major depressive disorder patients: A systematic review and meta-analysis

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There has not been comprehensive study assessing cognitive impairment among major depression patients despite the considerable risk of exposure. Therefore, this review was designed to assess global pooled prevalence of cognitive impairment among major depression patients. METHOD This systematic review was done according to PRISMA guidelines, with searches in electronic databases such as Google Scholar, PubMed, and Scopus. The quality of the studies included was determined by the JBI Quality Assessment Scale. Extraction of the data was performed with Microsoft Excel, and meta-analysis was done using STATA 17 software. The random-effects model was used to synthesize the pooled prevalence cognitive impairment in major depressive disorder. Besides, heterogeneity was explored using meta-regression and subgroup analysis. Publication bias was evaluated using funnel plots and Egger's statistical tests. Sensitivity analysis was also performed. RESULTS The pooled overall prevalence of cognitive impairment in patients with major depressive disorder (MDD) was 53% (95% CI: 41% – 64%). Heterogeneity between the studies was very high (I² = 98.76%). Subgroup analyses were conducted to examine the potential sources of heterogeneity. Study design was one of them. The subgroup pooled estimates are by Cross-sectional: 58.3%, Cohort: 42.5% and Case-control: 26.6%. Based on cognitive assessment tools suggested the highest overall proportion was observed in studies using the MMSE 66.9% and the lowest pooled prevalence was observed using the DASST tool 35.5%. The highest pooled prevalence was between studies that used the ICD-10 criteria to diagnose MDD, with rate of 70.4%, followed by clinical diagnosis 63.6%, DSM-V 58.1%, DSM-IV 40.4% and the MINI 7.0 study had the least estimate 32.7%. Publication bias was assessed using Begg's and Egger's tests, neither of which indicated bias at a significant level (p > 0.05). The trim-and-fill method confirmed the stability of the findings, with no imputed absent studies. Sensitivity analysis demonstrated that the overall effect size remained stable in all iterations. CONCLUSIONS This meta-analysis found the pooled prevalence of cognitive impairment in major depressive disorder (MDD) was relatively high across the included studies However, there was notable variability was observed based on diagnostic criteria, study design, and cognitive measurement instruments. Although the findings suggest the potential value of including routine cognitive screening and cognitive symptoms in treatment protocols, the limited number of studies (n = 11) warrants cautious interpretation. Future research using consistent diagnostic and cognitive assessment methods is recommended to confirm these findings and guide clinical practice. Psychiatry Cognitive impairment Major Depressive Disorder prevalence meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction MDD is a widespread and disabling psychiatric disorder characterized by persistent low mood, anhedonia, and a variety of cognitive and somatic symptoms interfering with daily functioning. According to the DSM-5-TR, the symptoms must last a minimum of two weeks and cause significant distress or impairment (APA, 2022) 1 . Apart from its affective and emotional symptoms, MDD is also being increasingly recognized as being accompanied by significant cognitive impairments, which are variously described as cognitive decline, cognitive impairment, or cognitive dysfunction. Cognitive impairment is a disturbance in one or more of the cognitive functions (e.g., memory, attention, or executive function) that is significantly greater than would be expected for the person's age and educational level and is a result of the depressive disorder and not another psychiatric or neurological disorder 2 . These impairments in MDD can be state-dependent (i.e., present during depressive episodes) or trait-like (still present during periods of remission) 3 . Globally, over 280 million people live with MDD, which has a sizeable proportion of the global disease burden 4 . It is a significant source of disability in the world and, in addition, a considerable contributor to years lived with disability (YLDs) 5 . Studies show that up to 80% of individuals with acute MDD have cognitive impairment, and 20–30% continue to have deficits even during remission 2 , 6 . Study conducted in China found that cognitive impairment is highly prevalent during acute phase of MDD, which the incidence ranging from 76.9% and 94.0% during prodromal and acute phases 7 . Cognitive dysfunction is now recognized as a core feature of MDD and not just a secondary symptom 8 . Most commonly involved domains are working memory, processing speed, attention, and executive functioning. Cognitive impairment in MDD is associated with poor treatment response, reduced quality of life, impaired work and social functioning, and increased risk of relapse 9 . There are some potential pharmacological interventions and non-pharmacological interventions that can improve cognitive outcome 10 . The increasing prevalence of cognitive impairment (CI) in major depressive disorder (MDD) patients, and the associated high health care cost, is a gigantic social, medical, and economic burden. It is an added load on already strained global health care. Cognitive impairment is a serious barrier in the effective treatment of MDD as it impairs the effect of treatment and functional recovery. Early recognition of CI in MDD patients is crucial—not only to support cognitive recovery and delay further impairment, but also to enhance medication adherence. Scientific evidence regarding the size of the cognitive deficit in MDD needs to be established in order to begin estimating healthcare requirements reliably and designing useful and targeted mental health services. Despite this, there are limited comprehensive data on the prevalence of cognitive impairment among Major depressive disorder patients. We therefore designed this review to assess the global pooled prevalence of Cognitive impairment among major depressive disorder patients. Methods Protocol Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines have been followed in this systematic review and meta-analysis 11 . Article selection was performed based on the checklist in (supplementary materials). Research protocol has been registered as CDR420251007851 in the International Prospective Register of Systematic Reviews (PROSPERO). Publication search strategy The current systematic review and meta-analysis aimed to determine the pooled prevalence cognitive impairment among major depressive disorder patients. Duplications were prevented by investigating whether such systematic reviews and meta-analyses on this subject have already been published. Comprehensive searches for research on the prevalence cognitive impairment among major depressive