Zhu N, Zhang Q, Huang J, Tong J, Gong HF, Zhu MH, Lu W, Zhang J, Sun XR. Using the THINC-integrated tool to compare the characteristics of cognitive dysfunction in patients with unipolar and bipolar depression. World J Psychiatry 2025; 15(3): 99408 [DOI: 10.5498/wjp.v15.i3.99408]
Corresponding Author of This Article
Xi-Rong Sun, MD, Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, No. 165 Sanlin Road, Pudong New Area, Shanghai 200124, China. sunxr1807@163.com
Research Domain of This Article
Psychology, Clinical
Article-Type of This Article
Clinical Trials Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Na Zhu, Qi Zhang, Jie Tong, Heng-Fen Gong, Ming-Huan Zhu, Wei Lu, Jie Zhang, Xi-Rong Sun, Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
Jia Huang, Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
Co-corresponding authors: Jie Zhang and Xi-Rong Sun.
Author contributions: Zhu N and Zhang Q conceptualized and designed the research; Tong J, Zhu MH, Gong HF, and Lu W screened patients and acquired clinical data; Zhu N performed data analysis and writing-original draft preparation and revising; All the authors have read and approved the final manuscript. Zhu N proposed, designed and conducted THINC-integrated tool analysis, performed data analysis and prepared the first draft of the manuscript. Zhang Q was responsible for patient screening, enrollment, and collection of clinical data. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Sun XR and Zhang J have played important and indispensable roles in the experimental design, data interpretation and manuscript preparation as the co-corresponding authors. Sun XR applied for and obtained the funds for this research project. Zhang J conceptualized, designed, and supervised the whole process of the project. She searched the literature, revised the early version of the manuscript. Sun XR was instrumental and responsible for data re-analysis and re-interpretation, figure plotting, comprehensive literature search, preparation and submission of the manuscript. This collaboration between Sun XR and Zhang J is crucial for the publication of this manuscript and other manuscripts still in preparation.
Supported by Science and Technology Development Fund of Shanghai Pudong New Area, No. PKJ2023-Y20; Key Discipline Construction Fund of the Shanghai Pudong New Area Municipal Commission of Health and Family Planning, No. PWZxk2022-18; and Pudong New Area Construction Project of National Traditional Chinese Medicine Development Comprehensive Reform Pilot Zone, No. PDZY-2022-0501.
Institutional review board statement: This research was conducted under the approval of the Institutional Review Board (IRB) of the Shanghai Pudong Area Mental Health Centre. The IRB carefully evaluated the research protocol, which encompassed the study design, methods of data collection, and procedures for participant recruitment and consent. The review process ensured that the study adhered to ethical guidelines and protected the rights and welfare of the participants. Informed consent was obtained from all participants after they were fully informed about the nature, purpose, and potential risks of the study. The confidentiality of participant information was maintained throughout the research process. The study was approved under IRB protocol number [PDJWLL2020037].
Clinical trial registration statement: This clinical trial was prospectively registered with ClinicalTrials.gov under the registration number [NCT 05053204]. The details provided in the registration encompassed the trial's primary and secondary objectives, study design, inclusion and exclusion criteria for participants, interventions and comparator treatments, outcome measures, planned sample size, and the estimated trial duration. This registration process is in line with the requirements and guidelines set forth to ensure transparency and reproducibility in clinical research. It allows for public access to key trial information, enabling other researchers, healthcare providers, and the general public to be informed about the trial's progress and results. The investigators of this trial are committed to following the protocol as registered and to reporting the trial outcomes in a timely and accurate manner.
Informed consent statement: All participants in this study were provided with comprehensive information regarding the nature, purpose, procedures, potential benefits, and possible risks of the research. This information was presented in a clear and understandable manner, either verbally and in written form. Before their inclusion in the study, participants were given sufficient time to review and consider the details. They were informed that their participation was entirely voluntary and that they could withdraw at any time without penalty or negative consequences. Written informed consent was obtained from each participant. The consent forms were signed and dated, and a copy was provided to the participants for their records. The research team ensured that all questions and concerns raised by the participants were addressed promptly and satisfactorily. The privacy and confidentiality of the participants were protected throughout the study, and any identifiable information was handled in accordance with applicable privacy laws and ethical guidelines. This process of obtaining informed consent was carried out in strict accordance with the requirements and recommendations of the Institutional Review Board of Shanghai Pudong Area Mental Health Centre to safeguard the rights and well-being of the individuals involved in the research.