disorder patients were carried out using various electronic databases, namely Google Scholar, PubMed, and Scopus. Suitable search terms, in accordance with the Medical Subject Heading (MeSH), were thoroughly chosen, and those search terms were individually as well as in combinations using Boolean operators such as "OR" and "AND". The keywords employed in this research included words like "prevalence, magnitude, proportion, cognitive impairment, cognitive dysfunction, cognitive decline, cognitive function, MDD, Major depressive disorder, depression." The duplicates were excluded and three independent reviewers (AMM, BB, and BWY) meticulously screened the title and abstract of all potentially included studies. Full copies of potentially helpful research studies documenting the prevalence cognitive impairment among major depressive disorder patients were subsequently accessed. Disagreement among authors regarding data extraction was resolved through discussion. The entire search was conducted between January 2015 and April of 2025. Eligibility criteria Inclusion Criteria Eligible studies were cross-sectional, cohort, or case-control studies. Participants should have been adults aged 18–65 years with Major Depressive Disorder (MDD). Cognitive impairment should have been quantified using standardized cognitive assessment tools such as the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), or any other similar valid instrument. Studies published in English or with English-language abstracts alone were included. The research had to be published from 2015 to 2025 and had to include studies from any geographic location unless specified by the scope of study otherwise. Exclusion Criteria Case reports, editorials, letters to the editor, animal studies, or non-human subject studies were not included. Studies that did not involve patients with Major Depressive Disorder (MDD) or included populations with comorbid neurological disease (e.g., Alzheimer's disease, Parkinson's disease) were not included. Studies that did not quantify cognitive decline using standardized tests of cognition or the sole use of subjective report were not included. Non-English language studies were also not included. Outcome of concern The key focus of this systematic review and meta-analysis is to assess the global pooled prevalence of cognitive impairment among major depressive disorder patients. It was calculated using the metan prevalence standard error command after generating prevalence and standard error of prevalence. Cognitive impairment among major depressive disorder patients was confirmed through MMSE, MoCA, Neurocognitive battery, PDQ-D, DASST cognitive impairment assessment tools. Data extraction and quality assessment Three reviewers (AMM, AMG, and BWY) independently selected the papers by title, abstract, and full text using the provided eligibility criteria. Afterwards, the reviewers used Microsoft Excel Sheets for data extraction from the full text of potentially eligible papers. Information on the primary author's name, year of publication, country, study design, cognitive assessment tool, Major depressive disorder diagnostic method, prevalence of cognitive impairment, and episode of depression were collected from each study. The JBI was employed to evaluate the general quality of the included studies since it was specifically designed to screen the quality of observational studies 12 . Extracted results of the reviewers were compared and any discrepancies present in the inclusion and quality assessment of individual articles were resolved through mutual consensus. Statistical analysis The essential information was retrieved from each qualified original study that met the inclusion criteria using a format of Microsoft Excel spread sheet. The data were subsequently transferred to STATA version 17 for analysis using metan commands. Forest plots, ascertained by I 2 and tests of Cochrane's Q 13 , 14 , were utilized to present the heterogeneity among these studies or the variation in their findings. The I² values were interpreted as: 0–40% (low heterogeneity), 40–60% (moderate), 60–90% (substantial), and 90–100% (considerable heterogeneity) 15 . Because of the considerable heterogeneity found, a random-effects model using the DerSimonian and Laird (D + L) approach was used to pool the prevalence 16 . The result was presented using a forest plot. Publication bias was assessed through visual inspection using a funnel plot and statistically with Egger's regression test 15 , 17 . An asymmetrical funnel plot and p-value of < 0.05 in Egger's test indicated the presence of significant publication bias. Moreover, trim-and-fill method was utilized to "estimate the number of missing studies that might exist in a meta-analysis and the impact of these studies might have had on its outcome" 17 . Subgroup analysis was also performed based on the study design, MDD diagnostic methods, cognitive assessment tools, country, and MDD first episod. Further, sensitivity analysis was carried out to ascertain the influence of one single study on the overall pooled estimate. Description of the included studies A total of 6251studies were obtained from database searches, and 1948 duplicates were removed. Then 4303 remaining articles were screened at title and abstract. Thereafter, 432 full-text articles were filtered in detail against the inclusion criteria. Among these, 420 full-text articles were removed. Ultimately, 11 articles met the inclusion criteria for the systematic review and meta-analysis (Fig. 1). Results Characteristics of the studies included In this review, 11 original studies with a total of 6275 study participants were included 18 – 29 . Three of the three studies were conducted in Pakistan, three in China, one in Ethiopia, one in Japan, one in Malaysia, one in south Korea and one in multicounty of Asia. Regarding types study design, one study on case control, two cohort studies, and 8 cross sectional studies. Regarding MDD diagnosis methods, 2 studies were based on clinical diagnosis, 2 studies based on ICD 10, 1 study based on MINI 7.0, 4 studies based on DSM IV, and 2 studies based on DSM V (Table 1 ). Table 1 Baseline characteristics of included articles on the prevalence and related factors of cognitive impairment in MDD patients (N = 11). Author year Countries Study design Sample size MDD criteria Cognitive tool Prevalence Quality Malik M et al. (2019) Pakistan Cross-sectional 382 Clinical diagnosis MMSE 75.7 9 Minhas et al. (2019) Pakistan Cross-sectional 2599 ICD-10 MMSE 70.7 8 Jat MI et al. (2022) Pakistan Cross-sectional 250 ICD-10 PDQ-D 67.6 9 Dominiquee et al. (2016) South Korea Cross-sectional 312 DSM IV PDQ-D 50.2 9 Ariaratnam et al. (2019) Malaysia Cross-sectional 245 MINI 7.0 MoCA 32.7 8 Liu et al. (2023) China Cohort 153 DSM IV Neurocognitive battery 50 8 Zhang et al. (2022) China Case-control 631 DSM IV Neurocognitive battery 26.6 8 Hammer-Helmich et al. (2021) Japan Cohort 518 DSM IV DASST 35.6 8 Ambaw & Desalegn (2019) Ethiopia Cross-sectional 395 DSM V MMSE 54 9 Chen et al. (2024) China Cross-sectional 126 DSM V MoCA 63.4 8 Srisurapanont et al. (2017) Multi_country Cross-sectional 664 Clinical diagnosis PDQ-D 51.7 9 Prevalence of Cognitive impairment among major depressive disorder The pooled estimated prevalence of Cognitive impairment among major depressive disorder was presented using a forest plot. A greater disparity in the Cognitive impairment among major depressive disorder prevalence was found in the studies. The prevalence range is from that was reported in China 26.6% to Pakistan reported 75.7%. The overall prevalence of Cognitive impairment among major depressive disorder patients pooled by random effects model was 53% (95% CI: 41% – 64%). There was considerable heterogeneity between studies (I² = 98.76%, p < 0.001). The between-study variance (Tau²) was 0.04, which again supported the use of a random-effects model (Fig. 2 ). Sub-group analysis by Study designs Subgroup analysis by the Study design showed that pooled prevalence cognitive impairment in MDD patients was 52.6% (95% CI: 41.3–63.8%), The subgroup pooled estimates are by Cross-sectional: 58.3% (95% CI: 48.9%, 67.7%), Cohort: 42.5% (95% CI: 28.0%, 57.0%) and Case-control: 26.6% (95% CI: 23.3%, 30.2%). There is very high heterogeneity between all subgroups (I² > 90%) and also there is statistical significance between groups (Q = 41.22, p < 0.001), substantiating real differences by study design. Sub-group analysis by Cognitive assessment tools The overall prevalence of cognitive impairment in major depressive disorder patients varied considerably according to the cognitive screening instrument used. The highest overall proportion was observed in studies using the MMSE (66.9%, 95% CI: 56.8–77.0%, I² = 95.8%, 3 studies), followed by PDQ-D (56.4%, 95% CI: 46.4–66.4%, I² = 91.6%, 3 studies). MoCA-based research provided a pooled prevalence of 47.9% (95% CI: 17.7–78.1%, I² = 97.1%, 2 studies), while neurocognitive test battery-based research provided a lower estimate of 38.2% (95% CI: 15.0–61.4%, I² = 96.5%, 2 studies). The lowest pooled prevalence was derived using the DASST tool (35.5%, 95% CI: 31.5–39.7%, 1 study). There was overall heterogeneity between tools (I² = 98.8%, Q = 806.36, p < 0.001), and between subgroup heterogeneity (p < 0.001) (Fig. 3). Sub-group analysis by MDD diagnostic Tools Subgroup analysis by the Study design showed that pooled prevalence cognitive impairment in MDD patients was 53% (95% CI: 41.3–63.8%). %). The highest pooled proportion was between studies that used ICD-10 criteria (70.4%, 95% CI: 68.7–72.1%, I² = 0.0%, 2 studies), followed by clinical diagnosis (63.6%, 95% CI: 40.1–87.2%, I² = 98.5%, 2 studies) and DSM-V (58.1%, 95% CI: 48.8–67.4%, I² = 73.0%, 2 studies). The studies using DSM-IV had less prevalence (40.4%, 95% CI: 29.0–51.8%, I² = 95.5%, 4 studies) and the MINI 7.0 study had the least estimate (32.7%, 95% CI: 27.1–38.8%). Publication bias Assessment of publication bias using Begg's and Egger's tests did not yield evidence of small-study effects. Begg's test was not significant (Kendall's z = 0.31, p = 0.7555), and Egger's test also provided no indication of publication bias (z = 0.03, p = 0.9730). These findings suggest that publication bias would be unlikely to have affected the pooled estimates in this meta-analysis. Trim and fill analysis The nonparametric trim-and-fill analysis using a random-effects model identified no missing studies from publication bias since no studies were imputed. The overall effect size remained unchanged at 0.526 (95% CI: 0.430, 0.621) and showed no indication of publication bias. Sensitivity analysis Leave-one-out sensitivity analysis was conducted to assess the influence of each individual study on the estimate of pooled overall prevalence. When individual studies were sequentially deleted, the pooled estimate was consistent across the 50.2% (95% CI: 38.3–62.2%) to 55.2% (95% CI: 45.5–65.0%) range. These finding suggest that no single study disproportionally influenced the overall meta analytic result, Indicating stable and reliable of the pooled prevalence estimate. Discussion This is the first systematic review and meta-analysis that examines the prevalence of cognitive impairment in patients with major depressive disorder (MDD). According to the review the pooled prevalence of cognitive impairment in patients with major depressive disorder (MDD) is found to be 53% (95% CI: 41% – 64%), which suggests that more than half of individuals with MDD have clinically significant cognitive impairment. Despite the high pooled prevalence, high heterogeneity (I² = 98.76%, p < 0.001) was observed, suggesting great variability between studies. Subgroup analyses were conducted to examine the potential sources of heterogeneity. Study design was one of them. The subgroup pooled estimates are by Cross-sectional: 58.3%, Cohort: 42.5% and Case-control: 26.6%. This suggests that study design can influence effect estimates, possibly due to variations in patient recruitment, measurement tools, or adjustment for confounding factors 30 . Subgroup analyses were conducted based on cognitive assessment tools suggested the highest overall proportion was observed in studies using the MMSE 66.9% and the lowest pooled prevalence was observed using the DASST tool 35.5%(1 study) 31 . This discrepancy could be a result of variation between the sensitivity and specificity of cognitive assessments for the detection of cognitive impairment in MDD patients 32 , 33 . The MMSE, while widely employed, is not specifically tailored for psychiatric groups and may overestimate cognitive impairment when compared to more subtle assessment instruments 33 . These findings suggest that choice of cognitive assessment instrument can contribute significantly to measured prevalence and must be applied cautiously in subsequent research. The highest pooled prevalence was between studies that used the ICD-10 criteria to diagnose MDD, with rate of 70.4%, followed by clinical diagnosis 63.6%, DSM-V 58.1%, DSM-IV 40.4% and the MINI 7.0 study had the least estimate 32.7%. The association differed Significant depending on the diagnostic criteria used. The ICD-10 method yielded the most stable and strongest results 34 , 35 . This variation most likely results from variation in the definition and diagnosis of MDD in these instruments, which dictate which patients are included in the studies and therefore impact the estimated prevalence of cognitive impairment 35 . Interestingly, publication bias test findings (Begg's and Egger's) and trim-and-fill analysis results revealed that there was no statistically significant publication bias on the overall outcome. Further, sensitivity analyses revealed that no single study improperly influenced the outcome, to enhance the credibility to the findings 36 , 37 . Between-studies heterogeneity across demographic variables, severity of depression, and duration