Conflict-of-interest statement: The authors declare no conflict of interest.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: Data used in this research can be shared upon request. please contact the first author at zhuna1987524@aliyun.com. The requester must provide a detailed research plan explaining how the data will be used and must sign a data use agreement which ensures that the data will not be used for commercial purposes and that the privacy of the participants will be protected.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xi-Rong Sun, MD, Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, No. 165 Sanlin Road, Pudong New Area, Shanghai 200124, China. sunxr1807@163.com
Received: July 22, 2024 Revised: December 16, 2024 Accepted: January 22, 2025 Published online: March 19, 2025 Processing time: 219 Days and 0.9 Hours
Abstract
BACKGROUND
Major depressive disorder (MDD) and bipolar depression (BD-D) are both intricate, enduring, and profound psychiatric conditions characterized primarily by depressive episodes and cognitive dysfunction. However, distinguishing the characteristics and influencing factors of cognitive impairment in unipolar and BD-D is crucial for identification and intervention.
AIM
To compare neurocognitive characteristics and investigate associations between cognitive function and clinical features in unipolar and BD-D.
METHODS
The THINC-integrated tool (THINC-it) as a cognitive assessment tool was applied to 295 individuals: 75 patients with depressive disorders (MDD), 120 individuals with BD-D, and 100 healthy controls. The Hamilton Depression Scale-17 (HAMD), Hamilton Anxiety Scale-14 (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess depression, anxiety, and sleep. Neurocognitive function characteristics and the relationships between cognitive impairment and general clinical attributes were analyzed.
RESULTS
There were no statistically significant differences in the overall THINC-it with each objective subscale. However, the subjective subscale (Perceived Deficits Questionnaire for Depression-5-item) showed significant differences between MDD and BD-D (P < 0.001). Linear regression analyses were explored to determine associations. Age, years of education, age at onset, and HAMD were significantly co-associated with the overall THINC-it and each subscale in both MDD and BD-D (P < 0.05). Furthermore, years of education showed a positive correlation with objective cognitive impairment (e.g., Codebreaker, Trails) (P < 0.05). There was a notable difference in that the number of depressive episodes, disease duration, hospitalizations, HAMA, and PSQI were significantly associated with the overall THINC-it with each subscale between MDD and BD-D (P < 0.05).
CONCLUSION
Although both unipolar and BD-D showed similar objective cognitive impairments, there was a significant difference in subjective cognitive impairment. Our findings suggest that factors like age, years of education, age at onset, and depression severity might not be significantly difference in the influence of cognitive impairment. Furthermore, we found that education was a protective factor for cognitive impairment in both unipolar and BD-D. Our analysis revealed that distinct factors including disease duration, number of depressive episodes, hospitalizations, anxiety levels, and sleep quality influenced cognitive impairment between unipolar and BD-D. Therefore, it was important to investigate the specific characteristics of cognitive impairment and influencing factors to identify differentiating unipolar and BD-D.
Core Tip: The THINC-integrated tool was applied to 75 patients with depressive disorders, 120 individuals with bipolar depression, and 100 healthy controls. In our study, neurocognitive function characteristics were analyzed as well as the relationships between cognitive impairment and general clinical attributes. We found that objective cognitive impairment exhibited similarities between individuals with unipolar and bipolar depression, whereas subjective cognitive impairment showed differences. Additionally, there were no disparities in the impacts of age, age at onset, years of education, and depression level on cognitive impairment in individuals with unipolar and bipolar depression. Furthermore, years of education emerged as a protective factor against objective cognitive impairment. Interestingly, other clinical features had different effects on cognitive impairment varying between the two depression groups. These provide some theoretical support for the identification of unipolar and bipolar depression.