of illness, comorbidity, and treatment status may have driven the large estimates of prevalence. Also, differences in culture, health systems, and resources among countries may have influenced cognitive impairment prevalence but were not fully adjusted for in this meta-analysis 38 , 39 . Strengths and Limitations This meta-analysis has several strengths, including comprehensive search strategies, rigorous quality assessments, and cautious examination of sources of heterogeneity. However, there are some limitations that must be noted. First, the majority of studies included were cross-sectional in nature, and therefore, the causation between MDD and cognitive impairment is limited. Second, the heterogeneity of cognitive testing tools and diagnostic criteria reduced comparability across studies. Third, the majority of the studies were conducted in Asian and African countries, limiting generalizability to other continents. Conclusion This meta-analysis found the pooled prevalence of cognitive impairment in major depressive disorder (MDD) was relatively high across the included studies However, there was notable variability was observed based on diagnostic criteria, study design, and cognitive measurement instruments. Although the findings suggest the potential value of including routine cognitive screening and cognitive symptoms in treatment protocols, the limited number of studies (n = 11) warrants cautious interpretation. Future research using consistent diagnostic and cognitive assessment methods is recommended to confirm these findings and guide clinical practice. Declarations Acknowledgements Authors thank all the authors of the included studies in this systematic review and meta-analysis. Author contributions AMM: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. AMG, BW and BB conceived the idea and provided funding acquisition and data extraction, analysis and draft writing. AMM and AMG helped in the analysis, manuscript preparation, and manuscript revision. All authors have read and approved the final manuscript to be published. Funding No funding from an organization was received by the authors for this research. Availability of data and materials The data sets presented in this study are available upon reasonable request from the corresponding author. Ethics approval Consent to participate: Not applicable. This study is based on already published literature and does not involve direct contact with human participants. Clinical trial number: Not applicable. 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version.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/05991b632de07f060342907f.png"},{"id":82136301,"identity":"a61a4dcd-13ee-4aa1-a5af-86b6c49c0a86","added_by":"auto","created_at":"2025-05-07 06:12:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82365,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2 Forest plot showing the prevalence of cognitive impairment among MDD patients (N = 11).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/2cf3ac7dd628ea6c23a3b189.png"},{"id":82134551,"identity":"7770070a-6702-4c5c-bbb5-d3722961a789","added_by":"auto","created_at":"2025-05-07 06:04:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106252,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Sub group analysis based on study design of cognitive impairment among patients with diabetes in Africa (N = 11).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/923305cedec528d7a598b550.png"},{"id":82134557,"identity":"68e996e5-aba0-4a53-a9cd-a9bc019bb1a3","added_by":"auto","created_at":"2025-05-07 06:04:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133865,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Sub group analysis based on cognitive assessment tools of cognitive impairment among patients with diabetes in Africa (N = 11).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/b79e1b858a86f480e36eca17.png"},{"id":82134555,"identity":"d035ec09-1149-41a3-96ce-adf9b9f3492e","added_by":"auto","created_at":"2025-05-07 06:04:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129908,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Sub group analysis based on MDD diagnostic criteria of cognitive impairment among patients with diabetes in Africa (N = 11).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/1f2d4f270eff8d3a2b2e1e4e.png"},{"id":82137728,"identity":"59a9f6ab-21d6-4589-a461-893a84386454","added_by":"auto","created_at":"2025-05-07 06:20:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44291,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/c370fa362603b0b35dbb2b66.png"},{"id":82134553,"identity":"736d8aad-0aa9-4a8d-b558-cb84ec803d64","added_by":"auto","created_at":"2025-05-07 06:04:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":19402,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Result of Trim and fill analysis is on the prevalence and associated factors of cognitive impairment among patients with MDD (N = 11).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/79ff2e1de1f8e2ea156f2323.png"},{"id":82134556,"identity":"25f1c9bd-d541-49da-ae89-b876e9ca345d","added_by":"auto","created_at":"2025-05-07 06:04:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":21861,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Result of the sensitivity analysis on the prevalence and associated factors of cognitive impairment among patients with MDD (N = 11).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/23d8dc7ede7063a43cea935f.png"},{"id":82139767,"identity":"3e808b72-b84c-42e5-b35c-cc11de03a668","added_by":"auto","created_at":"2025-05-07 06:28:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1162703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6573789/v1/0d6bf706-1c55-42b9-a014-12f22dea5a74.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePrevalence of Cognitive Impairment among major depressive disorder patients: A systematic review and meta-analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMDD is a widespread and disabling psychiatric disorder characterized by persistent low mood, anhedonia, and a variety of cognitive and somatic symptoms interfering with daily functioning. According to the DSM-5-TR, the symptoms must last a minimum of two weeks and cause significant distress or impairment (APA, 2022)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Apart from its affective and emotional symptoms, MDD is also being increasingly recognized as being accompanied by significant cognitive impairments, which are variously described as cognitive decline, cognitive impairment, or cognitive dysfunction.\u003c/p\u003e \u003cp\u003eCognitive impairment is a disturbance in one or more of the cognitive functions (e.g., memory, attention, or executive function) that is significantly greater than would be expected for the person's age and educational level and is a result of the depressive disorder and not another psychiatric or neurological disorder \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These impairments in MDD can be state-dependent (i.e., present during depressive episodes) or trait-like (still present during periods of remission)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobally, over 280\u0026nbsp;million people live with MDD, which has a sizeable proportion of the global disease burden\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It is a significant source of disability in the world and, in addition, a considerable contributor to years lived with disability (YLDs)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Studies show that up to 80% of individuals with acute MDD have cognitive impairment, and 20\u0026ndash;30% continue to have deficits even during remission\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Study conducted in China found that cognitive impairment is highly prevalent during acute phase of MDD, which the incidence ranging from 76.9% and 94.0% during prodromal and acute phases\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Cognitive dysfunction is now recognized as a core feature of MDD and not just a secondary symptom\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Most commonly involved domains are working memory, processing speed, attention, and executive functioning. Cognitive impairment in MDD is associated with poor treatment response, reduced quality of life, impaired work and social functioning, and increased risk of relapse\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. There are some potential pharmacological interventions and non-pharmacological interventions that can improve cognitive outcome \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe increasing prevalence of cognitive impairment (CI) in major depressive disorder (MDD) patients, and the associated high health care cost, is a gigantic social, medical, and economic burden. It is an added load on already strained global health care. Cognitive impairment is a serious barrier in the effective treatment of MDD as it impairs the effect of treatment and functional recovery. Early recognition of CI in MDD patients is crucial\u0026mdash;not only to support cognitive recovery and delay further impairment, but also to enhance medication adherence. Scientific evidence regarding the size of the cognitive deficit in MDD needs to be established in order to begin estimating healthcare requirements reliably and designing useful and targeted mental health services. Despite this, there are limited comprehensive data on the prevalence of cognitive impairment among Major depressive disorder patients. We therefore designed this review to assess the global pooled prevalence of Cognitive impairment among major depressive disorder patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eProtocol\u003c/h3\u003e\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines have been followed in this systematic review and meta-analysis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Article selection was performed based on the checklist in (supplementary materials). Research protocol has been registered as CDR420251007851 in the International Prospective Register of Systematic Reviews (PROSPERO).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublication search strategy\u003c/h2\u003e \u003cp\u003eThe current systematic review and meta-analysis aimed to determine the pooled prevalence cognitive impairment among major depressive disorder patients. Duplications were prevented by investigating whether such systematic reviews and meta-analyses on this subject have already been published. Comprehensive searches for research on the prevalence cognitive impairment among major depressive disorder patients were carried out using various electronic databases, namely Google Scholar, PubMed, and Scopus. Suitable search terms, in accordance with the Medical Subject Heading (MeSH), were thoroughly chosen, and those search terms were individually as well as in combinations using Boolean operators such as \"OR\" and \"AND\". The keywords employed in this research included words like \"prevalence, magnitude, proportion, cognitive impairment, cognitive dysfunction, cognitive decline, cognitive function, MDD, Major depressive disorder, depression.\" The duplicates were excluded and three independent reviewers (AMM, BB, and BWY) meticulously screened the title and abstract of all potentially included studies. Full copies of potentially helpful research studies documenting the prevalence cognitive impairment among major depressive disorder patients were subsequently accessed. Disagreement among authors regarding data extraction was resolved through discussion. The entire search was conducted between January 2015 and April of 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003eEligible studies were cross-sectional, cohort, or case-control studies. Participants should have been adults aged 18\u0026ndash;65 years with Major Depressive Disorder (MDD). Cognitive impairment should have been quantified using standardized cognitive assessment tools such as the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), or any other similar valid instrument. Studies published in English or with English-language abstracts alone were included. The research had to be published from 2015 to 2025 and had to include studies from any geographic location unless specified by the scope of study otherwise.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003eCase reports, editorials, letters to the editor, animal studies, or non-human subject studies were not included. Studies that did not involve patients with Major Depressive Disorder (MDD) or included populations with comorbid neurological disease (e.g., Alzheimer's disease, Parkinson's disease) were not included. Studies that did not quantify cognitive decline using standardized tests of cognition or the sole use of subjective report were not included. Non-English language studies were also not included.\u003c/p\u003e\n\u003ch3\u003eOutcome of concern\u003c/h3\u003e\n\u003cp\u003eThe key focus of this systematic review and meta-analysis is to assess the global pooled prevalence of cognitive impairment among major depressive disorder patients. It was calculated using the metan prevalence standard error command after generating prevalence and standard error of prevalence. Cognitive impairment among major depressive disorder patients was confirmed through MMSE, MoCA, Neurocognitive battery, PDQ-D, DASST cognitive impairment assessment tools.