Citation: Zhu N, Zhang Q, Huang J, Tong J, Gong HF, Zhu MH, Lu W, Zhang J, Sun XR. Using the THINC-integrated tool to compare the characteristics of cognitive dysfunction in patients with unipolar and bipolar depression. World J Psychiatry 2025; 15(3): 99408
Major depressive disorder (MDD) is often termed unipolar depressive disorder to be distinguished from depressive episodes of bipolar depression (BD-D). MDD and BD-D are both intricate, enduring, and profound psychiatric conditions characterized primarily by depressive episodes and cognitive dysfunction. A Chinese study[1] estimated the lifetime prevalence of mood disorders to be 7.4%, encompassing incidence rates of 3.4% and 0.6% for MDD and bipolar disorders, respectively. A 2017 survey[2] revealed that the combined impact of BD-D and MDD on global health resulted in over 40 million Disability-Adjusted Life Years. These high numbers impose a significant burden on both the patients’ families and society. Due to the overlapping features of co-occurring depressive episodes, the clinical identification and treatment of unipolar and BD-D often present challenges. Approximately 20.8%[3] of BD-D patients in China were incorrectly diagnosed with MDD, with the misdiagnosis rate abroad reaching as high as 60%[4]. Cognitive impairment is a prevalent characteristic of both MDD and BD-D with Depressive Episodes[5,6], approximately 40%-60% of individuals with BD-D and two-thirds of those with MDD experience varying degrees of cognitive impairment[7-10], which mainly showed abnormalities in attention, memory and executive function (EF)[11,12]. Furthermore, cognitive impairment might persist even during remission[13,14]. The high prevalence, misdiagnosis rates, and disability rates linked with unipolar and BD-D impact patients' Quality of Life and social and occupational abilities, contributing to long-term mental disabilities. Therefore, there is an urgent need for early detection and treatment of cognitive impairment in unipolar and BD-D.
Nevertheless, the effective differentiation between unipolar and BD-D has proven to be an arduous task[15]. Multiple studies[16,17] discovered that impairment patterns in these two types of depression are comparable, e.g., Lai et al[18] found that cognitive impairments were more common in BD II depression patients compared to MDD patients, particularly for visual learning. Xu et al[19] identified subtle distinctions between unipolar and BD-D in processing speed, visual memory, and EF. Mak et al[20] mainly found that treatment-naïve young individuals with BPII had cognitive function distinguished from unipolar depressed individuals by EF and verbal memory. However, Liu et al[21] found that depressed bipolar II depression and MDD patients may be characterized by similar profiles of cognitive domain deficits, and no differences in these cognitive domains among psychomotor speed, working memory, visual memory, attention switching, and verbal fluency. As a result, a disagreement remains on the differences in cognitive impairment, especially distinguishing the subjective and objective cognitive impairment, necessitating further research.
Currently, more researchers have shifted their focus toward examining the impact of cognitive impairment in unipolar and BD-D. Although the cause of cognitive dysfunction in patients with unipolar and BD-D was still unclear, many studies had found that it was related to obesity, sleep disorders, diet and exercise, disease duration, education, number of previous depressive episodes, number of previous hospitalizations and other factors[22-27]. At present, there are different conclusions on the influencing factors of cognitive impairment in unipolar and BD-D, and the existing reports seldom distinguish the differences between the influencing factors of cognitive impairment. Therefore, for individuals with unipolar and BD-D, early detection and classification of many contributing factors to cognitive impairment is critical to personalized treatment and long-term prognosis.
Despite the widespread use of various assessment tools in clinical practice, many limitations hinder their effectiveness. These limitations include complex procedures, high costs, lengthy administration times, and incompatibility with large-scale clinical settings. Consequently, a previous report found that only 3% of existing tools are suitable for MDD, and none are currently appropriate for BD-D[28]. In our study, we present a new cognitive screening tool- the THINC-integrated tool (THINC-it), that has recently been nationally and internationally validated for unipolar and BD-D[29-31]. It offers several advantages, including simplicity, speed, and the fact that it is free. It comprises four objective cognitive tests (spotter, codebreaker, symbol check, and trails) and one subjective cognitive test, called the Perceived Deficits Questionnaire for Depression-5-item (PDQ-5-D). The overall test procedure took approximately 10-15 minutes, with the use of short exercises and instructions, which can lower the overall treatment cost and save on medical resources. The THINC-it has been widely applied, and a series of research studies have been carried out internationally. e.g., found the importance of treating comorbid anxiety as a way to solve patients' cognitive impairment[32]; subjective cognitive impairment (measured by PDQ-5-D) was significantly associated with psychosocial dysfunction[33]; depressed patients with comorbid pain were more likely to show cognitive dysfunction[34]; subjective and objective cognitive impairment was associated with perceived sleep quality and depression severity to varying degrees[35].
Herein, we used the THINC-it as a quick, easy-to-use, and practical screening tool to assess the daily cognitive functions of Chinese adults with unipolar and BD-D. This study investigated the characteristics and underlying factors influencing cognitive impairment in unipolar and BD-D. By understanding these differences, clinicians can gain valuable insights for more accurate diagnosis, personalized treatment plans, and risk stratification. This will improve patient prognosis, improve identification of those at higher risk for recurrence, and support the development of preventive and treatment strategies.
MATERIALS AND METHODS
Research participants
Patient group: A total of 122 patients with BD-D were randomly recruited, with two being excluded for failing the screening test and experiencing anxiety. A total of 78 MDD patients were randomly recruited, one of whom was excluded due to having a suspicious manic/hypomanic episode. Two individuals who did not complete the full set of objective and subjective THINC-it tasks were also excluded. Herein, 195 patients (120 and 75 patients in the BD-D and MDD groups, respectively) were included after implementing the selection process. The patients were continuously enrolled from outpatients and wards of the psychiatric hospitals in Shanghai, China, between December 2020 and August 2022.
The inclusion criteria were as follows: (1) Participants aged 18-65 years who had completed at least a junior high school education; (2) Participants diagnosed with BD-D or MDD based on the International Classification of Diseases-10 (ICD-10) diagnostic criteria; and (3) If patients need to take antidepressants, they should choose Selective Serotonin Reuptake Inhibitors as the preferred antidepressants.
The exclusion criteria were as follows: (1) Participants who were on drugs known to affect cognitive impairment, such as central stimulants or corticosteroids; (2) Participants who used benzodiazepines within 12 hours or consumed alcohol within 8 hours before the cognitive assessment; (3) Participants with severe physical illnesses, or cognitive or speech impairments that could adversely affect the validity of the neuropsychological test data; and (4) Participants who underwent electroconvulsive therapy within the six months before the cognitive assessment.
The healthy control group: 103 participants were recruited through social channels, from the Pudong New Area of Shanghai, including college students, employees, teachers, freelancers, and social workers. Among them, 2 volunteers did not complete all the tests, and 1 volunteer taking a benzodiazepine was not included. Finally, 100 volunteers were enrolled. The healthy controls (HCs) matched the case group regarding gender, age, and education. The inclusion criteria for the HC group were as follows: (1) No diagnosis of any mental disorder according to the ICD-10 diagnostic criteria and did not have any history of neurological disorders, alcohol dependency, or family history of mental disorders among their first-degree relatives; and (2) Adequate audiovisual skills required to complete the tests mandated by the institute.
Research methods
This study was conducted cross-sectionally during a single visit, and the patients' ongoing treatment remained uninterrupted.
Measurement tools: Demographic data were collected, and the severity of affective symptoms was evaluated using the Hamilton Depression Scale-17 (HAMD), Hamilton Anxiety Scale-14 (HAMA), Pittsburgh Sleep Quality Index (PSQI), and THINC-it.
The severity of depression in patients with a score < 7 (indicating no depression symptoms) was assessed using the Seventeen HAMD items.
The HAMA is a psychological questionnaire for evaluating the severity of anxiety. Higher scores imply a more severe degree of anxiety, whereas patients with scores < 7 are regarded as showing no anxiety symptoms.
Subjective sleep quality over the previous month was assessed using the PSQI, with higher scores indicating poorer sleep quality.
Cognitive function was assessed using the THINC-it, and this assessment’s sub-tests, measured indicators, and cognitive dimensions are presented in Table 1. The raw scores of the THINC-it, specifically Symbol Check, Codebreaker, and PDQ-5-D exhibited an inverse association with the degree of cognitive impairment in the statistical analysis. The PDQ-5-D, Spotter, and Trails scores were standardized using Z-scores and subsequently multiplied by -1 to ensure consistency in interpreting results across all tests. These five score tests were each weighted at 20%, and their results were combined to calculate overall scores. Higher total scores on this composite measure indicated better cognitive functioning.
Table 1 Cognitive dimensions and measurement indicators reflected in each sub-test of the THINC-integrated tool.