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData extraction and quality assessment\u003c/h2\u003e \u003cp\u003eThree reviewers (AMM, AMG, and BWY) independently selected the papers by title, abstract, and full text using the provided eligibility criteria. Afterwards, the reviewers used Microsoft Excel Sheets for data extraction from the full text of potentially eligible papers. Information on the primary author's name, year of publication, country, study design, cognitive assessment tool, Major depressive disorder diagnostic method, prevalence of cognitive impairment, and episode of depression were collected from each study. The JBI was employed to evaluate the general quality of the included studies since it was specifically designed to screen the quality of observational studies \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Extracted results of the reviewers were compared and any discrepancies present in the inclusion and quality assessment of individual articles were resolved through mutual consensus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe essential information was retrieved from each qualified original study that met the inclusion criteria using a format of Microsoft Excel spread sheet. The data were subsequently transferred to STATA version 17 for analysis using metan commands. Forest plots, ascertained by I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and tests of Cochrane's Q \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, were utilized to present the heterogeneity among these studies or the variation in their findings. The I\u0026sup2; values were interpreted as: 0\u0026ndash;40% (low heterogeneity), 40\u0026ndash;60% (moderate), 60\u0026ndash;90% (substantial), and 90\u0026ndash;100% (considerable heterogeneity)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Because of the considerable heterogeneity found, a random-effects model using the DerSimonian and Laird (D\u0026thinsp;+\u0026thinsp;L) approach was used to pool the prevalence\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The result was presented using a forest plot.\u003c/p\u003e \u003cp\u003ePublication bias was assessed through visual inspection using a funnel plot and statistically with Egger's regression test \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. An asymmetrical funnel plot and p-value of \u0026lt;\u0026thinsp;0.05 in Egger's test indicated the presence of significant publication bias. Moreover, trim-and-fill method was utilized to \"estimate the number of missing studies that might exist in a meta-analysis and the impact of these studies might have had on its outcome\" \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Subgroup analysis was also performed based on the study design, MDD diagnostic methods, cognitive assessment tools, country, and MDD first episod. Further, sensitivity analysis was carried out to ascertain the influence of one single study on the overall pooled estimate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescription of the included studies\u003c/h3\u003e\n\u003cp\u003eA total of 6251studies were obtained from database searches, and 1948 duplicates were removed. Then 4303 remaining articles were screened at title and abstract. Thereafter, 432 full-text articles were filtered in detail against the inclusion criteria. Among these, 420 full-text articles were removed. Ultimately, 11 articles met the inclusion criteria for the systematic review and meta-analysis (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the studies included\u003c/h2\u003e \u003cp\u003eIn this review, 11 original studies with a total of 6275 study participants were included\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Three of the three studies were conducted in Pakistan, three in China, one in Ethiopia, one in Japan, one in Malaysia, one in south Korea and one in multicounty of Asia. Regarding types study design, one study on case control, two cohort studies, and 8 cross sectional studies. Regarding MDD diagnosis methods, 2 studies were based on clinical diagnosis, 2 studies based on ICD 10, 1 study based on MINI 7.0, 4 studies based on DSM IV, and 2 studies based on DSM V (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\u003eBaseline characteristics of included articles on the prevalence and related factors of cognitive impairment in MDD patients (N\u0026thinsp;=\u0026thinsp;11).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMDD criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCognitive tool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalik M et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinhas et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJat MI et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDQ-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominiquee et al. 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(2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMINI 7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeurocognitive battery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeurocognitive battery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHammer-Helmich et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDASST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbaw \u0026amp; Desalegn (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSrisurapanont et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti_country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDQ-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of Cognitive impairment among major depressive disorder\u003c/h2\u003e \u003cp\u003eThe pooled estimated prevalence of Cognitive impairment among major depressive disorder was presented using a forest plot. A greater disparity in the Cognitive impairment among major depressive disorder prevalence was found in the studies. The prevalence range is from that was reported in China 26.6% to Pakistan reported 75.7%. The overall prevalence of Cognitive impairment among major depressive disorder patients pooled by random effects model was 53% (95% CI: 41% \u0026ndash; 64%). There was considerable heterogeneity between studies (I\u0026sup2; = 98.76%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The between-study variance (Tau\u0026sup2;) was 0.04, which again supported the use of a random-effects model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSub-group analysis by Study designs\u003c/h2\u003e \u003cp\u003eSubgroup analysis by the Study design showed that pooled prevalence cognitive impairment in MDD patients was 52.6% (95% CI: 41.3\u0026ndash;63.8%), The subgroup pooled estimates are by Cross-sectional: 58.3% (95% CI: 48.9%, 67.7%), Cohort: 42.5% (95% CI: 28.0%, 57.0%) and Case-control: 26.6% (95% CI: 23.3%, 30.2%). There is very high heterogeneity between all subgroups (I\u0026sup2; \u0026gt; 90%) and also there is statistical significance between groups (Q\u0026thinsp;=\u0026thinsp;41.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), substantiating real differences by study design.