Sub-test
Cognitive dimensions
Measurement outcomes
Codebreaker
Speed of processing, attention/alertness
Count (correctly)
Spotter
Attention/alertness, reaction speed
Average reaction time (seconds)
Trails
Executive functions
Completion time (seconds)
Symbol check
Attention, working memory, reaction speed
Accuracy
PDQ-5-D
Pay attention, plan/organize, review and prospective memory
Total points
Statistical analysis
All statistical analyses were performed using SPSS 20.0. Measurement data were presented as mean ± SD, while the counting data were expressed as composition ratios. A one-way ANOVA was performed if continuous variables exhibited a non-normal distribution, but after converting their ranks to a normal distribution. One-way ANOVA, χ2 test, and Kruskal-Wallis H test were applied to perform intergroup comparisons between unipolar and BD-D and HCs. Furthermore, The Kruskal-Wallis H test compared each THINC-it item between MDD and BD-D. Linear regression models were estimated using the total THINC-it, Spotter, Codebreaker, Trails, Symbol Check and PDQ-5-D as the dependent variables, and age, years of education, age at onset, disease duration, number of depressive episodes, hospitalizations, depression severity, anxiety levels, and sleep quality as the independent variable. Linear regression analysis was performed to examine the relationship between cognitive impairment and clinical variables. P values equal to or below 0.05 (two-tailed) were considered significant.
RESULTS
General information
No statistically significant variations in age and gender were detected across the three groups. Compared with BD-D and HC groups, the MDD group exhibited lower education levels (P < 0.001). Our analysis revealed that most participants had a college education or higher in our study. Significant differences were found across the groups in HAMD, HAMA, and PSQI scores (P < 0.001; Table 2).
Table 2 Demographic and clinical characteristics of the study cohorts, n (%).
Comparison of neurocognitive impairment between unipolar and BD-D
The one-way ANOVA revealed statistically significant differences in the total THINC-it, Spotter, Codebreaker, Trails, and PDQ-5-D scores between individuals with unipolar and BD-D and the HC (P < 0.001). Subsequently, the Kruskal-Wallis H test was employed to compare the MDD and BD-D, and the results showed no statistically significant differences in the total THINC-it, Spotter, Codebreaker, and Trails scores between them (Z = -29.811, -24.657, -21.036, -0.031, P > 0.05). However, there was a statistically significant difference in the PDQ-5-D scores between the MDD and BD-D (Z = -48.188, P < 0.001; Table 3).
Table 3 Comparison of cognitive function between unipolar and bipolar depression and healthy controls.
Item
MDD (n = 75)
BD-D (n = 120)
HC (n = 100)
F
P value
Spotter
-0.485 ± 1.215
-0.089 ± 0.906
0.440 ± 0.732
20.861
< 0.001
Symbol check
0.006 ± 0.955
-0.085 ± 1.093
0.094 ± 0.916
0.871
0.419
Codebreaker
-0.349 ± 1.097
-0.058 ± 1.049
0.305 ± 0.759
9.671
< 0.001
Trails
-0.047 ± 0.654
-0.218 ± 1.379
0.291 ± 0.445
7.480
0.001
PDQ-5-D
-0.714 ± 0.907
-0.025 ± 1.019
0.539 ± 0.666
41.471
< 0.001
THINC-it
-0.318 ± 0.652
-0.095 ± 0.671
0.334 ± 0.401
28.042
< 0.001
The general clinical features influencing cognitive function in patients with unipolar and BD-D
Through linear regression analysis, it was found that age, years of education, and age at onset were significantly co-associated with the overall THINC-it with each subscale between MDD and BD-D (P < 0.05). Furthermore, years of education showed a positive correlation with objective cognitive impairment (e.g., Codebreaker, Trails) (P < 0.05). The number of depressive episodes, disease duration, and hospitalizations were significantly associated with the THINC-it with subtests in BD-D, not patients with MDD (P < 0.05; Table 4).
Table 4 Correlation the THINC-integrated tool with age, education, depressive episodes, disease duration, age at onset, and hospitalizations between unipolar and bipolar depression.
The associations among cognitive function and depression, anxiety, and sleep in unipolar and BD-D
Linear regression analysis revealed that the HAMD was negatively correlated with the overall THINC-it, Codebreaker, Trails, and Symbol Check in patients with MDD (P < 0.05), but was negatively correlated with the PDQ-5-D in BD-D (P < 0.001). Significant negative correlations were also found between PSQI and the total THINC-it, Symbol Check, and PDQ-5-D in patients with MDD, rather than BD-D (P < 0.05). Meanwhile, the HAMA exhibited significant negative effects on the PDQ-5-D in BD-D only (P < 0.001; Table 5).
Table 5 Correlation the THINC-integrated tool with Hamilton Depression Scale-17, Hamilton Anxiety Scale-14, and Pittsburgh Sleep Quality Index total scores between unipolar and bipolar depression.
This cross-sectional study investigated the characteristics of cognitive impairment and its relationship to clinical features in patients with unipolar or BD-D. Our analysis revealed that objective cognitive impairment exhibited similarities between individuals with unipolar and BD-D, whereas subjective cognitive impairment showed differences. Additionally, there were no disparities in the impacts of age, age at onset, years of education, and depression level on cognitive impairment in individuals with unipolar and BD-D. Furthermore, years of education emerged as a protective factor against objective cognitive impairment. Interestingly, other clinical features had different effects on cognitive impairment varying between the two depression groups. These provide some theoretical support for the identification of unipolar and BD-D.
According to previous research, individuals diagnosed with BD-D and MDD primarily experience deficits in EF, processing speed, attention, memory, and other cognitive domains[6,36]. Our findings further corroborate these reports by demonstrating the similarities between objective neurocognitive impairments in BD-D and MDD, specifically, including attention/alertness, reaction and information processing speeds, and EF. Jin et al[37] also found that there were differences in specific objective cognitive dimensions (visuospatial/constructive) between patients with unipolar and BD-D. Some scholars[38] further found that MDD patients performed better cognitively than BD-D patients, notably in the most objective cognitive domains. Xu et al[19] suggested that the difference in objective cognitive impairment (processing speed, visual memory, and EF) between BD-D and MDD could be the trait markers for differentiating between the two disorders. However, several studies found no or only slightly statistically significant differences in objective cognitive impairment between the two conditions[39-41], which was different from our present study in that there were differences in subjective cognitive impairment between the two disorders. Notably, in Miskowiak et al’s study[42], which included 45 unipolar depression and 54 BD-D patients in remission, no significant differences were observed between the groups in subjectively reported cognitive dysfunction. A potential explanation for the discrepancy could be the difference in sample size. Additionally, our inclusion criteria may have differed, potentially capturing patients at various stages of illness during a depressive episode, or partly remitted phase, compared to the remitted state in Miskowiak et al's study[42]. As a result, we revealed notable distinctions between these two types of depression in subjective cognitive impairment. These differences reflected the uniqueness of patients in the dimension of subjective cognitive feelings, suggesting that subjective cognitive experiences might be influenced by factors beyond the degree of objective cognitive impairment, such as differences in emotional regulation, staged characteristics of the disease, and individual psychological traits. Further exploration of its underlying mechanisms and influencing factors is warranted.
Here, we continued to explore the influencing factors of cognitive impairment in unipolar and BD-D. We found that there were no disparities in the detrimental impacts of age, age at onset, years of education, and depression level on cognitive impairment in both unipolar and BD-D. This finding also verifies that an earlier onset of depression, whether unipolar or bipolar, is associated with a higher likelihood of cognitive impairment. Our research supports that providing more educational support and early intervention for depressive symptoms may effectively reduce cognitive impairments. Consistent with Huang's research[43], their results have confirmed that both age and years of education significantly influence objective cognitive impairment in individuals with unipolar and BD-D. Thus, multiple factors like depression severity, years of education, age, and age at onset should be considered, rather than relying on a single indicator, to accurately assess the condition and develop suitable intervention plans in the following study.
Years of education as a common influencing factor were found to mitigate the adverse effects on objective cognitive impairment in unipolar and BD-D. Yang[44] also reported that education levels somehow represent the IQ level, which, to some extent, can offset the influence of cognitive impairment. A higher level of education could increase the cognitive reserve of the brain. It could play a compensatory role and delayed the emergence of cognitive impairment symptoms. Meanwhile, education could also enhance patients' self-management abilities, enabling them to better understand their own conditions and cooperate with treatment and rehabilitation training. Nevertheless, the participants exhibited a generally high level of education and were underrepresented in this study, thus necessitating further validation of the application of THINC-it testing in individuals with lower educational attainment.