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSub-group analysis by Cognitive assessment tools\u003c/h2\u003e \u003cp\u003eThe overall prevalence of cognitive impairment in major depressive disorder patients varied considerably according to the cognitive screening instrument used. The highest overall proportion was observed in studies using the MMSE (66.9%, 95% CI: 56.8\u0026ndash;77.0%, I\u0026sup2; = 95.8%, 3 studies), followed by PDQ-D (56.4%, 95% CI: 46.4\u0026ndash;66.4%, I\u0026sup2; = 91.6%, 3 studies). MoCA-based research provided a pooled prevalence of 47.9% (95% CI: 17.7\u0026ndash;78.1%, I\u0026sup2; = 97.1%, 2 studies), while neurocognitive test battery-based research provided a lower estimate of 38.2% (95% CI: 15.0\u0026ndash;61.4%, I\u0026sup2; = 96.5%, 2 studies). The lowest pooled prevalence was derived using the DASST tool (35.5%, 95% CI: 31.5\u0026ndash;39.7%, 1 study). There was overall heterogeneity between tools (I\u0026sup2; = 98.8%, Q\u0026thinsp;=\u0026thinsp;806.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between subgroup heterogeneity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSub-group analysis by MDD diagnostic Tools\u003c/h2\u003e \u003cp\u003eSubgroup analysis by the Study design showed that pooled prevalence cognitive impairment in MDD patients was 53% (95% CI: 41.3\u0026ndash;63.8%). %). The highest pooled proportion was between studies that used ICD-10 criteria (70.4%, 95% CI: 68.7\u0026ndash;72.1%, I\u0026sup2; = 0.0%, 2 studies), followed by clinical diagnosis (63.6%, 95% CI: 40.1\u0026ndash;87.2%, I\u0026sup2; = 98.5%, 2 studies) and DSM-V (58.1%, 95% CI: 48.8\u0026ndash;67.4%, I\u0026sup2; = 73.0%, 2 studies). The studies using DSM-IV had less prevalence (40.4%, 95% CI: 29.0\u0026ndash;51.8%, I\u0026sup2; = 95.5%, 4 studies) and the MINI 7.0 study had the least estimate (32.7%, 95% CI: 27.1\u0026ndash;38.8%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePublication bias\u003c/h2\u003e \u003cp\u003eAssessment of publication bias using Begg's and Egger's tests did not yield evidence of small-study effects. Begg's test was not significant (Kendall's z\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.7555), and Egger's test also provided no indication of publication bias (z\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.9730). These findings suggest that publication bias would be unlikely to have affected the pooled estimates in this meta-analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTrim and fill analysis\u003c/h2\u003e \u003cp\u003eThe nonparametric trim-and-fill analysis using a random-effects model identified no missing studies from publication bias since no studies were imputed. The overall effect size remained unchanged at 0.526 (95% CI: 0.430, 0.621) and showed no indication of publication bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eLeave-one-out sensitivity analysis was conducted to assess the influence of each individual study on the estimate of pooled overall prevalence. When individual studies were sequentially deleted, the pooled estimate was consistent across the 50.2% (95% CI: 38.3\u0026ndash;62.2%) to 55.2% (95% CI: 45.5\u0026ndash;65.0%) range. These finding suggest that no single study disproportionally influenced the overall meta analytic result, Indicating stable and reliable of the pooled prevalence estimate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first systematic review and meta-analysis that examines the prevalence of cognitive impairment in patients with major depressive disorder (MDD). According to the review the pooled prevalence of cognitive impairment in patients with major depressive disorder (MDD) is found to be 53% (95% CI: 41% \u0026ndash; 64%), which suggests that more than half of individuals with MDD have clinically significant cognitive impairment. Despite the high pooled prevalence, high heterogeneity (I\u0026sup2; = 98.76%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was observed, suggesting great variability between studies.\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted to examine the potential sources of heterogeneity. Study design was one of them. The subgroup pooled estimates are by Cross-sectional: 58.3%, Cohort: 42.5% and Case-control: 26.6%. This suggests that study design can influence effect estimates, possibly due to variations in patient recruitment, measurement tools, or adjustment for confounding factors \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted based on cognitive assessment tools suggested the highest overall proportion was observed in studies using the MMSE 66.9% and the lowest pooled prevalence was observed using the DASST tool 35.5%(1 study)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This discrepancy could be a result of variation between the sensitivity and specificity of cognitive assessments for the detection of cognitive impairment in MDD patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The MMSE, while widely employed, is not specifically tailored for psychiatric groups and may overestimate cognitive impairment when compared to more subtle assessment instruments\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These findings suggest that choice of cognitive assessment instrument can contribute significantly to measured prevalence and must be applied cautiously in subsequent research.\u003c/p\u003e \u003cp\u003eThe highest pooled prevalence was between studies that used the ICD-10 criteria to diagnose MDD, with rate of 70.4%, followed by clinical diagnosis 63.6%, DSM-V 58.1%, DSM-IV 40.4% and the MINI 7.0 study had the least estimate 32.7%. The association differed Significant depending on the diagnostic criteria used. The ICD-10 method yielded the most stable and strongest results \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This variation most likely results from variation in the definition and diagnosis of MDD in these instruments, which dictate which patients are included in the studies and therefore impact the estimated prevalence of cognitive impairment\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, publication bias test findings (Begg's and Egger's) and trim-and-fill analysis results revealed that there was no statistically significant publication bias on the overall outcome. Further, sensitivity analyses revealed that no single study improperly influenced the outcome, to enhance the credibility to the findings \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Between-studies heterogeneity across demographic variables, severity of depression, and duration of illness, comorbidity, and treatment status may have driven the large estimates of prevalence. Also, differences in culture, health systems, and resources among countries may have influenced cognitive impairment prevalence but were not fully adjusted for in this meta-analysis \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eThis meta-analysis has several strengths, including comprehensive search strategies, rigorous quality assessments, and cautious examination of sources of heterogeneity. However, there are some limitations that must be noted. First, the majority of studies included were cross-sectional in nature, and therefore, the causation between MDD and cognitive impairment is limited. Second, the heterogeneity of cognitive testing tools and diagnostic criteria reduced comparability across studies. Third, the majority of the studies were conducted in Asian and African countries, limiting generalizability to other continents.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis meta-analysis found the pooled prevalence of cognitive impairment in major depressive disorder (MDD) was relatively high across the included studies However, there was notable variability was observed based on diagnostic criteria, study design, and cognitive measurement instruments. Although the findings suggest the potential value of including routine cognitive screening and cognitive symptoms in treatment protocols, the limited number of studies (n\u0026thinsp;=\u0026thinsp;11) warrants cautious interpretation. Future research using consistent diagnostic and cognitive assessment methods is recommended to confirm these findings and guide clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eAuthors thank all the authors of the included studies in this systematic review and meta-analysis.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eAMM: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization,\u003c/p\u003e\n\u003cp\u003eValidation, Supervision, Software, Resources, Project administration,\u003c/p\u003e\n\u003cp\u003eMethodology, Investigation, Funding acquisition, Formal analysis, Data\u003c/p\u003e\n\u003cp\u003ecuration, Conceptualization. AMG, BW and BB conceived the idea and\u003c/p\u003e\n\u003cp\u003eprovided funding acquisition and data extraction, analysis and draft writing. AMM and AMG helped in the analysis, manuscript preparation, and manuscript revision. All authors have read and approved the final manuscript to be published.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding from an organization was received by the authors for this research.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data sets presented in this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eConsent to participate: Not applicable. This study is based on already published literature and does not involve direct contact with human participants.\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSvenaeus F, Diagnosing mental disorders and saving the normal: American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders, American Psychiatric Publishing:, Washington (2014) DC. 991 pp., ISBN: 978-0890425558. Price: \u003cspan\u003e$\u003c/span\u003e122.70. Medicine, Health Care and Philosophy, 17: pp. 241\u0026ndash;244\u003c/span\u003e \u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan Z et al (2019) Cognitive impairment in major depressive disorder. 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Lancet Psychiatry 6(10):851\u0026ndash;861\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Materials","content":"\u003cp\u003eThe Supplementary Materials file is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Teda Health science college","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive impairment, Major Depressive Disorder, prevalence, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-6573789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6573789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eINTRODUCTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive impairment is a common but undetected symptom among Major Depressive Disorder patients that can impact daily functioning and quality of life. There has not been comprehensive study assessing cognitive impairment among major depression patients despite the considerable risk of exposure. Therefore, this review was designed to assess global pooled prevalence of cognitive impairment among major depression patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHOD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review was done according to PRISMA guidelines, with searches in electronic databases such as Google Scholar, PubMed, and Scopus. The quality of the studies included was determined by the JBI Quality Assessment Scale. Extraction of the data was performed with Microsoft Excel, and meta-analysis was done using STATA 17 software. The random-effects model was used to synthesize the pooled prevalence cognitive impairment in major depressive disorder. Besides, heterogeneity was explored using meta-regression and subgroup analysis. Publication bias was evaluated using funnel plots and Egger's statistical tests. Sensitivity analysis was also performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled overall prevalence of cognitive impairment in patients with major depressive disorder (MDD) was 53% (95% CI: 41% – 64%). Heterogeneity between the studies was very high (I² = 98.76%). Subgroup analyses were conducted to examine the potential sources of heterogeneity. Study design was one of them. The subgroup pooled estimates are by Cross-sectional: 58.3%, Cohort: 42.5% and Case-control: 26.6%. Based on cognitive assessment tools suggested the highest overall proportion was observed in studies using the MMSE 66.9% and the lowest pooled prevalence was observed using the DASST tool 35.5%. The highest pooled prevalence was between studies that used the ICD-10 criteria to diagnose MDD, with rate of 70.4%, followed by clinical diagnosis 63.6%, DSM-V 58.1%, DSM-IV 40.4% and the MINI 7.0 study had the least estimate 32.7%. Publication bias was assessed using Begg's and Egger's tests, neither of which indicated bias at a significant level (p \u0026gt; 0.05). The trim-and-fill method confirmed the stability of the findings, with no imputed absent studies. Sensitivity analysis demonstrated that the overall effect size remained stable in all iterations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis meta-analysis found the pooled prevalence of cognitive impairment in major depressive disorder (MDD) was relatively high across the included studies However, there was notable variability was observed based on diagnostic criteria, study design, and cognitive measurement instruments. Although the findings suggest the potential value of including routine cognitive screening and cognitive symptoms in treatment protocols, the limited number of studies (n = 11) warrants cautious interpretation. Future research using consistent diagnostic and cognitive assessment methods is recommended to confirm these findings and guide clinical practice.\u003c/p\u003e","manuscriptTitle":"Prevalence of Cognitive Impairment among major depressive disorder patients: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:04:03","doi":"10.21203/rs.3.rs-6573789/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a2916de-8ca7-475d-921f-5812d4d7196f","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48052345,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-05-23T08:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:04:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6573789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6573789","identity":"rs-6573789","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}