Our study identified depression severity as the other common key factor influencing cognitive impairment in both unipolar and BD-D. Furthermore, the depressive levels of BD-D were closely related to subjective cognitive impairment, but this levels were closely related to objective cognitive impairment in unipolar depression in our study, which was concordant with the findings of Cha et al[35] and Shen et al[45]. Regarding BD-D, most participants were in remission, experiencing only residual symptoms of depression and anxiety in our study. This remission state could contribute to heightened subjective awareness of cognitive difficulties-patients may focus more on subtle cognitive issues once the more prominent emotional symptoms subside. Concerning MDD, our study highlights a key finding that patients often prioritize emotional changes over cognitive difficulties during depressive episodes. Therefore, this suggests a critical role for clinicians to consider cognitive impairment alongside emotional symptoms when diagnosing and treating depressive episodes. By acknowledging both aspects, early intervention and better management can be achieved.
We also discovered that there are differences in the influencing factors of cognitive function in individuals with unipolar and BD-D, including disease duration, number of depressive episodes, hospitalizations, anxiety level, and sleep quality. These findings strongly support the need to distinguish between unipolar and BD-D and to develop targeted treatment programs. According to previous research[36,46], cognitive impairment correlated with depression, anxiety, and subjective sleep quality to some extent. However, whether the influence relationship in unipolar and BD-D was consistent remains to be discussed. Moreover, long-term follow-up research and large-scale, multi-center studies should be conducted to verify this conclusion in the future. About sleep quality, Zhang et al[47] highlighted potential differences in the correlation between sleep indicators and learning and memory cognitive function in unipolar and BD-D patients, which was roughly consistent with our conclusion. Specifically, the impact of sleep on cognitive impairment was observed only in patients with MDD in our study, and Cha et al[35] was consistent with our study. Given this, our finding could be explained by the clinical observations that patients with poor sleep quality tended to experience greater psychological and ideological pressure, as well as more severe cognitive impairment.
Finally, this study also found a positive effect on the HAMA and the objective cognitive impairment (e.g., Spotter), age, age at onset, disease duration, and number of hospitalizations significantly positively affected the subjective cognitive impairment (PDQ-5-D), which were slightly different from clinical observations. On the one hand, such as Spotter, the operation of the task was relatively simple, and the enrolled patients had higher education levels and generally higher scores, which could not be indistinguishable from the real test situation. On the other hand, some patients assessed under fatigue conditions were more likely to acquiesce without critical thinking, leading to responses that did not accurately reflect the true test situation.
Limitations
There were some limitations to this study. Firstly, since it was a cross-sectional study, it could not explore the impact of disease progression and psychotropic drug adjustments on cognition. Secondly, the potential impact of factors such as disease status, drug use, and psychological treatment on cognitive changes in the enrolled patients was not strictly controlled. Furthermore, factors such as the concentrated age (with an average age of approximately 30 years), the relatively young age of enrolled patients, and high levels of education may introduce statistical bias, potentially leading to discrepancies with clinical observations. Future research could consider introducing a longitudinal follow-up mechanism and refining disease subtypes. Meanwhile, relevant factors such as disease status and medication use should be controlled to reduce the interference of confounding factors.
CONCLUSION
Overall, we found that neurocognitive impairment characteristics of BD-D and MDD were comparable and that the distinction in subjective cognitive impairment held significance in clinical differentiation to some extent. However, it lacked sufficient objectivity to be considered a definitive indicator for differentiating between unipolar and BD-D. Furthermore, our results indicated that education was a protective factor in the objective cognitive domains between individuals with unipolar and BD-D. However, our study revealed that the same or different factors influence subjective and objective cognitive impairment across unipolar and BD-D. Further exploration could be carried out to find objective indicators that can distinguish between unipolar and BD-D, to improve the accuracy and reliability of the research and provide a stronger basis for clinical diagnosis and treatment in this field.
ACKNOWLEDGEMENTS
The authors would like to thank the Shanghai Pudong New Area Health Commission. The authors would also like to thank the subjects in this study.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: Zhang RY S-Editor: Li L L-Editor: A P-Editor: Zhao S
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